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Part II - Fundamentals of Cognitive Development from Infancy to Adolescence and Young Adulthood

Published online by Cambridge University Press:  24 February 2022

Edited by
Olivier Houdé  and
Grégoire Borst
Olivier Houdé
Affiliation:
Université de Paris V
Grégoire Borst
Affiliation:
Université de Paris V
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The Cambridge Handbook of Cognitive Development , pp. 139 - 534
Publisher: Cambridge University Press
Print publication year: 2022

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References

References

Aron, A., Robbins, T., & Poldrack, R. (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8, 170–177.CrossRefGoogle ScholarPubMed
Aron, A., Robbins, T., & Poldrack, R. (2014). Inhibition and the right inferior frontal cortex: One decade on. Trends in Cognitive Sciences, 18, 177–185.CrossRefGoogle ScholarPubMed
Antell, S., & Keating, D. (1983). Perception of numerical invariance in neonates. Child Development, 54, 695–701.CrossRefGoogle ScholarPubMed
Baillargeon, R. (1995). Physical reasoning in infancy. In Gazzaniga, M. S. (ed.), The Cognitive Neurosciences (pp. 181–204). Cambridge, MA: MIT Press.Google Scholar
Bell, M., & Fox, N. (1992). The relations between frontal brain electrical activity and cognitive development during infancy. Child Development, 63, 1142–1163.CrossRefGoogle ScholarPubMed
Bjorklund, D., & Harnishfeger, K. (1990). The resources construct in cognitive development: Diverse sources of evidence and a theory of inefficient inhibition. Developmental Review, 10, 48–71.CrossRefGoogle Scholar
Borst, G., & Houdé, O. (2014). Inhibitory control as a core mechanism for cognitive development and learning at school. Perspectives on Language and Literacy, 17, 41–44.Google Scholar
Borst, G., Moutier, S., & Houdé, O. (2013a). Negative priming in logicomathematical reasoning: The cost of blocking your intuition. In De Neys, W., & Osman, M. (eds.), New Approaches in Reasoning Research – Current Issues in Thinking & Reasoning (pp. 34–50). New York: Psychology Press.Google Scholar
Borst, G., Pineau, A., Poirel, N., Cassotti, M., & Houdé, O. (2013b). Inhibitory control efficiency in a Piaget-like class-inclusion task in school-age children and adults: A developmental negative priming study. Developmental Psychology, 49, 1366–1374.CrossRefGoogle Scholar
Borst, G., Poirel, N., Pineau, A., Cassotti, M., & Houdé, O. (2012). Inhibitory control in number-conservation and class-inclusion tasks: A neo-Piagetian inter-tasks priming study. Cognitive Development, 27, 283–298.CrossRefGoogle Scholar
Casey, B., Tottenham, N., Liston, C., & Durston, S. (2005). Imaging the developing brain: What have we learned about cognitive development? Trends in Cognitive Sciences, 9, 104–110.CrossRefGoogle ScholarPubMed
De Neys, W., Rossi, S., & Houdé, O. (2013). Bats, balls, and substitution sensitivity: Cognitive misers are no happy fools. Psychonomic Bulletin & Review, 20, 269–273.CrossRefGoogle ScholarPubMed
Demetriou, A. (ed.) (1988). The Neo-Piagetian Theories of Cognitive Development. Amsterdam: North-Holland.Google Scholar
Dempster, F., & Brainerd, C. (eds.) (1995). Interference and Inhibition in Cognition. New York: Academic Press.CrossRefGoogle Scholar
Diamond, A. (1991). Neuropsychological insights into the meaning of object concept development. In Carey, S., & Gelman, R. (eds.), The Epigenesis of Mind: Essays on Biology and Cognition (pp. 67–110). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Diamond, A. (1998). Understanding the A-not-B error: Working memory vs. reinforced response, or active trace vs. latent trace. Developmental Science, 1, 185–189.CrossRefGoogle Scholar
Diamond, A., & Goldman-Rakic, P. (1989). Comparison of human infants and rhesus monkeys on Piaget’s AB task. Experimental Brain Research, 74, 24–40.CrossRefGoogle ScholarPubMed
Evans, J. (1989). Bias in Human Reasoning. London: Erlbaum.Google Scholar
Evans, J. (2003). In two minds: dual-process accounts of reasoning. Trends in Cognitive Sciences, 7, 454–459.CrossRefGoogle ScholarPubMed
Fischer, K., & Kaplan, U. (2003). Jean Piaget. In Nadel, L. (ed.), The Encyclopedia of Cognitive Science (Vol. 1, pp. 679–682). London: Nature Publishing Group, Macmillan.Google Scholar
Gelman, R. (1972). Logical capacity of very young children. Child Development, 43, 75–90.CrossRefGoogle Scholar
Gelman, R. (1997). Constructing and using conceptual competence. Cognitive Development, 12, 305–313.CrossRefGoogle Scholar
Gelman, R., & Meck, E. (1983). Preschooler’s counting. Cognition, 13, 343–359.CrossRefGoogle ScholarPubMed
Gelman, R., Meck, E., & Merkin, S. (1986). Young children’s numerical competence. Cognitive Development, 1, 1–29.CrossRefGoogle Scholar
Gopnik, A. (2012). Scientific thinking in young children: Theoretical advances, empirical research, and policy education. Science, 337, 1623–1627.CrossRefGoogle Scholar
Gopnik, A., Meltzoff, A., & Kuhl, P. (1999). The Scientist in the Crib. New York: William Morrow and Cie.Google Scholar
Harnishfeger, K. (1995). The development of cognitive inhibition: Theories, definition, and research evidence. In Dempster, F., & Brainerd, C. (eds.), Interference and Inhibition in Cognition (pp. 176–204). New York: Academic Press.Google Scholar
Houdé, O. (2000). Inhibition and cognitive development: Object, number, categorization, and reasoning. Cognitive Development, 15, 63–73.CrossRefGoogle Scholar
Houdé, O. (2007). First insights on neuropedagogy of reasoning. Thinking & Reasoning, 13, 81–89.Google Scholar
Houdé, O. (2015). Cognitive development during infancy and early childhood across cultures. In Wright, J. D. (ed.), International Encyclopedia of the Social and Behavioral Sciences (pp. 43–50). Oxford: Elsevier Science.CrossRefGoogle Scholar
Houdé, O. (2019). 3-System Theory of the Cognitive Brain: A Post-Piagetian Approach. New York: Routledge.CrossRefGoogle Scholar
Houdé, O., & Guichart, E. (2001). Negative priming effect after inhibition of number/length interference in a Piaget-like task. Developmental Science, 4, 71–74.CrossRefGoogle Scholar
Houdé, O., Pineau, A., Leroux, G., Poirel, N., Perchey, G., Lanoë, C., Lubin, A., Turbelin, M.-R., Rossi, S., Simon, G., Delcroix, N., Lamberton, F., Vigneau, M., Wisniewski, G., Vicet, J.-R., & Mazoyer, B. (2011). Functional MRI study of Piaget’s conservation-of-number task in preschool and school-age children: A neo-Piagetian approach. Journal of Experimental Child Psychology, 110, 332–346.CrossRefGoogle ScholarPubMed
Houdé, O., & Tzourio-Mazoyer, N. (2003). Neural foundations of logical and mathematical cognition. Nature Reviews Neuroscience, 4, 507–514.CrossRefGoogle ScholarPubMed
Houdé, O., Zago, L., Mellet, E., Moutier, S., Pineau, A., Mazoyer, B., & Tzourio-Mazoyer, N. (2000). Shifting from the perceptual brain to the logical brain: The neural impact of cognitive inhibition training. Journal of Cognitive Neuroscience, 12, 721–728.CrossRefGoogle Scholar
Johnson, M. (2010). Interactive specialization: A domain-general framework for human functional brain development. Developmental Cognitive Neuroscience, 1, 7–21.CrossRefGoogle ScholarPubMed
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.Google Scholar
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under Uncertainty: Heuristics and Biases. New York: Cambridge University Press.Google Scholar
Kahneman, D., & Tversky, A. (2000). Choices, Values, and Frames. New York: Cambridge University Press.CrossRefGoogle Scholar
Lipton, J., & Spelke, E. (2003). Origins of number sense: Large number discrimination in human infants. Psychological Science, 14, 396–401.Google Scholar
Loosbroek, E., & Smitsman, A. (1990). Visual perception of numerosity in infancy. Developmental Psychology, 26, 916–922.CrossRefGoogle Scholar
Mandler, J. (1988). How to build a baby: On the development of an accessible representational system. Cognitive Development, 3, 113–136.CrossRefGoogle Scholar
Mehler, J.,& Bever, T. (1967). Cognitive capacity of very young children. Science, 158, 141–142.CrossRefGoogle ScholarPubMed
O’Reilly, R. (1998). Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences, 11, 455–462.CrossRefGoogle Scholar
Parpart, P., Jones, M., & Love, B. (2018). Heuristics as Bayesian inference under extreme priors. Cognitive Development, 102, 127–144.Google Scholar
Perret, P., Blaye, A., & Paour, J.-L. (2003). Respective contributions of inhibition and knowledge levels in class inclusion development: A negative priming study. Developmental Science, 6, 283–286.CrossRefGoogle Scholar
Piaget, J. (1954). The Construction of Reality in the Child. New York: Basic Books.CrossRefGoogle Scholar
Piaget, J. (1983). Piaget’s theory. In Mussen, P. H. (ed.), Handbook of Child Psychology (Vol. 1, pp. 103–128). New York: Wiley.Google Scholar
Poirel, N., Borst, G., Simon, G., Rossi, S., Cassotti, M., Pineau, A., & Houdé, O. (2012). Number conservation is related to children’s prefrontal inhibitory control: An fMRI study of a Piagetian task. PLoS ONE, 7, e40802.CrossRefGoogle ScholarPubMed
Siegler, R. (1996). Emerging Minds: The Process of Change in Children’s Thinking. New York: Oxford University Press.CrossRefGoogle Scholar
Siegler, R. (1999). Strategic development. Trends in Cognitive Sciences, 3, 430–435.CrossRefGoogle ScholarPubMed
Spelke, E. (2000). Core knowledge. American Psychologist, 55, 1233–1243.CrossRefGoogle ScholarPubMed
Téglás, E., Vul, E., Girotto, V., Gonzales, M., Tenenbaum, J.-B., & Bonatti, L. (2011). Pure reasoning in 12-month-old infants as probabilistic inference. Science, 332, 1054–1059.CrossRefGoogle ScholarPubMed
Wright, I., Waterman, M., Prescott, H., & Murdoch-Eaton, D. (2003). A new Stroop-like measure of inhibitory function development: Typical developmental trends. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 44, 561–575.Google Scholar
Wynn, K. (1992). Addition and subtraction by human infants. Nature, 358, 749–750.CrossRefGoogle ScholarPubMed
Wynn, K. (1998). Psychological foundations of number: Numerical competence in human infants. Trends in Cognitive Sciences, 2, 296–303.CrossRefGoogle ScholarPubMed

References

Amsterdam, B. (1972). Mirror self-image reactions before age two. Psychobiology, 5, 297–305.CrossRefGoogle ScholarPubMed
Astington, J. W. (1993). The Child’s Discovery of the Mind. Cambridge, MA: Harvard University Press.Google Scholar
Bassano, D., & Maillochon, I. (1994). Early grammatical and prosodic marking of utterance modality in French. A longitudinal case study. Journal of Child Language, 21, 649–675.CrossRefGoogle ScholarPubMed
Bates, E. (1976). Language and Context: The Acquisition of Pragmatics. New York: Academic Press.Google Scholar
Bates, E. (1979). The Emergence of Symbols. Cognition and Communication in Infancy. New York: Academic Press.Google Scholar
Beran, M. J. (2004). Long-term retention of the differential values of Arabic numerals by chimpanzees (Pan troglodytes). Animal Cognition, 7, 86–92.CrossRefGoogle ScholarPubMed
Beran, M. J., Parrish, A. E., & Evans, T. A. (2015). Numerical cognition and quantitative abilities in nonhuman primates. In Geary, D., Berch, D., & Mann Koepke, K. (eds.), Evolutionary Origins and Early Development of Number Processing (pp. 91–119). New York: Elsevier.Google Scholar
Bickerton, D. (1990). Language and Species. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Boesch, C. (1991). Teaching among wild chimpanzees. Animal Behaviour, 41, 530–532.Google Scholar
Boesch, C., & Boesch, H. (1984). Mental maps in wild chimpanzees: An analysis of hammer transports for nut cracking. Primates, 25, 160–170.CrossRefGoogle Scholar
Bovet, D., & Vauclair, J. (1998). Functional categorization of objects and of their pictures in baboons (Papio anubis). Learning & Motivation, 29, 309–322.CrossRefGoogle Scholar
Bovet, D., & Vauclair, J. (2001). Judgement of conceptual identity in monkeys. Psychonomic Bulletin & Review, 8, 470–475.Google Scholar
Bovet, D., & Washburn, D. A. (2003). Rhesus macaques (Macaca mulatta) categorize unknown conspecifics according to their dominance relations. Journal of Comparative Psychology, 117, 400–405.Google Scholar
Boysen, S. T., & Berntson, G. G. (1989). Numerical competence in a chimpanzee (Pan troglodytes). Journal of Comparative Psychology, 103, 23–31.Google Scholar
Boysen, S. T., Berntson, G. G., Shreyer, T. A., & Hannan, M. B. (1995). Indicating acts during counting by a chimpanzee (Pan troglodytes). Journal of Comparative Psychology, 109, 47–51.CrossRefGoogle ScholarPubMed
Brosnan, S. F. (2014). Precursors of morality: Evidence for moral behaviors in non-human primates. In Christen, M. E., van Schaik, C. E., Fischer, J. E., Huppenbauer, M. E., & Tanner, C. E. (eds.), Empirically Informed Ethics: Morality between Facts and Norms (pp. 85–98). Cham, Switzerland: Springer.CrossRefGoogle Scholar
Bruner, J. S. (1983). Child’s Talk: Learning to Use Language. New York: W. W. Norton & Company Inc.Google Scholar
Byrne, R., & Whiten, A. (1988). Machiavellian Intelligence. Oxford: Oxford University Press.Google Scholar
Call, J., & Tomasello, M. (1999). A nonverbal false belief task: The performance of children and great apes. Child Development, 70, 381–395.CrossRefGoogle ScholarPubMed
Chang, L., Fang, Q., Zhang, S., Poo, M., & Gong, N. (2015). Mirror-induced self-directed behaviors in rhesus monkeys after visual-somatosensory training. Current Biology, 25, 1–6.CrossRefGoogle ScholarPubMed
Chomsky, N. (1968). Language and Mind. New York: Harcourt, Brace & World.Google Scholar
Dasser, V. (1988). A social concept in Java monkeys. Animal Behaviour, 36, 225–230.CrossRefGoogle Scholar
de Saussure, F. (1966). Course in General Linguistics, ed. Bally, C., & Sechehaye, A.. New York: McGraw-Hill.Google Scholar
de Waal, F. B. M. (1996). Good Natured: The Origins of Right and Wrong in Primates and Other Animals. Cambridge, MA: Harvard University Press.Google Scholar
Dennett, D. (1983). Intentional systems in cognitive ethology: The “Panglossian paradigm” defended. The Behavioral and Brain Sciences, 6, 343–390.CrossRefGoogle Scholar
Flemming, T. M., Thompson, R. K. R., & Fagot, J. (2013). Baboons, like humans, solve analogy by categorical abstraction of relations. Animal Cognition, 16, 519–524.CrossRefGoogle ScholarPubMed
Gallup, G. G. (1970). Chimpanzees: Self recognition. Science, 167, 86–87.Google Scholar
Hagège, C. (1985). L’homme de paroles. Paris: Fayard.Google Scholar
Hall, K., & Brosnan, S. F. (2016). A comparative perspective on the evolution of moral behaviour. In Shackelford, T. K., & Hansen, R. D. (eds.), The Evolution of Morality (pp. 157–176). Cham, Switzerland: Springer.CrossRefGoogle Scholar
Halliday, T. R., & Slater, P. J. B. (eds.) (1983). Animal Behaviour, Vol.3: Genes, Development and Learning. Oxford: Blackwell Scientific Publications.Google Scholar
Hamlin, J. K. (2013). Moral judgment and action in preverbal infants and toddlers: Evidence for an innate moral core. Current Directions in Psychological Science, 22, 186–193.Google Scholar
Hare, B., Addessi, E., Call, J., Tomasello, M., & Visalberghi, E. (2003). Do capuchin monkeys, Cebus apella, know what conspecifics do and do not see? Animal Behaviour, 65, 131–142.Google Scholar
Hare, B., Call, J., & Tomasello, M. (2001). Do chimpanzees know what conspecifics know and do not know? Animal Behaviour, 61, 139–151.CrossRefGoogle Scholar
Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298, 1569–1579.CrossRefGoogle ScholarPubMed
Herrnstein, R. J. (1990). Levels of stimulus control: A functional approach. Cognition, 37, 133–166.CrossRefGoogle ScholarPubMed
Hobaiter, C., & Byrne, R. W. (2011). The gestural repertoire of the wild chimpanzee. Animal Cognition, 14, 745–767.CrossRefGoogle ScholarPubMed
Hockett, C. F. (1960). The origin of speech. Scientific American, 203, 88–96.CrossRefGoogle ScholarPubMed
Hopkins, W. D., & Vauclair, J. (2012). Evolution of behavioral and brain asymmetries in primates. In Tallerman, M., & Gibson, K. (eds.), Handbook of Language Evolution (pp. 184–197). Oxford: Oxford University Press.Google Scholar
Humphrey, N. (1976). The social function of intellect. In Bateson, P. P. G., & Hinde, R. A. (eds.), Growing Points in Ethology (pp. 303–317). New York: Cambridge University Press.Google Scholar
Koehler, W. (1925). The Mentality of Apes. New York: Harcourt, Brace & Company Inc.Google Scholar
Krupenye, C., Kano, F., Hirata, S., Call, J., & Tomasello, M. (2016). Great apes anticipate that other individuals will act according to false beliefs. Science, 354, 110–114.CrossRefGoogle ScholarPubMed
Kummer, H. (1968). Social Organization of Hamadryas Baboons. Chicago, IL: University of Chicago Press.Google Scholar
Kummer, H. (1982). Social knowledge in free-ranging primates. In Griffin, D. R. (ed.), Animal Mind–Human Mind (pp. 113–130). Berlin: Springer Verlag.Google Scholar
Leroi-Gourhan, A. (1993). Gesture and Speech. Cambridge, MA: MIT Press.Google Scholar
Meguerditchian, A., & Vauclair, J. (2008). Vocal and gestural communication in nonhuman primates and the question of the origin of language. In Roska-Hardy, L. S., & Neumann-Held, E. M. (eds.), Learning from Animals? (pp. 61–85). London: Psychology Press.Google Scholar
Moore, R. (2013). Social learning and teaching in chimpanzees. Biology & Philosophy, 28, 879–901.CrossRefGoogle Scholar
Nieder, A. (2009). Prefrontal cortex and the evolution of symbolic reference. Current Opinion in Neurobiology, 19, 99–108.CrossRefGoogle ScholarPubMed
Parrish, A. E., & Brosnan, S. F. (2012). Primate cognition. In Ramachandran, V. S. (ed.), The Encyclopedia of Human Behavior (vol. 3, pp. 174–180). New York: Academic Press.Google Scholar
Pinker, S. (1994). The Language Instinct: How the Mind Creates Language. New York: W. Morrow and Co.CrossRefGoogle Scholar
Pinker, S. (2013). Language, Cognition and Human Nature. New York: Oxford University Press.CrossRefGoogle Scholar
Pollick, A. S., & de Waal, F. B. M. (2007). Ape gestures and language evolution. Proceedings of the National Academy of Sciences (USA), 104, 8184–8189.Google Scholar
Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? The Behavioral and Brain Sciences, 1, 515–526.Google Scholar
Sanz, C., & Morgan, D. (2007). Chimpanzee tool technology in the Goualougo triangle, Republic of Congo. Journal of Human Evolution, 52, 420–433.Google Scholar
Sanz, C., Morgan, D., & Gulick, S. (2004). New insights into chimpanzees, tools, and termites from the Congo Basin. American Naturalist, 164, 567–581.CrossRefGoogle ScholarPubMed
Sarnecka, B. W., & Carey, S. (2008). How counting represents number: What children must learn and when they learn it. Cognition, 108, 662–674.Google Scholar
Savage-Rumbaugh, E. S. (1986). Ape Language. From Conditioned Response to Symbol. New York: Columbia University Press.CrossRefGoogle Scholar
Savage-Rumbaugh, E. S., McDonald, K., Sevcik, R. A., Hopkins, W. D., & Rubert, E. (1986). Spontaneous symbol acquisition and communicative use by pygmy chimpanzees (Pan paniscus). Journal of Experimental Psychology: General, 115, 211–235.CrossRefGoogle ScholarPubMed
Seyfarth, R. M., & Cheney, D. L. (2012). Primate social cognition as a precursor to language. In Gibson, K., & Tallerman, M. (eds.), Oxford Handbook of Language Evolution (pp. 59–70). Oxford: Oxford University Press.Google Scholar
Seyfarth, R. M., Cheney, D. L., & Marler, P. (1980). Monkey responses to three different alarm calls: Evidence of predator classification and semantic communication. Science, 210, 801–803.CrossRefGoogle ScholarPubMed
Slocombe, K. E., & Zuberbühler, K. (2005). Functionally referential communication in a chimpanzee. Current Biology, 15, 1779–1784.CrossRefGoogle Scholar
Southgate, V., Senju, A., & Csibra, G. (2007). Action anticipation through attribution of false belief in 2-year-olds. Psychological Science, 18, 587–592.Google Scholar
Thompson, R. K. R., & Oden, D. L. (2000). Categorical perception and conceptual judgments by nonhuman primates: The paleological monkey and the analogical ape. Cognitive Science, 24, 363–396.CrossRefGoogle Scholar
Tomasello, M. (2016). A Natural History of Human Morality. Cambridge, MA: Harvard University Press.Google Scholar
Tomasello, M. (2019). Becoming Human: A Theory of Ontogeny. Cambridge, MA: The Belknap Press.Google Scholar
Tomasello, M., & Call, J. (1997). Primate Cognition. New York: Oxford University Press.CrossRefGoogle Scholar
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: The ontogeny of cultural cognition. The Behavioral and Brain Sciences, 28, 675–735.CrossRefGoogle Scholar
Tomasello, M., Kruger, A. C., & Ratner, H. H. (1993). Cultural learning. The Behavioral and Brain Sciences, 16, 495–552.Google Scholar
Vauclair, J. (1990). Primate cognition: From representation to language. In Parker, S. T., & Gibson, K. (eds.), Language and Intelligence in Monkeys and Apes: Comparative Developmental Perspectives (pp. 312–329). Cambridge, UK: Cambridge University Press.Google Scholar
Vauclair, J. (1996). Animal Cognition: Recent Developments in Comparative Psychology. Cambridge, MA: Harvard University Press.Google Scholar
Vauclair, J. (2003). Would humans without language be apes? In Valsiner, J. (Series ed.) & Toomela, A. (Vol. ed.), Cultural Guidance in the Development of the Human Mind: Vol. 7. Advances in Child Development within Culturally Structured Environments (pp. 9–26). Greenwich, CT: Ablex Publishing Corporation.Google Scholar
Vauclair, J. (2012). Piaget and the comparative psychology of animal cognition. In Marti, E., & Rodríguez, C. (eds.), After Piaget (pp. 59–72). New Brunswick, NJ: Transaction Publishers.Google Scholar
Vauclair, J., & Cochet, H. (2013). Ontogeny and phylogeny of communicative gestures, speech-gestures relationships and left hemisphere specialization for language. In Botha, R. and Everaert, M. (eds.), Oxford Studies in the Evolution of Language: The Evolutionary Emergence of Human Language (pp. 160–180). Oxford: Oxford University Press.Google Scholar
Vidal, J. M., & Vauclair, J. (1996). Un Animal politique autre qu’humain? Epokhè, 6, 35–55.Google Scholar
Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128.CrossRefGoogle ScholarPubMed
Winnicott, D. W. (1971). Playing and Reality. London: Routledge.Google Scholar
Yerkes, R. M. (1916). The Mental Life of Monkeys and Apes: A Study of Ideational Behavior. New York: Holt & Co.CrossRefGoogle Scholar

References

Aguiar, A., & Baillargeon, R. (1999). 2.5-month-old infants’ reasoning about when objects should and should not be occluded. Cognitive Psychology, 39, 116–157.Google Scholar
Aguiar, A., & Baillargeon, R. (2002). Developments in young infants’ reasoning about occluded objects. Cognitive Psychology, 45, 267–336.CrossRefGoogle ScholarPubMed
Ahmed, A., & Ruffman, T. (1998). Why do infants make A not B errors in a search task, yet show memory for the location of hidden objects in a nonsearch task? Developmental Psychology, 34, 441–453.CrossRefGoogle ScholarPubMed
Anderson, E. M., Hespos, S. J., & Rips, L. J. (2018). Five-month-old infants have expectations for the accumulation of nonsolid substances. Cognition, 175, 1–10.CrossRefGoogle ScholarPubMed
Angelone, B. L., Levin, D. T., & Simons, D. J. (2003). The relationship between change detection and recognition of centrally attended objects in motion pictures. Perception, 32, 947–962.Google Scholar
Applin, J. B., & Kibbe, M. M. (2019). Six-month-old infants predict agents’ goal-directed actions on occluded objects. Infancy, 24, 392–410.CrossRefGoogle ScholarPubMed
Baillargeon, R. (1987). Object permanence in 3.5- and 4.5-month-old infants. Developmental Psychology, 23, 655–664.Google Scholar
Baillargeon, R. (1991). Reasoning about the height and location of a hidden object in 4.5- and 6.5-month-old infants. Cognition, 38, 13–42.CrossRefGoogle ScholarPubMed
Baillargeon, R. (1993). The object concept revisited: New directions in the investigation of infants’ physical knowledge. In Granrud, C. E. (ed.), Visual Perception and Cognition in Infancy (pp. 265–315). Hillsdale, NJ: Erlbaum.Google Scholar
Baillargeon, R. (1995). A model of physical reasoning in infancy. In Rovee-Collier, C., & Lipsitt, L. P. (eds.), Advances in Infancy Research (Vol. 9, pp. 305–371). Norwood, NJ: Ablex.Google Scholar
Baillargeon, R. (2008). Innate ideas revisited: For a principle of persistence in infants’ physical reasoning. Perspectives on Psychological Science, 3, 2–13.Google Scholar
Baillargeon, R., & Carey, S. (2012). Core cognition and beyond: The acquisition of physical and numerical knowledge. In Pauen, S. (ed.), Early Childhood Development and Later Outcome (pp. 33–65). Cambridge: Cambridge University Press.Google Scholar
Baillargeon, R., & DeJong, G. F. (2017). Explanation-based learning in infancy. Psychonomic Bulletin & Review, 24, 1511–1526.CrossRefGoogle ScholarPubMed
Baillargeon, R., & DeVos, J. (1991). Object permanence in young infants: Further evidence. Child Development, 62, 1227–1246.Google Scholar
Baillargeon, R., & Graber, M. (1987). Where’s the rabbit? 5.5-month-old infants’ representation of the height of a hidden object. Cognitive Development, 2, 375–392.Google Scholar
Baillargeon, R., Graber, M., DeVos, J., & Black, J. (1990). Why do young infants fail to search for hidden objects? Cognition, 36, 225–284.Google Scholar
Baillargeon, R., Li, J., Gertner, Y., & Wu, D. (2011). How do infants reason about physical events? In Goswami, U. (ed.), The Wiley-Blackwell Handbook of Childhood Cognitive Development, 11 (2nd ed., pp. 11–48). Oxford: Blackwell.Google Scholar
Baillargeon, R., Li, J., Ng, W., & Yuan, S. (2009a). An account of infants’ physical reasoning. In Woodward, A., & Needham, A. (eds.), Learning and the Infant Mind (pp. 66–116). New York: Oxford University Press.Google Scholar
Baillargeon, R., Needham, A., & DeVos, J. (1992). The development of young infants’ intuitions about support. Early Development and Parenting, 1, 69–78.Google Scholar
Baillargeon, R., Spelke, E. S., & Wasserman, S. (1985). Object permanence in five-month-old infants. Cognition, 20, 191–208.Google Scholar
Baillargeon, R., Stavans, M., Wu, D., Gertner, Y., Setoh, P., Kittredge, A. K., & Bernard, A. (2012). Object individuation and physical reasoning in infancy: An integrative account. Language Learning and Development, 8, 4–46.Google Scholar
Baillargeon, R., Wu, D., Yuan, S., Li, J., & Luo, Y. (2009b). Young infants’ expectations about self-propelled objects. In Hood, B., & Santos, L. (eds.), The Origins of Object Knowledge (pp. 285–352). Oxford: Oxford University Press.CrossRefGoogle Scholar
Bogartz, R. S., Shinskey, J. L., & Speaker, C. J. (1997). Interpreting infant looking: The event set × event set design. Developmental Psychology, 33, 408–422.CrossRefGoogle ScholarPubMed
Bonatti, L., Frot, E., Zangl, R., & Mehler, J. (2002). The human first hypothesis: Identification of conspecifics and individuation of objects in the young infant. Cognitive Psychology, 44, 388–426.CrossRefGoogle ScholarPubMed
Boudreau, J. P., & Bushnell, E. W. (2000). Spilling thoughts: Configuring attentional resources in infants’ goal-directed actions. Infant Behavior and Development, 23, 543–566.CrossRefGoogle Scholar
Cacchione, T., Schaub, S., & Rakoczy, H. (2013). Fourteen-month-old infants infer the continuous identity of objects on the basis of nonvisible causal properties. Developmental Psychology, 49, 1325–1329.CrossRefGoogle ScholarPubMed
Carey, S. (2011). The Origin of Concepts. New York: Oxford University Press.Google ScholarPubMed
Casasola, M. (2008). The development of infants’ spatial categories. Current Directions in Psychological Science, 17, 21–25.CrossRefGoogle Scholar
Cashon, C. H., & Cohen, L. B. (2000). Eight‐month‐old infants’ perception of possible and impossible events. Infancy, 1, 429–446.Google Scholar
Dan, N., Omori, T., & Tomiyasu, Y. (2000). Development of infants’ intuitions about support relations: Sensitivity to stability. Developmental Science, 3, 171–180.Google Scholar
Daum, M. M., Prinz, W., & Aschersleben, G. (2009). Means-end behavior in young infants: The interplay of action perception and action production. Infancy, 14, 613–640.CrossRefGoogle ScholarPubMed
Decarli, G., Franchin, L., Piazza, M., & Surian, L. (2020). Infants’ use of motion cues in object individuation processes. Journal of Experimental Child Psychology, 197, 104868.Google Scholar
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168.CrossRefGoogle ScholarPubMed
Fischer, J., Mikhael, J. G., Tenenbaum, J. B., & Kanwisher, N. (2016). Functional neuroanatomy of intuitive physical inference. Proceedings of the National Academy of Sciences (USA), 113, E5072–E5081.CrossRefGoogle ScholarPubMed
Futó, J., Téglás, E., Csibra, G., & Gergely, G. (2010). Communicative function demonstration induces kind-based artifact representation in preverbal infants. Cognition, 117, 1–8.CrossRefGoogle ScholarPubMed
Gelman, R. (1990). First principles organize attention to and learning about relevant data: Number and the animate-inanimate distinction as examples. Cognitive Science, 14, 79–106.Google Scholar
Goldman, E. J., & Wang, S. H. (2019). Comparison facilitates the use of height information by 5-month-olds in containment events. Developmental Psychology, 55, 2475–2482.Google Scholar
Gordon, R. D., & Irwin, D. E. (1996). What’s in an object file? Evidence from priming studies. Perception & Psychophysics, 58, 1260–1277.CrossRefGoogle Scholar
Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and its role in object recognition. Vision Research, 41, 1409–1422.Google Scholar
Haith, M. M. (1998). Who put the cog in infant cognition? Is rich interpretation too costly? Infant Behavior and Development, 21, 167–179.CrossRefGoogle Scholar
Hauf, P., Paulus, M., & Baillargeon, R. (2012). Infants use compression information to infer objects’ weights: Examining cognition, exploration, and prospective action in a preferential-reaching task. Child Development, 83, 1978–1995.CrossRefGoogle Scholar
Hespos, S. J., & Baillargeon, R. (2001a). Infants’ knowledge about occlusion and containment events: A surprising discrepancy. Psychological Science, 12, 141–147.Google Scholar
Hespos, S. J., & Baillargeon, R. (2001b). Reasoning about containment events in very young infants. Cognition, 78, 207–245.CrossRefGoogle ScholarPubMed
Hespos, S. J., & Baillargeon, R. (2006). Décalage in infants’ knowledge about occlusion and containment events: Converging evidence from action tasks. Cognition, 99, B31–B41.CrossRefGoogle ScholarPubMed
Hespos, S. J., & Baillargeon, R. (2008). Young infants’ actions reveal their developing knowledge of support variables: Converging evidence for violation-of-expectation findings. Cognition, 107, 304–316.CrossRefGoogle ScholarPubMed
Hespos, S. J., Ferry, A. L., Anderson, E. M., Hollenbeck, E. N., & Rips, L. J. (2016). Five-month-old infants have general knowledge of how nonsolid substances behave and interact. Psychological Science, 27, 244–256.Google Scholar
Hollingworth, A., Williams, C. C., & Henderson, J. M. (2001). To see and remember: Visually specific information is retained in memory from previously attended objects in natural scenes. Psychonomic Bulletin & Review, 8, 761–768.Google Scholar
Huettel, S. A., & Needham, A. (2000). Effects of balance relations between objects on infant’s object segregation. Developmental Science, 3, 415–427.CrossRefGoogle Scholar
Huttenlocher, J., Duffy, S., & Levine, S. (2002). Infants and toddlers discriminate amount: Are they measuring? Psychological Science, 13, 244–249.Google Scholar
Huttenlocher, J., Hedges, L. V., & Duncan, S. (1991). Categories and particulars: Prototype effects in estimating spatial location. Psychological Review, 98, 352–376.Google Scholar
Hyde, D. C., Aparicio Betancourt, M., & Simon, C. E. (2015). Human temporal-parietal junction spontaneously tracks others’ beliefs: A functional near-infrared spectroscopy study. Human Brain Mapping, 36, 4831–4846.CrossRefGoogle ScholarPubMed
Hyde, D. C., Simon, C. E., Ting, F., & Nikolaeva, J. I. (2018). Functional organization of the temporal–parietal junction for theory of mind in preverbal infants: A near-infrared spectroscopy study. Journal of Neuroscience, 38, 4264–4274.CrossRefGoogle ScholarPubMed
Jin, K. S., Houston, J. L., Baillargeon, R., Groh, A. M., & Roisman, G. I. (2018). Young infants expect an unfamiliar adult to comfort a crying baby: Evidence from a standard violation-of-expectation task and a novel infant-triggered-video task. Cognitive Psychology, 102, 1–20.Google Scholar
Kahneman, D., Treisman, A., & Gibbs, B. J. (1992). The reviewing of object files: Object-specific integration of information. Cognitive Psychology, 24, 175–219.CrossRefGoogle ScholarPubMed
Káldy, Z., & Leslie, A. M. (2003). Identification of objects in 9-month-old infants: Integrating “what” and “where” information. Developmental Science, 6, 360–373.Google Scholar
Káldy, Z., & Leslie, A. M. (2005). A memory span of one? Object identification in 6.5-month-old infants. Cognition, 97, 153–177.CrossRefGoogle ScholarPubMed
Kampis, D., Parise, E., Csibra, , G., & Kovács, Á. M. (2015). Neural signatures for sustaining object representations attributed to others in preverbal human infants. Proceedings of the Royal Society B: Biological Sciences, 282, 20151683.CrossRefGoogle ScholarPubMed
Keen, R. E., & Berthier, N. E. (2004). Continuities and discontinuities in infants’ representation of objects and events. In Kail, R. V. (ed.), Advances in Child Development and Behavior (Vol. 32, pp. 243–279). San Diego, CA: Elsevier Academic Press.Google Scholar
Keil, F. C. (1995). The growth of causal understandings of natural kinds. In Sperber, D., Premack, D., & Premack, A. J. (eds.), Causal Cognition: A Multidisciplinary Debate (pp. 234–262). Oxford: Clarendon Press.Google Scholar
Kibbe, M. M., & Leslie, A. M. (2011). What do infants remember when they forget? Location and identity in 6-month-olds’ memory for objects. Psychological Science, 22, 1500–1505.Google Scholar
Kibbe, M. M., & Leslie, A. M. (2013). What’s the object of object working memory in infancy? Unraveling “what” and “how many.” Cognitive Psychology, 66, 380–404.CrossRefGoogle Scholar
Kibbe, M. M., & Leslie, A. M. (2019). Conceptually rich, perceptually sparse: Object representations in 6-month-old infants’ working memory. Psychological Science, 30, 362–375.CrossRefGoogle ScholarPubMed
Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition, 83, B35–B42.Google Scholar
Kominsky, J. F., Strickland, B., Wertz, A. E., Elsner, C., Wynn, K., & Keil, F. C. (2017). Categories and constraints in causal perception. Psychological Science, 28, 1649–1662.CrossRefGoogle ScholarPubMed
Kosugi, D., & Fujita, K. (2002). How do 8-month-old infants recognize causality in object motion and that in human action? Japanese Psychological Research, 44, 66–78.CrossRefGoogle Scholar
Kotovsky, L., & Baillargeon, R. (1994). Calibration-based reasoning about collision events in 11-month-old infants. Cognition, 51, 107–129.Google Scholar
Kotovsky, L., & Baillargeon, R. (1998). The development of calibration-based reasoning about collision events in young infants. Cognition, 67, 311–351.CrossRefGoogle ScholarPubMed
Kotovsky, L., & Baillargeon, R. (2000). Reasoning about collision events involving inert objects in 7.5-month-old infants. Developmental Science, 3, 344–359.CrossRefGoogle Scholar
Kovács, Á. M., Téglás, E., & Endress, A. D. (2010). The social sense: Susceptibility to others’ beliefs in human infants and adults. Science, 330, 1830–1834.CrossRefGoogle ScholarPubMed
Leslie, A. M. (1994). ToMM, ToBy, and Agency: Core architecture and domain specificity. In Hirschfeld, L. A., & Gelman, S. A. (eds.), Mapping the Mind: Domain Specificity in Cognition and Culture (pp. 119–148). New York: Cambridge University Press.Google Scholar
Leslie, A. M. (1995). A theory of agency. In Sperber, D., Premack, D., & Premack, A. J. (eds.), Causal Cognition: A Multidisciplinary Debate (pp. 121–149). Oxford: Clarendon Press.Google Scholar
Leslie, A. M., & Keeble, S. (1987). Do six-month-old infants perceive causality? Cognition, 25, 265–288.CrossRefGoogle ScholarPubMed
Leslie, A. M., Xu, F., Tremoulet, P. D., & Scholl, B. J. (1998). Indexing and the object concept: developing “what” and “where” systems. Trends in Cognitive Sciences, 2, 10–18.CrossRefGoogle Scholar
Lin, Y., & Baillargeon, R. (2018). Infants individuate objects with distinct prior event roles. Paper presented at the Biennial International Congress of Infant Studies, June 2018, Philadelphia, PA.Google Scholar
Lin, Y., & Baillargeon, R. (2019). Testing a new two-system model of early individuation. Paper presented at the Biennial Meeting of the Cognitive Development Society, September 2019, Louisville, KY.Google Scholar
Lin, Y., Li, J., Gertner, Y., Ng, W., Fisher, C. L., & Baillargeon, R. (2021). How do the object-file and physical-reasoning systems interact? Evidence from priming effects with object arrays or novel labels. Cognitive Psychology, 125, 101368.Google Scholar
Lin, Y., Stavans, M., & Baillargeon, R. (2019). Infants can use many types of categories to individuate objects. Paper presented at the Biennial Meeting of the Society for Research in Child Development, March 2019, Baltimore, MD.Google Scholar
Liu, S., Brooks, N. B., & Spelke, E. S. (2019). Origins of the concepts cause, cost, and goal in prereaching infants. Proceedings of the National Academy of Sciences (USA), 116, 17747–17752.Google Scholar
Luo, Y., & Baillargeon, R. (2005). When the ordinary seems unexpected: Evidence for incremental physical knowledge in young infants. Cognition, 95, 297–328.Google Scholar
Luo, Y., Kaufman, L., & Baillargeon, R. (2009). Young infants’ reasoning about physical events involving inert and self-propelled objects. Cognitive Psychology, 58, 441–486.CrossRefGoogle ScholarPubMed
Mascalzoni, E., Regolin, L., Vallortigara, G., & Simion, F. (2013). The cradle of causal reasoning: Newborns’ preference for physical causality. Developmental Science, 16, 327–335.CrossRefGoogle ScholarPubMed
McCurry, S., Wilcox, T., & Woods, R. (2009). Beyond the search barrier: A new task for assessing object individuation in young infants. Infant Behavior and Development, 32, 429–436.CrossRefGoogle Scholar
Merced-Nieves, F. M., Aguiar, A., Dzwilewski, K. L. C., Musaad, S., Korrick, S. A., & Schantz, S. L. (2020). Association of prenatal maternal perceived stress with a sexually dimorphic measure of cognition in 4.5-month-old infants. Neurotoxicology and Teratology, 77, 106850.Google Scholar
Mitroff, S. R., Simons, D. J., & Levin, D. T. (2004). Nothing compares 2 views: Change blindness can occur despite preserved access to the changed information. Perception & Psychophysics, 66, 1268–1281.Google Scholar
Mou, Y., & Luo, Y. (2017). Is it a container? Young infants’ understanding of containment events. Infancy, 22, 256–270.Google Scholar
Needham, A., & Baillargeon, R. (1993). Intuitions about support in 4.5-month-old infants. Cognition, 47, 121–148.Google Scholar
Newcombe, N., Huttenlocher, J., & Learmonth, A. (1999). Infants’ coding of location in continuous space. Infant Behavior and Development, 22, 483–510.CrossRefGoogle Scholar
Oakes, L. M., Ross-Sheehy, S., & Luck, S. J. (2006). Rapid development of feature binding in visual short-term memory. Psychological Science, 17, 781–787.Google Scholar
Pauen, S. (2002). The global-to-basic level shift in infants’ categorical thinking: First evidence from a longitudinal study. International Journal of Behavioral Development, 26, 492–499.CrossRefGoogle Scholar
Piaget, J. (1952). The Origins of Intelligence in Children. New York: International Universities Press.Google Scholar
Piaget, J. (1954). The Construction of Reality in the Child. New York: Basic Books.Google Scholar
Pylyshyn, Z. (1989). The role of location indexes in spatial perception: A sketch of the FINST spatial-index model. Cognition, 32, 65–97.CrossRefGoogle ScholarPubMed
Pylyshyn, Z. W. (2007). Things and Places: How the Mind Connects with the World. Cambridge, MA: MIT Press.Google Scholar
Rensink, R. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention to perceive changes in scenes. Psychological Science, 8, 368–373.Google Scholar
Rips, L. J., Blok, S., & Newman, G. (2006). Tracing the identity of objects. Psychological Review, 113, 1–30.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928.CrossRefGoogle ScholarPubMed
Saffran, J. R., & Kirkham, N. Z. (2018). Infant statistical learning. Annual Review of Psychology, 69, 181–203.CrossRefGoogle ScholarPubMed
Saxe, R., Tenenbaum, J., & Carey, S. (2005). Secret agents: 10- and 12-month-old infants’ inferences about hidden causes. Psychological Science, 16, 995–1001.CrossRefGoogle ScholarPubMed
Saxe, R., Tzelnic, T., & Carey, S. (2006). Five-month-old infants know humans are solid, like inanimate objects. Cognition, 101, B1–B8.Google Scholar
Saxe, R., Tzelnic, T., & Carey, S. (2007). Knowing who dunnit: Infants identify the causal agent in an unseen causal interaction. Developmental Psychology, 43, 149–158.Google Scholar
Setoh, P., Wu, D., Baillargeon, R., & Gelman, R. (2013). Young infants have biological expectations about animals. Proceedings of the National Academy of Sciences (USA), 110, 15937–15942.CrossRefGoogle ScholarPubMed
Shinskey, J. L. (2002). Infants’ object search: Effects of variable object visibility under constant means-end demands. Journal of Cognition and Development, 3, 119–142.CrossRefGoogle Scholar
Simons, D. J., Chabris, C. F., Schnur, T., & Levin, D. T. (2002). Evidence for preserved representations in change blindness. Consciousness and Cognition, 11, 78–97.Google Scholar
Simons, D. J., & Levin, D. T. (1998). Failure to detect changes to people during a real-world interaction. Psychonomic Bulletin & Review, 5, 644–649.CrossRefGoogle Scholar
Southgate, V., & Vernetti, A. (2014). Belief-based action prediction in preverbal infants. Cognition, 130, 1–10.Google Scholar
Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99, 605–632.Google Scholar
Spelke, E. S., Kestenbaum, R., Simons, D. J., & Wein, D. (1995a). Spatiotemporal continuity, smoothness of motion and object identity in infancy. British Journal of Developmental Psychology, 13, 113–142.CrossRefGoogle Scholar
Spelke, E. S., Phillips, A., & Woodward, A. L. (1995b). Infants’ knowledge of object motion and human action. In Sperber, D., Premack, D., & Premack, A. J. (eds.), Causal Cognition: A Multidisciplinary Debate (pp. 44–78). Oxford: Clarendon Press.Google Scholar
Stahl, A. E., & Feigenson, L. (2015). Observing the unexpected enhances infants’ learning and exploration. Science, 348, 91–94.CrossRefGoogle ScholarPubMed
Stavans, M., & Baillargeon, R. (2018). Four-month-old infants individuate and track simple tools following functional demonstrations. Developmental Science, 21, e12500.CrossRefGoogle ScholarPubMed
Stavans, M., Lin, Y., Wu, D., & Baillargeon, R. (2019). Catastrophic individuation failures in infancy: A new model and predictions. Psychological Review, 126, 196–225.CrossRefGoogle ScholarPubMed
Strickland, B., & Scholl, B. J. (2015). Visual perception involves event-type representations: The case of containment versus occlusion. Journal of Experimental Psychology: General, 144, 570–580.Google Scholar
Surian, L., & Caldi, S. (2010). Infants’ individuation of agents and inert objects. Developmental Science, 13, 143–150.CrossRefGoogle ScholarPubMed
Thelen, E., & Smith, L. B. (1994), A Dynamic Systems Approach to the Development of Perception and Action. Cambridge, MA: MIT Press.Google Scholar
Ullman, T. D., Spelke, E., Battaglia, P., & Tenenbaum, J. B. (2017). Mind games: Game engines as an architecture for intuitive physics. Trends in Cognitive Sciences, 21, 649–665.CrossRefGoogle ScholarPubMed
Van de Walle, G. A., Carey, S., & Prevor, M. (2000). Bases for object individuation in infancy: Evidence from manual search. Journal of Cognition and Development, 1, 249–280.Google Scholar
Wang, S. (2011). Priming 4.5-month-old infants to use height information by enhancing retrieval. Developmental Psychology, 47, 26–38.Google Scholar
Wang, S. (2019). Regularity detection and explanation-based learning jointly support learning about physical events in early infancy. Cognitive Psychology, 113, 101219.CrossRefGoogle ScholarPubMed
Wang, S., & Baillargeon, R. (2005). Inducing infants to detect a physical violation in a single trial. Psychological Science, 16, 542–549.CrossRefGoogle Scholar
Wang, S., & Baillargeon, R. (2006). Infants’ physical knowledge affects their change detection. Developmental Science, 9, 173–181.Google Scholar
Wang, S., & Baillargeon, R. (2008a). Can infants be “taught” to attend to a new physical variable in an event category? The case of height in covering events. Cognitive Psychology, 56, 284–326.CrossRefGoogle Scholar
Wang, S., & Baillargeon, R. (2008b). Detecting impossible changes in infancy: A three-system account. Trends in Cognitive Sciences, 12, 17–23.Google Scholar
Wang, S., Baillargeon, R., & Brueckner, L. (2004). Young infants’ reasoning about hidden objects: Evidence from violation-of-expectation tasks with test trials only. Cognition, 93, 167–198.Google Scholar
Wang, S., Baillargeon, R., & Paterson, S. (2005). Detecting continuity violations in infancy: A new account and new evidence from covering and tube events. Cognition, 95, 129–173.CrossRefGoogle ScholarPubMed
Wang, S., & Goldman, E. J. (2016). Infants actively construct and update their representations of physical events: Evidence from change detection by 12-month-olds. Child Development Research, article 3102481.Google Scholar
Wang, S., Kaufman, L., & Baillargeon, R. (2003). Should all stationary objects move when hit? Developments in infants’ causal and statistical expectations about collision events. Infant Behavior and Development, 26, 529–568.CrossRefGoogle ScholarPubMed
Wang, S., & Kohne, L. (2007). Visual experience enhances infants’ use of task-relevant information in an action task. Developmental Psychology, 43, 1513–1522.Google Scholar
Wang, S., & Mitroff, S. R. (2009). Preserved visual representations despite change blindness in infants. Developmental Science, 12, 681–687.Google Scholar
Wang, S., & Onishi, K. H. (2017). Enhancing young infants’ representations of physical events through improved retrieval (not encoding) of information. Journal of Cognition and Development, 18, 289–308.CrossRefGoogle Scholar
Wang, S., Zhang, Y., & Baillargeon, R. (2016). Young infants view physically possible support events as unexpected: New evidence for rule learning. Cognition, 157, 100–105.Google Scholar
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337–375.Google Scholar
Wilcox, T. (1999). Object individuation: Infants’ use of shape, size, pattern, and color. Cognition, 72, 125–166.CrossRefGoogle ScholarPubMed
Wilcox, T., & Baillargeon, R. (1998). Object individuation in infancy: The use of featural information in reasoning about occlusion events. Cognitive Psychology, 37, 97–155.Google Scholar
Wilcox, T., & Chapa, C. (2004). Priming infants to attend to color and pattern information in an individuation task. Cognition, 90, 265–302.CrossRefGoogle Scholar
Wilcox, T., Nadel, L., & Rosser, R. (1996). Location memory in healthy preterm and full-term infants. Infant Behavior and Development, 19, 309–323.Google Scholar
Wilcox, T., & Schweinle, A. (2002). Object individuation and event mapping: Developmental changes in infants’ use of featural information. Developmental Science, 5, 132–150.CrossRefGoogle Scholar
Wilcox, T., Smith, T., & Woods, R. (2011). Priming infants to use pattern information in an object individuation task: The role of comparison. Developmental Psychology, 47, 886.CrossRefGoogle Scholar
Wilson, R. A., & Keil, F. C. (2000). The shadows and shallows of explanation. In Keil, F. C., & Wilson, R. A. (eds.), Explanation and Cognition (pp. 87–114). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Wynn, K. (1992). Addition and subtraction by human infants. Nature, 358, 749–750.CrossRefGoogle ScholarPubMed
Xu, F. (2002). The role of language in acquiring object kind concepts in infancy. Cognition, 85, 223–250.CrossRefGoogle ScholarPubMed
Xu, F., & Baker, A. (2005). Object individuation in 10-month-old infants using a simplified manual search method. Journal of Cognition and Development, 6, 307–323.CrossRefGoogle Scholar
Xu, F., & Carey, S. (1996). Infants’ metaphysics: The case of numerical identity. Cognitive Psychology, 30, 111–153.Google Scholar
Xu, F., Carey, S., & Quint, N. (2004). The emergence of kind-based object individuation in infancy. Cognitive Psychology, 49, 155–190.Google Scholar
Zhang, Y, & Wang, S. (2019). Violation to infant faulty knowledge induces object exploration by 7.5-month-olds in support events. Paper presented at the Biennial Meeting of the Cognitive Development Society, September 2019, Louisville, KY.Google Scholar

References

Antell, S. E., & Caron, A. J. (1985). Neonatal perception of spatial relationships. Infant Behavior and Development, 8, 15–23.Google Scholar
Baillargeon, R. (1987). Object permanence in 3 1/2- and 4 1/2-month-old infants. Developmental Psychology, 23, 655–664.Google Scholar
Behl-Chadha, G. (1996). Basic-level and superordinate-like categorical representations in early infancy. Cognition, 60, 105–141.Google Scholar
Benton, D. T., & Rakison, D. H. (2018). Computational Modeling and What It Can Tell You about Behavior. Thousand Oaks, CA: SAGE Research Methods Cases.Google Scholar
Best, C. A., Yim, H., & Sloutsky, V. M. (2013). The cost of selective attention in category learning: Developmental differences between adults and infants. Journal of Experimental Child Psychology, 116, 105–119.CrossRefGoogle ScholarPubMed
Bomba, P. C., & Siqueland, E. R. (1983). The nature and structure of infant form categories. Journal of Experimental Child Psychology, 35, 294–328.CrossRefGoogle Scholar
Brooks, L. (1978). Nonanalytic concept formation and memory for instances. In Rosch, E. H., and Lloyd, B. B. (eds.), Cognition and Categorisation (pp. 169–211). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Bruner, J. S., Olver, R. R., & Greenfield, P. M. (1966). Studies in Cognitive Growth. New York: Wiley.Google Scholar
Bruner, R., Goodnow, J. J., and Austin, G. A. (eds.) (1956). A Study of Thinking (pp. 3–170). New York: Wiley.Google Scholar
Casasola, M. (2005). Can language do the driving? The effect of linguistic input on infants’ categorization of support spatial relations. Developmental Psychology, 41, 183–192.CrossRefGoogle ScholarPubMed
Casasola, M., & Bhagwat, J. (2007). Do novel words facilitate 18‐month‐olds’ spatial categorization? Child Development, 78, 1818–1829.Google Scholar
Casasola, M., & Cohen, L. B. (2002). Infant categorization of containment, support and tight‐fit spatial relationships. Developmental Science, 5, 247–264.Google Scholar
Casasola, M., Cohen, L. B., & Chiarello, E. (2003). Six‐month‐old infants’ categorization of containment spatial relations. Child Development, 74, 679–693.CrossRefGoogle ScholarPubMed
Casasola, M., & Park, Y. (2013). Developmental changes in infant spatial categorization: When more is best and when less is enough. Child Development, 84, 1004–1019.Google Scholar
Choi, S. (2006). Influence of language-specific input on spatial cognition: Categories of containment. First Language, 26, 207–232.Google Scholar
Choi, S., & Bowerman, M. (1991). Learning to express motion events in English and Korean: The influence of language-specific lexicalization patterns. Cognition, 41, 83–121.Google Scholar
Choi, S., McDonough, L., Bowerman, M., & Mandler, J. M. (1999). Early sensitivity to language-specific spatial categories in English and Korean. Cognitive Development, 14(2), 241–268.Google Scholar
Cromer, R. F. (1974). The development of language and cognition: The cognition hypothesis. In Foss, B. M. (ed.), New Perspectives in Child Development (pp. 184–252). London: Penguin.Google Scholar
de Boysson-Bardies, B., & Vihman, M. M. (1991). Adaptation to language: Evidence from babbling and first words in four languages. Language, 67, 297–319.Google Scholar
Dromi, E. (1987). Early Lexical Development. New York. Cambridge University Press.Google Scholar
Eimas, P. D., & Quinn, P. C. (1994). Studies on the formation of perceptually based basic-level categories in young infants. Child Development, 65, 903–917.CrossRefGoogle ScholarPubMed
Freeman, N. H., Lloyd, S., & Sinha, C. G. (1980). Infant search tasks reveal early concepts of containment and canonical usage of objects. Cognition, 8, 243–262.Google Scholar
French, R. M., Mareschal, D., Mermillod, M., & Quinn, P. C. (2004). The role of bottom-up processing in perceptual categorization by 3- to 4-month old infants: Simulations and data. Journal of Experimental Psychology: General, 133, 382–397.CrossRefGoogle Scholar
Gava, L., Valenza, E., & Turati, C. (2009). Newborns’ perception of left–right spatial relations. Child Development, 80, 1797–1810.CrossRefGoogle ScholarPubMed
Gelman, R. (1990). First principles organize attention to and learning about relevant data: Number and the animate-inanimate distinction as examples. Cognitive Science, 14, 79–106.Google Scholar
Gelman, R., Durgin, F., & Kaufman, L. (1995). Distinguishing between animate and inanimates: Not by motion alone. In Sperber, D., Premack, D., & Premack, A. J. (eds.), Causal Cognition (pp. 150–184). Oxford: Clarendon.Google Scholar
Gelman, S. A., & Coley, J. D. (1990). The importance of knowing a dodo is a bird: Categories and inferences in 2-year-old children. Developmental Psychology, 26, 796.Google Scholar
Gentner, D., Özyürek, A., Gürcanli, Ö., & Goldin-Meadow, S. (2013). Spatial language facilitates spatial cognition: Evidence from children who lack language input. Cognition, 127, 318–330.CrossRefGoogle ScholarPubMed
Goldfield, B. A., & Reznick, J. S. (1990). Early lexical acquisition: Rate, content, and the vocabulary spurt. Journal of Child Language, 17, 171–183.CrossRefGoogle ScholarPubMed
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3.Google Scholar
Gopnik, A., & Meltzoff, A. (1987). The development of categorization in the second year and its relation to other cognitive and linguistic developments. Child Development, 58, 1523–1531.Google Scholar
Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557.Google Scholar
Hespos, S. J., & Spelke, E. S. (2004). Conceptual precursors to language. Nature, 430, 453.CrossRefGoogle ScholarPubMed
Hurley, K. B., & Oakes, L. M. (2015). Experience and distribution of attention: Pet exposure and infants’ scanning of animal images. Journal of Cognition and Development, 16, 11–30.Google Scholar
James, W. (2013). The Principles of Psychology. Redditch, Worcestershire: Read Books Ltd.Google Scholar
Ji, L. J., Zhang, Z., & Nisbett, R. E. (2004). Is it culture or is it language? Examination of language effects in cross-cultural research on categorization. Journal of Personality and Social Psychology, 87, 57.Google Scholar
Johnson, C., & Rakison, D. H. (2006). Early categorization of animate/inanimate concepts in young children with autism. Journal of Developmental and Physical Disabilities, 18, 73–89.Google Scholar
Jones, S. S., & Smith, L. B. (1993). The place of perception in children’s concepts. Cognitive Development, 8, 113–139.CrossRefGoogle Scholar
Keil, F. C. (1981). Constraints on knowledge and cognitive development. Psychological Review, 88, 197–227.CrossRefGoogle Scholar
Kovack-Lesh, K. A., McMurray, B., & Oakes, L. M. (2014). Four-month-old infants’ visual investigation of cats and dogs: Relations with pet experience and attentional strategy. Developmental Psychology, 50, 402.Google Scholar
Langlois, J. H., Roggman, L. A., Casey, R. J., Ritter, J. M., Rieser-Danner, L. A., & Jenkins, V. Y. (1987). Infant preferences for attractive faces: Rudiments of a stereotype? Developmental Psychology, 23, 363.Google Scholar
Leslie, A. (1994). ToMM, ToBy, and Agency: Core architecture and domain specificity. In Hirschfeld, L., & Gelman, S. (eds.), Mapping the Mind: Domain Specificity in Cognition and Culture (pp. 119–148). New York: Cambridge University Press.Google Scholar
Leslie, A. (1995). A theory of agency. In Sperber, D., Premack, D., & Premack, A. J. (eds.), Causal Cognition (pp. 121–141). Oxford: Clarendon.Google Scholar
Madole, K. L., & Cohen, L. B. (1995). The role of object parts in infants’ attention to form-function correlations. Developmental Psychology, 31, 637.Google Scholar
Mandler, J. M. (1992). How to build a baby: II. Conceptual primitives. Psychological Review, 99, 587–604.CrossRefGoogle ScholarPubMed
Mandler, J. M. (2000). Perceptual and conceptual processes in infancy. Journal of Cognition and Development, 1, 3–36.Google Scholar
Mandler, J. M. (2003). Conceptual categorization. In Rakison, D. H., & Oakes, L. M. (eds.), Early Category and Concept Development: Making Sense of the Blooming, Buzzing Confusion (pp. 103–131). New York: Oxford University Press.Google Scholar
Mandler, J. M., & Bauer, P. J. (1988). The cradle of categorization: Is the basic level basic? Cognitive Development, 3, 247–264.Google Scholar
Mandler, J. M., Bauer, P. J., & McDonough, L. (1991). Separating the sheep from the goats: Differentiating global categories. Cognitive Psychology, 23, 263–298.CrossRefGoogle Scholar
Mandler, J. M., & McDonough, L. (1996). Drinking and driving don’t mix: Inductive generalization in infancy. Cognition, 59, 307–335.Google Scholar
Mareschal, D., French, R. M., & Quinn, P. C. (2000). A connectionist account of asymmetric category learning in early infancy. Developmental Psychology, 36, 635.Google Scholar
Mareschal, D., Quinn, P. C., & French, R. M. (2002). Asymmetric interference in 3- to 4-month olds’ sequential category learning. Cognitive Science, 26, 377–389.Google Scholar
McDonough, L., Choi, S., & Mandler, J. M. (2003). Understanding spatial relations: Flexible infants, lexical adults. Cognitive Psychology, 46, 229–259.Google Scholar
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.Google Scholar
Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19, 242–279.CrossRefGoogle ScholarPubMed
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.CrossRefGoogle ScholarPubMed
Nazzi, T., & Gopnik, A. (2001). Linguistic and cognitive abilities in infancy: When does language become a tool for categorization? Cognition, 80, B11–B20.Google Scholar
Neisser, U. (1987). From direct perception to conceptual structure. In Neisser, U. (ed.), Concepts and Conceptual Development (pp. 11–24). London: Cambridge University Press.Google Scholar
Nelson, K. (1973). Some evidence for the cognitive primacy of categorisation and its functional basis. Merrill-Palmer Quarterly, 19, 21–39.Google Scholar
Newcombe, N., & Huttenlocher, J. (2000). Making Space. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Oakes, L. M. (2010). Using habituation of looking time to assess mental processes in infancy. Journal of Cognition and Development, 11, 255–268.CrossRefGoogle ScholarPubMed
Oakes, L. M., & Cohen, L. B. (1990). Infant perception of a causal event. Cognitive Development, 5, 193–207.Google Scholar
Oakes, L. M., Coppage, D. J., & Dingel, A. (1997). By land or by sea: The role of perceptual similarity in infants’ categorization of animals. Developmental Psychology, 33, 396.Google Scholar
Oakes, L. M. & Madole, K. L. (2003). Principles of developmental change in infants’ category formation. In Rakison, D. H., & Oakes, L. M. (eds.), Early Category and Concept Development: Making Sense of the Blooming, Buzzing Confusion (pp. 159–192). New York: Oxford University Press.Google Scholar
Park, Y., & Casasola, M. (2015). Plain or decorated? Object visual features matter in infant spatial categorization. Journal of Experimental Child Psychology, 140, 105–119.Google Scholar
Piaget, J. (1952). The Origins of Intelligence in Children. New York: W. W. Norton & Co.Google Scholar
Premack, D. (1990). The infants’ theory of self-propelled objects. Cognition, 36, 1–16.CrossRefGoogle ScholarPubMed
Quinn, P. C. (1994). The categorization of above and below spatial relations by young infants. Child Development, 65, 58-69.Google Scholar
Quinn, P. C., Cummins, M., Kase, J., Martin, E., & Weissman, S. (1996). Development of categorical representations for above and below spatial relations in 3-to 7-month-old infants. Developmental Psychology, 32, 942–950.CrossRefGoogle Scholar
Quinn, P. C., & Eimas, P. D. (1996). Perceptual organization and categorization. In Rovee-Collier, C., & Lipsitt, L. (eds.), Advances in Infancy Research (Vol. 10, pp. 1–36). Norwood, NJ: Ablex Publishing.Google Scholar
Quinn, P. C., & Eimas, P. D. (1997). A reexamination of the perceptual-to-conceptual shift in mental representations. Review of General Psychology, 1, 171–187.Google Scholar
Quinn, P. C., & Eimas, P. D. (2000). The emergence of category representations during infancy: Are separate perceptual and conceptual processes required? Journal of Cognition and Development, 1, 55–61.CrossRefGoogle Scholar
Quinn, P. C., Eimas, P. D., & Rosenkrantz, S. L. (1993). Evidence for representations of perceptually similar natural categories by 3-month-old and 4-month-old infants. Perception, 22, 463–475.Google Scholar
Quinn, P. C., Eimas, P. D., & Tarr, M. J. (2001). Perceptual categorization of cat and dog silhouettes by 3-to 4-month-old infants. Journal of Experimental Child Psychology, 79, 78–94.Google Scholar
Quinn, P. C., & Johnson, M. H. (2000). Global-before-basic object categorization in connectionist networks and 2-month-old infants. Infancy, 1, 31–46.CrossRefGoogle ScholarPubMed
Quinn, P. C., Johnson, M. H., Mareschal, D., Rakison, D. H., & Younger, B. A. (2000). Understanding early categorization: One process or two? Infancy, 1, 111–122.Google Scholar
Quinn, P. C., Norris, C. M., Pasko, R. N., Schmader, T. M., & Mash, C. (1999). Formation of a categorical representation for the spatial relation between by 6-to 7-month-old infants. Visual Cognition, 6, 569–585.Google Scholar
Rakison, D. H. (2003). Parts, categorization, and the animate-inanimate distinction in infancy. In Rakison, D. H., & Oakes, L. M. (eds.), Early Category and Concept Development: Making Sense of the Blooming Buzzing Confusion (pp. 159–192). New York: Oxford University Press.Google Scholar
Rakison, D. H. (2005). A secret agent? How infants learn about the identity of objects in a causal scene. Journal of Experimental Child Psychology, 91, 271–296.Google Scholar
Rakison, D. H., & Benton, D. T. (2019). Second‐order correlation learning of dynamic stimuli: Evidence from infants and computational modeling. Infancy, 24, 57–78.CrossRefGoogle ScholarPubMed
Rakison, D. H., & Butterworth, G. E. (1998a). Infants’ attention to object structure in early categorization. Developmental Psychology, 34, 1310–1325.Google Scholar
Rakison, D. H., & Butterworth, G. E. (1998b). Infants’ use of object parts in early categorization. Developmental Psychology, 34, 49–62.CrossRefGoogle ScholarPubMed
Rakison, D. H., & Cohen, L. B. (1999). Infants’ use of functional parts in basic-like categorization. Developmental Science, 2, 423–432.Google Scholar
Rakison, D. H., & Hahn, E. (2004). The mechanisms of early categorization and induction: Smart or dumb infants? In Kail, R. (ed.), Advances in Child Development and Behavior (Vol. 32, pp. 281–322). New York: Academic Press.Google Scholar
Rakison, D. H., & Lupyan, G. (2008). Developing object concepts in infancy: An associative learning perspective. Monographs of the Society for Research in Child Development, 73, 1–110.Google Scholar
Rakison, D. H., & Poulin-Dubois, D. (2001). Developmental origin of the animate-inanimate distinction. Psychological Bulletin, 127, 209–228.Google Scholar
Rakison, D. H., & Poulin-Dubois, D. (2002). You go this way and I’ll go that way: Developmental changes in infants’ attention to correlations among dynamic parts in motion events. Child Development, 73, 682–699.Google Scholar
Rakison, D. H., & Yermolayeva, Y. (2010). Infant categorization. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 894–905.Google ScholarPubMed
Regier, T., & Carlson, L. A. (2001). Grounding spatial language in perception: An empirical and computational investigation. Journal of Experimental Psychology: General, 130, 273.Google Scholar
Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology General, 104, 192–233.Google Scholar
Rosch, E. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.Google Scholar
Rosch, E. (1978). Principles of categorisation. In Rosch, E., & Lloyd, B. (eds.), Cognition and Categorisation (pp. 27–48). Lawrence Erlbaum, NJ, Hillsdale.Google Scholar
Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.Google Scholar
Sapir, E. (1921). An Introduction to the Study of Speech. New York: Harcourt, Brace.Google Scholar
Smith, L. B., Colunga, E., & Yoshida, H. (2003). Making an ontology: Cross-linguistic evidence. In Rakison, D. H., & Oakes, L. M. (eds.), Early Category and Concept Development: Making Sense of the Blooming Buzzing Confusion (pp. 275–302). New York: Oxford University Press.Google Scholar
Smith, L. B., & Heise, D. (1992). Perceptual similarity and conceptual structure. In Burns, B. (ed.), Percepts, Concepts, and Categories (Vol. 93, pp. 233–272). Amsterdam: Elsevier.Google Scholar
Smith, L. B., Jones, S. S., & Landau, B. (1992). Count nouns, adjectives, and perceptual properties in children's novel word interpretations. Developmental Psychology, 28, 273–286.Google Scholar
Smith, L. B., Jones, S. S., & Landau, B. (1996). Naming in young children: A dumb attentional mechanism? Cognition, 60, 143–171.CrossRefGoogle ScholarPubMed
Smith, L. B., & Samuelson, L. K. (2003). Different is good: Connectionism and dynamic systems theory are complementary emergentist approaches to development. Developmental Science, 6, 434–439.CrossRefGoogle Scholar
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10, 89–96.Google Scholar
Spelke, E. S., & Kinzler, K. D. (2009). Innateness, learning, and rationality. Child Development Perspectives, 3, 96–98.CrossRefGoogle ScholarPubMed
Spelke, E. S., Phillips, A., & Woodward, A. L. (1995). Infants’ knowledge of object motion and human action. In Sperber, D., Premack, D., & Premack, A. J. (eds.), Causal Cognition (pp. 150–184). Oxford: Clarendon.Google Scholar
Waxman, S., & Booth, A. (2003). The origins and evolution of links between word learning and conceptual organization: New evidence from 11‐month‐olds. Developmental Science, 6, 128–135.CrossRefGoogle Scholar
Waxman, S. R., & Hall, D. G. (1993). The development of a linkage between count nouns and object categories: Evidence from fifteen‐to twenty‐one‐month‐old infants. Child Development, 64, 1224–1241.Google Scholar
Waxman, S. R., & Markow, D. B. (1995). Words as invitations to form categories: Evidence from 12-to 13-month-old infants. Cognitive Psychology, 29, 257–302.CrossRefGoogle Scholar
Whorf, B. L. (1940). Science and Linguistics (pp. 207–219). Indianapolis, IN: Bobbs-Merrill.Google Scholar
Whorf, B. L. (1956). Language, Thought, and Reality: Selected Writings of Benjamin Lee Whorf. Carroll, J. B. (Ed.). Cambridge, MA: Technology Press of MIT.Google Scholar
Wynn, K. (1992). Addition and subtraction by human infants. Nature, 358, 749–750.CrossRefGoogle ScholarPubMed
Yermolayeva, Y., & Rakison, D. H. (2014). Connectionist modeling of developmental changes in infancy: Approaches, challenges, and contributions. Psychological Bulletin, 140, 224.Google Scholar
Yoshida, H., & Smith, L. B. (2003). Known and novel noun extensions: Attention at two levels of abstraction. Child Development, 74, 564–577.Google Scholar
Younger, B. A., & Cohen, L. B. (1986). Developmental change in infants' perception of correlations among attributes. Child Development, 57, 803–815.CrossRefGoogle ScholarPubMed

References

Baddeley, A. D. (1996). Exploring the central executive. The Quarterly Journal of Experimental Psychology Section A, 49, 5–28.Google Scholar
Baddeley, A. D. (2002). Is working memory still working? European Psychologist, 7, 85–97.Google Scholar
Baddeley, A. D., & Hitch, G. (1974). Working memory. In Bower, G. H. (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 8, pp. 47–89). New York: Academic Press.Google Scholar
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663.Google Scholar
Briars, D., & Siegler, R. S. (1984). A featural analysis of preschoolers’ counting knowledge. Developmental Psychology, 20, 607–618.Google Scholar
Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33, 205–228.CrossRefGoogle ScholarPubMed
Bull, R., & Lee, K. (2014). Executive functioning and mathematics achievement. Child Development Perspectives, 8, 36–41.Google Scholar
Bull, R., & Scerif, G. (2001). Executive functioning as a predictor of children’s mathematics ability: inhibition, switching, and working memory. Developmental Neuropsychology, 19, 273–293.Google Scholar
Butterworth, B. (2005). The development of arithmetical abilities. Journal of Child Psychology and Psychiatry, 46, 3–18.Google Scholar
Butterworth, B. (2010). Foundational numerical capacities and the origins of dyscalculia. Trends in Cognitive Sciences, 14, 534–541.Google Scholar
Bynner, J. (1997). Basic skills in adolescents’ occupational preparation. The Career Development Quarterly, 45, 305–321.CrossRefGoogle Scholar
Carey, S. (2004). Bootstrapping and the origins of concepts. Daedalus, 133, 59–68.CrossRefGoogle Scholar
Carey, S. (2009). The Origin of Concepts. New York: Oxford University Press.CrossRefGoogle Scholar
Carey, S. (2011). Précis of the origin of concepts. Behavioral and Brain Sciences, 34, 113–124.CrossRefGoogle ScholarPubMed
Cheung, P., Rubenson, M., & Barner, D. (2017). To infinity and beyond: Children generalize the successor function to all possible numbers years after learning to count. Cognitive Psychology, 92, 22–36.Google Scholar
Chu, F. W., vanMarle, K., & Geary, D. C. (2013). Quantitative deficits of preschool children at risk for mathematical learning disability. Frontiers in Psychology, 4, 195.Google Scholar
Chu, F. W., vanMarle, K., & Geary, D. C. (2015). Early numerical foundations of young children’s mathematical development. Journal of Experimental Child Psychology, 132, 205–212.Google Scholar
Chu, F. W., vanMarle, K., & Geary, D. C. (2016). Predicting children’s reading and mathematics achievement from early quantitative knowledge and domain-general cognitive abilities. Frontiers in Psychology, 7, 1–14.Google Scholar
Chu, F. W., vanMarle, K., Rouder, J., & Geary, D. C. (2018). Children’s early understanding of number predicts their later problem-solving sophistication in addition. Journal of Experimental Child Psychology, 169, 73–92.Google Scholar
Clark, C. A. C., Pritchard, V. E., & Woodward, L. J. (2010). Preschool executive functioning abilities predict early mathematics achievement. Developmental Psychology, 46, 1176–1191.Google Scholar
Coubart, A., Izard, V., Spelke, E. S., Marie, J., & Streri, A. (2014). Dissociation between small and large numerosities in newborn infants. Developmental Science, 17, 11–22.Google Scholar
Cowan, R., Donlan, C., Shepherd, D. L., Cole-Fletcher, R., Saxton, M., & Hurry, J. (2011). Basic calculation proficiency and mathematics achievement in elementary school children. Journal of Educational Psychology, 103, 786–803.CrossRefGoogle Scholar
Cowan, R., & Powell, D. (2014). The contributions of domain-general and numerical factors to third-grade arithmetic skills and mathematical learning disability. Journal of Educational Psychology, 106, 214–229.CrossRefGoogle ScholarPubMed
Davidson, K., Eng, K., & Barner, D. (2012). Does learning to count involve a semantic induction? Cognition, 123, 162–173.Google Scholar
de Hevia, M. D., Izard, V., Coubart, A., Spelke, E. S., & Streri, A. (2014). Representations of space, time, and number in neonates. Proceedings of the National Academy of Sciences, 111, 4809–4813.Google Scholar
De Smedt, B., & Gilmore, C. K. (2011). Defective number module or impaired access? Numerical magnitude processing in first graders with mathematical difficulties. Journal of Experimental Child Psychology, 108, 278–292.Google Scholar
De Smedt, B., Janssen, R., Bouwens, K., Verschaffel, L., Boets, B., & Ghesquière, P. (2009). Working memory and individual differences in mathematics achievement: A longitudinal study from first grade to second grade. Journal of Experimental Child Psychology, 103, 186–201.CrossRefGoogle ScholarPubMed
De Visscher, A., & Noël, M.-P. (2014). Arithmetic facts storage deficit: The hypersensitivity-to-interference in memory hypothesis. Developmental Science, 17, 434–442.Google Scholar
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 13–21.Google Scholar
Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20, 487–506.Google Scholar
Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., … Sexton, H. (2007). School readiness and later achievement. Developmental Psychology, 43, 1428–1446.Google Scholar
Eason, S. H., & Levine, S. C. (2017). Math learning begins at home. Zero to Three, 37, 35–44.Google Scholar
Edwards, L. A., Wagner, J. B., Simon, C. E., & Hyde, D. C. (2016). Functional brain organization for number processing in pre‐verbal infants. Developmental Science, 19, 757–769.Google Scholar
Espy, K. A., McDiarmid, M. M., Cwik, M. F., Stalets, M. M., Hamby, A., & Senn, T. E. (2004). The contribution of executive functions to emergent mathematic skills in preschool children. Developmental Neuropsychology, 26, 465–486.CrossRefGoogle ScholarPubMed
Feigenson, L., & Carey, S. (2003). Tracking individuals via object-files: Evidence from infants’ manual search. Developmental Science, 6, 568–584.Google Scholar
Feigenson, L., Carey, S., & Hauser, M. (2002). The representations underlying infants’ choice of more: Object-files versus analog magnitudes. Psychological Science, 13, 150–156.Google Scholar
Feigenson, L., Libertus, M. E., & Halberda, J. (2013). Links between the intuitive sense of number and formal mathematics ability. Child Development Perspectives, 7, 74–79.CrossRefGoogle ScholarPubMed
Friso-van den Bos, I., Van Der Ven, S. H., Kroesbergen, E. H., & Van Luit, J. E. (2013). Working memory and mathematics in primary school children: A meta-analysis. Educational Research Review, 10, 29–44.Google Scholar
Fuchs, L. S., Geary, D. C., Compton, D. L., Fuchs, D., Hamlett, C. L., Seethaler, P. M., … Schatschneider, C. (2010). Do different types of school mathematics development depend on different constellations of numerical versus general cognitive abilities? Developmental Psychology, 46, 1731–1746.CrossRefGoogle ScholarPubMed
Fuhs, M. W., & McNeil, N. M. (2013). ANS acuity and mathematics ability in preschoolers from low‐income homes: Contributions of inhibitory control. Developmental Science, 16, 136–148.Google Scholar
Gallistel, C. R., & Gelman, R. (1992). Preverbal and verbal counting and computation. Cognition, 44, 43–74.Google Scholar
Gallistel, C. R., & Gelman, R. (2000). Non-verbal numerical cognition: From reals to integers. Trends in Cognitive Sciences, 4, 59–65.Google Scholar
Gallistel, C. R., & Gelman, R. (2005). Mathematical cognition. In Holyoak, K., & Morrison, R. (eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 559–588). New York: Cambridge University Press.Google Scholar
Gathercole, S. E., & Pickering, S. J. (2000). Working memory deficits in children with low achievements in the national curriculum at 7 years of age. British Journal of Educational Psychology, 70, 177–194.Google Scholar
Geary, D. C. (1993). Mathematical disabilities: Cognitive, neuropsychological, and genetic components. Psychological Bulletin, 114, 345–362.Google Scholar
Geary, D. C. (2005). The Origin of Mind: Evolution of Brain, Cognition, and General Intelligence. Washington, DC: American Psychological Association.Google Scholar
Geary, D. C. (2011). Cognitive predictors of achievement growth in mathematics: A 5-year longitudinal study. Developmental Psychology, 47, 1539.Google Scholar
Geary, D. C., Bailey, D. H., & Hoard, M. K. (2009). Predicting mathematical achievement and mathematical learning disability with a simple screening tool: The number sets test. Journal of Psychoeducational Assessment, 27, 265–279.Google Scholar
Geary, D. C., Brown, S. C., & Samaranayake, V. A. (1991). Cognitive addition: A short longitudinal study of strategy choice and speed-of-processing differences in normal and mathematically disabled children. Developmental Psychology, 27, 787–797.CrossRefGoogle Scholar
Geary, D. C., Hoard, M. K., Byrd‐Craven, J., Nugent, L., & Numtee, C. (2007). Cognitive mechanisms underlying achievement deficits in children with mathematical learning disability. Child Development, 78, 1343–1359.Google Scholar
Geary, D. C., Hoard, M. K., & Nugent, L. (2012b). Independent contributions of the central executive, intelligence, and in-class attentive behavior to developmental change in the strategies used to solve addition problems. Journal of Experimental Child Psychology, 113, 49–65.CrossRefGoogle ScholarPubMed
Geary, D. C., Hoard, M. K., Nugent, L., & Bailey, D. H. (2013). Adolescents’ functional numeracy is predicted by their school entry number system knowledge. PLoS ONE, 8, e54651.Google Scholar
Geary, D. C., & Moore, A. M. (2016). Cognitive and brain systems underlying early mathematical development. Progress in Brain Research, 227, 75–103.Google Scholar
Geary, D. C., Nicholas, A., Li, Y., & Sun, J. (2017). Developmental change in the influence of domain-general abilities and domain-specific knowledge on mathematics achievement: An eight-year longitudinal study. Journal of educational Psychology, 109, 680–693.Google Scholar
Geary, D. C., & vanMarle, K. (2016). Young children’s core symbolic and nonsymbolic quantitative knowledge in the prediction of later mathematics achievement. Developmental Psychology, 52, 2130–2144.Google Scholar
Geary, D. C., & vanMarle, K. (2018). Growth of symbolic number knowledge accelerates after children understand cardinality. Cognition, 177, 69–78.Google Scholar
Geary, D. C., vanMarle, K., Chu, F. W., Rouder, J., Hoard, M. K., & Nugent, L. (2018). Early conceptual understanding of cardinality predicts superior school-entry number-system knowledge. Psychological Science, 29, 191–205.Google Scholar
Gelman, R. (1972). Logical capacity of very young children: Number invariance rules. Child Development, 43, 75–90.Google Scholar
Gelman, R. (1993). A rational-constructivist account of early learning about numbers and objects. Learning and Motivation, 30, 61–96.Google Scholar
Gelman, R., & Gallistel, C. R. (1978). The Child’s Understanding of Number. Cambridge, MA: Harvard University Press.Google Scholar
Gelman, R., & Greeno, J. G. (1989). On the nature of competence: Principles for understanding in a domain. In Resnick, L. B. (ed.), Knowing and Learning: Issues for a Cognitive Science of Instruction (pp. 125–186). Hillsdale, NJ: Erlbaum.Google Scholar
Gelman, R., & Meck, E. (1983). Preschoolers’ counting: Principles before skill. Cognition, 13, 343–359.Google Scholar
Gilmore, C. K., Attridge, N., Clayton, S., Cragg, L., Johnson, S., Marlow, N., & Inglis, M. (2013). Individual differences in inhibitory control, not non-verbal number acuity, correlate with mathematics achievement. PLoS ONE, 8, e67374.Google Scholar
Gilmore, C. K., McCarthy, S. E., & Spelke, E. S. (2010). Non-symbolic arithmetic abilities and mathematics achievement in the first year of formal schooling. Cognition, 115, 394–406.Google Scholar
Ginsburg, H. P., & Baroody, A. J. (2003). Test of Early Mathematical Ability (3rd ed.). Austin, TX: Pro-Ed.Google Scholar
Gunderson, E. A., Spaepen, E., & Levine, S. C. (2015). Approximate number word knowledge before the cardinal principle. Journal of Experimental Child Psychology, 130, 35–55.Google Scholar
Halberda, J., & Feigenson, L. (2008). Developmental change in the acuity of the “Number Sense”: The approximate number system in 3-, 4-, 5-, and 6-year-olds and adults. Developmental Psychology, 44, 1457.CrossRefGoogle ScholarPubMed
Halberda, J., Mazzocco, M. M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlate with maths achievement. Nature, 455, 665.Google Scholar
Huttenlocher, J., Jordan, N. C., & Levine, S. C. (1992). A mental model for early arithmetic. Journal of Experimental Psychology: General, 123, 284–296.CrossRefGoogle Scholar
Hyde, D. C. (2011). Two systems of non-symbolic numerical cognition. Frontiers in Human Neuroscience, 5, 1–8.Google Scholar
Iuculano, T., Tang, J., Hall, C. W., & Butterworth, B. (2008). Core information processing deficits in developmental dyscalculia and low numeracy. Developmental Science, 11, 669–680.CrossRefGoogle ScholarPubMed
Izard, V., Sann, C., Spelke, E. S., & Streri, A. (2009). Newborn infants perceive abstract numbers. Proceedings of the National Academy Sciences (USA), 106, 10382–10385.Google Scholar
Kahneman, D., Treisman, A., & Gibbs, B. J. (1992). The reviewing of object files: Object specific integration of information. Cognitive Psychology, 24, 174–219.Google Scholar
Klibanoff, R. S., Levine, S. C., Huttenlocher, J., Vasilyeva, M., & Hedges, L. V. (2006). Preschool children’s mathematical knowledge: The effect of teacher “math talk.” Developmental Psychology, 42, 59–69.Google Scholar
Le Corre, M., & Carey, S. (2007). One, two, three, four, nothing more: An investigation of the conceptual sources of the verbal counting principles. Cognition, 105, 395–438.CrossRefGoogle ScholarPubMed
Lee, M. D., & Sarnecka, B. W. (2011). Number-knower levels in young children: Insights from Bayesian modeling. Cognition, 120, 391–402.Google Scholar
Leslie, A. M., Gallistel, C. R., & Gelman, R. (2007). Where integers come from. In Carruthers, P., Laurence, S., & Stich, S. (eds.), The Innate Mind, Vol. 3: Foundations and the Future (pp. 109–138). New York: Oxford University Press.Google Scholar
Leslie, A. M., Gelman, R., & Gallistel, C. R. (2008). The generative basis of natural number concepts. Trends in Cognitive Sciences, 12, 213–218.Google Scholar
Libertus, M. E. (2015). The role of intuitive approximation skills for school math abilities. Mind, Brain, and Education, 9, 112–120.Google Scholar
Libertus, M. E. (2019). Understanding the link between the approximate number system and math abilities. In Geary, D. C., Berch, D. B., & Mann-Koepke, K. (eds.), Mathematical Cognition and Learning: Cognitive Foundations for Improving Mathematical Learning, (Vol. 5, pp. 91–106). New York: Elsevier.Google Scholar
Libertus, M. E., & Brannon, E. M. (2010). Stable individual differences in number discrimination in infancy. Developmental Science, 13, 900–906.Google Scholar
Libertus, M. E., Feigenson, L., & Halberda, J. (2011). Preschool acuity of the approximate number system correlates with school math ability. Developmental Science, 14, 1292–1300.Google Scholar
Lipton, J. S., & Spelke, E. S. (2003). Origins of number sense: Large number discrimination in human infants. Psychological Science, 14, 396–401.Google Scholar
Lyons, I. M., & Beilock, S. L. (2011). Numerical ordering ability mediates the relation between number-sense and arithmetic competence. Cognition, 121, 256–261.Google Scholar
Lyons, I. M., & Beilock, S. L. (2013). Ordinality and the nature of symbolic numbers. Journal of Neuroscience, 33, 17052–17061.Google Scholar
Lyons, I. M., Price, G. R., Vaessen, A., Blomert, L., & Ansari, D. (2014). Numerical predictors of arithmetic success in grades 1–6. Developmental Science, 17, 714–726.Google Scholar
Mazzocco, M. M., Feigenson, L., & Halberda, J. (2011). Impaired acuity of the approximate number system underlies mathematical learning disability (dyscalculia). Child Development, 82, 1224–1237.Google Scholar
Moore, A. M., vanMarle, K., & Geary, D. C. (2016). Kindergartners’ fluent processing of symbolic numerical magnitude is predicted by their cardinal knowledge and implicit understanding of arithmetic 2 years earlier. Journal of Experimental Child Psychology, 150, 31–47.Google Scholar
Nosworthy, N., Bugden, S., Archibald, L., Evans, B., & Ansari, D. (2013). A two-minute paper-and-pencil test of symbolic and nonsymbolic numerical magnitude processing explains variability in primary school children’s arithmetic competence. PLoS ONE, 8, e67918.Google Scholar
Parsons, S., & Bynner, J. (1997). Numeracy and employment. Education and Training, 39, 43–51.Google Scholar
Peng, P., & Fuchs, D. (2016). A meta-analysis of working memory deficits in children with learning difficulties: Is there a difference between verbal domain and numerical domain? Journal of Learning Disabilities, 49, 3–20.Google Scholar
Piaget, J. (1952). The Child’s Concept of Number. London: Routledge & Kegan Paul.Google Scholar
Piazza, M., Facoetti, A., Trussardi, A. N., Berteletti, I., Conte, S., Lucangeli, D., ... Zorzi, M. (2010). Developmental trajectory of number acuity reveals a severe impairment in developmental dyscalculia. Cognition, 116, 33–41.Google Scholar
Price, G. R., Holloway, I., Räsänen, P., Vesterinen, M., & Ansari, D. (2007). Impaired parietal magnitude processing in developmental dyscalculia. Current Biology, 17, R1042–R1043.Google Scholar
Pylyshyn, Z. W., & Storm, R. W. (1988). Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision, 3, 179–197.Google Scholar
Ramani, G. B., Rowe, M. L., Eason, S. H., & Leech, K. A. (2015). Math talk during informal learning activities in Head Start families. Cognitive Development, 35, 15–33.Google Scholar
Ritchie, S. J., & Bates, T. C. (2013). Enduring links from childhood mathematics and reading achievement to adult socioeconomic status. Psychological Science, 24, 1301–1308.Google Scholar
Rivera-Batiz, F. (1992). Quantitative literacy and the likelihood of employment among young adults in the United States. Journal of Human Resources, 27, 313–328.Google Scholar
Rose, H., & Betts, J. R. (2004). The effect of high school courses on earnings. Review of Economics and Statistics, 86, 497–513.Google Scholar
Rousselle, L., & Noël, M.-P. (2007). Basic numerical skills in children with mathematical learning disabilities: A comparison of symbolic vs. non-symbolic number magnitude processing. Cognition, 102, 361–395.Google Scholar
Sasanguie, D., Defever, E., Maertens, B., & Reynvoet, B. (2014). The approximate number system is not predictive for symbolic number processing in kindergarteners. The Quarterly Journal of Experimental Psychology, 67, 271–280.Google Scholar
Scholl, B. J. (2001). Objects and attention: The state of the art. Cognition, 80, 1–46.Google Scholar
Shalev, R. S., Manor, O., & Gross-Tsur, V. (2005). Developmental dyscalculia: A prospective six-year follow-up. Developmental Medicine and Child Neurology, 47, 121–125.Google Scholar
Siegel, L. S., & Ryan, E. B. (1989). The development of working memory in normally achieving and subtypes of learning disabled children. Child Development, 60, 973–980.Google Scholar
Siegler, R. S. (1988). Individual differences in strategy choices: Good students, not-so-good students, and perfectionists. Child Development, 59, 833–851.Google Scholar
Siegler, R. S., & Booth, J. L. (2004). Development of numerical estimation in young children. Child Development, 75, 428–444.Google Scholar
Soltész, F., Szűcs, D., & Szűcs, L. (2010). Relationships between magnitude representation, counting and memory in 4-to 7-year-old children: A developmental study. Behavioral and Brain Functions, 6, 13.Google Scholar
Starr, A., Libertus, M. E., & Brannon, E. M. (2013). Number sense in infancy predicts mathematical abilities in childhood. Proceedings of the National Academy of Sciences, 110, 18116–18120.Google Scholar
Swanson, H. L., & Sachse-Lee, C. (2001). Mathematical problem solving and working memory in children with learning disabilities: Both executive and phonological processes are important. Journal of Experimental Child Psychology, 79, 294–321.Google Scholar
Vanbinst, K., & De Smedt, B. (2016). Individual differences in children’s mathematics achievement: The roles of symbolic numerical magnitude processing and domain-general cognitive functions. Progress in Brain Research, 227, 105–130.Google Scholar
vanMarle, K. (2013). Infants use different mechanisms to make small and large number ordinal judgments. Journal of Experimental Child Psychology, 114, 102–110.Google Scholar
vanMarle, K., Chu, F. W., Li, Y., & Geary, D. C. (2014). Acuity of the approximate number system and preschoolers’ quantitative development. Developmental Science, 17, 492–505.Google Scholar
vanMarle, K., Chu, F. W., Mou, Y., Seok, J. H., Rouder, J., & Geary, D. C. (2018). Attaching meaning to the number words: Contributions of the object tracking and approximate number systems. Developmental Science, 21, e12495.Google Scholar
Walberg, H. J. (1984). Improving the productivity of America’s schools. Educational Leadership, 41, 19–27.Google Scholar
Wechsler, D. (2001). Wechsler Individual Achievement Test – Abbreviated II. San Antonio, TX: Psychological Corp.Google Scholar
Wechsler, D. (2002). Wechsler Preschool and Primary Scale of Intelligence (3rd ed.). San Antonio, TX: Psychological Corp.Google Scholar
Wood, J. N., & Spelke, E. S. (2005). Infants’ enumeration of actions: Numerical discrimination and its signature limits. Developmental Science, 8, 173–181.Google Scholar
Wynn, K. (1990). Children’s understanding of counting. Cognition, 36, 155–193.Google Scholar
Wynn, K. (1992). Children’s acquisition of the number words and the counting system. Cognitive Psychology, 24, 220–251.Google Scholar
Xu, F., & Spelke, E. S. (2000). Large number discrimination in 6-month-old infants. Cognition, 74, B1–B11.Google Scholar

References

Adrián, J. E., Clemente, R. A., & Villanueva, L. (2007). Mothers’ use of cognitive state verbs in picture-book reading and the development of children’s understanding of mind: A longitudinal study. Child Development, 78, 1052–1067.Google Scholar
Antilici, F., & Baillargeon, R. (2020). 2.5-year-olds pass an explicit unexpected-transfer false-belief task when processing demands are reduced. Paper presented at the Biennial Meeting of the International Congress of Infant Studies, July 2020, Glasgow, Scotland.Google Scholar
Apperly, I. A., & Butterfill, S. A. (2009). Do humans have two systems to track beliefs and belief-like states? Psychological Review, 116, 953–970.Google Scholar
Baillargeon, R., Buttelmann, D., & Southgate, V. (2018). Invited Commentary: Interpreting failed replications of early false-belief findings: Methodological and theoretical considerations. Cognitive Development, 46, 112–124.Google Scholar
Baillargeon, R., He, Z., Setoh, P., Scott, R. M., Sloane, S., & Yang, D. Y. J. (2013). False-belief understanding and why it matters. In Mahzarin, R., & Gelman, S. A. (eds.), Navigating the Social World: What Infants, Children, and Other Species Can Teach Us (pp. 88–95). Oxford: Oxford University Press.Google Scholar
Baillargeon, R., Scott, R. M., & Bian, L. (2016). Psychological reasoning in infancy. Annual Review of Psychology, 67, 159–186.Google Scholar
Baillargeon, R., Scott, R. M., He, Z., Sloane, S., Setoh, P., Jin, K., … Bian, L. (2015). Psychological and sociomoral reasoning in infancy. In Mikulincer, M., Shaver, P. R. (eds.), Borgida, E., & Bargh, J. A. (assoc. eds.), APA Handbook of Personality and Social Psychology: Vol.1. Attitudes and Social Cognition (pp. 79–150). Washington, DC: American Psychological Association.Google Scholar
Bardi, L., Desmet, C., Nijhof, A., Wiersema, J. R., & Brass, M. (2017). Brain activation for spontaneous and explicit false belief tasks overlaps: New fMRI evidence on belief processing and violation of expectation. Social Cognitive and Affective Neuroscience, 12, 391–400.Google Scholar
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a “theory of mind”? Cognition, 21, 37–46.Google Scholar
Barrett, H. C., Broesch, T., Scott, R. M., He, Z., Baillargeon, R., Wu, D., … Laurence, S. (2013). Early false-belief understanding in traditional non-Western societies. Proceedings of the Royal Society of London B: Biological Sciences, 280, 20122654.Google Scholar
Bartsch, K. (1996). Between desires and beliefs: Young children’s action predictions. Child Development, 67, 1671–1685.Google Scholar
Begus, K., & Southgate, V. (2012). Infant pointing serves an interrogative function. Developmental Science, 15, 611–617.Google Scholar
Bergelson, E., & Swingley, D. (2012). At 6–9 months, human infants know the meanings of many common nouns. Proceedings of the National Academy of Sciences, USA, 109, 3253–3258.Google Scholar
Bian, L., He, Z., & Baillargeon, R. (2017). False-belief understanding in young infants: Evidence from anticipatory-looking and violation-of-expectation measures. Paper presented at the Biennial Meeting of the Society for Research in Child Development, April 2017, Austin, TX.Google Scholar
Bíro, S., Verschoor, S., & Coenen, L. (2011). Evidence for a unitary goal concept in 12-month-old infants. Developmental Science, 14, 1255–1260.Google Scholar
Buresh, J. S., & Woodward, A. L. (2007). Infants track action goals within and across agents. Cognition, 104, 287–314.Google Scholar
Butler, A. G. (2013). Exploring the role of social reasoning and self-efficacy in the mathematics problem-solving performance of lower-and higher-income children. Journal of Educational Research and Practice, 3, 93–119.Google Scholar
Buttelmann, D., Carpenter, M., & Tomasello, M. (2009). Eighteen-month-old infants show false belief understanding in an active helping paradigm. Cognition, 112, 337–342.Google Scholar
Buttelmann, F., Suhrke, J., & Buttelmann, D. (2015). What you get is what you believe: Eighteen-month-olds demonstrate belief understanding in an unexpected-identity task. Journal of Experimental Child Psychology, 131, 94–103.Google Scholar
Butterfill, S., & Apperly, I. A. (2013). How to construct a minimal theory of mind. Mind and Language, 28, 606–637.Google Scholar
Butterworth, G., & Jarrett, N. (1991). What minds have in common is space: Spatial mechanisms serving joint visual attention in infancy. British Journal of Developmental Psychology, 9, 55–72.Google Scholar
Carlson, S. M., & Moses, L. J. (2001). Individual differences in inhibitory control and children’s theory of mind. Child Development, 72, 1032–1053.Google Scholar
Carruthers, P. (2016). Two systems for mindreading? Review of Philosophy and Psychology, 7, 141–162.Google Scholar
Carruthers, P. (2018). Young children flexibly attribute mental states to others. Proceedings of the National Academy of Sciences, USA, 115, 11351–11353.Google Scholar
Cesana-Arlotti, N., Kovács, Á. M., & Téglás, E. (2020). Infants recruit logic to learn about the social world. Nature Communications, 11, 5999.Google Scholar
Choi, Y. J., & Luo, Y. (2015). 13-month-olds’ understanding of social interactions. Psychological Science, 26, 274–283.Google Scholar
Choi, Y., Luo, Y., & Baillargeon, R. (in press). Can 5-month-old infants consider the perspective of a novel eyeless agent? New evidence for early mentalistic reasoning. Child Development.Google Scholar
Choi, Y. J., Mou, Y., & Luo, Y. (2018). How do 3-month-old infants attribute preferences to a human agent? Journal of Experimental Child Psychology, 172, 96–106.Google Scholar
Cowell, J. M., Lee, K., Malcolm-Smith, S., Selcuk, B., Zhou, X., & Decety, J. (2017). The development of generosity and moral cognition across five cultures. Developmental Science, 20, e12403.Google Scholar
Crivello, C., & Poulin-Dubois, D. (2018). Infants’ false belief understanding: A non-replication of the helping task. Cognitive Development, 46, 51–57.Google Scholar
Csibra, G. (2008). Goal attribution to inanimate agents by 6.5-month-old infants. Cognition, 107, 705–717.Google Scholar
Csibra, G., Bíró, S., Koós, O., & Gergely, G. (2003). One-year-old infants use teleological representations of actions productively. Cognitive Science, 27, 111–133.Google Scholar
Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13, 148–153.Google Scholar
Csibra, G., Gergely, G., Bíró, S., Koos, O., & Brockbank, M. (1999). Goal attribution without agency cues: The perception of ‘pure reason’ in infancy. Cognition, 72, 237–267.CrossRefGoogle ScholarPubMed
Cutting, A. L., & Dunn, J. (1999). Theory of mind, emotion understanding, language, and family background: Individual differences and interrelations. Child Development, 70, 853–865.Google Scholar
de Villiers, J. & de Villiers, P. (2003). Language for thought: Coming to understand false beliefs. In Gentner, D., & Goldin-Meadow, S. (eds.), Language in Mind: Advances in the Study of Language and Thought (pp. 335–384). Harvard, MA: MIT Press.Google Scholar
Dennett, D. C. (1987). The Intentional Stance. Cambridge, MA: MIT Press.Google Scholar
Devine, R. T., & Hughes, C. (2014). Relations between false belief understanding and executive function in early childhood: A meta-analysis. Child Development, 85, 1777–1794.Google Scholar
Devine, R. T., & Hughes, C. (2018). Family correlates of false belief understanding in early childhood: A meta-analysis. Child Development, 89, 971–987.Google Scholar
Dörrenberg, S., Rakoczy, H., & Liszkowski, U. (2018). How (not) to measure infant Theory of Mind: Testing the replicability and validity of four non-verbal measures. Cognitive Development, 46, 12–30.Google Scholar
Duh, S., Paik, J. H., Miller, P. H., Gluck, S. C., Li, H., & Himelfarb, I. (2016). Theory of mind and executive function in Chinese preschool children. Developmental Psychology, 52, 582–591.Google Scholar
Dunfield, K. A., & Kuhlmeier, V. A. (2010). Intention-mediated selective helping in infancy. Psychological Science, 21, 523–527.Google Scholar
Egyed, K., Király, I., & Gergely, G. (2013). Communicating shared knowledge in infancy. Psychological Science, 24, 1348–1353.Google Scholar
Ensor, R., & Hughes, C. (2008). Content or connectedness? Mother–child talk and early social understanding. Child Development, 79, 201–216.Google Scholar
Forgács, B., Gervain, J., Parise, E., Csibra, G., Gergely, G., Baross, J., & Király, I. (2020). Electrophysiological investigation of infants’ understanding of understanding. Developmental Cognitive Neuroscience, 43, 100783.Google Scholar
Forgács, B., Parise, E., Csibra, G., Gergely, G., Jacquey, L., & Gervain, J. (2019). Fourteen-month-old infants track the language comprehension of communicative partners. Developmental Science, 22, e12751.Google Scholar
Garnham, W. A., & Ruffman, T. (2001). Doesn’t see, doesn’t know: Is anticipatory looking really related to understanding or belief? Developmental Science, 4, 94–100.Google Scholar
Gergely, G., & Csibra, G. (2003). Teleological reasoning in infancy: The naive theory of rational action. Trends in Cognitive Sciences, 7, 287–292.Google Scholar
Gergely, G., Nádasdy, Z., Csibra, G., & Bíró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165–193.Google Scholar
Glenwright, M., Scott, R. M., Bilevicius, E., Pronovost, M., & Hanlon-Dearman, A. (2021). Children with autism spectrum disorder can attribute false beliefs in a spontaneous-response preferential-looking task. Frontiers in Communication, 6, 146.Google Scholar
Gopnik, A., & Astington, J. W. (1988). Children’s understanding of representational change and its relation to the understanding of false belief and the appearance-reality distinction. Child Development, 59, 26–37.Google Scholar
Gopnik, A., & Wellman, H. M. (1994). The theory theory. In Hirschfeld, L. A., & Gelman, S. A. (eds.), Mapping the Mind: Domain Specificity in Cognition and Culture (pp. 257–293). New York: Cambridge University Press.Google Scholar
Grosse Wiesmann, C. G., Friederici, A. D., Disla, D., Steinbeis, N., & Singer, T. (2018). Longitudinal evidence for 4-year-olds’ but not 2-and 3-year-olds’ false belief-related action anticipation. Cognitive Development, 46, 58–68.Google Scholar
Grosso, S. S., Schuwerk, T., Kaltefleiter, L. J., & Sodian, B. (2019). 33-month-old children succeed in a false-belief task with reduced processing demands: A replication of Setoh et al. (2016). Infant Behavior and Development, 54, 151–155.Google Scholar
Hamlin, J. K. (2013). Failed attempts to help and harm: Intention versus outcome in preverbal infants’ social evaluations. Cognition, 18, 451–474.Google Scholar
Hamlin, J. K., & Wynn, K. (2011). Young infants prefer prosocial to antisocial others. Cognitive Development, 26, 30–39.Google Scholar
Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557–559.Google Scholar
Hansen, M. B. (2010). If you know something, say something: Young children’s problem with false beliefs. Frontiers in Psychology, 1, 23.Google Scholar
Hayne, H. (2004). Infant memory development: Implications for childhood amnesia. Developmental Review, 24, 33–73.Google Scholar
He, Z., Bolz, M., & Baillargeon, R. (2011). False-belief understanding in 2.5-year-olds: Evidence from violation-of-expectation change-of-location, and unexpected-contents tasks. Developmental Science, 14, 292–305.Google Scholar
He, Z., Bolz, M., & Baillargeon, R. (2012). 2.5-year-olds succeed at a verbal anticipatory-looking false-belief task. British Journal of Developmental Psychology, 30, 14–29.CrossRefGoogle Scholar
Helming, K. A., Strickland, B., & Jacob, P. (2016). Solving the puzzle about early belief-ascription. Mind & Language, 31, 438–469.Google Scholar
Henderson, A. M., & Woodward, A. L. (2012). Nine-month-old infants generalize object labels, but not object preferences across individuals. Developmental Science, 15, 641–652.Google Scholar
Hespos, S. J., & Baillargeon, R. (2001). Infants’ knowledge about occlusion and containment events: A surprising discrepancy. Psychological Science, 12, 141–147.Google Scholar
Heyes, C. (2014). False belief in infancy: A fresh look. Developmental Science, 17, 647–659.Google Scholar
Hofmann, S. G., Doan, S. N., Sprungc, M., Wilson, A., Ebesutanie, C., Andrews, L. A. … Harris, P. L. (2016). Training children’s theory-of-mind: A meta-analysis of controlled studies. Cognition, 150, 200–212.Google Scholar
Holmes, H. A., Black, C., & Miller, S. A. (1996). A cross-task comparison of false belief understanding in a Head Start population. Journal of Experimental Child Psychology, 63, 263–285.Google Scholar
Hughes, C., Adlam, A., Happé, F., Jackson, J., Taylor, A., & Caspi, A. (2000). Good test–retest reliability for standard and advanced false-belief tasks across a wide range of abilities. Journal of Child Psychology and Psychiatry, 41, 483–490.Google Scholar
Hyde, D. C., Aparicio Betancourt, M., & Simon, C. E. (2015). Human temporal‐parietal junction spontaneously tracks others’ beliefs: A functional near-infrared spectroscopy study. Human Brain Mapping, 36, 4831–4846.Google Scholar
Hyde, D. C., Simon, C. E., Ting, F., & Nikolaeva, J. I. (2018). Functional organization of the temporal–parietal junction for theory of mind in preverbal infants: A near-infrared spectroscopy study. Journal of Neuroscience, 38, 4264–4274.Google Scholar
Imuta, K., Henry, J. D., Slaughter, V., Selcuk, B. & Ruffman, T. (2016). Theory of mind and prosocial behavior in childhood: A meta-analytic review. Developmental Psychology, 52, 1192–1205.Google Scholar
Jin, K. S., Houston, J. L., Baillargeon, R., Groh, A. M., & Roisman, G. I. (2018). Young infants expect an unfamiliar adult to comfort a crying baby: Evidence from a standard violation-of-expectation task and a novel infant-triggered-video task. Cognitive Psychology, 102, 1–20.Google Scholar
Jin, K. S., Kim, Y., Song, M., Kim, Y. J., Lee, H., Lee, Y., … Song, H. J. (2019). Fourteen-to eighteen-month-old infants use explicit linguistic information to update an agent’s false belief. Frontiers in Psychology, 10, 2508.Google Scholar
Jin, K. S., & Song, H. J. (2017). You changed your mind! Infants interpret a change in word as signaling a change in an agent’s goals. Journal of Experimental Child Psychology, 162, 149–162.Google Scholar
Johnson, S. C., Shimizu, Y. A., & Ok, S. J. (2007). Actors and actions: The role of agent behavior in infants’ attribution of goals. Cognitive Development, 22, 310–322.Google Scholar
Kamewari, K., Kato, M., Kanda, T., Ishiguro, H., & Hiraki, K. (2005). Six-and-a-half-month-old children positively attribute goals to human action and to humanoid-robot motion. Cognitive Development, 20, 303–320.Google Scholar
Kampis, D., & Hamlin, K. (2019). ManyBabies 2: Theory of mind in infancy. Paper presented at the Biennial Meeting of the Society for Research in Child Development, March 2019, Baltimore, MD.Google Scholar
Kampis, D., Parise, E., Csibra, G., & Kovács, Á. M. (2015). Neural signatures for sustaining object representations attributed to others in preverbal human infants. Proceedings of the Royal Society B: Biological Sciences, 282, 20151683.Google Scholar
Kim, E. Y., & Song, H. J. (2015). Six-month-olds actively predict others’ goal-directed actions. Cognitive Development, 33, 1–13.Google Scholar
Király, I., Oláh, K., Csibra, G., & Kovács, Á. M. (2018). Retrospective attribution of false beliefs in 3-year-old children. Proceedings of the National Academy of Sciences, USA, 115, 11477–11482.Google Scholar
Kloo, D., Kristen-Antonow, S., & Sodian, B. (2020). Progressing from an implicit to an explicit false belief understanding: A matter of executive control? International Journal of Behavioral Development, 44, 107–115.Google Scholar
Knudsen, B., & Liszkowski, U. (2012). 18-month-olds predict specific action mistakes through attribution of false belief, not ignorance, and intervene accordingly. Infancy, 17, 672–691.Google Scholar
Koenig, M. A., & Woodward, A. L. (2010). Sensitivity of 24-month-olds to the prior inaccuracy of the source: Possible mechanisms. Developmental Psychology, 46, 815–826.Google Scholar
Kulke, L., Reiß, M., Krist, H., & Rakoczy, H. (2018a). How robust are anticipatory looking measures of Theory of Mind? Replication attempts across the life span. Cognitive Development, 46, 97–111.Google Scholar
Kulke, L., von Duhn, B., Schneider, D., & Rakoczy, H. (2018b). Is implicit theory of mind a real and robust phenomenon? Results from a systematic replication study. Psychological Science, 29, 888–900.Google Scholar
Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621–647.Google Scholar
Lecce, S., & Hughes, C. (2010). The Italian job?: Comparing theory of mind performance in British and Italian children. British Journal of Developmental Psychology, 28, 747–766.Google Scholar
Leslie, A. M. (1987). Pretense and representation: The origins of “theory of mind.” Psychological Review, 94, 412–426.Google Scholar
Leslie, A. M. (1994). ToMM, ToBy, and agency: Core architecture and domain specificity. In Hirschfeld, L. A., & Gelman, S. A. (eds.), Mapping the Mind: Domain Specificity in Cognition and Culture (pp. 119–148). New York: Cambridge University Press.Google Scholar
Leslie, A. M., Friedman, O., & German, T. P. (2004). Core mechanisms in ‘theory of mind’. Trends in Cognitive Sciences, 8, 528–533.Google Scholar
Liszkowski, U., Carpenter, M., & Tomasello, M. (2008). Twelve-month-olds communicate helpfully and appropriately for knowledgeable and ignorant partners. Cognition, 108, 732–739.Google Scholar
Liu, D., Wellman, H. M., Tardif, T., & Sabbagh, M. A. (2008). Theory of mind development in Chinese children: A meta-analysis of false-belief understanding across cultures and languages. Developmental Psychology, 44, 523–531.Google Scholar
Liu, S., & Sun, R. (2018). Do great minds prefer alike? Thirteen-month-old infants generalize personal preferences across objects of like kind but not across people. Frontiers in Psychology, 9, 2636.Google Scholar
Low, J., Apperly, I. A., Butterfill, S. A., & Rakoczy, H. (2016). Cognitive architecture of belief reasoning in children and adults: A primer on the two-systems account. Child Development Perspectives, 10, 184–189.Google Scholar
Low, J., Drummond, W., Walmsley, A., & Wang, B. (2014). Representing how rabbits quack and competitors act: Limits on preschoolers’ efficient ability to track perspective. Child Development, 85, 1519–1534.Google Scholar
Low, J., & Watts, J. (2013). Attributing false beliefs about object identity reveals a signature blind spot in humans’ efficient mind-reading system. Psychological Science, 24, 305–311.Google Scholar
Luo, Y. (2011). Three-month-old infants attribute goals to a non-human agent. Developmental Science, 14, 453–460.Google Scholar
Luo, Y., & Baillargeon, R. (2005). Can a self-propelled box have a goal? Psychological reasoning in 5-month-old infants. Psychological Science, 16, 601–608.Google Scholar
Luo, Y., & Baillargeon, R. (2007). Do 12.5-month-old infants consider what objects others can see when interpreting their actions? Cognition, 105, 489–512.Google Scholar
Luo, Y., & Johnson, S. C. (2009). Recognizing the role of perception in action at 6 months. Developmental Science, 12, 142–149.Google Scholar
Ma, F., Xu, F., Heyman, G. D., & Lee, K. (2011). Chinese children’s evaluations of white lies: Weighing the consequences for recipients. Journal of Experimental Child Psychology, 108, 308–321.Google Scholar
Margoni, F., Baillargeon, R., & Surian, L. (2018). Infants distinguish between leaders and bullies. Proceedings of the National Academy of Sciences, USA, 115, E8835–E8843.Google Scholar
Margoni, F., & Surian, L. (2020). Conceptual continuity in the development of moral judgment. Journal of Experimental Child Psychology, 194, 104812.Google Scholar
Martin, A., Onishi, K. H., & Vouloumanos, A. (2012). Understanding the abstract role of speech in communication at 12 months. Cognition, 123, 50–60.Google Scholar
Mayer, A., & Träuble, B. E. (2013). Synchrony in the onset of mental state understanding across cultures? A study among children in Samoa. International Journal of Behavioral Development, 37, 21–28.Google Scholar
McAlister, A., & Peterson, C. C. (2006). Mental playmates: Siblings, executive functioning, and theory of mind. British Journal of Developmental Psychology, 24, 733–751.Google Scholar
Meltzoff, A. N. (1995). Understanding the intentions of others: Re-enactment of intended acts by 18-month-old children. Developmental Psychology, 31, 838–850.Google Scholar
Meristo, M., & Surian, L. (2013). Do infants detect indirect reciprocity? Cognition, 129, 102–113.Google Scholar
Milligan, K., Astington, J. W., & Dack, L. A. (2007). Language and theory of mind: Meta-analysis of the relation between language ability and false-belief understanding. Child Development, 78, 622–646.Google Scholar
Moll, H., Khalulyan, A., & Moffett, L. (2017). 2.5-year-olds express suspense when others approach reality with false expectations. Child Development, 88, 114–122.Google Scholar
Naito, M., & Koyama, K. (2006). The development of false-belief understanding in Japanese children: Delay and difference? International Journal of Behavioral Development, 30, 290–304.Google Scholar
Onishi, K. H., & Baillargeon, R. (2005). Do 15-month-old infants understand false beliefs? Science, 308, 255–258.Google Scholar
Perner, J. (1991). Understanding the Representational Mind. Cambridge, MA: MIT Press.Google Scholar
Perner, J. (2010). Who took the cog out of cognitive science? Mentalism in an era of anti-cognitivism. In Frensch, P. A., & Schwarzer, R. (eds.), Cognition and Neuropsychology: International Perspectives on Psychological Science (Vol. 1, pp. 241–261). Hove: Psychology Press.Google Scholar
Perner, J., Leekam, S. R., & Wimmer, H. (1987). Three-year-olds’ difficulty with false belief: The case for a conceptual deficit. British Journal of Developmental Psychology, 5, 125–137.Google Scholar
Peterson, C. C., Wellman, H. M., & Slaughter, V. (2012). The mind behind the message: Advancing theory-of-mind scales for typically developing children, and those with deafness, autism, or Asperger syndrome. Child Development, 83, 469–485.Google Scholar
Poulin-Dubois, D., Brooker, I., & Polonia, A. (2011). Infants prefer to imitate a reliable person. Infant Behavior and Development, 34, 303–309.Google Scholar
Poulin-Dubois, D., Polonia, A., & Yott, J. (2013). Is false belief skin-deep? The agent’s eye status influences infants’ reasoning in belief-inducing situations. Journal of Cognition and Development, 14, 87–99.Google Scholar
Poulin-Dubois, D., Rakoczy, H., Burnside, K., Crivello, C., Dörrenberg, S., Edwards, K., … Perner, J. (2018). Do infants understand false beliefs? We don’t know yet – A commentary on Baillargeon, Buttelmann, and Southgate’s commentary. Cognitive Development, 48, 302–315.Google Scholar
Powell, L. J., Hobbs, K., Bardis, A., Carey, S., & Saxe, R. (2018). Replications of implicit theory of mind tasks with varying representational demands. Cognitive Development, 46, 40–50.Google Scholar
Priewasser, B., Rafetseder, E., Gargitter, C., & Perner, J. (2018). Helping as an early indicator of a theory of mind: Mentalism or teleology? Cognitive Development, 46, 69–78.Google Scholar
Rhodes, M., & Brandone, A. C. (2014). Three-year-olds’ theories of mind in actions and words. Frontiers in Psychology, 5, 263.Google Scholar
Richardson, H., Lisandrelli, G., Riobueno-Naylor, A., & Saxe, R. (2018). Development of the social brain from age three to twelve years. Nature Communications, 9, 1027.Google Scholar
Roby, E., & Scott, R. M. (2016). Rethinking the relationship between social experience and false-belief understanding: A mentalistic account. Frontiers in Psychology, 7, 1721.Google Scholar
Roby, E., & Scott, R. M. (2018). The relationship between parental mental-state language and 2.5-year-olds’ performance on a nontraditional false-belief task. Cognition, 180, 10–23.Google Scholar
Rubio-Fernández, P., & Geurts, B. (2013). How to pass the false-belief task before your fourth birthday. Psychological Science, 24, 27–33.Google Scholar
Rubio-Fernández, P., & Geurts, B. (2016). Don’t mention the marble! The role of attentional processes in false-belief tasks. Review of Philosophy and Psychology, 7, 835–850.Google Scholar
Ruffman, T. (2014). To belief or not belief: Children’s theory of mind. Developmental Review, 34, 265–293.Google Scholar
Ruffman, T., Slade, L., & Crowe, E. (2002). The relation between children’s and mothers’ mental state language and theory-of-mind understanding. Child Development, 73, 734–751.Google Scholar
Schulze, C., & Buttelmann, D. (2021). Small procedural differences matter: Conceptual and direct replication attempts of the communication-intervention effect on infants’ false-belief ascriptions. Cognitive Development, 59, 101054Google Scholar
Schuwerk, T., Priewasser, B., Sodian, B., & Perner, J. (2018). The robustness and generalizability of findings on spontaneous false belief sensitivity: A replication attempt. Royal Society Open Science, 5, 172273.Google Scholar
Scott, R. M. (2017a). Surprise! 20-month-old infants understand the emotional consequences of false beliefs. Cognition, 159, 33–47.Google Scholar
Scott, R. M. (2017b). The developmental origins of false-belief understanding. Current Directions in Psychological Science, 26, 68–74.Google Scholar
Scott, R. M., & Baillargeon, R. (2009). Which penguin is this? Attributing false beliefs about object identity at 18 months. Child Development, 80, 1172–1196.Google Scholar
Scott, R. M., & Baillargeon, R. (2017). Early false-belief understanding. Trends in Cognitive Sciences, 21, 237–249.Google Scholar
Scott, R. M., & Baillargeon, R. (2013). Do infants really expect others to act efficiently? A critical test of the rationality principle. Psychological Science, 24, 466–474.Google Scholar
Scott, R. M., Baillargeon, R., Song, H. J., & Leslie, A. M. (2010). Attributing false beliefs about non-obvious properties at 18 months. Cognitive Psychology, 61, 366–395.Google Scholar
Scott, R. M., He, Z., Baillargeon, R., & Cummins, D. (2012). False-belief understanding in 2.5-year-olds: Evidence from two novel verbal spontaneous-response tasks. Developmental Science, 15, 181–193.Google Scholar
Scott, R. M., Richman, J. C., & Baillargeon, R. (2015). Infants understand deceptive intentions to implant false beliefs about identity: New evidence for early mentalistic reasoning. Cognitive Psychology, 82, 32–56.Google Scholar
Scott, R. M., & Roby, E. (2015). Processing demands impact 3-year-olds’ performance in a spontaneous-response task: New evidence for the processing-load account of early false-belief understanding. PLoS ONE, 10, e0142405.Google Scholar
Scott, R. M., Roby, E., & Setoh, P. (2020). 2.5-year-olds succeed in identity and location elicited-response false-belief tasks with adequate response practice. Journal of Experimental Child Psychology, 198, 104890.Google Scholar
Senju, A., Southgate, V., Miura, Y., Matsui, T., Hasegawa, T., Tojo, Y., … Csibra, G. (2010). Absence of spontaneous action anticipation by false belief attribution in children with autism spectrum disorder. Development and Psychopathology, 22, 353–360.Google Scholar
Senju, A., Southgate, V., Snape, C., Leonard, M., & Csibra, G. (2011). Do 18-month-olds really attribute mental states to others? A critical test. Psychological Science, 22, 878–880.Google Scholar
Senju, A., Southgate, V., White, S., & Frith, U. (2009). Mindblind eyes: An absence of spontaneous theory of mind in Asperger syndrome. Science, 325, 883–885.Google Scholar
Setoh, P., Scott, R. M., & Baillargeon, R. (2016). Two-and-a-half-year-olds succeed at a traditional false-belief task with reduced processing demands. Proceedings of the National Academy of Sciences, USA, 113, 13360–13365.Google Scholar
Slaughter, V., Peterson, C. C., & Mackintosh, E. (2007). Mind what mother says: Narrative input and theory of mind in typical children and those on the autism spectrum. Child Development, 78, 839–858.Google Scholar
Sloane, S., Baillargeon, R., & Premack, D. (2012). Do infants have a sense of fairness? Psychological Science, 23, 196–204.Google Scholar
Song, H. J., & Baillargeon, R. (2008). Infants’ reasoning about others’ false perceptions. Developmental Psychology, 44, 1789–1795.Google Scholar
Song, H. J., Baillargeon, R., & Fisher, C. (2014). The development of infants’ use of novel verbal information when reasoning about others’ actions. PLoS ONE, 9, e92387.Google Scholar
Song, H. J., Onishi, K. H., Baillargeon, R., & Fisher, C. (2008). Can an agent’s false belief be corrected by an appropriate communication? Psychological reasoning in 18-month-old infants. Cognition, 109, 295–315.Google Scholar
Southgate, V., Chevallier, C., & Csibra, G. (2010). Seventeen-month-olds appeal to false beliefs to interpret others’ referential communication. Developmental Science, 13, 907–912.Google Scholar
Southgate, V., Senju, A., & Csibra, G. (2007). Action anticipation through attribution of false belief by 2-year-olds. Psychological Science, 18, 587–592.Google Scholar
Southgate, V., & Vernetti, A. (2014). Belief-based action prediction in preverbal infants. Cognition, 130, 1–10.Google Scholar
Spaepen, E., & Spelke, E. (2007). Will any doll do? 12-month-olds’ reasoning about goal objects. Cognitive Psychology, 54, 133–154.Google Scholar
Takagishi, H., Kameshima, S., Schug, J., Koizumi, M., & Yamagishi, T. (2010). Theory of mind enhances preference for fairness. Journal of Experimental Child Psychology, 105, 130–137.Google Scholar
Ting, F., He, Z., & Baillargeon, R. (2021). Five-month-old infants attribute inferences based on general knowledge to agents. Journal of Experimental Child Psychology, 208, 105126.Google Scholar
Tomasello, M. (1999). Having intentions, understanding intentions, and understanding communicative intentions. In Zelazo, P. D., Astington, J. W., & Olson, D. R. (eds.), Developing Theories of Intention: Social Understanding and Self-Control (pp. 63–75). Mahwah, NJ: Erlbaum.Google Scholar
Tomasello, M., & Haberl, K. (2003). Understanding attention: 12-and 18-month-olds know what is new for other persons. Developmental Psychology, 39, 906–912.Google Scholar
Vouloumanos, A., Martin, A., & Onishi, K. H. (2014). Do 6-month-olds understand that speech can communicate? Developmental Science, 17, 872–879.Google Scholar
Wang, S., Baillargeon, R., & Brueckner, L. (2004). Young infants’ reasoning about hidden objects: Evidence from violation-of-expectation tasks with test trials only. Cognition, 93, 167–198.Google Scholar
Wang, Y., & Su, Y. (2009). False belief understanding: Children catch it from classmates of different ages. International Journal of Behavioral Development, 33, 331–336.Google Scholar
Welder, A. N., & Graham, S. A. (2001). The influence of shape similarity and shared labels on infants’ inductive inferences about nonobvious object properties. Child Development, 72, 1653–1673.Google Scholar
Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of theory of mind development: The truth about false belief. Child Development, 72, 655–684.Google Scholar
Wellman, H. M., Fang, F., & Peterson, C. C. (2011). Sequential progressions in a theory-of-mind scale: Longitudinal perspectives. Child Development, 82, 780–792.Google Scholar
Westra, E., & Carruthers, P. (2017). Pragmatic development explains the Theory-of-Mind scale. Cognition, 158, 165–176.Google Scholar
Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128.Google Scholar
Woodward, A. L. (1998). Infants selectively encode the goal object of an actor’s reach. Cognition, 69, 1–34.Google Scholar
Woodward, A. L. (1999). Infants’ ability to distinguish between purposeful and non-purposeful behaviors. Infant Behavior and Development, 22, 145–160.Google Scholar
Woodward, A. L. (2005). The infant origins of intentional understanding. Advances in Child Development and Behavior, 33, 229–262.Google Scholar
Woodward, A. L., Sommerville, J. A., & Guajardo, J. J. (2001). How infants make sense of intentional action. In Malle, B. F., Moses, L. J., & Baldwin, D. A. (eds.), Intentions and Intentionality: Foundations of Social Cognition (pp. 149–169). Cambridge, MA: MIT Press.Google Scholar
Yazdi, A. A., German, T. P., Defeyter, M. A., & Siegal, M. (2006). Competence and performance in belief-desire reasoning across two cultures: The truth, the whole truth, and nothing but the truth about false belief? Cognition, 100, 343–368.Google Scholar
Yott, J., & Poulin-Dubois, D. (2012). Breaking the rules: Do infants have a true understanding of false belief? British Journal of Developmental Psychology, 30, 156–171.Google Scholar
Yott, J., & Poulin-Dubois, D. (2016). Are infants’ theory-of-mind abilities well integrated? Implicit understanding of intentions, desires, and beliefs. Journal of Cognition and Development, 17, 683–698.Google Scholar
Zmyj, N., Buttelmann, D., Carpenter, M., & Daum, M. M. (2010). The reliability of a model influences 14-month-olds’ imitation. Journal of Experimental Child Psychology, 106, 208–220.Google Scholar

References

Anisfeld, M. (1996). Neonatal imitation. Developmental Review, 11, 60–97.Google Scholar
Apperly, I. A., & Butterfill, S. A. (2009). Do humans have two systems to track beliefs and belief-like states? Psychological Review, 116, 953–970.Google Scholar
Atran, S. (1995). Causal constraints on categories and categorical constraints on biological reasoning across cultures. In Sperber, D., Premack, D., & Premack, A. (eds.), Causal Cognition, a Multidisciplinary Debate (pp. 205–233). Oxford: Clarendon Press.Google Scholar
Baumard, N., André, J.-B., & Sperber, D. (2013). A mutualistic approach to morality: The evolution of fairness by partner choice. Behavioral and Brain Sciences, 36, 59–122.Google Scholar
Begus, K., Gliga, T., & Southgate, V. (2016). Infants’ preferences for native speakers are associated with an expectation of information. Proceedings of the National Academy of Sciences, 113, 12397–12402.Google Scholar
Bian, L., Sloane, S., & Baillargeon, R. (2018). Infants expect ingroup support to override fairness when resources are limited. Proceedings of the National Academy of Sciences, 115, 2705–2710.Google Scholar
Biro, S., & Leslie, A. (2007). Infants’ perception of goal-directed action: Development through cue-based bootstrapping. Developmental Science, 10, 379–398.Google Scholar
Boyer, P. (2018). Minds Make Societies, How Cognition Explains the World Humans Create. New Haven, CT: Yale University Press.Google Scholar
Brandone, A. C., Leslie, S. J., Cimpian, A., & Gelman, S. A. (2012). Do lions have manes? For children, generics are about kinds rather than quantities. Child Development, 83, 423–433.Google Scholar
Brewer, M. B. (1979). In-group bias in the minimal intergroup situation: Cognitive–motivational analysis. Psychological Bulletin, 86, 307–324.Google Scholar
Buon, M., Habib, M., & Frey, D. (2016). Moral development: Conflicts and compromises. In Somerville, J. A., & Decety, J. (eds.), Social Cognition: Development Across the Life Span (pp. 129–150). New York: Psychology Press.Google Scholar
Buon, M, Jacob, P., Loissel, E., & Dupoux, E. (2013). A non-mentalistic cause-based heuristic in human social evaluations. Cognition, 126, 149–155.Google Scholar
Butler, L. P., & Markman, E. M. (2012). Preschoolers use intentional and pedagogical cues to guide inductive inferences and exploration. Child Development, 83, 1416–1428.Google Scholar
Buyukozer Dawkins, M., Sloane, S., & Baillargeon, R. (2019). Do Infants in the first year of life expect equal resource allocations? Frontiers in Psychology, 10, 116.Google Scholar
Byrne, R. W., & Whiten, A. (eds.) (1988). Machiavellian Intelligence: Social Expertise and the Evolution of Intellect in Monkeys, Apes and Humans. Oxford: Blackwell.Google Scholar
Carey, S. (1995) On the origin of causal understanding. In Sperber, D., Premack, D., & Premack, A. (eds.), (1995). Causal Cognition, a Multidisciplinary Debate (pp. 205–233). Oxford: Clarendon Press.Google Scholar
Clément, F., Koenig, M. A., & Harris, P. L. (2004). The ontogenesis of trust. Mind and Language, 19, 360–379.Google Scholar
Cooper, R. P., & Aslin, R. N. (1990). Preference for infant‐directed speech in the first month after birth. Child Development, 61, 1584–1595.Google Scholar
Cosmides, L., Tooby, J., & Kurzban, R. (2003). Perceptions of race. Trends in Cognitive Sciences, 7, 173–179.Google Scholar
Csibra, G. (2010). Recognizing communicative intentions in infancy. Mind and Language, 25, 141–168.Google Scholar
Csibra, G., Bíró, S., Koós, O., & Gergely, G. (2003). One-year-old infants use teleological representations of actions productively. Cognitive Science, 27, 111–133.Google Scholar
Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Sciences, 13, 148–153.Google Scholar
Csibra, G., Gergely, G., Bíró, S., Koós, O., & Brockbank, M. (1999). Goal attribution without agency cues: The perception of “pure reason” in infancy. Cognition, 72, 237–267.Google Scholar
Csibra, G., & Volein, Á. (2008). Infants can infer the presence of hidden objects from referential gaze information. British Journal of Developmental Psychology, 26, 1–11.Google Scholar
Cushman, F. A., Sheketoff, R., Wharton, S., & Carey, S. (2013). The development of intent-based moral judgment. Cognition, 127, 6–21.Google Scholar
Cushman, F. A., Young, L., & Hauser, M. D. (2006). The role of conscious reasoning and intuition in moral judgment: Testing three principles of harm. Psychological Science, 17, 1082–1089.Google Scholar
Darwin, C. (1871). The Descent of Man, and Selection in Relation to Sex (1st ed.). London: John Murray.Google Scholar
Dörrenberg, S., Rakoczy, H., & Liszkowski, U. (2018). How (not) to measure infant Theory of Mind: Testing the replicability and validity of four non-verbal measures. Cognitive Development, 46, 12–30.Google Scholar
Dunbar, R. I. M. (1992). Neocortex size as a constraint on group size in primates. Journal of Human Evolution, 22, 469–493.Google Scholar
Dunbar, R. I. M. (2003). The social brain: Mind, language, and society in evolutionary perspective. Annual Review of Anthropology, 32, 163–181.Google Scholar
Egyed, K., Király, I., & Gergely, G. (2013). Communicating shared knowledge in infancy. Psychological Science, 24, 1348–1353.Google Scholar
Falck-Ytter, T., Gredebäck, G., & von Hofsten, C. (2006). Infants predict other people’s action goals. Nature Neuroscience, 9, 878–879.Google Scholar
Farroni, T., Johnson, M. H., Menon, E., Zulian, L., Faraguna, D., & Csibra, G. (2005). Newborns’ preference for face-relevant stimuli: Effects of contrast polarity. Proceedings of the National Academy of Sciences, 102, 17245–17250.Google Scholar
Farroni, T., Mansfield, E. M., Lai, C., & Johnson, M. H. (2003). Infants perceiving and acting on the eyes: Tests of an evolutionary hypothesis. Journal of Experimental Child Psychology, 85, 199–212.Google Scholar
Futó, J., Téglás, E., Csibra, G., & Gergely, G. (2010). Communicative function demonstration induces kind-based artifact representation in preverbal infants. Cognition, 117, 1–8.Google Scholar
Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119, 593–609.Google Scholar
Gao, T., Newman, G. E., & Scholl, B. J. (2009). The psychophysics of chasing: A case study in the perception of animacy. Cognitive Psychology, 59, 154–179.Google Scholar
Gelman, S., & Hirschfeld, L. (1999). How biological is essentialism? In Atran, S., & Medin, D. (eds.), Folk Biology (pp. 403–446). Cambridge, MA: MIT Press.Google Scholar
Gergely, G., Bekkering, H., & Kiraly, I. (2002). Rational imitation in preverbal infants. Nature, 415, 755.Google Scholar
Gergely, G., Nádasdy, Z., Csibra, G., & Bíró, S. (1995) Taking the intentional stance at 12 months of age. Cognition, 56, 165–193.Google Scholar
Gervain, J., & Mehler, J. (2010). Speech perception and language acquisition in the first year of life. Annual Review of Psychology, 61, 191–218.Google Scholar
Greene, J. D., Nystrom, L. E., Engell, A. D., Darley, J. M., & Cohen, J. D. (2004). The neural bases of cognitive conflict and control in moral judgment. Neuron, 44, 389–400.Google Scholar
Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108, 814–834.Google Scholar
Haidt, J. (2012). The Righteous Mind: Why Good People Are Divided by Politics and Religion. New York: Pantheon.Google Scholar
Hamlin, J. K. (2013). Moral judgment and action in preverbal infants and toddlers: Evidence for an innate moral core. Current Directions in Psychological Science, 22, 186–193.Google Scholar
Hamlin, J. K., Hallinan, E. V., & Woodward, A. L. (2008). Do as I do: 7-month-old infants selectively reproduce others’ goals. Developmental Science, 11, 487–494.Google Scholar
Hamlin, J. K., Ullman, T., Tenenbaum, J., Goodman, N., & Baker, C. (2013). The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model. Developmental Science, 16, 209–226.Google Scholar
Hamlin, J. K., & Wynn, K. (2012). Who knows what’s good to eat? Infants fail to match the food preferences of antisocial others. Cognitive Development, 27, 227–239.Google Scholar
Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature, 450, 557–559.Google Scholar
Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298, 1569–1579.Google Scholar
Heyes, C. (2014). False belief in infancy: A fresh look. Developmental Science, 17, 647–659.Google Scholar
Heyes, C. (2018). Cognitive Gadgets, the Cultural Evolution of Thinking. Cambridge, MA: Harvard University Press.Google Scholar
Hirschfeld, L. (1995a). Do children have a theory of race? Cognition, 54, 209–252.Google Scholar
Hirschfeld, L. (1995b). Anthropology, psychology, and the meanings of social causality. In Sperber, D., Premack, D., & Premack, A. (eds.) (1995). Causal Cognition, a Multidisciplinary Debate (pp. 205–233). Oxford: Clarendon Press.Google Scholar
Hume, D. (1738). A Treatise of Human Nature: Being an Attempt to Introduce the Experimental Method of Reasoning into Moral Subjects. New York: Oxford University Press.Google Scholar
Humphrey, N. (1976). The social function of the intellect. In Bateson, P. P. G., & Hinde, R. A. (eds.), Growing Points on Ethology (pp. 303–317). Cambridge: Cambridge University Press.Google Scholar
Hyde, D. C., Simon, C. E., Ting, F., & Nikolaeva, J. I. (2018). Functional organization of the temporal–parietal junction for theory of mind in preverbal infants: A near-infrared spectroscopy study. Journal of Neuroscience, 38, 4264–4274.Google Scholar
Jacob, P. (2012). Sharing and ascribing goals. Mind and Language, 27, 202–229.Google Scholar
Jin, K., & Baillargeon, R. (2017). Infants possess an abstract expectation of ingroup support. Proceedings of the National Academy of Sciences (USA), 114, 8199–8204.Google Scholar
Johnson, M. H., Dziurawiec, S., Ellis, H., & Morton, J. (1991). Newborns' preferential tracking of face-like stimuli and its subsequent decline. Cognition, 40, 1–19.Google Scholar
Jovanovic, B., Király, I., Elsner, B., Gergely, G., Prinz, W., & Aschersleben, G. (2007). The role of effects for infants’ perception of action goals. Psychologia, 50, 273–290.Google Scholar
Kampis, D., Somogyi, E., Itakura, S., & Király, I. (2013). Do infants bind mental states to agents? Cognition, 129, 232–240.Google Scholar
Kant, I. (1785). Groundwork of the Metaphysics of Morals, various printings.Google Scholar
Keil, F. C. (2014). Developmental Psychology: The Growth of Mind and Behavior. New York: W. W. Norton.Google Scholar
Kelly, D. J., Quinn, P. C., Slater, A. M., Lee, K., Gibson, A., Smith, M, Ge, L., & Pascalis, O. (2005). Three-month-olds, but not newborns, prefer own-race faces. Developmental Science, 8, F31–F36.Google Scholar
Kinzler, K. D., & DeJesus, J. M. (2013). Children’s sociolinguistic evaluations of nice foreigners and mean Americans. Developmental Psychology, 49, 655–664.Google Scholar
Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2007). The native language of social cognition. Proceedings of the National Academy of Sciences (USA), 104, 12577–12580.Google Scholar
Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2012). “Native” objects and collaborators: Infants’ object choices and acts of giving reflect favor for native over foreign speakers. Journal of Cognitive Development, 13, 67–81.Google Scholar
Kinzler, K. D., Shutts, K., DeJesus, J., & Spelke, E. S. (2009). Accent trumps race in guiding children’s social preferences. Social Cognition, 27, 623–634.Google Scholar
Kiràly, I., Jovanovic, B., Prinz, W., Aschersleben, G., & Gergely, G. (2003). The early origins of goal attribution in infancy. Consciousness and Cognition, 12, 752–769.Google Scholar
Knudsen, B., & Liszkowski, U. (2012). 18-month-olds predict specific action mistakes through attribution of false belief, not ignorance, and intervene accordingly. Infancy, 17, 672–691.Google Scholar
Kohlberg, L. (1976). Moral stages and moralization: The cognitive-developmental. In Lickona, T. (ed.), Moral Development and Behavior: Theory, Research and Social Issues (pp. 31–53). New York: Holt, Rinehart and Winston.Google Scholar
Krebs, D. L., & Denton, K. (2005). Toward a more pragmatic approach to morality: A critical evaluation of Kohlberg’s model. Psychological Review, 112, 629–649.Google Scholar
Kuhlmeier, V., Wynn, K., & Bloom, P. (2003). Attribution of dispositional states by 12-month-olds. Psychological Science, 14, 402–408.Google Scholar
Kurtines, W., & Greif, E. B. (1974). The development of moral thought: Review and evaluation of Kohlberg’s approach. Psychological Bulletin, 81, 453–470.Google Scholar
Kurzban, R., Tooby, J., & Cosmides, L. (2001). Can race be erased? Coalitional computation and social categorization. Proceedings of the National Academy of Sciences (USA), 98, 15387–15392.Google Scholar
Leslie, S. J. (2007). Generics and the structure of the mind. Philosophical Perspectives, 21, 375–405.Google Scholar
Leslie, S. J. (2008). Generics: Cognition and acquisition. Philosophical Review, 117, 1–49.Google Scholar
Liberman, Z., Woodward, A. L., & Kinzler, K. D. (2017). Preverbal infants infer third-party social relationships based on language. Cognitive Science, 41, 622–634.Google Scholar
Liszkowski, U., Carpenter, M., Striano, T., & Tomasello, M. (2006). 12- and 18-month-olds point to provide information for others. Journal of Cognition and Development, 7, 173–187.Google Scholar
Locke, J. (1698). An Essay Concerning Human Understanding. London.Google Scholar
Luo, Y. (2011a). Three-month-old infants attribute goals to a non-human agent. Developmental Science, 14, 453–460.Google Scholar
Luo, Y. (2011b). Do 10-month-old infants understand others’ false beliefs? Cognition, 121, 289–298.Google Scholar
Luo, Y., & Baillargeon, R. (2005). Can a self-propelled box have a goal?: Psychological reasoning in 5-month-old infants. Psychological Science, 16, 601–608.Google Scholar
Martin, A., Onishi, K. H., & Vouloumanos, A. (2012). Understanding the abstract role of speech in communication at 12 months. Cognition, 123, 50–60.Google Scholar
Mascaro, O., & Csibra, G. (2012). Representation of stable social dominance relations by human infants. Proceedings of the National Academy of Sciences (USA), 109, 6862–6867.Google Scholar
Mascaro, O., & Sperber, D. (2009). The moral, epistemic, and mindreading components of children’s vigilance towards deception. Cognition, 112, 367–380.Google Scholar
Maynard-Smith, J., & Harper, D. (2003). Animal Signals. New York: Oxford University Press.Google Scholar
Meltzoff, A. N. (1988). Infant imitation after a 1-week delay: Long-term memory for novel acts and multiple stimuli. Developmental Psychology, 24, 470–476.Google Scholar
Meltzoff, A. N. (2002). Imitation as a mechanism of social cognition: Origins of empathy, theory of mind, and the representation of action. In Goswami, U. (ed.), Blackwell Handbook of Childhood Cognitive Development (pp. 6–25). Oxford: Blackwell.Google Scholar
Meltzoff, A. N. (2005). Imitation and other minds: The “Like me” hypothesis. In Hurley, S., & Chater, N. (eds.), Perspectives on Imitation: From Neuroscience to Social Science (Vol. 2, pp. 55–77). Cambridge, MA: MIT Press.Google Scholar
Meltzoff, A. N. (2007). “Like me”: A foundation for social cognition. Developmental Science, 10, 126–134.Google Scholar
Meltzoff, A. N., & Moore, M. K. (1977). Imitation of facial and manual gestures by human neonates. Science, 198, 5.Google Scholar
Meltzoff, A. N., & Moore, M. K. (1983). Newborn infants imitate adult facial gestures. Child Development, 54, 702–709.Google Scholar
Meltzoff, A. N., & Moore, M. K. (1997). Explaining facial imitation: A theoretical model. Early Development and Parenting, 6, 179–192.Google Scholar
Meltzoff, A. N., Murray, L., Simpson, E., Heimann, M., Nagy, E., Nadel, J., Pedersen, E. J., Brooks, R., Messinger, D. S., Pascalis, L. D., Subiaul, F., Paukner, A., & Ferrari, P. F. (2018). Re-examination of Oostenbroek et al. (2016): Evidence for neonatal imitation of tongue protrusion. Developmental Science, 21, e12609.Google Scholar
Mikhail, J. (2007). Universal moral grammar: Theory, evidence and the future. Trends in Cognitive Sciences, 11, 143–152.Google Scholar
New, J., Cosmides, L., & Tooby, J. (2007). Category-specific attention for animals reflects ancestral priorities, not expertise. Proceedings of the National Academy of Sciences (USA), 104, 16598–16603.Google Scholar
Nyström, P. (2008). The infant mirror neuron system studied with high density EEG. Social Neuroscience, 3, 334–347.Google Scholar
Nyström, P., Ljunghammar, T., Rosander, K., & von Hofsten, C. (2010). Using mu rhythm perturbations to measure mirror neuron activity in infants. Developmental Science, 14, 327–335.Google Scholar
Onishi, K. H., & Baillargeon, R. (2005). Do 15-month-old infants understand false beliefs? Science, 308, 255–258.Google Scholar
Oostenbroek, J., Slaughter, V., Nielsen, M., & Suddendorf, T. (2013). Why the confusion around neonatal imitation? A review. Journal of Reproductive and Infant Psychology, 31, 328–341.Google Scholar
Oostenbroek, J., Suddendorf, T., Nielsen, M., Redshaw, J., Kennedy-Costantini, S., Davis, J., & Slaughter, V. (2016). Comprehensive longitudinal study challenges the existence of neonatal imitation in humans. Current Biology, 26, 1334–1338.Google Scholar
Perner, J., & Ruffman, T. (2005). Infants’ insight into the mind: How deep? Science, New Series, 308, 214–216.Google Scholar
Piaget, J. (1932). The Moral Judgment of the Child. London: Kegan, Paul, Trench, Trubner & Co.Google Scholar
Pietraszewski, D., & German, T. C. (2013). Coalitional psychology on the playground: Reasoning about indirect social consequences in preschoolers and adults. Cognition, 126, 352–363.Google Scholar
Plötner, M., Over, H., Carpenter, M., & Tomasello, M. (2015). The effects of collaboration and minimal-group membership on children’s prosocial behavior, liking, affiliation, and trust. Journal of Experimental Child Psychology, 139, 161–173.Google Scholar
Powell, L. J., & Spelke, E. S. (2013). Preverbal infants expect members of social groups to act alike. Proceedings of the National Academy of Sciences (USA), 23, E3965–E3972.Google Scholar
Powell, L. J., & Spelke, E. S. (2018). Human infants’ understanding of social imitation: Inferences of affiliation from third party observations. Cognition, 170, 31–48.Google Scholar
Premack, D., & Premack, A. (1997). Infants attribute value ± to the goal-directed actions of self-propelled objects. Journal of Cognitive Neuroscience, 9, 848–856.Google Scholar
Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences, 1, 515–526.Google Scholar
Reid, V. M., Dunn, K., Young, R. J., Amu, J., Donovan, T., & Reissland, N. (2017). The human fetus preferentially engages with face-like visual stimuli. Current Biology, 27, 1825–1828.Google Scholar
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192.Google Scholar
Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3, 131–141.Google Scholar
Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience, 2, 661–670.Google Scholar
Rizzolatti, G., & Sinigaglia, C. (2010). The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nature Reviews Neuroscience, 11, 264–274.Google Scholar
Rosander, K., & von Hofsten, C. (2011). Predictive gaze shifts elicited during observed and performed actions in 10-month-old infants and adults. Neuropsychologia, 49, 2911–2917.Google Scholar
Royzman, E., Kim, K., & Leeman, R. (2015). The curious tale of Julie and Mark: Unraveling the moral dumfounding effect. Judgment and Decision Making, 10, 296–313.Google Scholar
Salvadori, E., Blazsekova, T., Volein, A., Karap, Z., Tatone, D., Mascaro, O., & Csibra, G. (2015). Probing the strength of infants’ preference for helpers over hinderers: Two replication attempts of Hamlin and Wynn (2011). PLoS ONE, 10, e0140570.Google Scholar
Scott-Phillips, T. C. (2014). Speaking Our Minds. London: Palgrave MacMillan.Google Scholar
Senju, A., & Csibra, G. (2008). Gaze following in human infants depends on communicative signals. Current Biology, 18, 668–671.Google Scholar
Sheshkin, M., Bloom, P., & Wynn, K. (2014). Anti-equality: Social comparison in young children. Cognition, 130, 152–156.Google Scholar
Sheshkin, M., Chevallier, C., Lambert, S., & Baumard, N. (2014). Life-history theory explains childhood moral development. Trends in Cognitive Sciences, 18, 613–615.Google Scholar
Shutts, K., Kinzler, K. D., McKee, C. B., & Spelke, E. S. (2009). Social information guides infants' selection of foods. Journal of Cognition and Development, 10, 1–17.Google Scholar
Simion, F., Regolin, L., & Bulf, H. (2008). A predisposition for biological motion in the newborn baby. Proceedings of the National Academy of Sciences (USA), 105, 809–813.Google Scholar
Skerry, A. E., Carey, S. E., & Spelke, E. S. (2013). First-person action experience reveals sensitivity to action efficiency in prereaching infants. Proceedings of the National Academy of Sciences (USA), 110, 18728–18733.Google Scholar
Sloane, S., Baillargeon, R., & Premack, D. (2012). Do infants have a sense of fairness? Psychological Science, 23, 196–204.Google Scholar
Sommerville, J. A., Woodward, A. L., & Needham, A. (2005). Action experience alters 3-month-old infants’ perception of others’ actions. Cognition, 96, B1–B11.Google Scholar
Southgate, V. (2020). Are infants altercentric? The other and the self in early social cognition. Psychological Review, 127, 505–523.Google Scholar
Southgate, V., Johnson, M. H., & Csibra, G. (2008). Infants attribute goals even to biomechanically impossible actions. Cognition, 107, 1059–1069.Google Scholar
Southgate, V., Johnson, M. H., Osborne, T., & Csibra, G. (2009). Predictive motor activation during action observation in human infants. Biology Letters, 5, 769–772.Google Scholar
Southgate, V., & Vernetti, A. (2014). Belief-based action prediction in preverbal infants. Cognition, 130, 1–10.Google Scholar
Tajfel, H., Billig, M. G., Bundy, R. P., & Flament, C. (1971). Social categorization and intergroup behavior. European Journal of Social Psychology, 1, 149–177.Google Scholar
Tomasello, M. (2008). Origins of Human Communication. Cambridge, MA: MIT Press.Google Scholar
Tomasello, M. (2014). A Natural History of Human Thinking, Cambridge MA: Harvard University Press.Google Scholar
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: The origins of cultural cognition. Behavioral and Brain Sciences, 28, 675–691.Google Scholar
Tooby, J., & Cosmides, L. (2010). Groups in mind: The coalitional roots of war and morality. In Høgh-Olesen, H. (ed.), Human Morality & Sociality: Evolutionary & Comparative Perspectives (pp. 191–234). New York: Palgrave MacMillan.Google Scholar
Troje, N. F., & Westhoff, C. (2006). The inversion effect in biological motion perception: Evidence for a “life detector”? Current Biology, 16, 821–824.Google Scholar
Turiel, E. (1983). The Development of Social Knowledge: Morality and Convention. Cambridge: Cambridge University Press.Google Scholar
Turiel, E., Killen, M., & Helwig, C. C. (1987). Morality: Its structure, function, and vagaries. In Kagan, J., & Lamb, S. (eds.), The Emergence of Morality in Young Children (pp. 155–243). Chicago, IL: University of Chicago Press.Google Scholar
Umiltà, M. A., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C., & Rizzolatti, G. (2001). “I know what you are doing”: A neurophysiological study. Neuron, 32, 91–101.Google Scholar
Valenza, E., Simion, F., Cassia, V. M., & Umiltà, C. (1996). Face preference at birth. Journal of Experimental Psychology: Human Perception and Performance, 22, 892.Google Scholar
Vallortigara, G., Regolin, L., & Marconato, F. (2005). Visually inexperienced chicks exhibit spontaneous preference for biological motion. PLoS Biology, 3, e208.Google Scholar
Vouloumanos, A., & Werker, J. F. (2007). Listening to language at birth: Evidence for a bias for speech in neonates. Developmental Science, 10, 159–164.Google Scholar
Warnecken, F., & Tomasello, M. (2006). Altruistic helping in human infants and young chimpanzees. Science, 311, 1301–1302.Google Scholar
Warnecken, F., & Tomasello, M. (2007). Helping and cooperation at 14 months of age. Infancy, 11, 271–294.Google Scholar
Warnecken, F., & Tomasello, M. (2009). Varieties of altruism in children and chimpanzees. Trends in Cognitive Sciences, 13, 397–402.Google Scholar
Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of theory-of-mind development: The truth about false belief. Child Development, 72, 655–684.Google Scholar
Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128.Google Scholar
Woodward, A. L. (1998). Infants selectively encode the goal object of an actor’s reach. Cognition, 69, 1–34.Google Scholar
Woodward, A. L. (1999). Infants’ ability to distinguish between purposeful and non-purposeful behaviors. Infant Behavior and Development, 22, 145–160.Google Scholar
Woodward, A. L., Sommerville, J. A., Gerson, S., Henderson, A. M. E., & Buresh, J. (2009). The emergence of intention attribution in infancy. In Ross, B. (ed.), Psychology of Learning and Motivation (Vol. 51, pp. 187–222). New York: Academic Press.Google Scholar
Xu, F., & Carey, S. (1996). Infants’ metaphysics: The case of numerical identity. Cognitive Psychology, 30, 111–153.Google Scholar
Yoon, J. M. D., Johnson, M. H., & Csibra, G. (2008). Communication-induced memory biases in preverbal infants. Proceedings of the National Academy of Sciences (USA), 105, 13690–13695.Google Scholar
Yu, C., & Smith, L. B. (2013). Joint attention without gaze following: Human infants and their parents coordinate visual attention to objects through eye–hand coordination. PLoS ONE, 8, e79659.Google Scholar

References

Arend, R., Gove, F. L., & Sroufe, L. A. (1979). Continuity of individual adaptation from infancy to kindergarten: A predictive study of ego-resiliency and curiosity in preschoolers. Child Development, 50, 950–959.Google Scholar
Astington, J. W., Harris, P. L., & Olson, D. R. (eds.) (1988). Developing Theories of Mind. Cambridge: Cambridge University Press.Google Scholar
Baillargeon, R. (1987). Object permanence in 3½- and 4½-month-old infants. Developmental Psychology, 23, 655–664.Google Scholar
Baillargeon, R. (1998). Infants' understanding of the physical world. In Sabourin, M., Craik, F., & Robert, M. (eds.), Advances in Psychological Science, Vol. 2. Biological and Cognitive Aspects (pp. 503–529). Hove: Psychology Press.Google Scholar
Beck, S. R., & Riggs, K. J. (2013). Counterfactuals and reality. In Taylor, M. (ed.), The Oxford Handbook of the Development of Imagination (pp. 325–341). New York: Oxford University Press.Google Scholar
Beck, S. R., Robinson, E. J., Carroll, D. J., & Apperly, I. A. (2006). Children’s thinking about counterfactuals and future hypotheticals as possibilities. Child Development, 77, 413–426.Google Scholar
Bonawitz, E., Denison, S., Gopnik, A., & Griffiths, T. L. (2014a). Win-stay, lose-sample: A simple sequential algorithm for approximating Bayesian inference. Cognitive Psychology, 74, 35–65.Google Scholar
Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. (2014b). Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences, 18, 497–500.Google Scholar
Bonawitz, E. B., Ferranti, D., Saxe, R., Gopnik, A., Meltzoff, A. N., Woodward, J., & Schulz, L. E. (2010). Just do it? Investigating the gap between prediction and action in toddlers’ causal inferences. Cognition, 115, 104–117.Google Scholar
Bonawitz, E. B., & Lombrozo, T. (2012). Occam’s rattle: Children’s use of simplicity and probability to constrain inference. Developmental Psychology, 48, 1156.Google Scholar
Butler, L. P. (2020). The empirical child? A framework for investigating the development of scientific habits of mind. Child Development Perspectives, 14, 34–40.Google Scholar
Carey, S. (1985). Conceptual Change in Childhood. Cambridge, MA: MIT Press.Google Scholar
Carey, S. (2009) The Origin of Concepts. New York: Oxford University Press.Google Scholar
Cesana-Arlotti, N., Martín, A., Téglás, E., Vorobyova, L., Cetnarski, R., & Bonatti, L. L. (2018). Precursors of logical reasoning in preverbal human infants. Science, 359, 1263–1266.Google Scholar
Cimpian, A., & Steinberg, O. D. (2014). The inherence heuristic across development: Systematic differences between children’s and adults’ explanations for everyday facts. Cognitive Psychology, 75, 130–154.Google Scholar
Christie, S., & Gentner, D. (2010). Where hypotheses come from: Learning new relations by structural alignment. Journal of Cognition and Development, 11, 356–373.Google Scholar
Christie, S. & Gentner, D. (2014). Language helps children succeed on a classic analogy task. Cognitive Science, 38, 383–397.Google Scholar
Cook, C., Goodman, N. D., & Schulz, L. E. (2011). Where science starts: Spontaneous experiments in preschoolers’ exploratory play. Cognition, 120, 341–349.Google Scholar
Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (2013). Rational variability in children’s causal inferences: The sampling hypothesis. Cognition, 126, 285–300.Google Scholar
Flavell, J. H. (1963). The Developmental Psychology of Jean Piaget. New York: Van Nostrand Reinhold Company.Google Scholar
Gelman, S. A. (2003). The Essential Child. New York: Oxford University PressGoogle Scholar
Gelman, S. A., Coley, J. D., & Gottfried, G. M. (1994). Essentialist beliefs in children: The acquisition of concepts and theories. In Hirschfeld, L. A., & Gelman, S. A. (eds.), Mapping the Mind: Domain Specificity in Cognition and Culture (pp. 341–365). Cambridge: Cambridge University Press.Google Scholar
Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23, 183–209.Google Scholar
Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P., & Forbus, K. D. (1997). Analogical reasoning and conceptual change: A case study of Johannes Kepler. The Journal of the Learning Sciences, 6, 3–40.Google Scholar
Giles, J. W., Gopnik, A., & Heyman, G. D. (2002). Source monitoring reduces the suggestibility of preschool children. Psychological Science, 13, 288–291.Google Scholar
Goddu, M. K., & Gopnik, A. (2020). Learning what to change: Young children use “difference-making” to identify causally relevant variables. Developmental Psychology, 56, 275.Google Scholar
Goddu, M. K., Lombrozo, T., & Gopnik, A. (2020). Transformations and transfer: Preschool children understand abstract relations and reason analogically in a causal task. Child Development, 91, 1898–1915.Google Scholar
Goddu, M., K. & Walker, C. M. (2018). Toddlers and adults simultaneously track multiple hypotheses in a causal learning task. Cognitive Science. Available from https://cogsci.mindmodeling.org/2018/papers/0330/index.html. Last accessed July 30, 2021.Google Scholar
Gopnik, A. (1984). Conceptual and semantic change in scientists and children: Why there are no semantic universals. Lingusitics, 21.Google Scholar
Gopnik, A. (1998). Explanation as orgasm. Minds and Machines, 8, 101–118.Google Scholar
Gopnik, A. (2012). Scientific thinking in young children: Theoretical advances, empirical research, and policy implications. Science, 337, 1623–1627.Google Scholar
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3.Google Scholar
Gopnik, A., & Meltzoff, A. N. (1997). Words, Thoughts, & Theories. Cambridge, MA: MIT Press.Google Scholar
Gopnik, A., & Sobel, D.M. (2000). Detecting Blickets: How young children use information about novel causal powers in categorization and induction. Child Development, 71, 1205–1222.Google Scholar
Gopnik, A., Sobel, D. M., Schulz, L. E., & Glymour, C. (2001). Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology, 37, 620.Google Scholar
Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin, 138, 1085.Google Scholar
Gottlieb, J., Oudeyer, P. Y., Lopes, M., & Baranes, A. (2013). Information-seeking, curiosity, and attention: Computational and neural mechanisms. Trends in Cognitive Sciences, 17, 585–593.Google Scholar
Gottlieb, S., Keltner, D., & Lombrozo, T. (2018). Awe as a scientific emotion. Cognitive Science, 42, 2081–2094.Google Scholar
Greco, C., Hayne, H., & Rovee-Collier, C. (1990). Roles of function, reminding, and variability in categorization by 3-month-old infants. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 617.Google Scholar
Gweon, H., & Schulz, L. (2011). 16-month-olds rationally infer causes of failed actions. Science, 332, 1524.Google Scholar
Inagaki, K. (1990). The effects of raising animals on children’s biological knowledge. British Journal of Developmental Psychology, 8, 119–129.Google Scholar
Johnston, A. M., Johnson, S. G., Koven, M. L., & Keil, F. C. (2017). Little Bayesians or little Einsteins? Probability and explanatory virtue in children’s inferences. Developmental Science, 20, e12483.Google Scholar
Kalish, C. (1998). Reasons and causes: Children’s understanding of conformity to social rules and physical laws. Child Development, 69, 706–720.Google Scholar
Karmiloff-Smith, A., & Inhelder, B. (1974). If you want to get ahead, get a theory. Cognition, 3, 195–212.Google Scholar
Keil, F. C. (1989). Concepts, Kinds, and Cognitive Development. Cambridge, MA: MIT Press.Google Scholar
Keil, F. C. (2006). Explanation and understanding. Annual Review of Psychology, 57, 227–254.Google Scholar
Keil, F. C. (2012). Running on empty? How folk science gets by with less. Current Directions in Psychological Science, 21, 329–334.Google Scholar
Keil, F. C., Lockhart, K. L., & Schlegel, E. (2010). A bump on a bump? Emerging intuitions concerning the relative difficulty of the sciences. Journal of Experimental Psychology: General, 139, 1.Google Scholar
Kelemen, D. (1999a). The scope of teleological thinking in preschool children. Cognition, 70, 241–272.Google Scholar
Kelemen, D. (1999b). Why are rocks pointy? Children’s preference for teleological explanations of the natural world. Developmental Psychology, 35, 1440.Google Scholar
Kelemen, D., & DiYanni, C. (2005). Intuitions about origins: Purpose and intelligent design in children’s reasoning about nature. Journal of Cognition and Development, 6, 3–31.Google Scholar
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10, 307–321.Google Scholar
Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88, 449–460.Google Scholar
Kuhn, T. (1962) The Structure of Scientific Revolutions. Chicago, IL: The University of Chicago Press.Google Scholar
Kuhn, D. (2002). What is scientific thinking and how does it develop? In Goswami, U. (ed.), Blackwell Handbook of Childhood Cognitive Development (pp. 371–393). Oxford: Blackwell Publishing.Google Scholar
Kushnir, T., & Gopnik, A. (2007). Conditional probability versus spatial contiguity in causal learning: Preschoolers use new contingency evidence to overcome prior spatial assumptions. Developmental Psychology, 43, 186.Google Scholar
Lapidow, E., & Walker, C. M. (2020). Informative experimentation in intuitive science: Children select and learn from their own causal interventions. Cognition, 201, 104315.Google Scholar
Leahy, B. P.,& Carey, S. E. (2020). The acquisition of modal concepts. Trends in Cognitive Science, 24, 65–78.Google Scholar
Legare, C. H. (2012). Exploring explanation: Explaining inconsistent evidence informs exploratory, hypothesis‐testing behavior in young children. Child Development, 83, 173–185.Google Scholar
Legare, C. H. (2014). The contributions of explanation and exploration to children’s scientific reasoning. Child Development Perspectives, 8, 101–106.Google Scholar
Legare, C. H., Gelman, S. A., & Wellman, H. M. (2010). Inconsistency with prior knowledge triggers children’s causal explanatory reasoning. Child Development, 81, 929–944.Google Scholar
Lockhart, K. L., Goddu, M. K., & Keil, F. C. (2017). Overoptimism about future knowledge: Early arrogance? The Journal of Positive Psychology, 12, 36–46.Google Scholar
Lockhart, K. L., Goddu, M. K., Smith, E. D., & Keil, F. C. (2016). What could you really learn on your own?: Understanding the epistemic limitations of knowledge acquisition. Child Development, 87, 477–493.Google Scholar
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116, 75–98.Google Scholar
Lucas, C. G., Bridgers, S., Griffiths, T. L., & Gopnik, A. (2014). When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships. Cognition, 131, 284–299.Google Scholar
Magid, R. W., Sheskin, M., & Schulz, L. E. (2015). Imagination and the generation of new ideas. Cognitive Development, 34, 99–110.Google Scholar
Mikulincer, M. (1997). Adult attachment style and information processing: Individual differences in curiosity and cognitive closure. Journal of Personality and Social Psychology, 72, 1217.Google Scholar
Mills, C. M., & Keil, F. C. (2004). Knowing the limits of one’s understanding: The development of an awareness of an illusion of explanatory depth. Journal of Experimental Child Psychology, 87, 1–32.Google Scholar
Mills, C. M., Legare, C. H., Bills, M., & Mejias, C. (2010). Preschoolers use questions as a tool to acquire knowledge from different sources. Journal of Cognition and Development, 11, 533–560.Google Scholar
Mody, S., & Carey, S. (2016). The emergence of reasoning by the disjunctive syllogism in early childhood. Cognition, 154, 40–48.Google Scholar
Nersessian, N. J. (1999). Model-based reasoning in conceptual change. In Magnani, L., Nersessian, N. J., & Thagard, P. (eds.), Model-Based Reasoning in Scientific Discovery (pp. 5–22). Boston, MA: Springer.Google Scholar
Newman, G. E., Herrmann, P., Wynn, K., & Keil, F. C. (2008). Biases towards internal features in infants’ reasoning about objects. Cognition, 107, 420–432.Google Scholar
O’Neill, D. K., & Gopnik, A. (1991). Young children’s ability to identify the sources of their beliefs. Developmental Psychology, 27, 390.Google Scholar
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. New York: Cambridge University Press.Google Scholar
Pearl, J. (2009). Causality. New York: Cambridge University Press.Google Scholar
Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. New York: Basic Books.Google Scholar
Piaget, J. (1929). The Child’s Conception of the World. London: Kegan Paul.Google Scholar
Quine, W. V. O. (1960). Word and Object (Studies in Communication). New York: Technology Press of MIT.Google Scholar
Rafetseder, E., Cristi‐Vargas, R., & Perner, J. (2010). Counterfactual reasoning: Developing a sense of “nearest possible world.” Child Development, 81, 376–389.Google Scholar
Redshaw, J., & Suddendorf, T. (2016). Children’s and apes’ preparatory responses to two mutually exclusive possibilities. Current Biology, 26, 1758–1762.Google Scholar
Redshaw, J., & Suddendorf, T. (2020). Temporal junctures in the mind. Trends in Cognitive Sciences, 24, 52–64.Google Scholar
Repacholi, B. M., & Gopnik, A. (1997). Early reasoning about desires: Evidence from 14-and 18-month-olds. Developmental Psychology, 33, 12.Google Scholar
Ronfard, S., Zambrana, I. M., Hermansen, T. K., & Kelemen, D. (2018). Question-asking in childhood: A review of the literature and a framework for understanding its development. Developmental Review, 49, 101–120.Google Scholar
Ross, N., Medin, D., Coley, J. D., & Atran, S. (2003). Cultural and experiential differences in the development of folk biological induction. Cognitive Development, 18, 25–47.Google Scholar
Ruchlis, H. (1963). Discovering Scientific Method. New York: Harper & Row.Google Scholar
Ruggeri, A., & Lombrozo, T. (2015). Children adapt their questions to achieve efficient search. Cognition, 143, 203–216.Google Scholar
Ruggeri, A., Sim, Z. L., & Xu, F. (2017). “Why is Toma late to school again?” Preschoolers identify the most informative questions. Developmental Psychology, 53, 1620–1632.Google Scholar
Salmon, W. C. (1984). Scientific Explanation and the Causal Structure of the World. Princeton, NJ: Princeton University Press.Google Scholar
Saxe, R., Tenenbaum, J. B., & Carey, S. (2005). Secret agents: Inferences about hidden causes by 10-and 12-month-old infants. Psychological Science, 16, 995–1001.Google Scholar
Schulz, L. E., Bonawitz, E. B., & Griffiths, T. L. (2007a). Can being scared give you a tummy ache? Naive theories, ambiguous evidence and preschoolers’ causal inferences. Developmental Psychology, 43, 1124–1139.Google Scholar
Schulz, L. E., Goodman, N. D., Tenenbaum, J. B., & Jenkins, A. C. (2008). Going beyond the evidence: Abstract laws and preschoolers’ responses to anomalous data. Cognition, 109, 211–223.Google Scholar
Schulz, L. E., & Gopnik, A. (2004). Causal learning across domains. Developmental Psychology, 40, 162.Google Scholar
Schulz, L. E., & Somerville, J. (2006). God does not play dice: Causal determinism and preschoolers’ causal inferences. Child Development, 77, 427–442.Google Scholar
Shtulman, A., & Carey, S. (2007). Improbable or impossible? How children reason about the possibility of extraordinary events. Child Development, 78, 1015–1032.Google Scholar
Shtulman, A., & Phillips, J. (2018). Differentiating “could” from “should”: Developmental changes in modal cognition. Journal of Experimental Child Psychology, 165, 161–182.Google Scholar
Silvia, P. J. (2008). Interest – The curious emotion. Current Directions in Psychological Science, 17, 57–60.Google Scholar
Simons, D. J., & Keil, F. C. (1995). An abstract to concrete shift in the development of biological thought: The insides story. Cognition, 56, 129–163.Google Scholar
Sobel, D. M., & Kushnir, T. (2006). The importance of decision making in causal learning from interventions. Memory & Cognition, 34, 411–419.Google Scholar
Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (1993). Causation, Prediction, and Search. Cambridge, MA: MIT press.Google Scholar
Stahl, A. E., & Feigenson, L. (2015). Observing the unexpected enhances infants’ learning and exploration. Science, 348, 91–94.Google Scholar
Taylor, M., Esbensen, B. M., & Bennett, R. T. (1994). Children’s understanding of knowledge acquisition: The tendency for children to report that they have always known what they have just learned. Child Development, 65, 1581–1604.Google Scholar
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285.Google Scholar
Valdesolo, P., Shtulman, A., & Baron, A. S. (2017). Science is awe-some: The emotional antecedents of science learning. Emotion Review, 9, 215–221.Google Scholar
Walker, C. M., Bonawitz, E., & Lombrozo, T. (2017). Effects of explaining on children’s preference for simpler hypotheses. Psychonomic Bulletin & Review, 24, 1538–1547.Google Scholar
Walker, C. M., Bridgers, S., & Gopnik, A. (2016). The early emergence and puzzling decline of relational reasoning: Effects of knowledge and search on inferring abstract concepts. Cognition, 156, 30–40.Google Scholar
Walker, C. M., & Gopnik, A. (2013). Causality and imagination. In Taylor, M. (ed.), The Oxford Handbook of the Development of Imagination (pp. 342–358). Oxford: Oxford University Press.Google Scholar
Walker, C. M., & Gopnik, A. (2014). Toddlers infer higher-order relational principles in causal learning. Psychological Science, 25, 161–169.Google Scholar
Walker, C. M., Lombrozo, T., Legare, C. H., & Gopnik, A. (2014). Explaining prompts children to privilege inductively rich properties. Cognition, 133, 343–357.Google Scholar
Wang, S. H., & Baillargeon, R. (2008). Can infants be “taught” to attend to a new physical variable in an event category? The case of height in covering events. Cognitive Psychology, 56, 284–326.Google Scholar
Weisberg, D. S., & Gopnik, A. (2013). Pretense, counterfactuals, and Bayesian causal models: Why what is not real really matters. Cognitive Science, 37, 1368–1381.Google Scholar
Wellman, H. M. (1992). The Child’s Theory of Mind. Cambridge, MA: The MIT Press.Google Scholar
Wellman, H. M., & Gelman, S. A. (1998). Knowledge acquisition in foundational domains. In Damon, W. (ed.), Handbook of Child Psychology: Vol. 2. Cognition, Perception, and Language (pp. 523–573). Hoboken, NJ: John Wiley & Sons Inc.Google Scholar
Wellman, H. M., & Woolley, J. D. (1990). From simple desires to ordinary beliefs: The early development of everyday psychology. Cognition, 35, 245–275.Google Scholar
Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128.Google Scholar
Woodward, A. L. (1999). Infants’ ability to distinguish between purposeful and non-purposeful behaviors. Infant Behavior and Development, 22, 145–160.Google Scholar
Woodward, J. (2003). Making Things Happen: A Theory of Causal Explanation. New York: Oxford University Press.Google Scholar
Xu, F., Dewar, K., & Perfors, A. (2009). Induction, overhypotheses, and the shape bias: Some arguments and evidence for rational constructivism. In Hood, B. M., & Santos, L. (eds.), The Origins of Object Knowledge (pp. 263–284). New York: Oxford University Press.Google Scholar
Xu, F., & Kushnir, T. (2013). Infants are rational constructivist learners. Current Directions in Psychological Science, 22, 28–32.Google Scholar

References

Aslin, R. N., Saffran, J. R., & Newport, E. L. (1998). Computation of conditional probability statistics by 8 month old infants. Psychological Science, 9, 321–324.Google Scholar
Baluja, S., & Fahlman, S. E. (1994). Reducing network depth in the cascade-correlation learning architecture. Technical Report CMU-CS-94-209, Carnegie Mellon University.Google Scholar
Berthiaume, V. G., Shultz, T. R., & Onishi, K. H. (2013). A constructivist connectionist model of transitions on false-belief tasks. Cognition, 126, 441–458.Google Scholar
Bonawitz, E., Denison, S., Gopnik, A., & Griffiths, T. L. (2014). Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference. Cognitive Psychology, 74, 35–65.Google Scholar
Bonawitz, E., & Shafto, P. (2016). Computational models of development, social influences. Current Opinion in Behavioral Sciences, 7, 95–100.Google Scholar
Bonawitz, E., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., & Schulz, L. (2011). The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery. Cognition, 120, 322–330.Google Scholar
Boom, J., Hoijtink, H., & Kunnen, S. (2001). Rules in the balance: Classes, strategies, or rules for the Balance Scale Task? Cognitive Development, 16, 717–735.Google Scholar
Boom, J., & ter Laak, J. (2007). Classes in the balance: Latent class analysis and the balance scale task. Developmental Review, 27, 127–149.Google Scholar
Buchsbaum, D., Gopnik, A., Griffiths, T. L., & Shafto, P. (2011). Children’s imitation of causal action sequences is influenced by statistical and pedagogical evidence. Cognition, 120, 331–340.Google Scholar
Bulf, H., Johnson, S. P., & Valenza, E. (2011). Visual statistical learning in the newborn infant. Cognition, 121, 127–132.Google Scholar
Cassidy, K. W. (1998). Three- and four-year-old children’s ability to use desire- and belief- based reasoning. Cognition, 66, B1.Google Scholar
Dandurand, F., & Shultz, T. R. (2010). Automatic detection and quantification of growth spurts. Behavior Research Methods, 42, 809–823.Google Scholar
Dandurand, F., & Shultz, T. R. (2014). A comprehensive model of development on the balance-scale task. Cognitive Systems Research, 31–32, 1–25.Google Scholar
Denison, S., Reed, C., & Xu, F. (2013). The emergence of probabilistic reasoning in very young infants: Evidence from 4.5- and 6-month-olds. Developmental Psychology, 49, 243–249.Google Scholar
Denison, S., & Xu, F. (2014). The origins of probabilistic inference in human infants. Cognition, 130, 335–347.Google Scholar
Elman, J. L. (1996). Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press.Google Scholar
Elman, J. L. (2005). Connectionist models of cognitive development: Where next? Trends in Cognitive Sciences, 9, 111–117.Google Scholar
Fahlman, S. E. (1988). An empirical study of learning speed in back-propagation networks. Neural Networks, 6, 1–19.Google Scholar
Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. In Touretzky, D. S. (ed.), Advances in Neural Information Processing Systems (pp. 524–532). Los Altos, CA: Morgan Kaufmann.Google Scholar
Ferretti, R. P., & Butterfield, E. C. (1986). Are children’s rule-assessment classifications invariant across instances of problem types? Child Development, 57, 1419–1428.Google Scholar
French, R. M., Mermillod, M., Mareschal, D., & Quinn, P. C. (2004). The role of bottom-up processing in perceptual categorization by 3- to 4-month-old infants: Simulations and data. Journal of Experimental Psychology: General, 133, 382–397.Google Scholar
Friedman, O., & Leslie, A. M. (2005). Processing demands in belief-desire reasoning: Inhibition or general difficulty? Developmental Science, 8, 218–225.Google Scholar
Gershman, S., & Beck, J. (2017). Complex probabilistic inference: from cognition to neural computation. In Moustafa, A. (ed.), Computational Models of Brain and Behavior (p. 453). Hoboken, NJ: Wiley-Blackwell.Google Scholar
Goodman, N. D., Baker, C. L., Bonawitz, E. B., Mansinghka, V. K., Gopnik, A., & Wellman, H. M. (2006). Intuitive theories of mind: A rational approach to false belief. In Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1382–1387). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review, 118, 110.Google Scholar
Gopnik, A., & Bonawitz, E. (2015). Bayesian models of child development. Wiley Interdisciplinary Reviews: Cognitive Science, 6, 75–86.Google Scholar
Gopnik, A., Glymour, C., Sobel, D., Schulz, L., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 1–31.Google Scholar
Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012). How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis. Psychological Bulletin, 138, 415–422.Google Scholar
Hamlin, K., Ullman, T., Tenenbaum, J., Goodman, N., & Baker, C. (2013). The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model. Developmental Science, 16, 209–226.Google Scholar
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10, 307–321.Google Scholar
Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition, 83, 4–5.Google Scholar
Klahr, D., Langley, P., & Neches, R. (1987). Production System Models of Learning and Development. Cambridge, MA: MIT Press.Google Scholar
Klahr, D., & Wallace, J. G. (1976). Cognitive Development: An Information Processing View. Hillsdale; NJ: Erlbaum.Google Scholar
Lochmann, T., & Deneve, S. (2011). Neural processing as causal inference. Current Opinion in Neurobiology, 21, 774–781.Google Scholar
Mareschal, D., & French, R. (2017). Tracx2: A connectionist autoencoder using graded chunks to model infant visual statistical learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 372, 20160057.Google Scholar
Marr, D. (2010). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Cambridge, MA: MIT Press.Google Scholar
Mayor, J., & Plunkett, K. (2010). A neurocomputational account of taxonomic responding and fast mapping in early word learning. Psychological Review, 117, 1–31.Google Scholar
Munakata, Y., & McClelland, J. L. (2003). Connectionist models of development. Developmental Science, 6, 413–429.Google Scholar
Nobandegani, A., & Shultz, T. (2018). Example generation under constraints using cascade correlation neural nets. CogSci. Available from https://cogsci.mindmodeling.org/2018/papers/0456/index.html. Last accessed August 23, 2021.Google Scholar
O’Loughlin, C., & Thagard, P. (2000). Autism and coherence: A computational model. Mind and Language, 15, 375–392.Google Scholar
Onishi, K., & Baillargeon, R. (2005). Do 15-month-old infants understand false beliefs? Science, 308, 255–258.Google Scholar
Pearl, J. (2000). Causality: Models, Reasoning and Inference. Cambridge, MA: MIT Press.Google Scholar
Perfors, A., Tenenbaum, J., Griffiths, T., & Xu, F. (2011a). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302–321.Google Scholar
Perfors, A., Tenenbaum, J., & Regier, T. (2011b). The learnability of abstract syntactic principles. Cognition, 118, 306–338.Google Scholar
Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2012). Bootstrapping in a language of thought: A formal model of numerical concept learning. Cognition, 123, 199–217.Google Scholar
Quinlan, P. T., van der Maas, H. L. J., Jansen, B. R. J., Booij, O., & Rendell, M. (2007). Re-thinking stages of cognitive development: An appraisal of connectionist models of the balance scale task. Cognition, 103, 413–459.Google Scholar
Restle, F. (1962). The selection of strategies in cue learning. Psychological Review, 69, 329–343.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928.Google Scholar
Schapiro, A. C., & McClelland, J. L. (2009). A connectionist model of a continuous developmental transition in the balance scale task. Cognition, 110, 395–411.Google Scholar
Schmidt, W., & Ling, C. (1996). A decision-tree model of balance scale development. Machine Learning, 24, 203–229.Google Scholar
Schulz, L. E., Bonawitz, E., & Griffiths, T. L. (2007). Can being scared cause tummy aches? Naive theories, ambiguous evidence, and preschoolers’ causal inferences. Developmental Psychology, 43, 1124–1139.Google Scholar
Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: The consequences of psychological reasoning for human learning. Perspectives on Psychological Science, 7, 341–351.Google Scholar
Shultz, T. R. (2003). Computational Developmental Psychology. Cambridge, MA: MIT Press.Google Scholar
Shultz, T. R. (2007). The Bayesian revolution approaches psychological development. Developmental Science, 10, 357–364.Google Scholar
Shultz, T. R. (2010). Computational modeling of infant concept learning: The developmental shift from features to correlations. In Oakes, L. M., Cashon, C. H., Casasola, M., & Rakison, D. H. (eds.), Infant Perception and Cognition: Recent Advances, Emerging Theories, and Future Directions (pp. 125–152). New York: Oxford University Press.Google Scholar
Shultz, T. R. (2012). A constructive neural-network approach to modeling psychological development. Cognitive Development, 27, 383–400.Google Scholar
Shultz, T. R. (2013). Computational models in developmental psychology. In Zelazo, P. D. (ed.), Oxford Handbook of Developmental Psychology, Vol. 1: Body and Mind (pp. 477–504). New York: Oxford University Press.Google Scholar
Shultz, T. R. (2017). Constructive artificial neural-network models for cognitive development. In Budwig, N., Turiel, E., & Zelazo, P. D. (eds.), New Perspectives on Human Development (pp. 13–26). Cambridge: Cambridge University Press.Google Scholar
Shultz, T. R., & Bale, A. C. (2006). Neural networks discover a near-identity relation to distinguish simple syntactic forms. Minds and Machines, 16, 107–139.Google Scholar
Shultz, T. R., & Cohen, L. B. (2004). Modeling age differences in infant category learning. Infancy, 5, 153–171.Google Scholar
Shultz, T. R., & Doty, E. (2014). Knowing when to quit on unlearnable problems: Another step towards autonomous learning. In Mayor, J., & Gomez, P. (ed.), Computational Models of Cognitive Processes (pp. 211–221). London: World Scientific.Google Scholar
Shultz, T. R., & Fahlman, S. E. (2010). Cascade-correlation. In Sammut, C., & Webb, G. (eds.), Encyclopedia of Machine Learning Part 4/C (pp. 139–147). Heidelberg, Germany: Elsevier.Google Scholar
Shultz, T. R., Mareschal, D., & Schmidt, W. C. (1994). Modeling cognitive development on balance scale phenomena. Machine Learning, 16, 57–86.Google Scholar
Shultz, T. R., Mysore, S. P., & Quartz, S. R. (2012). Why let networks grow? In Mareschal, D., Sirois, S., Westermann, G., & Johnson, M. H. (eds.), Neuroconstructivism: Perspectives and Prospects (Vol. 2, pp. 65–98). Oxford: Oxford University Press.Google Scholar
Shultz, T. R., & Nobandegani, A. S. (2020). Probability without counting and dividing: A fresh computational perspective. In Denison, S., Mack, M., Xu, Y., & Armstrong, B. (eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 1–7). Toronto ON: Cognitive Science Society.Google Scholar
Shultz, T. R., & Rivest, F. (2001). Knowledge-based cascade-correlation: Using knowledge to speed learning. Connection Science, 13, 43–72.Google Scholar
Shultz, T. R., Rivest, F., Egri, L., Thivierge, J.-P., & Dandurand, F. (2007). Could knowledge-based neural learning be useful in developmental robotics? The case of KBCC. International Journal of Humanoid Robotics, 4, 245–279.Google Scholar
Shultz, T. R., & Sirois, S. (2008). Computational models of developmental psychology. In Sun, R. (ed.), The Cambridge Handbook of Computational Psychology (pp. 451–476). New York: Cambridge University Press.Google Scholar
Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8, 481–520.Google Scholar
Siegler, R. S. (1996). Emerging Minds: The Process of Change in Children’s Thinking. New York: Oxford University Press.Google Scholar
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285.Google Scholar
Thomas, M. S. C., & Karmiloff-Smith, A. (2003). Connectionist models of development, developmental disorders and individual differences. In Sternberg, R. J., Lautrey, J., & Lubart, T. (eds.), Models of Intelligence: International Perspectives (pp. 133–150). Washington, DC: American Psychological Association.Google Scholar
Thompson, V. A., Prowse Turner, J. A., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63, 107–140.Google Scholar
Triona, L. M., Masnick, A. M., & Morris, B. J. (2019). What does it take to pass the false belief task? An ACT-R model. CogSci. Available from https://escholarship.org/uc/item/49c346x1. Last accessed August 23, 2021.Google Scholar
Tummeltshammer, K., Amso, D., French, R. M., & Kirkham, N. Z. (2017). Across space and time: Infants learn from backward and forward visual statistics. Developmental Science, 20, e12474.Google Scholar
Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2012). Theory learning as stochastic search in the language of thought. Cognitive Development, 27, 455–480.Google Scholar
Wellman, H. M., Cross, D., & Watson, J. (2001). Meta-analysis of theory-of-mind development: The truth about false belief. Child Development, 72, 655–684.Google Scholar
Westermann, G., Sirois, S., Shultz, T. R., & Mareschal, D. (2006). Modeling developmental cognitive neuroscience. Trends in Cognitive Sciences, 10, 227–232.Google Scholar
Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114, 245–272.Google Scholar
Younger, B. A., & Cohen, L. B. (1986). Developmental change in infants’ perception of correlations among attributes. Child Development, 57, 803–815.Google Scholar

References

Adrián, J., Clemente, R., & Villanueva, L. (2007). Mothers’ use of cognitive state verbs in picturebook reading and the development of children’s understanding of mind: A longitudinal study. Child Development, 78, 1052–1067.Google Scholar
Anggoro, F. K., Waxman, S. R., & Medin, D. L. (2008). Naming practices and the acquisition of key biological concepts: Evidence from English and Indonesian. Psychological Science, 19, 314–319.Google Scholar
Arthur, A. E., Bigler, R. S., Liben, L. S., Gelman, S. A., & Ruble, D. N. (2008). Gender stereotyping and prejudice in young children: A developmental intergroup perspective. In Levy, S. R., & Killen, M. (eds.), Intergroup Attitudes and Relations in Childhood through Adulthood (pp. 66–86). Oxford: Oxford University Press.Google Scholar
Astuti, R., Solomon, G. A., & Carey, S. (2004). Constraints on conceptual development: A case study of the acquisition of folkbiological and folksociological knowledge in Madagascar. Monographs of the Society for Research in Child Development, 69, 1–13.Google Scholar
Atran, S. (1998). Folk biology and the anthropology of science: Cognitive universals and cultural particulars. Behavioral and Brain Sciences, 21, 547–569.Google Scholar
Austin, K., Theakston, A., Lieven, E., & Tomasello, M. (2014). Young children’s understanding of denial. Developmental Psychology, 50, 2061–2070.Google Scholar
Baldwin, D. A., Markman, E. M., & Melartin, R. L. (1993). Infants’ ability to draw inferences about nonobvious object properties: Evidence from exploratory play. Child Development, 64, 711–728.Google Scholar
Banaji, M. R., & Gelman, S. A. (eds.) (2013). Navigating the Social World: What Infants, Children, and Other Species Can Teach Us. New York: Oxford University Press.Google Scholar
Bastian, B., & Haslam, N. (2007). Psychological essentialism and attention allocation: Preferences for stereotype-consistent versus stereotype-inconsistent information. The Journal of Social Psychology, 147, 531–541.Google Scholar
Bergelson, E., & Swingley, D. (2012). At 6–9 months, human infants know the meanings of many common nouns. Proceedings of the National Academy of Sciences (USA), 109, 3253–3258.Google Scholar
Bian, L., Leslie, S. J., & Cimpian, A. (2017). Gender stereotypes about intellectual ability emerge early and influence children’s interests. Science, 355, 389–391.Google Scholar
Bowerman, M. (2005). Why can’t you “open” a nut or “break” a cooked noodle? Learning covert object categories in action word meanings. In Gershkoff-Stowe, L., & Rakison, D. H. (eds.). Building Object Categories in Developmental Time (pp. 227–262). Hove: Psychology Press.Google Scholar
Bowerman, M., & Choi, S. (2001). Shaping meanings for language: Universal and language-specific in the acquisition of spatial semantic categories. In Levinson, S. C., & Bowerman, M. (eds.), Language Acquisition and Conceptual Development (No. 3, pp. 475–511). Cambridge: Cambridge University Press.Google Scholar
Brandone, A. C., Cimpian, A., Leslie, S. J., & Gelman, S. A. (2012). Do lions have manes? For children, generics are about kinds rather than quantities. Child Development, 83, 423–433.Google Scholar
Brandone, A. C., Gelman, S. A., & Hedglen, J. (2015). Children’s developing intuitions about the truth conditions and implications of novel generics versus quantified statements. Cognitive Science, 39, 711–738.Google Scholar
Casasola, M., & Ahn, Y. A. (2018). What develops in infants’ spatial categorization? Korean infants’ categorization of containment and tight‐fit relations. Child Development, 89, e382–e396.Google Scholar
Chouinard, M. M. (2007). Children’s questions: A mechanism for cognitive development. Monographs of the Society for Research in Child Development, 72, vii–ix, 1–112.Google Scholar
Cimpian, A., Brandone, A. C., & Gelman, S. A. (2010). Generic statements require little evidence for acceptance but have powerful implications. Cognitive Science, 34, 1452–1482.Google Scholar
Cimpian, A., & Markman, E. M. (2009). Information learned from generic language becomes central to children’s biological concepts: Evidence from their open-ended explanations. Cognition, 113, 14–25.Google Scholar
Cimpian, A., & Salomon, E. (2014). The inherence heuristic: An intuitive means of making sense of the world, and a potential precursor to psychological essentialism. Behavioral and Brain Sciences, 37, 461–480.Google Scholar
Cimpian, A., & Steinberg, O. D. (2014). The inherence heuristic across development: Systematic differences between children’s and adults’ explanations for everyday facts. Cognitive Psychology, 75, 130–154.Google Scholar
Clark, E. V. (1992). Conventionality and contrast: Pragmatic principles with lexical consequences. In Kittay, E. F., & Lehrer, A. (eds.), Frames, Fields, and Contrasts: New Essays in Semantic and Lexical Organization (pp. 171–188). Hillsdale, NJ: Erlbaum.Google Scholar
Clark, E. V. (2005). Meaning: Development. In Brown, K. (gen. ed.), Encyclopedia of Language and Linguistics (2nd ed., article 0840, pp. 577–584). London: Elsevier.Google Scholar
Coley, J. D. (2012). Where the wild things are: Informal experience and ecological reasoning. Child Development, 83, 992–1006.Google Scholar
Danovitch, J. H., & Keil, F. C. (2004). Should you ask a fisherman or a biologist?: Developmental shifts in ways of clustering knowledge. Child Development, 75, 918–931.Google Scholar
Davidson, N. S., & Gelman, S. A. (1990). Inductions from novel categories: The role of language and conceptual structure. Cognitive Development, 5, 151–176.Google Scholar
de Villiers, J. G., & de Villiers, P. A. (2014). The role of language in theory of mind development. Topics in Language Disorders, 34, 313–328.Google Scholar
DeJesus, J. M., Hwang, H. G., Dautel, J. B., & Kinzler, K. D. (2017). Bilingual children’s social preferences hinge on accent. Journal of Experimental Child Psychology, 164, 178–191.Google Scholar
del Río, M. F., & Strasser, K. (2011). Chilean children’s essentialist reasoning about poverty. British Journal of Developmental Psychology, 29, 722–743.Google Scholar
Dewar, K., & Xu, F. (2007). Do 9-month-old infants expect distinct words to refer to kinds? Developmental Psychology, 43, 1227–1238.Google Scholar
Diesendruck, G. (2013). Essentialism: The development of a simple, but potentially dangerous, idea. In Banaji, M. R., & Gelman, S. A. (eds.), Navigating the Social World: What Infants, Children, and Other Species Can Teach Us (pp. 263–268). New York: Oxford University Press.Google Scholar
Diesendruck, G., Goldfein-Elbaz, R., Rhodes, M., Gelman, S. A., & Neumark, N. (2013). Cross-cultural differences in children’s beliefs about the objectivity of social categories. Child Development, 84, 1906–1917.Google Scholar
Douglas, M. (1966). Purity and Danger: An Analysis of Concepts of Pollution and Taboo. Oxfordshire: Routledge and Keegan Paul.Google Scholar
Feiman, R., Carey, S., & Cushman, F. (2015). Infants’ representations of others’ goals: Representing approach over avoidance. Cognition, 136, 204–214.Google Scholar
Ferguson, C. A. (1975). Toward a characterization of English foreigner talk. Anthropological Linguistics, 17, 1–14.Google Scholar
Fisher, A. V., Godwin, E. K., & Matlen, B. (2015). Development of inductive generalization with familiar categories. Psychonomic Bulletin & Review, 22, 1149–1173.Google Scholar
Frank, M. C., Everett, D. L., Fedorenko, E., & Gibson, E. (2008). Number as a cognitive technology: Evidence from Pirahã language and cognition. Cognition, 108, 819–824.Google Scholar
Frazier, B. N., Gelman, S. A., & Wellman, H. M. (2009). Preschoolers’ search for explanatory information within adult–child conversation. Child Development, 80, 1592–1611.Google Scholar
Frazier, B. N., Gelman, S. A., & Wellman, H. M. (2016). Young children prefer and remember satisfying explanations. Journal of Cognition and Development, 17, 718–736.Google Scholar
Gelman, S. A. (2003). The Essential Child: Origins of Essentialism in Everyday Thought. New York: Oxford University Press.Google Scholar
Gelman, S. A. (2004). Psychological essentialism in children. Trends in Cognitive Sciences, 8, 404–409.Google Scholar
Gelman, S. A. (2009). Learning from others: Children’s construction of concepts. Annual Review of Psychology, 60, 115–140.Google Scholar
Gelman, S. A. (2010). Generics as a window onto young children’s concepts. In Pelletier, F. J. (ed.), Kinds, Things, and Stuff: The Cognitive Side of Generics and Mass Terms (New Directions in Cognitive Science, v. 12, pp. 100–121). New York: Oxford University Press.Google Scholar
Gelman, S. A., & Bloom, P. (2007). Developmental changes in the understanding of generics. Cognition, 105, 166–183.Google Scholar
Gelman, S. A., & Coley, J. D. (1990). The importance of knowing a dodo is a bird: Categories and inferences in 2-year-old children. Developmental Psychology, 26, 796–804.Google Scholar
Gelman, S. A., Coley, J. D., Rosengren, K., Hartman, E., & Pappas, A. (1998). Beyond labeling: The role of maternal input in the acquisition of richly-structured categories. Monographs of the Society for Research in Child Development, Serial No. 253, 63, 1–157.Google Scholar
Gelman, S. A., & Davidson, N. S. (2013). Conceptual influences on category-based induction. Cognitive Psychology, 66, 327–353.Google Scholar
Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23, 183–209.Google Scholar
Gelman, S. A., & Markman, E. M. (1987). Young children’s inductions from natural kinds: The role of categories and appearances. Child Development, 58, 1532–1541.Google Scholar
Gelman, S. A., & Rhodes, M. (2012). “Two-thousand years of stasis”: How psychological essentialism impedes evolutionary understanding. In Rosengren, K. S., Brem, S., Evans, E. M., & Sinatra, G. (eds.), Evolution Challenges: Integrating Research and Practice in Teaching and Learning about Evolution (pp. 3–21). Cambridge: Oxford University Press.Google Scholar
Gelman, S. A., & Roberts, S. O. (2017). How language shapes the cultural inheritance of categories. Proceedings of the National Academy of Sciences (USA), 114, 7900–7907.Google Scholar
Gelman, S. A., Taylor, M G., Nguyen, S. P., Leaper, C., & Bigler, R. S. (2004). Mother–child conversations about gender: Understanding the acquisition of essentialist beliefs. Monographs of the Society for Research in Child Development, 69, i–142.Google Scholar
Gelman, S. A., Ware, E. A., & Kleinberg, F. (2010). Effects of generic language on category content and structure. Cognitive Psychology, 61, 273–301.Google Scholar
Gelman, S. A., Wilcox, S. A., & Clark, E. V. (1989). Conceptual and lexical hierarchies in young children. Cognitive Development, 4, 309–326.Google Scholar
Gobbo, C., & Chi, M. (1986). How knowledge is structured and used by expert and novice children. Cognitive Development, 1, 221–237.Google Scholar
Gopnik, A., & Sobel, D. M. (2000). Detecting blickets: How young children use information about novel causal powers in categorization and induction. Child Development, 71, 1205–1222.Google Scholar
Graham, S. A., Kilbreath, C. S., & Welder, A. N. (2004). Thirteen‐month‐olds rely on shared labels and shape similarity for inductive inferences. Child Development, 75, 409–427.Google Scholar
Gunderson, E. A., Gripshover, S. J., Romero, C., Dweck, C. S., Goldin‐Meadow, S., & Levine, S. C. (2013). Parent praise to 1‐to 3‐year‐olds predicts children’s motivational frameworks 5 years later. Child Development, 84, 1526–1541.Google Scholar
Gustafsson, Å. (1979). Linnaeus’ peloria: The history of a monster. Theoretical and Applied Genetics, 54, 241–248.Google Scholar
Harris, P. L., Koenig, M. A., Corriveau, K. H., & Jaswal, V. K. (2018). Cognitive foundations of learning from testimony. Annual Review of Psychology, 69, 251–273.Google Scholar
Haslam, N., Rothschild, L., & Ernst, D. (2000). Essentialist beliefs about social categories. British Journal of Social Psychology, 39, 113–127.Google Scholar
Henderson, A. M., & Woodward, A. L. (2012). Nine-month-old infants generalize object labels, but not object preferences across individuals. Developmental Science, 15, 641–652.Google Scholar
Hollander, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986). Induction: Processes of Inference. Cambridge, MA: MIT Press.Google Scholar
Horton, M. S., & Markman, E. M. (1980). Developmental differences in the acquisition of basic and superordinate categories. Child Development, 51, 708–719.Google Scholar
Inagaki, K. (1990). The effects of raising animals on children’s biological knowledge. British Journal of Developmental Psychology, 8, 119–129.Google Scholar
Inhelder, B., & Piaget, J. (1964). The Early Growth of Logic in the Child. New York: Norton.Google Scholar
Jaswal, V. K., Lima, O. K., & Small, J. E. (2009). Compliance, conversion, and category induction. Journal of Experimental Child Psychology, 102, 182–195.Google Scholar
Jaswal, V. K., & Markman, E. M. (2007). Looks aren’t everything: 24-month-olds’ willingness to accept unexpected labels. Journal of Cognition and Development, 8, 93–111.Google Scholar
Keates, J., & Graham, S. A. (2008). Category markers or attributes: Why do labels guide infants’ inductive inferences? Psychological Science, 19, 1287–1293.Google Scholar
Keil, F. C., Stein, C., Webb, L., Billings, V. D., & Rozenblit, L. (2008). Discerning the division of cognitive labor: An emerging understanding of how knowledge is clustered in other minds. Cognitive Science, 32, 259–300.Google Scholar
Keller, J. (2005). In genes we trust: the biological component of psychological essentialism and its relationship to mechanisms of motivated social cognition. Journal of Personality and Social Psychology, 88, 686.Google Scholar
Kemler-Nelson, D. G., Egan, L. C., & Holt, M. B. (2004). When children ask, “What is it?” what do they want to know about artifacts? Psychological Science, 15(6), 384–389.Google Scholar
Kinzler, K. D. (2013). The development of language as a social category. In Banaji, M. R., & Gelman, S. A. (eds.), Oxford Series in Social Cognition and Social Neuroscience. Navigating the Social World: What Infants, Children, and Other Species Can Teach Us (pp. 314–317). New York: Oxford University Press.Google Scholar
Kinzler, K. D., & DeJesus, J. M. (2013). Northern = smart and Southern = nice: The development of accent attitudes in the United States. Quarterly Journal of Experimental Psychology, 66, 1146–1158.Google Scholar
Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2007). The native language of social cognition. Proceedings of the National Academy of Sciences (USA), 104, 12577–12580.Google Scholar
Kirby, S., Cornish, H., & Smith, K. (2008) Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences (USA), 105, 10681–10686.Google Scholar
Koenig, M. A., & Harris, P. L. (2005). Preschoolers mistrust ignorant and inaccurate speakers. Child Development, 76, 1261–1277.Google Scholar
Koenig, M. A., & Jaswal, V. K. (2011). Characterizing children’s expectations about expertise and incompetence: Halo or pitchfork effects? Child Development, 82, 1634–1647.Google Scholar
Labotka, D. & Gelman, S. A. (2019). The Effect of Register on Children’s Social Inferences about Addressees. Baltimore, MD: Society for Research in Child Development Biannual Meeting.Google Scholar
Lane, J. D., Harris, P. L., Gelman, S. A., & Wellman, H. M. (2014). More than meets the eye: Young children’s trust in claims that defy their perceptions. Developmental Psychology, 50, 865–871.Google Scholar
Lane, J. D., Wellman, H. M., & Gelman, S. A. (2013). Informants’ traits weigh heavily in young children’s trust in testimony and in their epistemic inferences. Child Development, 84, 1253–1268.Google Scholar
Leslie, S. J. (2013). Essence and natural kinds: When science meets preschooler intuition. Oxford Studies in Epistemology, 4, 108–165.Google Scholar
Leslie, S. J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347, 262–265.Google Scholar
Macnamara, J. (1987). A Border Dispute. Cambridge, MA: MIT Press.Google Scholar
Mandalaywala, T. M., Amodio, D. M., & Rhodes, M. (2018). Essentialism promotes racial prejudice by increasing endorsement of social hierarchies. Social Psychological and Personality Science, 9, 461–469.Google Scholar
Markman, E. M. (1989). Categorization and Naming in Children: Problems in Induction. Cambridge: MIT Press.Google Scholar
Maynard Smith, J., & Szathmary, E. (1997). The Major Transitions in Evolution. New York: Oxford University Press.Google Scholar
Medin, D. (1989). Concepts and conceptual structure. American Psychologist, 44, 1469–1481.Google Scholar
Medin, D., Waxman, S., Woodring, J., & Washinawatok, K. (2010). Human-centeredness is not a universal feature of young children’s reasoning: Culture and experience matter when reasoning about biological entities. Cognitive Development, 25, 197–207.Google Scholar
Mervis, C. B., & Crisafi, M. A. (1982). Order of acquisition of subordinate-, basic-, and superordinate-level categories. Child Development, 53, 258–266.Google Scholar
Moya, C., Boyd, R., & Henrich, J. (2015). Reasoning about cultural and genetic transmission: Developmental and cross‐cultural evidence from Peru, Fiji, and the United States on how people make inferences about trait transmission. Topics in Cognitive Science, 7, 595–610.Google Scholar
Murphy, G. (2002). The Big Book of Concepts. Cambridge, MA: MIT Press.Google Scholar
Olson, K. R., & Enright, E. A. (2018). Do transgender children (gender) stereotype less than their peers and siblings? Developmental Science, 21, e12606.Google Scholar
Orvell, A., Kross, E., & Gelman, S. A. (2017). How “you” makes meaning. Science, 355, 1299–1302.Google Scholar
Orvell, A., Kross, E., & Gelman, S. A. (2018). That’s how “you” do it: Generic you expresses norms in early childhood. Journal of Experimental Child Psychology, 165, 183–195.Google Scholar
Orvell, A., Kross, E., & Gelman, S. A. (2019). “You” and “I” in a foreign land: The persuasive force of generic-you. Journal of Experimental Social Psychology, 85, 103869.Google Scholar
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185.Google Scholar
Ozturk, O., & Papafragou, A. (2016). The acquisition of evidentiality and source monitoring. Language Learning and Development, 12, 199–230.Google Scholar
Pagel, M. (2017). Darwinian perspectives on the evolution of human languages. Psychonomic Bulletin and Review, 24, 151–157.Google Scholar
Perner, J., Ruffman, T., & Leekam, S. R. (1994). Theory of mind is contagious: You catch it from your sibs. Child Development, 65, 1228–1238.Google Scholar
Perszyk, D. R., & Waxman, S. R. (2018). Linking language and cognition in infancy. Annual Review of Psychology, 69, 231–250.Google Scholar
Pruden, S. M., Levine, S. C., & Huttenlocher, J. (2011). Children’s spatial thinking: Does talk about the spatial world matter? Developmental Science, 14, 1417–1430.Google Scholar
Reuter, T., Feiman, R., & Snedeker, J. (2018). Getting to no: Pragmatic and semantic factors in two‐ and three‐year‐olds’ understanding of negation. Child Development, 89, e364–e381.Google Scholar
Rhodes, M., & Gelman, S. A. (2009a). A developmental examination of the conceptual structure of animal, artifact, and human social categories across two cultural contexts. Cognitive Psychology, 59, 244–274.Google Scholar
Rhodes, M., & Gelman, S. A. (2009b). Five-year-olds’ beliefs about the discreteness of category boundaries for animals and artifacts. Psychonomic Bulletin & Review, 16, 920–924.Google Scholar
Rhodes, M., Gelman, S. A., & Karuza, J. C. (2014). Preschool ontology: The role of beliefs about category boundaries in early categorization. Journal of Cognition and Development, 15, 78–93.Google Scholar
Rhodes, M., Leslie, S. J., & Tworek, C. M. (2012). Cultural transmission of social essentialism. Proceedings of the National Academy of Sciences (USA), 109, 13526–13531.Google Scholar
Rhodes, M., & Liebenson, P. (2015). Continuity and change in the development of category-based induction: The test case of diversity-based reasoning. Cognitive Psychology, 82, 74–95.Google Scholar
Rhodes, M., & Mandalaywala, T. M. (2017). The development and developmental consequences of social essentialism. Wiley Interdisciplinary Reviews: Cognitive Science, 8, e1437.Google Scholar
Roberts, S. O., & Gelman, S. A. (2015). Do children see in black and white? Children’s and adults’ categorizations of multiracial individuals. Child Development, 86, 1830–1847.Google Scholar
Roberts, S. O., Gelman, S. A., & Ho, A. K. (2017a). So it is, so it shall be: Descriptive regularities license children’s prescriptive judgments. Cognitive Science, 41, 576–600.Google Scholar
Roberts, S. O., Ho, A. K., & Gelman, S. A. (2017b). Group presence, category labels, and generic statements foster children’s tendency to enforce group norms. Journal of Experimental Child Psychology, 158, 19–31.Google Scholar
Roberts, S. O., Ho, A. K., & Gelman, S. A. (2019). The role of group norms in evaluating uncommon and negative behaviors. Journal of Experimental Psychology: General, 148, 374–387.Google Scholar
Roberts, S. O., Ho, A. K., Rhodes, M., & Gelman, S. A. (2017c). Making boundaries great again: Essentialism and support for boundary-enhancing initiatives. Personality and Social Psychology Bulletin, 43, 1643–1658.Google Scholar
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.Google Scholar
Sabbagh, M. A., & Baldwin, D. A. (2001). Learning words from knowledgeable versus ignorant speakers: Links between preschoolers’ theory of mind and semantic development. Child Development, 72, 1054–1070.Google Scholar
Sabbagh, M. A., & Henderson, A. M. (2007). How an appreciation of conventionality shapes early word learning. New Directions in Child and Adolescent Development, 115, 25–37.Google Scholar
Salehuddin, K., & Winskel, H. (2009). An investigation into Malay numeral classifier acquisition through an elicited production task. First Language, 29, 289–311.Google Scholar
Schwab, J. F., Lew-Williams, C., & Goldberg, A. E. (2018). When regularization gets it wrong: Children over-simplify language input only in production. Journal of Child Language, 45, 1054–1072.Google Scholar
Shatz, M. (1987). Bootstrapping operations in child language. In Nelson, K. E., & Van Kleeck, A. (eds.), Children’s Language (Vol. 6, pp. 1–22). Hillsdale, NJ: Erlbaum.Google Scholar
Shatz, M., Tare, M., Nguyen, S. P., & Young, T. (2010). Acquiring non-object terms: The case for time words. Journal of Cognition and Development, 11, 16–6.Google Scholar
Shtulman, A., & Schulz, L. (2008). The relation between essentialist beliefs and evolutionary reasoning. Cognitive Science, 32, 1049–1062.Google Scholar
Shutts, K., Kenward, B., Falk, H., Ivegran, A., & Fawcett, C. (2017). Early preschool environments and gender: Effects of gender pedagogy in Sweden. Journal of Experimental Child Psychology, 162, 1–17.Google Scholar
Skinner, A. L., Meltzoff, A. N., & Olson, K. R. (2017). “Catching” social bias: Exposure to biased nonverbal signals creates social biases in preschool children. Psychological Science, 28, 216–224.Google Scholar
Smith, L. B., Colunga, E., & Yoshida, H. (2010). Knowledge as process: Contextually cued attention and early word learning. Cognitive Science, 34, 1287–1314.Google Scholar
Sobel, D. M., & Corriveau, K. H. (2010). Children monitor individuals’ expertise for word learning. Child Development, 81, 669–679.Google Scholar
Sobel, D. M., & Kushnir, T. (2013). Knowledge matters: How children evaluate the reliability of testimony as a process of rational inference. Psychological Review, 120, 779–797.Google Scholar
Susperreguy, M. I., & Davis-Kean, P. E. (2016). Maternal math talk in the home and math skills in preschool children. Early Education and Development, 27, 841–857.Google Scholar
Talmy, L. (1985). Lexicalization patterns: Semantic structure in lexical forms. Language Typology and Syntactic Description, 3, 36–149.Google Scholar
Taumoepeau, M., & Reese, E. (2013). Maternal reminiscing, elaborative talk, and children’s theory of mind: An intervention study. First Language, 33, 388–410.Google Scholar
Taylor, M.G., Rhodes, M., & Gelman, S.A. (2009). Boys will be boys, cows will be cows: Children’s essentialist reasoning about human gender and animal development. Child Development, 80, 461–481.Google Scholar
Tillman, K. A., Marghetis, T., Barner, D., & Srinivasan, M. (2017). Today is tomorrow’s yesterday: Children’s acquisition of deictic time words. Cognitive Psychology, 92, 87–100.Google Scholar
Tworek, C. M., & Cimpian, A. (2016). Why do people tend to infer “ought” from “is”? The role of biases in explanation. Psychological Science, 27, 1109–1122.Google Scholar
Unger, L., & Fisher, A. V. (2019). Rapid, experience-related changes in the organization of children’s semantic knowledge. Journal of Experimental Child Psychology, 179, 1–22.Google Scholar
Unger, L., Fisher, A. V., Nugent, R., Ventura, S. L., & MacLellan, C. J. (2016). Developmental changes in semantic knowledge organization. Journal of Experimental Child Psychology, 146, 202–222.Google Scholar
Vasilyeva, N., Gopnik, A., & Lombrozo, T. (2018). The development of structural thinking about social categories. Developmental Psychology, 54, 1735–1744.Google Scholar
Waxman, S. R. (1990). Linguistic biases and the establishment of conceptual hierarchies: Evidence from preschool children. Cognitive Development, 5, 123–150.Google Scholar
Waxman, S. R., & Gelman, S. A. (2009). Early word-learning entails reference, not merely associations. Trends in Cognitive Sciences, 13, 258–263.Google Scholar
Waxman, S. R., & Markow, D. B. (1995). Words as invitations to form categories: Evidence from 12- to 13-month-old infants. Cognitive Psychology, 29, 257–302.Google Scholar
Waxman, S., Medin, D., & Ross, N. (2007). Folkbiological reasoning from a cross-cultural developmental perspective: Early essentialist notions are shaped by cultural beliefs. Developmental Psychology, 43, 294–308.Google Scholar
Wellman, H. M. (2013). Universal social cognition. In Banaji, M., & Gelman, S. (eds.), Navigating the Social World: What Infants, Children, and Other Species Can Teach Us (pp. 69–74). New York: Oxford University Press.Google Scholar
Wellman, H. M., Fang, F., & Peterson, C. C. (2011). Sequential progressions in a theory‐of‐mind scale: Longitudinal perspectives. Child Development, 82, 780–792.Google Scholar
Wellman, H. M., & Liu, D. (2004). Scaling of theory‐of‐mind tasks. Child Development, 75, 523–541.Google Scholar
White, H., Jubran, R., Chroust, A., Heck, A., & Bhatt, R. S. (2018). Dichotomous perception of animal categories in infancy. Visual Cognition, 26, 764–779.Google Scholar
Williams, M. J., & Eberhardt, J. L. (2008). Biological conceptions of race and the motivation to cross racial boundaries. Journal of Personality and Social Psychology, 94, 1033–1047.Google Scholar
Xu, F., & Carey, S. (1996). Infants’ metaphysics: The case of numerical identity. Cognitive Psychology, 30, 111–153.Google Scholar
Yamamoto, K., & Keil, F. (2000). The acquisition of Japanese numeral classifiers: Linkage between grammatical forms and conceptual categories. Journal of East Asian Linguistics, 9, 379–409.Google Scholar

References

Agrillo, C., Piffer, L., Bisazza, A., & Butterworth, B. (2012). Evidence for two numerical systems that are similar in humans and guppies. PLoS ONE, 7, e31923.Google Scholar
Alibali, M. W., & Goldin-Meadow, S. (1993). Gesture-speech mismatch and mechanisms of learning: What the hands reveal about a child’s state of mind. Cognitive Psychology, 25, 468–523.Google Scholar
Andres, M., Michaux, N., & Pesenti, M. (2012). Common substrate for mental arithmetic and finger representation in the parietal cortex. NeuroImage, 62, 1520–1528.Google Scholar
Ashcraft, M. H. (1982). The development of mental arithmetic: A chronometric approach. Developmental Review, 2, 213–236.Google Scholar
Bailey, D. H., Siegler, R. S., & Geary, D. C. (2014). Early predictors of middle school fraction knowledge. Developmental Science, 17, 775–785.Google Scholar
Baroody, A. J., & Dowker, A. (eds.) (2003). The Development of Arithmetic Concepts and Skills: Constructing Adaptive Expertise. Mahwah, NJ: Erlbaum.Google Scholar
Berteletti, I., & Booth, J. R. (2015). Perceiving fingers in single-digit arithmetic problems. Frontiers in Psychology, 6, 226.Google Scholar
Berteletti, I., Lucangeli, D., Piazza, M., Dehaene, S., & Zorzi, M. (2010). Numerical estimation in preschoolers. Developmental Psychology, 41, 545–551.Google Scholar
Binet, A., & Simon, T. (1905). New methods for the diagnosis of the intellectual level of subnormals. L’Année Psychologique, 11, 191–244. Translated by Elizabeth S. Kite and reprinted in The Development of Intelligence in Children (1916). Baltimore: Williams & Wilkins.Google Scholar
Braithwaite, D. W., Pyke, A. A., & Siegler, R. S. (2017). A computational model of fraction arithmetic. Psychological Review, 124, 603–625.Google Scholar
Braithwaite, D. W., Tian, J., & Siegler, R. S. (2018). Do children understand fraction addition? Developmental Science, 21, e12601.Google Scholar
Brannon, E. M., & Merritt, D. J. (2011). Evolutionary foundations of the approximate number system. In Dehaene, S., & Brannon, E. (eds.), Space, Time and Number in the Brain: Searching for the Foundations of Mathematical Thought (pp. 207–224). New York: Elsevier.Google Scholar
Brown, J. S., & Van Lehn, K. (1982). Toward a generative theory of “bugs.” In Carpenter, T. P., Moser, J. M., & Romberg, T. A. (eds.), Addition and Subtraction: A Cognitive Perspective (pp. 117–136). Hillsdale, N.J.: ErlbaumGoogle Scholar
Campbell, J. I., & Xue, Q. (2001). Cognitive arithmetic across cultures. Journal of Experimental Psychology. General, 130, 299–315.Google Scholar
Carpenter, T. P., Corbitt, M. K., Kepner, H. S., Lindquist, M. M., & Reys, R. E. (1981). Results and Implications from the Second Mathematics Assessment of the National Assessment of Educational Progress. Reston, VA: National Council of Teachers of Mathematics.Google Scholar
Case, R., & Okamoto, Y. (1996). The role of central conceptual structures in the development of children’s thought. Monographs of the Society for Research in Child Development, 61, Nos. 1–2 (Serial No. 246).Google Scholar
Chen, Q., & Li, J. (2014). Association between individual differences in nonsymbolic number acuity and math performance: A meta-analysis. Acta Psychologica, 148, 163–172.Google Scholar
College Board. (2015). Advanced Placement Physics 1 Equations, Effective 2015 (pdf document). Available from https://secure-media.collegeboard.org/digitalServices/pdf/ap/ap-physics-1-equations-table.pdf. Last accessed August 2, 2021.Google Scholar
Cordes, S., & Brannon, E. M. (2008). Quantitative competencies in infancy. Developmental Science, 11, 803–808.Google Scholar
Dehaene, S. (2011). The Number Sense: How the Mind Creates Mathematics. New York: Oxford University Press.Google Scholar
Depaepe, F., Torbeyns, J., Vermeersch, N., Janssens, D., Janssen, R., Kelchtermans, G., … Van Dooren, W. (2015). Teachers’ content and pedagogical content knowledge on rational numbers: A comparison of prospective elementary and lower secondary school teachers. Teaching and Teacher Education, 47, 82–92.Google Scholar
Dotan, D., & Dehaene, S. (2013). How do we convert a number into a finger trajectory? Cognition, 129, 512–529.Google Scholar
Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., … Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43, 1428–1446.Google Scholar
Fazio, L. K., Bailey, D. H., Thompson, C. A., & Siegler, R. S. (2014). Relations of different types of numerical magnitude representations to each other and to mathematics achievement. Journal of Experimental Child Psychology, 123, 53–72.Google Scholar
Gauvain, M. (2001). The Social Context of Cognitive Development. New York: The Guilford Press.Google Scholar
Geary, D. C. (2006). Development of mathematical understanding. In Kuhn, D., & Siegler, R. S. (vol. eds.), Cognition, Perception, and Language, (pp. 777–810). W. Damon (gen. ed.), Handbook of child psychology (6th ed.). New York: John Wiley & Sons.Google Scholar
Geary, D. C. (2008). An evolutionarily informed education science. Educational Psychologist, 43, 179–195.Google Scholar
Geary, D. C., Hoard, M. K., Byrd-Craven, J., Nugent, L., & Numtee, C. (2007). Cognitive mechanisms underlying achievement deficits in children with mathematical learning disability. Child Development, 78, 1343–1359.Google Scholar
Geary, D. C., & vanMarle, K. (2016). Young children’s core symbolic and non-symbolic quantitative knowledge in the prediction of later mathematics achievement. Developmental Psychology, 52, 2130–2144.Google Scholar
Halberda, J., & Feigenson, L. (2008). Developmental change in the acuity of the “Number sense”: The approximate number system in 3-, 4-, 5-, and 6-year-olds and adults. Developmental Psychology, 44, 1457–1465.Google Scholar
Halberda, J., Ly, R., Wilmer, J. B., Naiman, D. Q., & Germine, L. (2012). Number sense across the lifespan as revealed by a massive Internet-based sample. Proceedings of the National Academy of Sciences (USA), 109, 11116–11120.Google Scholar
Halberda, J., Mazzocco, M. M. M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlates with math achievement. Nature, 455, 665–668.Google Scholar
Handel, M. J. (2016). What do people do at work? A profile of U.S. jobs from the survey of workplace Skills, Technology, and Management Practices (STAMP). Journal for Labour Market Research, 49, 177–197.Google Scholar
Hanushek, E. A. (2016). What matters for student achievement: Updating Coleman on the influence of families and schools. EducationNext, 16 , 23–30.Google Scholar
Iuculano, T., & Butterworth, B. (2011). Understanding the real value of fractions and decimals. The Quarterly Journal of Experimental Psychology, 64, 2088–2098.Google Scholar
Jordan, N. C., Hansen, N., Fuchs, L. S., Siegler, R. S., Gersten, R., & Micklos, D. (2013). Developmental predictors of fraction concepts and procedures. Journal of Experimental Child Psychology, 116, 45–58.Google Scholar
Jordan, N.C., Kaplan, D., Olah, L. N., & Locuniak, M. N. (2006). Number sense growth in kindergarten: A longitudinal investigation of children at risk for mathematics difficulties. Child Development, 77, 153–175.Google Scholar
Kant, I. (1781/2003). Critique of Pure Reason, trans. J. M. D. Meiklejohn. Mineola, NY: Dover.Google Scholar
Klahr, D., & MacWhinney, B. (1998). Information processing. In Damon, W. (Series ed.) & Kuhn, D. & Siegler, R. S. (vol. eds.), Handbook of Child Psychology: Vol. 2: Cognition, Perception & Language. (5th ed., pp. 631–678). New York: Wiley.Google Scholar
Le Corre, M., & Carey, S. (2007). One, two, three, four, nothing more: An investigation of the conceptual sources of the verbal counting principles. Cognition, 105, 395–438.Google Scholar
LeFevre, J. A., Sadesky, G. S., & Bisanz, J. (1996). Selection of procedures in mental addition: Reassessing the problem-size effect in adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 216–230.Google Scholar
LeFevre, J. A., Smith-Chant, B. L., Hiscock, K., Dale, K. E., & Morris, J. (2003). Young adults’ strategic choices in simple arithmetic: Implications for the development of mathematical representations. In Baroody, A. J., & Dowker, A. (eds.), The Development of Arithmetic Concepts and Skills: Constructing Adaptive Expertise (pp. 203–228). Mahwah, NJ: Erlbaum.Google Scholar
Lemaire, P., & Siegler, R. S. (1995). Four aspects of strategic change: Contributions to children’s learning of multiplication. Journal of Experimental Psychology: General, 124, 83–97.Google Scholar
Libertus, M., Feigenson, L., & Halberda, J. (2011). Preschool acuity of the approximate number system correlates with school math ability. Developmental Science, 14, 1292–1300.Google Scholar
Lortie-Forgues, H., & Siegler, R. S. (2017). Conceptual knowledge of decimal arithmetic. Journal of Educational Psychology, 109, 374–386.Google Scholar
Lortie-Forgues, H., Tian, J., & Siegler, R. S. (2015). Why is learning fraction and decimal arithmetic so difficult? Developmental Review, 38, 201–221.Google Scholar
Luo, F., Lo, J., & Leu, Y. (2011). Fundamental fraction knowledge of pre-service elementary teachers: A cross-national study in the United States and Taiwan. School Science and Mathematics, 111, 164–177.Google Scholar
Lyons, I. M., Price, G. R., Vaessen, A., Blomert, L., & Ansari, D. (2014). Numerical predictors of arithmetic success in grades 1-6. Developmental Science, 17, 714–726.Google Scholar
Ma, L. (1999). Knowing and Teaching Elementary Mathematics: Teachers Understanding of Fundamental Mathematics in China and the United States. Mahwah, NJ: Erlbaum.Google Scholar
McCrink, K., & Wynn, K. (2004). Large-number addition and subtraction by 9-month-old infants. Psychological Science, 15, 776–781.Google Scholar
McCrink, K., & Wynn, K. (2007). Ratio abstraction by 6-month-old infants. Psychological Science, 18, 740–745.Google Scholar
McNeil, N. M. (2014). A change-resistance account of children’s difficulties understanding mathematical equivalence. Child Development Perspectives, 8, 42–47.Google Scholar
Meert, G., Grégoire, J., & Noël, M.-P. (2010). Comparing the magnitude of two fractions with common components: Which representations are used by 10- and 12-year-olds? Journal of Experimental Child Psychology, 107, 244–259.Google Scholar
Miller, P. H., & Seier, W. L. (1994). Strategy utilization deficiencies in children: When, where, and why. In Reese, H. W. (ed.), Advances in Child Development and Behavior (Vol. 25, pp. 108–156). New York: Academic Press.Google Scholar
Möhring, W., Liu, R., & Libertus, M. E. (2017). Infants’ speed discrimination: Effects of different ratios and spatial orientations. Infancy, 22, 762–777.Google Scholar
National Mathematics Advisory Panel. (2008). Foundations for Success: The Final Report of the National Mathematics Advisory Panel. Washington, DC: US Department of Education.Google Scholar
Ni, Y., & Zhou, Y.-D. (2005). Teaching and learning fraction and rational numbers: The origins and implications of whole number bias. Educational Psychologist, 40, 27–52.Google Scholar
Nieder, A., & Dehaene, S. (2009). Representation of number in the brain. Annual Review of Neuroscience, 32, 185–208.Google Scholar
Park, J., Park, D. C., & Polk, T. A. (2013). Parietal functional connectivity in numerical cognition. Cerebral Cortex, 23, 2127–2135.Google Scholar
Parnas, M., Lin, A. C., Huetteroth, W., & Miesenböck, G. (2013). Odor discrimination in Drosophila: From neural population codes to behavior. Neuron, 79, 932–944.Google Scholar
Piaget, J. (1952). The Child’s Concept of Number. New York: W. W. Norton.Google Scholar
Piazza, M. (2011). Neurocognitive start-up tools for symbolic number representations. In Dehaene, S., & Brannon, E. (eds.), Space, Time, and Number in the Brain: Searching for the Foundations of Mathematical Thought (pp. 267–285). London: Elsevier.Google Scholar
Piffer, L., Petrazzini, M. E. M., & Agrillo, C. (2013). Large number discrimination in newborn fish. PLoS ONE, 8, e62466.Google Scholar
Ramani, G. B., & Siegler, R. S. (2008). Promoting broad and stable improvements in low-income children’s numerical knowledge through playing number board games. Child Development, 79, 375–394.Google Scholar
Ramani, G. B., & Siegler, R. S. (2011). Reducing the gap in numerical knowledge between low- and middle-income preschoolers. Journal of Applied Developmental Psychology, 32, 146–159.Google Scholar
Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In Duncan, G., & Murnane, R. (eds.), Whither Opportunity? Rising Inequality and the Uncertain Life Chances of Low-Income Children (pp. 91–116). New York: Russell Sage Foundation Press.Google Scholar
Reeve, R. A., Paul, J. M., & Butterworth, B. (2015). Longitudinal changes in young children’s 0–100 to 0–1000 number-line error signatures. Frontiers in Psychology, 6, Article 647.Google Scholar
Resnick, I., Jordan, N. C., Hansen, N., Rajan, V., Rodrigues, J., Siegler, R. S., & Fuchs, L. (2016). Developmental growth trajectories in understanding of fraction magnitude from fourth through sixth grade. Developmental Psychology, 52, 746–757.Google Scholar
Resnick, L. B., Nesher, P., Leonard, F., Magone, M., Omanson, S., & Peled, I. (1989). Conceptual bases of arithmetic errors: The case for decimal fractions. Journal for Research in Mathematics Education, 20, 8–27.Google Scholar
Riggs, K. J., Ferrand, L., Lancelin, D., Fryziel, L., Dumur, G., & Simpson, A. (2006). Subitizing in tactile perception. Psychological Science, 17, 271–272.Google Scholar
Ritchie, S. J., & Bates, T. C. (2013). Enduring links from childhood mathematics and reading achievement to adult socioeconomic status. Psychological Science, 24, 1301–1308.Google Scholar
Robinson, K. M. (2017). The understanding of additive and multiplicative arithmetic concepts. In Geary, D. C., Berch, D. B., Ochsendorf, R., & Mann Koepke, K. (eds.). Acquiring Complex Arithmetic Skills and Higher-Order Mathematical Concepts (Vol. 3, Mathematical Cognition and Learning, pp. 21–46). San Diego, CA: Elsevier Academic Press.Google Scholar
Schneider, M., Beeres, K., Coban, L., Merz, S., Schmidt, S., Stricker, J., & De Smedt, B. (2017). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis. Developmental Science, 20, e12372.Google Scholar
Schneider, M., Merz, S., Stricker, J., De Smedt, B., Torbeyns, J., Verschaffel, L., & Luwel, K. (2018). Associations of number line estimation with mathematical competence: A meta-analysis. Child Development, 89, 1467–1484.Google Scholar
Schneider, M., & Siegler, R. S. (2010). Representations of the magnitudes of fractions. Journal of Experimental Psychology: Human Perception and Performance, 36, 1227–1238.Google Scholar
Shrager, J., & Siegler, R. S. (1998). SCADS: A model of children’s strategy choices and strategy discoveries. Psychological Science, 9, 405–410.Google Scholar
Siegler, R. S. (1987). The perils of averaging data over strategies: An example from children’s addition. Journal of Experimental Psychology: General, 116, 250–264.Google Scholar
Siegler, R. S. (1988). Strategy choice procedures and the development of multiplication skill. Journal of Experimental Psychology: General, 117, 258–275.Google Scholar
Siegler, R. S. (1989). Hazards of mental chronometry: An example from children’s subtraction. Journal of Educational Psychology, 81, 497–506.Google Scholar
Siegler, R. S. (1996). Unidimensional thinking, multidimensional thinking, and characteristic tendencies of thought. In Sameroff, A. J., & Haith, M. M. (eds.), The Five to Seven Year Shift: The Age of Reason and Responsibility (pp. 63–84). Chicago, IL: University of Chicago Press.Google Scholar
Siegler, R. S. (2006). Microgenetic analyses of learning. In Damon, W., & Lerner, R. M. (Series eds.) & Kuhn, D. & Siegler, R. S. (vol. eds.), Handbook of Child Psychology: Volume 2: Cognition, Perception, and Language (6th ed., pp. 464–510). Hoboken, NJ: Wiley.Google Scholar
Siegler, R. S. (2016). Magnitude knowledge: The common core of numerical development. Developmental Science, 19, 341–361.Google Scholar
Siegler, R. S., & Booth, J. L. (2004). Development of numerical estimation in young children. Child Development, 75, 428–444.Google Scholar
Siegler, R. S., & Braithwaite, D. W. (2017). Numerical development. Annual Review of Psychology, 68, 187–213.Google Scholar
Siegler, R. S., & Crowley, K. (1994). Constraints on learning in non-privileged domains. Cognitive Psychology, 27, 194–227.Google Scholar
Siegler, R. S., Duncan, G. J., Davis-Kean, P. E., Duckworth, K., Claessens, A., Engel, M., … Chen, M. (2012). Early predictors of high school mathematics achievement. Psychological Science, 23, 691–697.Google Scholar
Siegler, R. S., & Jenkins, E. A. (1989). How Children Discover New Strategies. Hillsdale, NJ: Erlbaum.Google Scholar
Siegler, R. S., & Lemaire, P. (1997). Older and younger adults’ strategy choices in multiplication: Testing predictions of ASCM via the choice/no-choice method. Journal of Experimental Psychology: General, 126, 71–92.Google Scholar
Siegler, R. S., & Lortie-Forgues, H. (2015). Conceptual knowledge of fraction arithmetic. Journal of Educational Psychology, 107, 909–918.Google Scholar
Siegler, R. S., & Pyke, A. A. (2013). Developmental and individual differences in understanding fractions. Developmental Psychology, 49, 1994–2004.Google Scholar
Siegler, R. S., & Ramani, G. B. (2009). Playing linear number board games – but not circular ones – improves low-income preschoolers’ numerical understanding. Journal of Educational Psychology, 101, 545–560.Google Scholar
Siegler, R. S., & Shrager, J. (1984). Strategy choices in addition and subtraction: How do children know what to do? In Sophian, C. (ed.), The Origins of Cognitive Skills (pp. 229–293). Hillsdale, NJ: Erlbaum.Google Scholar
Siegler, R. S., Thompson, C. A., & Schneider, M. (2011). An integrated theory of whole number and fractions development. Cognitive Psychology, 62, 273–296.Google Scholar
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10, 89–96.Google Scholar
Sullivan, J., & Barner, D. (2014). The development of structural analogy in number-line estimation. Journal of Experimental Child Psychology, 128, 171–189.Google Scholar
Svenson, O., & Sjöberg, K. (1983). Evolution of cognitive processes for solving simple additions during the first three school years. Scandinavian Journal of Psychology, 24, 117–124.Google Scholar
Thompson, C. A., & Opfer, J. E. (2010). How 15 hundred is like 15 cherries: Effect of progressive alignment on representational changes in numerical cognition. Child Development, 81, 1768–1786.Google Scholar
Torbeyns, J., Schneider, M., Xin, Z. & Siegler, R. S. (2015). Bridging the gap: Fraction understanding is central to mathematics achievement in students from three different continents. Learning and Instruction, 37, 5–13.Google Scholar
Verschaffel, L., Greer, B., & De Corte, E. (2007). Whole number concepts and operations. In Lester, F. (ed.), Second Handbook of Research on Mathematics Teaching and Learning (pp. 557–628). Charlotte, NC: Information Age.Google Scholar
Watts, T. W., Duncan, G. J., Siegler, R. S., & Davis-Kean, P. E. (2014). What’s past is prologue: Relations between early mathematics knowledge and high school achievement. Educational Researcher, 43, 352–360.Google Scholar
Wynn, K. (1992). Addition and subtraction by human infants. Nature, 358, 749–750.Google Scholar
Xu, X., Chen, C., Pan, M., & Li, N. (2013). Development of numerical estimation in Chinese preschool children. Journal of Experimental Child Psychology, 116, 351–366.Google Scholar

References

Adleman, N. E., Menon, V., Blasey, C. M., White, C. D., Warsofsky, I. S., Glover, G. H., & Reiss, A. L. (2002). A developmental fMRI study of the Stroop color-word task. NeuroImage, 16, 61–75.Google Scholar
Ahr, E., Houdé, O., & Borst, G. (2016). Inhibition of the mirror generalization process in reading in school-aged children. Journal of Experimental Child Psychology, 145, 157–165.Google Scholar
Aïte, A., Berthoz, A., Vidal, J., Roëll, M., Zaoui, M., Houdé, O., & Borst, G. (2016). Taking a third-person perspective requires inhibitory control: Evidence from a developmental negative priming study. Child Development, 87, 1825–1840.Google Scholar
Amalric, M., & Dehaene, S. (2016). Origins of the brain networks for advanced mathematics in expert mathematicians. Proceedings of the National Academy of Sciences (USA), 113, 4909–4917.Google Scholar
Anobile, G., Cicchini, G. M., & Burr, D. C. (2014). Separate mechanisms for perception of numerosity and density. Psychological Science, 25, 265–270.Google Scholar
Anokhin, A. P., Heath, A. C., & Myers, E. (2004). Genetics, prefrontal cortex, and cognitive control: A twin study of event-related brain potentials in a response inhibition task. Neuroscience Letters, 368, 314–318.Google Scholar
Ansari, D., Fugelsang, J. A., Dhital, B., & Venkatraman, V. (2006). Dissociating response conflict from numerical magnitude processing in the brain: An event-related fMRI study. NeuroImage, 32, 799–805.Google Scholar
Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8, 170–177.Google Scholar
Bench, C. J., Frith, C. D., Grasby, P. M., Friston, K. J., Paulesu, E., Frackowiak, R. S. J., & Dolan, R. J. (1993). Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia, 31, 907–922.Google Scholar
Besner, D., & Coltheart, M. (1979). Ideographic and alphabetic processing in skilled reading of English. Neuropsychologia, 17, 467–472.Google Scholar
Blair, C., Knipe, H., & Gamson, D. (2008). Is there a role for executive functions in the development of mathematics ability? Mind, Brain, and Education, 2, 80–89.Google Scholar
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663.Google Scholar
Borgmann, K., Fugelsang, J., Ansari, D., & Besner, D. (2011). Congruency proportion reveals asymmetric processing of irrelevant physical and numerical dimensions in the size congruity paradigm. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 65, 98–104.Google Scholar
Borst, G., Ahr, E., Roell, M., & Houdé, O. (2015). The cost of blocking the mirror-generalization process in reading: Evidence for the role of inhibitory control in discriminating letters with lateral mirror-image counterparts. Psychonomic Bulletin & Review, 22, 228–234.Google Scholar
Borst, G., Cachia, A., Vidal, J., Simon, G., Fischer, C., Pineau, A., … Houdé, O. (2014). Folding of the anterior cingulate cortex partially explains inhibitory control during childhood: A longitudinal study. Developmental Cognitive Neuroscience, 9, 126–135.Google Scholar
Borst, G., Poirel, N., Pineau, A., Cassotti, M., & Houdé, O. (2013a). Inhibitory control efficiency in a Piaget-like class-inclusion task in school-age children and adults: A developmental negative priming study. Developmental Psychology, 49, 1366–1374.Google Scholar
Borst, G., Simon, G., Vidal, J., & Houdé, O. (2013b). Inhibitory control and visuo-spatial reversibility in Piaget’s seminal number conservation task: A high-density ERP study. Frontiers in Human Neuroscience, 7, 920.Google Scholar
Botvinick, M. M. (2007). Conflict monitoring and decision making: Reconciling two perspectives on anterior cingulate function. Cognitive, Affective, & Behavioral Neuroscience, 7, 356–366.Google Scholar
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652.Google Scholar
Brainerd, C. J., & Reyna, V. F. (2001). Fuzzy-trace theory: Dual processes in memory, reasoning, and cognitive neuroscience. Advances in Child Development and Behavior, 28, 41–100.Google Scholar
Bub, D. N., Masson, M. E. J., & Lalonde, C. E. (2006). Cognitive control in children: Stroop interference and suppression of word reading. Psychological Science, 17, 351–357.Google Scholar
Bugden, S., & Ansari, D. (2011). Individual differences in children’s mathematical competence are related to the intentional but not automatic processing of Arabic numerals. Cognition, 118, 32–44.Google Scholar
Bull, R., & Scerif, G. (2001). Executive functioning as a predictor of children’s mathematics ability: Inhibition, switching, and working memory. Developmental Neuropsychology, 19, 273–293.Google Scholar
Cachia, A., Borst, G., Tissier, C., Fisher, C., Plaze, M., Gay, O., … Raznahan, A. (2016). Longitudinal stability of the folding pattern of the anterior cingulate cortex during development. Developmental Cognitive Neuroscience, 19, 122–127.Google Scholar
Cappelletti, M., Didino, D., Stoianov, I., & Zorzi, M. (2014). Number skills are maintained in healthy ageing. Cognitive Psychology, 69, 25–45.Google Scholar
Cappelletti, M., Lee, H. L., Freeman, E. D., & Price, C. J. (2010). The role of right and left parietal lobes in the conceptual processing of numbers. Journal of Cognitive Neuroscience, 22, 331–346.Google Scholar
Chomsky, N. (2006). Language and Mind (3rd ed.). Cambridge: Cambridge University Press.Google Scholar
Clayton, S., & Gilmore, C. (2015). Inhibition in dot comparison tasks. ZDM: The International Journal on Mathematics Education, 47, 759–770.Google Scholar
Cohen Kadosh, R., Cohen Kadosh, K., & Henik, A. (2008). When brightness counts: The neuronal correlate of numerical-luminance interference. Cerebral Cortex, 18, 337–343.Google Scholar
Cragg, L., & Gilmore, C. (2014). Skills underlying mathematics: The role of executive function in the development of mathematics proficiency. Trends in Neuroscience and Education, 3, 63–68.Google Scholar
Dakin, S. C., Tibber, M. S., Greenwood, J. A., Kingdom, F. A. A., & Morgan, M. J. (2011). A common visual metric for approximate number and density. Proceedings of the National Academy of Sciences (USA), 108, 19552–19557.CrossRefGoogle ScholarPubMed
Daurignac, E., Houdé, O., & Jouvent, R. (2006). Negative priming in a numerical Piaget-like task as evidenced by ERP. Journal of Cognitive Neuroscience, 18, 730–736.Google Scholar
de Hevia, M. D., Girelli, L., Bricolo, E., & Vallar, G. (2008). The representational space of numerical magnitude: Illusions of length. The Quarterly Journal of Experimental Psychology, 61, 1496–1514.Google Scholar
de Hevia, M. D., Vanderslice, M., & Spelke, E. S. (2012). Cross-dimensional mapping of number, length and brightness by preschool children. PLoS ONE, 7, e35530.Google Scholar
De Neys, W., Lubin, A., & Houdé, O. (2014). The smart nonconserver: Preschoolers detect their number conservation errors. Child Development Research, 2014, 1–7.CrossRefGoogle Scholar
De Neys, W., & Vanderputte, K. (2011). When less is not always more: Stereotype knowledge and reasoning development. Developmental Psychology, 47, 432–441.Google Scholar
Defever, E., Reynvoet, B., & Gebuis, T. (2013). Task- and age-dependent effects of visual stimulus properties on children’s explicit numerosity judgments. Journal of Experimental Child Psychology, 116, 216–233.Google Scholar
Dehaene, S. (2011). The Number Sense: How the Mind Creates Mathematics (Rev. and updated ed). New York: Oxford University Press.Google Scholar
Dehaene, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and number magnitude. Journal of Experimental Psychology: General, 122, 371.Google Scholar
Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56, 384–398.CrossRefGoogle ScholarPubMed
Dehaene, S., Dehaene-Lambertz, G., & Cohen, L. (1998). Abstract representations of numbers in the animal and human brain. Trends in Neurosciences, 21, 355–361.Google Scholar
Dehaene, S., Izard, V., Spelke, E., & Pica, P. (2008). Log or linear? Distinct intuitions of the number scale in western and Amazonian indigene cultures. Science, 320, 1217–1220.Google Scholar
Dempster, F. N. (1992). The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive development and aging. Developmental Review, 12, 45–75.Google Scholar
Desmet, L., Grégoire, J., & Mussolin, C. (2010). Developmental changes in the comparison of decimal fractions. Learning and Instruction, 20, 521–532.Google Scholar
DeWind, N. K., Adams, G. K., Platt, M. L., & Brannon, E. M. (2015). Modeling the approximate number system to quantify the contribution of visual stimulus features. Cognition, 142, 247–265.Google Scholar
DeWind, N. K., & Brannon, E. M. (2012). Malleability of the approximate number system: Effects of feedback and training. Frontiers in Human Neuroscience, 6, 68.Google Scholar
DeWolf, M., & Vosniadou, S. (2015). The representation of fraction magnitudes and the whole number bias reconsidered. Learning and Instruction, 37, 39–49.Google Scholar
Diamond, A. (2000). Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development, 71, 44–56.Google Scholar
Diamond, A., & Goldman-Rakic, P. S. (1989). Comparison of human infants and rhesus monkeys on Piaget’s AB task: Evidence for dependence on dorsolateral prefrontal cortex. Experimental Brain Research, 74, 24–40.Google Scholar
Dillon, M. R., Huang, Y., & Spelke, E. S. (2013). Core foundations of abstract geometry. Proceedings of the National Academy of Sciences (USA), 110, 14191–14195.Google Scholar
Dormal, V., & Pesenti, M. (2009). Common and specific contributions of the intraparietal sulci to numerosity and length processing. Human Brain Mapping, 30, 2466–2476.Google Scholar
Dubois, B., Verin, M., Teixera-Ferreira, C., Thierry, A. M., Glowinski, J., Goldman-Rakic, P. S., & Christen, Y. (1994). Motor and Cognitive Functions of the Prefrontal Cortex. Berlin: Springer.Google Scholar
Duncan, E. M., & McFarland, C. E. (1980). Isolating the effects of symbolic distance, and semantic congruity in comparative judgments: An additive-factors analysis. Memory & Cognition, 8, 612–622.Google Scholar
Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22, 206–214.Google Scholar
Egner, T., & Hirsch, J. (2005). Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information. Nature Neuroscience, 8, 1784–1790.Google Scholar
Espy, K. A., McDiarmid, M. M., Cwik, M. F., Stalets, M. M., Hamby, A., & Senn, T. E. (2004). The contribution of executive functions to emergent mathematic skills in preschool children. Developmental Neuropsychology, 26, 465–486.Google Scholar
Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., … Petersen, S. E. (2009). Functional brain networks develop from a ‘local to distributed’ organization. PLoS Computational Biology, 5, e1000381.Google Scholar
Fjell, A. M., Walhovd, K. B., Brown, T. T., Kuperman, J. M., Chung, Y., Hagler, D. J., … Gruen, J. (2012). Multimodal imaging of the self-regulating developing brain. Proceedings of the National Academy of Sciences (USA), 109, 19620–19625.Google Scholar
Foltz, G. S., Poltrock, S. E., & Plotts, G. R. (1984). Mental comparison of size and magnitude: Size congruity effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 442–453.Google Scholar
Fuhs, M. W., & McNeil, N. M. (2013). ANS acuity and mathematics ability in preschoolers from low-income homes: Contributions of inhibitory control. Developmental Science, 16, 136–148.Google Scholar
Fuhs, M. W., McNeil, N. M., Kelley, K., O’Rear, C., & Villano, M. (2016). The role of non-numerical stimulus features in approximate number system training in preschoolers from low-income homes. Journal of Cognition and Development, 17, 737–764.CrossRefGoogle Scholar
Gabriel, F. C., Szucs, D., & Content, A. (2013). The development of the mental representations of the magnitude of fractions. PLoS ONE, 8, e80016.Google Scholar
Gebuis, T., & Gevers, W. (2011). Numerosities and space; indeed a cognitive illusion! A reply to de Hevia and Spelke (2009). Cognition, 121, 248–252.Google Scholar
Gebuis, T., Herfs, I. K., Kenemans, J. L., De Haan, E. H. F., & Van der Smagt, M. J. (2009). The development of automated access to symbolic and non-symbolic number knowledge in children: An ERP study. European Journal of Neuroscience, 30, 1999–2008.Google Scholar
Gebuis, T., Kenemans, J. L., de Haan, E. H. F., & van der Smagt, M. J. (2010). Conflict processing of symbolic and non-symbolic numerosity. Neuropsychologia, 48, 394–401.Google Scholar
Gebuis, T., & Reynvoet, B. (2012). The interplay between nonsymbolic number and its continuous visual properties. Journal of Experimental Psychology. General, 141, 642–648.CrossRefGoogle ScholarPubMed
Gebuis, T., & Reynvoet, B. (2013). The neural mechanisms underlying passive and active processing of numerosity. Neuroimage, 70, 301–307.CrossRefGoogle ScholarPubMed
Gelman, R. (1972). Logical capacity of very young children: Number invariance rules. Child Development, 43, 75.Google Scholar
Gilmore, C., Attridge, N., Clayton, S., Cragg, L., Johnson, S., Marlow, N., … Inglis, M. (2013). Individual differences in inhibitory control, not non-verbal number acuity, correlate with mathematics achievement. PLoS ONE, 8, e67374.Google Scholar
Gilmore, C., Cragg, L., Hogan, G., & Inglis, M. (2016). Congruency effects in dot comparison tasks: Convex hull is more important than dot area. Journal of Cognitive Psychology (Hove, England), 28, 923–931.Google Scholar
Gilmore, C. K., McCarthy, S. E., & Spelke, E. S. (2010). Non-symbolic arithmetic abilities and mathematics achievement in the first year of formal schooling. Cognition, 115, 394–406.Google Scholar
Girelli, L., Lucangeli, D., & Butterworth, B. (2000). The development of automaticity in accessing number magnitude. Journal of Experimental Child Psychology, 76, 104–122.Google Scholar
Hadland, K. A., Rushworth, M. F. S., Passingham, R. E., Jahanshahi, M., & Rothwell, J. C. (2001). Interference with performance of a response selection task that has no working memory component: An rTMS comparison of the dorsolateral prefrontal and medial frontal cortex. Journal of Cognitive Neuroscience, 13, 1097–1108.Google Scholar
Halberda, J., Mazzocco, M. M. M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlate with maths achievement. Nature, 455, 665–668.Google Scholar
Harris, I. M., & Miniussi, C. (2003). Parietal lobe contribution to mental rotation demonstrated with rTMS. Journal of Cognitive Neuroscience, 15, 315–323.Google Scholar
Henik, A., & Tzelgov, J. (1982). Is three greater than five: The relation between physical and semantic size in comparison tasks. Memory & Cognition, 10, 389–395.Google Scholar
Ho, C. S.-H., & Fuson, K. C. (1998). Children’s knowledge of teen quantities as tens and ones: Comparisons of Chinese, British, and American kindergartners. Journal of Educational Psychology, 90, 536–544.Google Scholar
Houdé, O. (2000). Inhibition and cognitive development: Object, number, categorization, and reasoning. Cognitive Development, 15, 63–73.Google Scholar
Houdé, O., & Borst, G. (2015). Evidence for an inhibitory-control theory of the reasoning brain. Frontiers in Human Neuroscience, 9, 148.Google Scholar
Houdé, O., & Guichart, E. (2001). Negative priming effect after inhibition of number/length interference in a Piaget-like task. Developmental Science, 4, 119–123.Google Scholar
Houdé, O., Pineau, A., Leroux, G., Poirel, N., Perchey, G., Lanoë, C., … Mazoyer, B. (2011). Functional magnetic resonance imaging study of Piaget’s conservation-of-number task in preschool and school-age children: A neo-Piagetian approach. Journal of Experimental Child Psychology, 110, 332–346.Google Scholar
Houdé, O., Rossi, S., Lubin, A., & Joliot, M. (2010). Mapping numerical processing, reading, and executive functions in the developing brain: An fMRI meta-analysis of 52 studies including 842 children: Meta-analysis of developmental fMRI data. Developmental Science, 13, 876–885.Google Scholar
Hubbard, E. M., Piazza, M., Pinel, P., & Dehaene, S. (2005). Interactions between number and space in parietal cortex. Nature Reviews Neuroscience, 6, 435–448.Google Scholar
Hurewitz, F., Gelman, R., & Schnitzer, B. (2006). Sometimes area counts more than number. Proceedings of the National Academy of Sciences (USA), 103, 19599–19604.Google Scholar
Jacobs, J. E., & Klaczynski, P. A. (2002). The development of judgment and decision making during childhood and adolescence. Current Directions in Psychological Science, 11, 145–149.Google Scholar
Joliot, M., Leroux, G., Dubal, S., Tzourio-Mazoyer, N., Houdé, O., Mazoyer, B., & Petit, L. (2009). Cognitive inhibition of number/length interference in a Piaget-like task: Evidence by combining ERP and MEG. Clinical Neurophysiology, 120, 1501–1513.Google Scholar
Kaufmann, L., Koppelstaetter, F., Delazer, M., Siedentopf, C., Rhomberg, P., Golaszewski, S., … Ischebeck, A. (2005). Neural correlates of distance and congruity effects in a numerical Stroop task: An event-related fMRI study. NeuroImage, 25, 888–898.Google Scholar
Kaufmann, L., Koppelstaetter, F., Siedentopf, C., Haala, I., Haberlandt, E., Zimmerhackl, L.-B., … Ischebeck, A. (2006). Neural correlates of the number-size interference task in children. NeuroReport, 17, 587–591.Google Scholar
Keller, L., & Libertus, M. (2015). Inhibitory control may not explain the link between approximation and math abilities in kindergarteners from middle class families. Frontiers in Psychology, 6, 685.Google Scholar
Knops, A., Thirion, B., Hubbard, E. M., Michel, V., & Dehaene, S. (2009a). Recruitment of an area involved in eye movements during mental arithmetic. Science, 324, 1583–1585.Google Scholar
Knops, A., Viarouge, A., & Dehaene, S. (2009b). Dynamic representations underlying symbolic and nonsymbolic calculation: Evidence from the operational momentum effect. Attention, Perception, & Psychophysics, 71, 803–821.Google Scholar
Knops, A., Zitzmann, S., & McCrink, K. (2013). Examining the presence and determinants of operational momentum in childhood. Frontiers in Psychology, 4, 325.Google Scholar
Koechlin, E., Dehaene, S., & Mehler, J. (1997). Numerical transformations in five-month-old human infants. Mathematical Cognition, 3, 89–104.Google Scholar
Kok, A. (1999). Varieties of inhibition: Manifestations in cognition, event-related potentials and aging. Acta Psychologica, 101, 129–158. https://doi.org/10.1016/S0001–6918(99)00003-7Google Scholar
Konishi, S., Nakajima, K., Uchida, I., Kameyama, M., Nakahara, K., Sekihara, K., & Miyashita, Y. (1998). Transient activation of inferior prefrontal cortex during cognitive set shifting. Nature Neuroscience, 1, 80–84.Google Scholar
Lammertyn, J., Fias, W., & Lauwereyns, J. (2002). Semantic influences on feature-based attention due to overlap of neural circuits. Cortex, 38, 878–882.Google Scholar
Leibovich, T., Henik, A., & Salti, M. (2015). Numerosity processing is context driven even in the subitizing range: An fMRI study. Neuropsychologia, 77, 137–147.Google Scholar
Leibovich, T., Katzin, N., Harel, M., & Henik, A. (2017). From ‘sense of number’ to ‘sense of magnitude’: The role of continuous magnitudes in numerical cognition. The Behavioral and Brain Sciences, 40, e164.Google Scholar
Leroux, G., Joliot, M., Dubal, S., Mazoyer, B., Tzourio-Mazoyer, N., & Houdé, O. (2006). Cognitive inhibition of number/length interference in a Piaget-like task in young adults: Evidence from ERPs and fMRI. Human Brain Mapping, 27, 498–509.Google Scholar
Leroux, G., Spiess, J., Zago, L., Rossi, S., Lubin, A., Turbelin, M.-R., … Joliot, M. (2009). Adult brains don’t fully overcome biases that lead to incorrect performance during cognitive development: An fMRI study in young adults completing a Piaget-like task. Developmental Science, 12, 326–338.Google Scholar
Lortie-Forgues, H., Tian, J., & Siegler, R. S. (2015). Why is learning fraction and decimal arithmetic so difficult? Developmental Review, 38, 201–221.Google Scholar
Lubin, A., Rossi, S., Lanoë, C., Vidal, J., Houdé, O., & Borst, G. (2016). Expertise, inhibitory control and arithmetic word problems: A negative priming study in mathematics experts. Learning and Instruction, 45, 40–48.Google Scholar
Lubin, A., Simon, G., Houdé, O., & De Neys, W. (2015). Inhibition, conflict detection, and number conservation. ZDM, 47, 793–800.Google Scholar
Lubin, A., Vidal, J., Lanoë, C., Houdé, O., & Borst, G. (2013). Inhibitory control is needed for the resolution of arithmetic word problems: A developmental negative priming study. Journal of Educational Psychology, 105, 701–708.Google Scholar
Luna, B., Padmanabhan, A., & O’Hearn, K. (2010). What has fMRI told us about the development of cognitive control through adolescence? Brain and Cognition, 72, 101–113.CrossRefGoogle ScholarPubMed
MacDonald, A. W. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288, 1835–1838.Google Scholar
Marshuetz, C., Smith, E. E., Jonides, J., DeGutis, J., & Chenevert, T. L. (2000). Order information in working memory: FMRI evidence for parietal and prefrontal mechanisms. Journal of Cognitive Neuroscience, 12(suppl 2), 130–144.Google Scholar
McCrink, K., Dehaene, S., & Dehaene-Lambertz, G. (2007). Moving along the number line: Operational momentum in nonsymbolic arithmetic. Attention, Perception, & Psychophysics, 69, 1324–1333.CrossRefGoogle ScholarPubMed
McNab, F., Leroux, G., Strand, F., Thorell, L., Bergman, S., & Klingberg, T. (2008). Common and unique components of inhibition and working memory: An fMRI, within-subjects investigation. Neuropsychologia, 46, 2668–2682.Google Scholar
Mehler, J., & Bever, T. G. (1967). Cognitive capacity of very young children. Science, 158, 141–142.Google Scholar
Miller, E. K. (2000). The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1, 59–65.Google Scholar
Moskal, B. M., & Magone, M. E. (2000). Making sense of what students know: Examining the referents, relationships and modes students displayed in response to a decimal task. Educational Studies in Mathematics, 43, 313–335.Google Scholar
Nieuwenhuis, S., Yeung, N., van den Wildenberg, W., & Ridderinkhof, K. R. (2003). Electrophysiological correlates of anterior cingulate function in a go/no-go task: Effects of response conflict and trial type frequency. Cognitive, Affective, & Behavioral Neuroscience, 3, 17–26.Google Scholar
Nys, J., & Content, A. (2012). Judgement of discrete and continuous quantity in adults: Number counts! Quarterly Journal of Experimental Psychology, 65, 675–690.Google Scholar
Nys, J., Ventura, P., Fernandes, T., Querido, L., Leybaert, J., & Content, A. (2013). Does math education modify the approximate number system? A comparison of schooled and unschooled adults. Trends in Neuroscience and Education, 2, 13–22.Google Scholar
Odic, D., Hock, H., & Halberda, J. (2014). Hysteresis affects approximate number discrimination in young children. Journal of Experimental Psychology. General, 143, 255–265.Google Scholar
Odic, D., Libertus, M. E., Feigenson, L., & Halberda, J. (2013). Developmental change in the acuity of approximate number and area representations. Developmental Psychology, 49, 1103–1112.Google Scholar
Pardo, J. V., Pardo, P. J., Janer, K. W., & Raichle, M. E. (1990). The anterior cingulate cortex mediates processing selection in the Stroop attentional conflict paradigm. Proceedings of the National Academy of Sciences (USA), 87, 256–259.Google Scholar
Peterson, B. S., Kane, M. J., Alexander, G. M., Lacadie, C., Skudlarski, P., Leung, H.-C., … Gore, J. C. (2002). An event-related functional MRI study comparing interference effects in the Simon and Stroop tasks. Cognitive Brain Research, 13, 427–440.Google Scholar
Piaget, J. (1952). The Child’s Conception of Number. London: Routledge and Kegan Paul (original in French, Piaget, J., & Szeminska, A., 1941).Google Scholar
Piaget, J. (1964). Part I: Cognitive development in children: Piaget development and learning. Journal of Research in Science Teaching, 2, 176–186.Google Scholar
Piaget, J. (1968). Quantification, conservation, and nativism. Science, 162, 976–979.Google Scholar
Piazza, M., De Feo, V., Panzeri, S., & Dehaene, S. (2018). Learning to focus on number. Cognition, 181, 35–45.Google Scholar
Piazza, M., Izard, V., Pinel, P., Le Bihan, D., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44, 547–555.Google Scholar
Piazza, M., Pica, P., Izard, V., Spelke, E. S., & Dehaene, S. (2013). Education enhances the acuity of the nonverbal approximate number system. Psychological Science, 24, 1037–1043.Google Scholar
Pica, P., Lemer, C., Izard, V., & Dehaene, S. (2004). Exact and approximate arithmetic in an Amazonian indigene group. Science, 306, 499–503.Google Scholar
Pinel, P., Piazza, M., Le Bihan, D., & Dehaene, S. (2004). Distributed and overlapping cerebral representations of number, size, and luminance during comparative judgments. Neuron, 41, 983–993.Google Scholar
Poirel, N., Borst, G., Simon, G., Rossi, S., Cassotti, M., Pineau, A., & Houdé, O. (2012). Number conservation is related to children’s prefrontal inhibitory control: An fMRI study of a Piagetian task. PLoS ONE, 7, e40802.Google Scholar
Posner, M., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.Google Scholar
Resnick, L. B., Nesher, P., Leonard, F., Magone, M., Omanson, S., & Peled, I. (1989). Conceptual bases of arithmetic errors: The case of decimal fractions. Journal for Research in Mathematics Education, 20, 8.Google Scholar
Roche, A. (2005). Longer is larger – Or is it? Australian Primary Mathematics Classroom, 10, 11–16.Google Scholar
Roell, M., Viarouge, A., Hilscher, E., Houdé, O., & Borst, G. (2019a). Evidence for a visuospatial bias in decimal number comparison in adolescents and in adults. Scientific Reports, 9, 14770.CrossRefGoogle ScholarPubMed
Roell, M., Viarouge, A., Houdé, O., & Borst, G. (2017). Inhibitory control and decimal number comparison in school-aged children. PLoS ONE, 12, e0188276.Google Scholar
Roell, M., Viarouge, A., Houdé, O., & Borst, G. (2019b). Inhibition of the whole number bias in decimal number comparison: A developmental negative priming study. Journal of Experimental Child Psychology, 177, 240–247.Google Scholar
Rousselle, L., & Noël, M.-P. (2008). The development of automatic numerosity processing in preschoolers: Evidence for numerosity-perceptual interference. Developmental Psychology, 44, 544–560.CrossRefGoogle ScholarPubMed
Rubinsten, O., Henik, A., Berger, A., & Shahar-Shalev, S. (2002). The development of internal representations of magnitude and their association with Arabic numerals. Journal of Experimental Child Psychology, 81, 74–92.Google Scholar
Sackur-Grisvard, C., & Léonard, F. (1985). Intermediate cognitive organizations in the process of learning a mathematical concept: The order of positive decimal numbers. Cognition and Instruction, 2, 157–174.Google Scholar
Schwarz, W., & Ischebeck, A. (2003). On the relative speed account of number-size interference in comparative judgments of numerals. Journal of Experimental Psychology: Human Perception and Performance, 29, 507–522.Google ScholarPubMed
Siegler, R. S. (1995). How does change occur: A microgenetic study of number conservation. Cognitive Psychology, 28, 225–273.Google Scholar
Siegler, R. S. (1998). Emerging Minds: The Process of Change in Children’s Thinking. New York: Oxford University Press.Google Scholar
Simon, T. J., Hespos, S. J., & Rochat, P. (1995). Do infants understand simple arithmetic? A replication of Wynn (1992). Cognitive Development, 10, 253–269.Google Scholar
Smith, L. B., & Sera, M. D. (1992). A developmental analysis of the polar structure of dimensions. Cognitive Psychology, 24, 99–142.Google Scholar
Soltesz, F., Szucs, D., & Szucs, L. (2010). Relationships between magnitude representation, counting and memory in 4- to 7-year-old children: A developmental study. Behavioral and Brain Functions, 6, 13.Google Scholar
Sophian, C., & Chu, Y. (2008). How do people apprehend large numerosities? Cognition, 107, 460–478.Google Scholar
Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10, 89–96.Google Scholar
St Clair-Thompson, H. L., & Gathercole, S. E. (2006). Executive functions and achievements in school: Shifting, updating, inhibition, and working memory. Quarterly Journal of Experimental Psychology, 59, 745–759.Google Scholar
Stacey, K., Helme, S., & Steinle, V. (2001). Confusions between decimals, fractions and negative numbers: A consequence of the mirror as a conceptual metaphor in three different ways. PME Conference, 4, 4–217.Google Scholar
Starkey, P., & Cooper, R. (1980). Perception of numbers by human infants. Science, New Series, 210, 1033–1035.Google Scholar
Starkey, P., Spelke, E., & Gelman, R. (1983). Detection of intermodal numerical correspondences by human infants. Science, 222, 179–181.CrossRefGoogle ScholarPubMed
Starr, A., Libertus, M. E., & Brannon, E. M. (2013). Infants show ratio-dependent number discrimination regardless of set size. Infancy, 18, 927–941.Google Scholar
Steinle, V., & Stacey, K. (2003). Grade-related trends in the prevalence and persistence of decimal misconceptions. International Group for the Psychology of Mathematics Education, 4, 259–266.Google Scholar
Szucs, D., Devine, A., Soltesz, F., Nobes, A., & Gabriel, F. (2013a). Developmental dyscalculia is related to visuo-spatial memory and inhibition impairment. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 49, 2674–2688.Google Scholar
Szűcs, D., Nobes, A., Devine, A., Gabriel, F. C., & Gebuis, T. (2013b). Visual stimulus parameters seriously compromise the measurement of approximate number system acuity and comparative effects between adults and children. Frontiers in Psychology, 4, 444.CrossRefGoogle ScholarPubMed
Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Nagase, T., … Kawashima, R. (2012). Regional gray and white matter volume associated with Stroop interference: Evidence from voxel-based morphometry. NeuroImage, 59, 2899–2907.Google Scholar
Tibber, M. S., Greenwood, J. A., & Dakin, S. C. (2012). Number and density discrimination rely on a common metric: Similar psychophysical effects of size, contrast, and divided attention. Journal of Vision, 12, 8.Google Scholar
Tipper, S. P. (2001). Does negative priming reflect inhibitory mechanisms? A review and integration of conflicting views. The Quarterly Journal of Experimental Psychology Section A, 54, 321–343.Google Scholar
Tipper, S. P., Weaver, B., Cameron, S., Brehaut, J. C., & Bastedo, J. (1991). Inhibitory mechanisms of attention in identification and localization tasks: Time course and disruption. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 681–692.Google ScholarPubMed
Tissier, C., Linzarini, A., Allaire-Duquette, G., Mevel, K., Poirel, N., Dollfus, S., … Cachia, A. (2018). Sulcal polymorphisms of the IFC and ACC contribute to inhibitory control variability in children and adults. eNeuro, 5.Google Scholar
Tokita, M., & Ishiguchi, A. (2010). How might the discrepancy in the effects of perceptual variables on numerosity judgment be reconciled? Attention, Perception, & Psychophysics, 72, 1839–1853.Google Scholar
Townsend, J., Adamo, M., & Haist, F. (2006). Changing channels: An fMRI study of aging and cross-modal attention shifts. NeuroImage, 31, 1682–1692.Google Scholar
Tzelgov, J., Meyer, J., & Henik, A. (1992). Automatic and intentional processing of numerical information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 166–179.Google Scholar
Vamvakoussi, X., Van Dooren, W., & Verschaffel, L. (2012). Naturally biased? In search for reaction time evidence for a natural number bias in adults. The Journal of Mathematical Behavior, 31, 344–355.Google Scholar
Van Dooren, W., Lehtinen, E., & Verschaffel, L. (2015). Unraveling the gap between natural and rational numbers. Learning and Instruction, 37, 1–4.Google Scholar
Van Hoof, J., Lijnen, T., Verschaffel, L., & Van Dooren, W. (2013). Are secondary school students still hampered by the natural number bias? A reaction time study on fraction comparison tasks. Research in Mathematics Education, 15, 154–164.Google Scholar
Viarouge, A., & de Hevia, M. D. (2013). The role of numerical magnitude and order in the illusory perception of size and brightness. Frontiers in Psychology, 4, 484.Google Scholar
Viarouge, A., Houdé, O., & Borst, G. (2019). Evidence for the role of inhibition in numerical comparison: A negative priming study in 7- to 8-year-olds and adults. Journal of Experimental Child Psychology, 186, 131–141.Google Scholar
Vogel, S. E., Grabner, R. H., Schneider, M., Siegler, R. S., & Ansari, D. (2013). Overlapping and distinct brain regions involved in estimating the spatial position of numerical and non-numerical magnitudes: An fMRI study. Neuropsychologia, 51, 979–989.Google Scholar
Walsh, V. (2003). A theory of magnitude: Common cortical metrics of time, space and quantity. Trends in Cognitive Sciences, 7, 483–488.Google Scholar
Westlye, L. T., Grydeland, H., Walhovd, K. B., & Fjell, A. M. (2011). Associations between regional cortical thickness and attentional networks as measured by the attention network test. Cerebral Cortex, 21, 345–356.Google Scholar
Wilkey, E. D., Barone, J. C., Mazzocco, M. M. M., Vogel, S. E., & Price, G. R. (2017). The effect of visual parameters on neural activation during nonsymbolic number comparison and its relation to math competency. NeuroImage, 159, 430–442.Google Scholar
Wilkey, E. D., Pollack, C., & Price, G. R. (2020). Dyscalculia and typical math achievement are associated with individual differences in number-specific executive function. Child Development, 91, 596–619.Google Scholar
Wood, G., Ischebeck, A., Koppelstaetter, F., Gotwald, T., & Kaufmann, L. (2009). Developmental trajectories of magnitude processing and interference control: An fMRI study. Cerebral Cortex, 19, 2755–2765.Google Scholar
Wynn, K. (1992). Addition and subtraction by human infants. Nature, 358, 749–750.Google Scholar
Wynn, K. (1995). Infants possess a system of numerical knowledge. Current Directions in Psychological Science, 4, 172–177.Google Scholar
Wynn, K. (1998). Psychological foundations of number: Numerical competence in human infants. Trends in Cognitive Sciences, 2, 296–303.Google Scholar
Wynn, K., Bloom, P., & Chiang, W.-C. (2002). Enumeration of collective entities by 5-month-old infants. Cognition, 83, B55–B62.Google Scholar
Xu, F., & Spelke, E. S. (2000). Large number discrimination in 6-month-old infants. Cognition, 74, B1–B11.Google Scholar
Zacks, J. M. (2008). Neuroimaging studies of mental rotation: A meta-analysis and review. Journal of Cognitive Neuroscience, 20, 31.Google Scholar
Zhou, X., Chen, Y., Chen, C., Jiang, T., Zhang, H., & Dong, Q. (2007). Chinese kindergartners’ automatic processing of numerical magnitude in Stroop-like tasks. Memory & Cognition, 35, 464–470.Google Scholar

References

Anscombe, G. E. M. (1957). Intention. Cambridge, MA: Harvard University Press.Google Scholar
Apperly, I. A., & Butterfill, S. A. (2009). Do humans have two systems to track beliefs and belief-like states? Psychological Review, 116, 953.Google Scholar
Apperly, I. A., & Robinson, E. (1998). Children’s mental representation of referential relations. Cognition, 67, 287–309.CrossRefGoogle ScholarPubMed
Astington, J. W., & Gopnik, A. (1988). Knowing you’ve changed your mind: Children’s understanding of representational change. In Astington, J. W., Harris, P. L., & Olson, D. R. (eds.), Developing Theories of Mind (pp. 193–206). Cambridge: Cambridge University Press.Google Scholar
Baillargeon, R., Buttelmann, D., & Southgate, V. (2018). Invited commentary: Interpreting failed replications of early false-belief findings: Methodological and theoretical considerations. Cognitive Development, 46, 112–124.Google Scholar
Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017). Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, 1, 0064.Google Scholar
Barlassina, L., & Gordon, R. M. (2017). Folk psychology as mental simulation. In Zalta, E. N. (ed.), The Stanford Encyclopedia of Philosophy (Summer 2017 ed.). Available from https://plato.stanford.edu/entries/folkpsych-simulation/. Last accessed 4 August 2021.Google Scholar
Barone, P., Corradi, G., & Gomila, A. (2019). Infants’ performance in spontaneous-response false belief tasks: A review and meta-analysis. Infant Behavior and Development, 57, 101350.Google Scholar
Beck, S. R., Carroll, D. J., Brunsdon, V. E., & Gryg, C. K. (2011). Supporting children’s counterfactual thinking with alternative modes of responding. Journal of Experimental Child Psychology, 108, 190–202.Google Scholar
Beck, S. R., & Guthrie, C. (2011). Almost thinking counterfactually: Children’s understanding of close counterfactuals. Child Development, 82, 1189–1198.Google Scholar
Beck, S. R., Riggs, K. J., & Gorniak, S. L. (2009). Relating developments in children’s counterfactual thinking and executive functions. Thinking & Reasoning, 15, 337–354.Google Scholar
Beck, S. R., Riggs, K. J., & Gorniak, S. L. (2010). The effect of causal chain length on counterfactual conditional reasoning. British Journal of Developmental Psychology, 28, 505–521.Google Scholar
Beck, S. R., Robinson, E. J., Carroll, D. J., & Apperly, I. A. (2006). Children’s thinking about counterfactuals and future hypotheticals as possibilities. Child Development, 77, 413–426.Google Scholar
Bello, P., & Cassimatis, N. (2006). Developmental Accounts of Theory-of-Mind Acquisition: Achieving Clarity via Computational Cognitive Modeling. Paper presented at the Proceedings of the Annual Meeting of the Cognitive Science Society, Vancouver, Canada.Google Scholar
Björklund, D. F. (2018). A metatheory for cognitive development (or ‘Piaget is dead’ revisited). Child Development, 89, 2288–2302.Google Scholar
Breheny, R. (2006). Communication and folk psychology. Mind & Language, 21, 74–107.CrossRefGoogle Scholar
Buchsbaum, D., Bridgers, S., Weisberg, D. S., & Gopnik, A. (2012). The power of possibility: Causal learning, counterfactual reasoning, and pretend play. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 367, 2202–2212.Google Scholar
Burns, P., Riggs, K. J., & Beck, S. R. (2012). Executive control and the experience of regret. Journal of Experimental Child Psychology, 111, 501–515.Google Scholar
Buttelmann, D., Carpenter, M., & Tomasello, M. (2009). Eighteen-month-old infants show false belief understanding in an active helping paradigm. Cognition, 112, 337–342.Google Scholar
Butterfill, S. A., & Apperly, I. A. (2013). How to construct a minimal theory of mind. Mind & Language, 28, 606–637.Google Scholar
Carey, S. (2009). Where our number concepts come from. The Journal of Philosophy, 106, 220.Google Scholar
Carlson, S. M., Claxton, L. J., & Moses, L. J. (2015). The relation between executive function and theory of mind is more than skin deep. Journal of Cognition and Development, 16, 186–197.Google Scholar
Carlson, S. M., Wong, A., Lemke, M., & Cosser, C. (2005). Gesture as a window on children’s beginning understanding of false belief. Child Development, 76, 73–86.Google Scholar
Clements, W. A., & Perner, J. (1994). Implicit understanding of belief. Cognitive Development, 9, 377–395.Google Scholar
Davidson, D. (1963). Actions, reasons, and causes. The Journal of Philosophy, 60, 685–700.Google Scholar
De Villiers, J. (2007). The interface of language and theory of mind. Lingua, 117, 1858–1878.Google Scholar
Devine, R. T., & Hughes, C. (2014). Relations between false belief understanding and executive function in early childhood: A meta‐analysis. Child Development, 85, 1777–1794.Google Scholar
Dias, M. G., & Harris, P. L. (1988). The effect of make‐believe play on deductive reasoning. British Journal of Developmental Psychology, 6, 207–221.Google Scholar
Diesendruck, G., Markson, L., & Bloom, P. (2003). Children’s reliance on creator’s intent in extending names for artifacts. Psychological Science, 14, 164–168.Google Scholar
Doherty, M. J., & Perner, J. (1998). Metalinguistic awareness and theory of mind: Just two words for the same thing? Cognitive Development, 13, 279–305.Google Scholar
Doherty, M. J., & Perner, J. (2020). Mental files: Developmental integration of dual naming and theory of mind. Developmental Review, 56, 100909.Google Scholar
Drayton, S., Turley-Ames, K. J., & Guajardo, N. R. (2011). Counterfactual thinking and false belief: The role of executive function. Journal of Experimental Child Psychology, 108, 532–548.Google Scholar
Edgington, D. (2011). Causation first: Why causation is prior to counterfactuals. In Hoerl, C., McCormack, T., & Beck, S. R. (eds.), Understanding Counterfactuals, Understanding Causation: Issues in Philosophy and Psychology (pp. 230–241). Oxford: Oxford University Press.Google Scholar
Ferguson, H. J., & Cane, J. E. (2015). Examining the cognitive costs of counterfactual language comprehension: Evidence from ERPs. Brain Research, 1622, 252–269.Google Scholar
Fodor, J. A. (1981). The current state of the innateness controversy. In Fodor, J. A. (ed.), Representations (pp. 257–316). Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (2008). LOT 2: The Language of Thought Revisited. Oxford: Oxford University Press.Google Scholar
Gallagher, S. (2007). Simulation trouble. Social Neuroscience, 2, 353–365.Google Scholar
Garnham, W. A., & Perner, J. (2001). Actions really do speak louder than words – but only implicitly: Young children’s understanding of false belief in action. British Journal of Developmental Psychology, 19, 413–432.Google Scholar
German, T. P., & Nichols, S. (2003). Children’s counterfactual inferences about long and short causal chains. Developmental Science, 6, 514–523.Google Scholar
Ginsberg, M. L. (1986). Counterfactuals. Artificial Intelligence, 30, 35–79.Google Scholar
Gollek, C., & Doherty, M. J. (2016). Metacognitive developments in word learning: Mutual exclusivity and theory of mind. Journal of Experimental Child Psychology, 148, 51–69.Google Scholar
Gopnik, A., & Meltzoff, A. N. (1998). Words, Thoughts, and Theories (Learning, Development, and Conceptual Change). Cambridge, MA: MIT Press.Google Scholar
Gordon, R. M. (1986). Folk psychology as simulation. Mind & Language, 1, 158–171.Google Scholar
Gordon, R. M. (2001). Simulation and reason explanation: the radical view. Philosophical Topics, 29, 175–192.Google Scholar
Grice, H. P. (1957). Meaning. The Philosophical Review, 66, 377–388.Google Scholar
Guajardo, N. R., Parker, J., & Turley‐Ames, K. (2009). Associations among false belief understanding, counterfactual reasoning, and executive function. British Journal of Developmental Psychology, 27, 681–702.Google Scholar
Harris, P. L. (1992). From simulation to folk psychology: The case for development. Mind & Language, 7, 120–144.Google Scholar
Harris, P. L. (1997). On realizing what might have happened instead. Polish Quarterly of Developmental Psychology, 3, 161–176.Google Scholar
Harris, P. L. (2000). The Work of the Imagination: Understanding Children’s Worlds. Malden, MA: Wiley-Blackwell.Google Scholar
Harris, P. L., German, T., & Mills, P. (1996). Children’s use of counterfactual thinking in causal reasoning. Cognition, 61, 233–259.Google Scholar
Harris, P. L., Kavanaugh, R. D., Wellman, H. M., & Hickling, A. K. (1993). Young children’s understanding of pretense. Monographs of the Society for Research in Child Development, 58, i–107.Google Scholar
Haryu, E. (1991). A developmental study of children’s use of ‘mutual exclusivity’ and context to interpret novel words. The Japanese Journal of Educational Psychology, 39, 11–20.Google Scholar
Haryu, E., & Imai, M. (1999). Controlling the application of the mutual exclusivity assumption in the acquisition of lexical hierarchies. Japanese Psychological Research, 41, 21–34.Google Scholar
Hauf, P., Aschersleben, G., & Prinz, W. (2007). Baby do–baby see!: How action production influences action perception in infants. Cognitive Development, 22, 16–32.Google Scholar
Hauf, P., & Prinz, W. (2005). The understanding of own and others’ actions during infancy: ‘You-like-Me’ or ‘Me-like-You’? Interaction Studies, 6, 429–445.Google Scholar
He, Z., Bolz, M., & Baillargeon, R. (2011). False‐belief understanding in 2.5‐year‐olds: Evidence from violation‐of‐expectation change‐of‐location and unexpected‐contents tasks. Developmental Science, 14, 292–305.Google Scholar
Heal, J. (1986). Replication and functionalism. In Butterfield, J. (ed.), Language, Mind, and Logic (pp. 135–150). Cambridge: Cambridge University Press.Google Scholar
Helming, K. A., Strickland, B., & Jacob, P. (2016). Solving the puzzle about early belief‐ascription. Mind & Language, 31, 438–469.Google Scholar
Huemer, M., Perner, J., & Leahy, B. (2018). Mental files theory of mind: When do children consider agents acquainted with different object identities? Cognition, 171, 122–129.Google Scholar
Kamp, H. (1990). Prolegomena to a structural account of belief and other attitudes. In Anderson, C. A. (ed.), Propositional Attitudes: The Role of Content in Logic, Language and Mind (pp. 27–90). Stanford, CA: Center for study of language and information, Lecture Notes Series.Google Scholar
Kampis, D., Parise, E., Csibra, G., & Kovács, Á. M. (2015). Neural signatures for sustaining object representations attributed to others in preverbal human infants. Proceedings of the Royal Society B, 282, 20151683.Google Scholar
Karttunen, L. (1976). Discourse referents. In McCawley, J. D. (ed.), Notes from the Linguistic Underground (Syntax and Semantics, vol. 7, pp. 363–385). New York: Academic Press.Google Scholar
Kovács, Á. M., Téglás, E., & Endress, A. D. (2010). The social sense: Susceptibility to others’ beliefs in human infants and adults. Science, 330, 1830–1834.Google Scholar
Kuczaj, S. A., & Daly, M. J. (1979). The development of hypothetical reference in the speech of young children. Journal of Child Language, 6, 563–579.Google Scholar
Kulakova, E., Aichhorn, M., Schurz, M., Kronbichler, M., & Perner, J. (2013). Processing counterfactual and hypothetical conditionals: An fMRI investigation. NeuroImage, 72, 265–271.Google Scholar
Kulke, L., & Rakoczy, H. (2018). Implicit theory of mind – An overview of current replications and non-replications. Data in Brief, 16, 101–104.Google Scholar
Kulke, L., von Duhn, B., Schneider, D., & Rakoczy, H. (2018). Is implicit theory of mind a real and robust phenomenon? Results from a systematic replication study. Psychological Science, 29, 888–900.Google Scholar
Leahy, B., Rafetseder, E., & Perner, J. (2014). Basic conditional reasoning: How children mimic counterfactual reasoning. Studia Logica, 102, 793–810.Google Scholar
Leslie, A. M. (1987). Pretense and representation: The origins of ‘theory of mind’. Psychological Review, 94, 412.Google Scholar
Leslie, A. M. (1994). ToMM, ToBy, and Agency: Core architecture and domain specificity. In Hirschfeld, L. A., & Gelman, S. A. (eds.). Mapping the Mind: Domain Specificity in Cognition and Culture (pp. 119–148). New York: Cambridge University Press.Google Scholar
Leslie, A. M., Friedman, O., & German, T. P. (2004). Core mechanisms in ‘theory of mind’. Trends in Cognitive Sciences, 8, 528–533.Google Scholar
Lewis, D. (1973). Counterfactuals. Oxford: Basil Blackwell.Google Scholar
Lillard, A. S. (1993). Young children’s conceptualization of pretense: Action or mental representational state? Child Development, 64, 372–386.Google Scholar
Liu, D., Sabbagh, M. A., Gehring, W. J., & Wellman, H. M. (2009). Neural correlates of children’s theory of mind development. Child Development, 80, 318–326.Google Scholar
Low, J., & Watts, J. (2013). Attributing false beliefs about object identity reveals a signature blind spot in humans’ efficient mind-reading system. Psychological Science, 24, 305–311.Google Scholar
Margolis, E., & Laurence, S. (2011). Learning matters: The role of learning in concept acquisition. Mind & Language, 26, 507–539.Google Scholar
Milligan, K., Astington, J. W., & Dack, L. A. (2007). Language and theory of mind: Meta‐analysis of the relation between language ability and false‐belief understanding. Child Development, 78, 622–646.Google Scholar
Norman, D., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In Davidson, R. J., Schwarts, G. E., & Shapiro, D. (eds.), Consciousness and Self-regulation: Advances in Research and Theory (pp. 1–18). New York: Plenum Press.Google Scholar
Nyhout, A., Henke, L., & Ganea, P. A. (2017). Children’s counterfactual reasoning about causally overdetermined events. Child Development, 90, 610–622.Google Scholar
Onishi, K. H., & Baillargeon, R. (2005). Do 15-month-old infants understand false beliefs? Science, 308, 255–258.Google Scholar
Paulus, M. (2012). Is it rational to assume that infants imitate rationally a theoretical analysis and critique. Human Development, 55, 107–121.Google Scholar
Paulus, M., Hunnius, S., Vissers, M., & Bekkering, H. (2011). Imitation in infancy: Rational or motor resonance? Child Development, 82, 1047–1057.Google Scholar
Perner, J. (1998). The meta-intentional nature of executive functions and theory of mind. In Carruthers, P., & Boucher, J. (eds.), Language and Thought: Interdisciplinary Themes (pp. 270–283). Cambridge: Cambridge University Press.Google Scholar
Perner, J. (2000). About + belief + counterfactual. In Mitchell, P., & Riggs, K. J. (eds.), Children’s Reasoning and the Mind (pp. 367–401). Hove, East Sussex: Psychology Press.Google Scholar
Perner, J., & Brandl, J. L. (2005). File change semantics for preschoolers: Alternative naming and belief understanding. Interaction Studies, 6, 483–501.Google Scholar
Perner, J., & Howes, D. (1992). ‘He thinks he knows’: And more developmental evidence against the simulation (role taking) theory. Mind & Language, 7, 72–86.Google Scholar
Perner, J., Huemer, M., & Leahy, B. (2015). Mental files and belief: A cognitive theory of how children represent belief and its intensionality. Cognition, 145, 77–88.Google Scholar
Perner, J., & Lang, B. (1999). Development of theory of mind and executive control. Trends in Cognitive Sciences, 3, 337–344.Google Scholar
Perner, J., Lang, B., & Kloo, D. (2002). Theory of mind and self‐control: More than a common problem of inhibition. Child Development, 73, 752–767.Google Scholar
Perner, J., & Leahy, B. (2016). Mental files in development: Dual naming, false belief, identity and intensionality. Review of Philosophy and Psychology, 7, 491–508.Google Scholar
Perner, J., Mauer, M. C., & Hildenbrand, M. (2011). Identity: Key to children’s understanding of belief. Science, 333, 474–477.Google Scholar
Perner, J., & Roessler, J. (2010). Teleology and causal understanding in children’s theory of mind. In Aguilar, J. H., & Buckareff, A. A. (eds.), Causing Human Actions: New Perspectives on the Causal Theory of Action. Cambridge, MA: MIT Press.Google Scholar
Perner, J., Sprung, M., & Steinkogler, B. (2004). Counterfactual conditionals and false belief: A developmental dissociation. Journal of Cognition and Development, 19, 179–201.Google Scholar
Peterson, D. M., & Riggs, K. J. (1999). Adaptive modelling and mindreading. Mind & Language, 14, 80–112.Google Scholar
Phillips, J., Ong, D. C., Surtees, A. D., Xin, Y., Williams, S., Saxe, R., & Frank, M. C. (2015). A second look at automatic theory of mind: Reconsidering Kovács, Téglás, and Endress (2010). Psychological Science, 26, 1353–1367.Google Scholar
Powell, L. J., & Carey, S. (2017). Executive function depletion in children and its impact on theory of mind. Cognition, 164, 150–162.Google Scholar
Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences, 1, 515–526.Google Scholar
Priewasser, B., Fowles, F., Schweller, K., & Perner, J. (2020). Mistaken max befriends Duplo girl: No difference between a standard and an acted-out false belief task. Journal of Experimental Child Psychology, 191, 104756.Google Scholar
Priewasser, B., Rafetseder, E., Gargitter, C., & Perner, J. (2018). Helping as an early indicator of a theory of mind: Mentalism or teleology? Cognitive Development, 46, 69–78.Google Scholar
Rafetseder, E., Cristi‐Vargas, R., & Perner, J. (2010). Counterfactual reasoning: Developing a sense of ‘nearest possible world’. Child Development, 81, 376–389.Google Scholar
Rafetseder, E., & Perner, J. (2018). Belief and counterfactuality. Zeitschrift für Psychologie, 226, 110–121.Google Scholar
Rafetseder, E., Schwitalla, M., & Perner, J. (2013). Counterfactual reasoning: From childhood to adulthood. Journal of Experimental Child Psychology, 114, 389–404.Google Scholar
Rakoczy, H., Bergfeld, D., Schwarz, I., & Fizke, E. (2015). Explicit theory of mind is even more unified than previously assumed: Belief ascription and understanding aspectuality emerge together in development. Child Development, 86, 486–502.Google Scholar
Recanati, F. (2012). Mental Files. Oxford: Oxford University Press.Google Scholar
Riggs, K. J., Peterson, D. M., Robinson, E. J., & Mitchell, P. (1998). Are errors in false belief tasks symptomatic of a broader difficulty with counterfactuality? Cognitive Development, 13, 73–90.Google Scholar
Robinson, E. J., & Mitchell, P. (1995). Masking of children’s early understanding of the representational mind: Backwards explanation versus prediction. Child Development, 66, 1022–1039.Google Scholar
Rubio-Fernández, P., & Geurts, B. (2013). How to pass the false-belief task before your fourth birthday. Psychological Science, 24, 27–33.Google Scholar
Ruffman, T. (1996). Do children understand the mind by means of simulation or a theory? Evidence from their understanding of inference. Mind & Language, 11, 388–414.Google Scholar
Ruffman, T., Garnham, W., Import, A., & Connolly, D. (2001). Does eye gaze indicate implicit knowledge of false belief? Charting transitions in knowledge. Journal of Experimental Child Psychology, 80, 201–224.Google Scholar
Russell, J. (1987). ‘Can we say…?’ Children’s understanding of intensionality. Cognition, 25, 289–308.Google Scholar
Russell, J. (1996). Agency. Its Role in Mental Development. Hove: Erlbaum.Google Scholar
Russell, J., Mauthner, N., Sharpe, S., & Tidswell, T. (1991). The ‘windows task’ as a measure of strategic deception in preschoolers and autistic subjects. British Journal of Developmental Psychology, 9, 331–349.Google Scholar
Scanlon, T. (1998). What We Owe to Each Other. Cambridge, MA: Harvard University Press.Google Scholar
Schneider, D., Lam, R., Bayliss, A. P., & Dux, P. E. (2012). Cognitive load disrupts implicit theory-of-mind processing. Psychological Science, 23, 842–847.Google Scholar
Scott, F. J., Baron‐Cohen, S., & Leslie, A. (1999). ‘If pigs could fly’: A test of counterfactual reasoning and pretence in children with autism. British Journal of Developmental Psychology, 17, 349–362.Google Scholar
Scott, R. M., & Baillargeon, R. (2017). Early false-belief understanding. Trends in Cognitive Sciences, 21, 237–249.Google Scholar
Scott, R. M., He, Z., Baillargeon, R., & Cummins, D. (2012). False‐belief understanding in 2.5‐year‐olds: Evidence from two novel verbal spontaneous‐response tasks. Developmental Science, 15, 181–193.Google Scholar
Sommerville, J. A., Woodward, A. L., & Needham, A. (2005). Action experience alters 3-month-old infants’ perception of others’ actions. Cognition, 96, 1–11.Google Scholar
Southgate, V., Chevallier, C., & Csibra, G. (2010). Seventeen‐month‐olds appeal to false beliefs to interpret others’ referential communication. Developmental Science, 13, 907–912.Google Scholar
Southgate, V., Senju, A., & Csibra, G. (2007). Action anticipation through attribution of false belief by 2-year-olds. Psychological Science, 18, 587–592.Google Scholar
Southgate, V., & Vernetti, A. (2014). Belief-based action prediction in preverbal infants. Cognition, 130, 1–10.Google Scholar
Stalnaker, R. (1968). A theory of conditionals. In Jackson, F. (ed.), Conditionals (pp. 98–112). Oxford: Oxford University Press.Google Scholar
Stuhlmüller, A., & Goodman, N. D. (2014). Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs. Cognitive Systems Research, 28, 80–99.Google Scholar
Surian, L., Caldi, S., & Sperber, D. (2007). Attribution of beliefs by 13-month-old infants. Psychological Science, 18, 580–586.Google Scholar
Surian, L., & Geraci, A. (2012). Where will the triangle look for it? Attributing false beliefs to a geometric shape at 17 months. British Journal of Developmental Psychology, 30, 30–44.Google Scholar
Thompson, V. A., & Byrne, R. M. (2002). Reasoning counterfactually: Making inferences about things that didn’t happen. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 1154.Google Scholar
Träuble, B., Marinović, V., & Pauen, S. (2010). Early theory of mind competencies: Do infants understand others’ beliefs? Infancy, 15, 434–444.Google Scholar
Weisberg, D. P., & Beck, S. R. (2012). The development of children’s regret and relief. Cognition & Emotion, 26, 820–835.CrossRefGoogle ScholarPubMed
Weisberg, D. S., & Gopnik, A. (2013). Pretense, counterfactuals, and Bayesian causal models: Why what is not real really matters. Cognitive Science, 37, 1368–1381.Google Scholar
Wellman, H. M., Cross, D., & Watson, J. (2001). Meta‐analysis of theory‐of‐mind development: The truth about false belief. Child Development, 72, 655–684.Google Scholar
Westra, E., & Carruthers, P. (2017). Pragmatic development explains the Theory-of-Mind Scale. Cognition, 158, 165–176.Google Scholar
Wiesmann, C. G., Schreiber, J., Singer, T., Steinbeis, N., & Friederici, A. D. (2017). White matter maturation is associated with the emergence of Theory of Mind in early childhood. Nature Communications, 8, 14692.Google Scholar
Wimmer, H. (1989). Common-sense Mentalismus und Emotion: einige entwicklungspsychologische Implikationen Denken und Fühlen (pp. 56–66). Berlin: Springer.Google Scholar
Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13, 103–128.Google Scholar
Woodward, A. L. (1998). Infants selectively encode the goal object of an actor’s reach. Cognition, 69, 1–34.Google Scholar
Woodward, J. (2011). Psychological studies of causal and counterfactual reasoning. In Hoerl, C., McCormack, T., & Beck, S. R. (eds.), Understanding Counterfactuals, Understanding Causation. Issues in Philosophy and Psychology (pp. 16–53). Oxford: Oxford University Press.Google Scholar

References

Allan, N. P., Hume, L. E., Allan, D. M., Farrington, A. L., & Lonigan, C. J. (2014). Relations between inhibitory control and the development of academic skills in preschool and kindergarten: A meta-analysis. Developmental Psychology, 50, 2368–2379.Google Scholar
Allan, N. P., & Lonigan, C. J. (2014). Exploring dimensionality of effortful control using hot and cool tasks in a sample of preschool children. Journal of Experimental Child Psychology, 122, 33–47.Google Scholar
Alloway, T. P., Gathercole, S. E., Kirkwood, H., & Elliott, J. (2009). The Working Memory Rating Scale: A classroom-based behavioral assessment of working memory. Learning and Individual Differences, 19, 242–245.Google Scholar
Archibald, S. J., & Kerns, K. A. (1999). Identification and description of new tests of executive functioning in children. Child Neuropsychology, 5, 115–129.Google Scholar
Bargh, J. A., & Morsella, E. (2008). The unconscious mind. Perspectives on Psychological Science, 3, 73–79.Google Scholar
Barnes, J. J. M., Dean, A. J., Nandam, L. S., O’Connell, R. G., & Bellgrove, M. A. (2011). The molecular genetics of executive function: Role of monoamine system genes. Biological Psychiatry, 69, e127–e143.Google Scholar
Bassett, H. H., Denham, S., Wyatt, T. M., & Warren-Khot, H. K. (2012). Refining the Preschool Self-Regulation Assessment for use in preschool classrooms. Infant and Child Development, 21, 596–616.Google Scholar
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15.Google Scholar
Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215–225.Google Scholar
Becker, M. G., Isaac, W., & Hynd, G. W. (1987). Neuropsychological development of nonverbal behaviors attributed to “frontal lobe” functioning. Developmental Neuropsychology, 3, 275–298.Google Scholar
Bernier, A., Carlson, S. M., Deschênes, M., & Matte‐Gagné, C. (2012). Social factors in the development of early executive functioning: A closer look at the caregiving environment. Developmental Science, 15, 12–24.Google Scholar
Bernier, A., Carlson, S. M., & Whipple, N. (2010). From external regulation to self‐regulation: Early parenting precursors of young children’s executive functioning. Child Development, 81, 326–339.Google Scholar
Bernstein, A., Hadash, Y., Lichtash, Y., Tanay, G., Shepherd, K., & Fresco, D. M. (2015). Decentering and related constructs: A critical review and metacognitive processes model. Perspectives on Psychological Science, 10, 599–617.Google Scholar
Best, J. R. Miller, P. H., & Naglieri, J. A. (2011). Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learning and Individual Differences, 21, 327–336.Google Scholar
Blair, C., Granger, D., & Razza, R. P. (2005). Cortisol reactivity is positively related to executive function in preschool children attending head start. Child Development, 76, 554–567.Google Scholar
Blair, C., Granger, D., Willoughby, M., Mills-Koonce, R., Cox, M., Greenberg, M. T., et al. (2011). Salivary cortisol mediates effects of poverty and parenting on executive functions in early childhood. Child Development, 82, 1970–1984.Google Scholar
Blair, C., & Raver, C. C. (2015). School readiness and self-regulation: A developmental psychobiological approach. Annual Review of Psychology, 66, 711–731.Google Scholar
Blair, C., & Raver, C. C. (2016). Poverty, stress, and brain development: New directions for prevention and intervention. Academic Pediatrics, 16, S30–S36.Google Scholar
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–680.Google Scholar
Bodrova, E., & Leong, D. J. (2001). Tools of the mind: A case study of implementing the Vygotskian approach in American early childhood and primary classrooms. Innodata Monographs, 7. Geneva: UNESCO International Bureau of Education.Google Scholar
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences, 8, 539–546.Google Scholar
Bradley, R. H., McKelvey, L. M., & Whiteside‐Mansell, L. (2011). Does the quality of stimulation and support in the home environment moderate the effect of early education programs? Child Development, 82, 2110–2122.Google Scholar
Brock, L. L., Rimm-Kaufman, S. E., Nathanson, L., & Grimm, K. J. (2009). The contributions of “hot” and “cool” executive function to children’s academic achievement, learning-related behaviors, and engagement in kindergarten. Early Childhood Research Quarterly, 24, 337–349.Google Scholar
Bronfenbrenner, U. (1979). The Ecology of Human Development: Experiments by Nature and Design. Cambridge, MA: Harvard University Press.Google Scholar
Bruce, J., Fisher, P. A., Pears, K. C., & Levine, S. (2008). Morning cortisol levels in preschool-aged foster children: Differential effects of maltreatment type. Developmental Psychobiology, 51, 14–23.Google Scholar
Brydges, C. R., Reid, C. L., Fox, A. M., & Anderson, M. (2012). A unitary executive function predicts intelligence in children. Intelligence, 40, 458–469.Google Scholar
Bugental, D. B., Schwartz, A. & Lynch, C. (2010). Effects of an early family intervention on children’s memory: The mediating effects of cortisol levels. Mind, Brain, and Education, 4, 159–170.Google Scholar
Bunge, S. A. (2004). How we use rules to select actions: A review of evidence from cognitive neuroscience. Cognitive, Affective, and Behavioral Neuroscience, 4, 564–579.Google Scholar
Bunge, S. A., & Wallis, J. D. (2008). Perspectives on Rule Guided Behavior. New York: Oxford University Press.Google Scholar
Bunge, S. A., & Zelazo, P. D. (2006). A brain-based account of the development of rule use in childhood. Current Directions in Psychological Science, 15, 118–121.Google Scholar
Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental Neuropsychology, 28, 595–616.Google Scholar
Carlson, S. M., & Moses, L. J. (2001). Individual differences in inhibitory control and children’s theory of mind. Child Development, 72, 1032–1053.Google Scholar
Carlson, S. M., White, R. E., & Davis-Unger, A. C. (2014). Evidence for a relation between executive function and pretense representation in preschool children. Cognitive Development, 29, 1–16.Google Scholar
Carlson, S. M., & Zelazo, P. D. (2014). Minnesota Executive Function Scale. Saint Paul, MN: Reflection Sciences, LLC.Google Scholar
Carthy, T., Horesh, N., Apter, A., Edge, M. D., & Gross, J. J. (2010). Emotional reactivity and cognitive regulation in anxious children. Behaviour Research and Therapy, 48, 384–393.Google Scholar
Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H. L., Israel, S., et al. (2014). The p factor: One general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2, 119–137.Google Scholar
Castellanos, F. X., Sonuga-Barke, E. J. S., Milham, M. P., & Tannock, R. (2006). Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences, 10, 117–123.Google Scholar
Castellanos-Ryan, N., Brière, F. N., O’Leary-Barrett, M., Banaschewski, T., Bokde, A., Bromberg, U., et al. (2016). The structure of psychopathology in adolescence and its common personality and cognitive correlates. Journal of Abnormal Psychology, 125, 1039–1052.Google Scholar
Checa, P., & Fernández-Berrocal, P. (2019). Cognitive control and emotional intelligence: Effect of the emotional content of the task: Brief Reports. Frontiers in Psychology, 10, 195.Google Scholar
Cicchetti, D. (1984). The emergence of developmental psychopathology. Child Development, 55, 1–7.Google Scholar
Cicchetti, D., & Rogosch, F. A. (2001). Diverse patterns of neuroendocrine activity in maltreated children. Development and Psychopathology, 13, 677–693.Google Scholar
Cicchetti, D., & Tucker, D. (1994). Development and self-regulatory structures of the mind. Development and Psychopathology, 6, 533–549.Google Scholar
Cirino, P. T., Ahmed, Y., Miciak, J., Taylor, W. P., Gerst, E. H., & Barnes, M. A. (2018). A framework for executive function in the late elementary years. Neuropsychology, 32, 176–189.Google Scholar
Ciurli, P., Bivona, U., Barba, C., Onder, G., Silvestro, D., Azicnuda, E., et al. (2010). Metacognitive unawareness correlates with executive function impairment after severe traumatic brain injury. Journal of the International Neuropsychological Society, 16, 360.Google Scholar
Clark, A. S., & Goldman-Rakic, P. S. (1989). Gonadal hormones influence the emergence of cortical function in nonhuman primates. Behavioral Neuroscience, 103, 1287–1295.Google Scholar
Clark, C. A., Martinez, M. M., Nelson, J. M., Wiebe, S. A., & Andrews Espy, K. (2014). Children's self‐regulation and executive control: Critical for later years. Wellbeing: A Complete Reference Guide (pp. 1–30). Wiley Online Library.Google Scholar
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16, 1348–1355.Google Scholar
Conger, R. D., Wallace, L. B., Sun, Y., Simons, R. L., McLoyd, V., & Brody, G. H. (2002). Economic pressure in African American families: A replication and extension of the family stress model. Developmental Psychology, 38, 179–193.Google Scholar
Conway, A., & Stifter, C. A. (2012). Longitudinal antecedents of executive function in preschoolers. Child Development, 83, 1022–1036.Google Scholar
Crone, E., & Steinbeis, N. (2017). Neural perspectives on cognitive control development during childhood and adolescence. Trends in Cognitive Sciences, 21, 205–215.Google Scholar
Cunningham, W. A., & Zelazo, P. D. (2007). Attitudes and evaluations: A social cognitive neuroscience perspective. Trends in Cognitive Sciences, 11, 97–104.Google Scholar
Delis, D., Kramer, J., Kaplan, E., & Holdnack, J. (2004). Reliability and validity of the Delis-Kaplan Executive Function System: An update. Journal of the International Neuropsychological Society, 10, 301–303.Google Scholar
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168.Google Scholar
Diamond, A., & Doar, B. (1989). The performance of human infants on a measure of frontal cortex function, the delayed response task. Developmental Psychobiology, 22, 271–294.Google Scholar
Diamond, A., & Ling, D. S. (2016). Conclusions about interventions, programs and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48.Google Scholar
DiStefano, R., Galinsky, E., McClelland, M. M., Zelazo, P. D., & Carlson, S. M. (2018). Autonomy-supportive parenting and associations with child and parent executive function. Journal of Applied Developmental Psychology, 58, 77–85.Google Scholar
Duncan, J. (2013). The structure of cognition: Attentional episodes in mind and brain. Neuron, 80, 35–50.Google Scholar
Enlow, M. B., Petty, C. R., Svelnys, C., Gusman, M., Huezo, M., Malin, A., et al. (2019). Differential effects of stress exposures, caregiving quality, and temperament in early life on working memory versus inhibitory control in preschool-aged children. Developmental Neuropsychology, 44, 339–356.Google Scholar
Eslinger, P. J., Flaherty-Craig, C. V., Benton, A. L. (2004). Developmental outcomes after early prefrontal cortex damage. Brain and Cognition, 55, 84–103.Google Scholar
Espinet, S. D., Anderson, J. E., & Zelazo, P. D. (2012). N2 amplitude as a neural marker of executive function in young children: An ERP study of children who switch versus perseverate on the Dimensional Change Card Sort. Developmental Cognitive Neuroscience, 2, S49–S58.Google Scholar
Espinet, S. D., Anderson, J. E., & Zelazo, P. D. (2013). Reflection training improves executive function in preschool children: Behavioral and neural effects. Developmental Cognitive Neuroscience, 4, 3–15.Google Scholar
Espy, K. A. (1997). The shape school: Assessing executive function in preschool children. Developmental Neuropsychology, 13, 495–499.Google Scholar
Espy, K. A., Kaufmann, P. M., McDiarmid, M. D., & Glisky, M. L. (1999). Executive functioning in preschool children: Performance on A-not-B and other delayed response format tasks. Brain and Cognition, 41, 178–199.Google Scholar
Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59, 77–92.Google Scholar
Evans, G. W., & Schamberg, M. A. (2009). Childhood poverty, chronic stress, and adult working memory. Proceedings of the National Academy of Sciences (USA), 106, 6545–6549.Google Scholar
Farah, M. J., Shera, D. M., Savage, J. H., Betancourt, L., Giannetta, J. M., Brodsky, N. L., et al. (2006). Childhood poverty: Specific associations with neurocognitive development. Brain Research, 1110, 166–174.Google Scholar
Fellows, L. K., & Farah, M. J. (2005). Different underlying impairments in decision-making following ventromedial and dorsolateral frontal lobe damage in humans. Cerebral Cortex, 15, 58–63.Google Scholar
Fitzpatrick, C., McKinnon, R. D., Blair, C. B., & Willoughby, M. T. (2014). Do preschool executive function skills explain the school readiness gap between advantaged and disadvantaged children? Learning and Instruction, 30, 25–31.Google Scholar
Fonseca, R. P., Zimmermann, N., Cotrena, C., Cardoso, C., Kristensen, C. H., & Grassi-Oliveira, R. (2012). Neuropsychological assessment of executive functions in traumatic brain injury: Hot and cold components. Psychology & Neuroscience, 5, 183–190.Google Scholar
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137, 201–225.Google Scholar
Frye, D., Zelazo, P. D., & Palfai, T. (1995). Theory of mind and rule-based reasoning. Cognitive Development, 10, 483–527.Google Scholar
Gandolfi, E., Viterbori, P., Traverso, L., & Usai, M. C. (2014). Inhibitory processes in toddlers: A latent-variable approach. Frontiers in Psychology, 5, 381.Google Scholar
Gathercole, S. E. (1998). The development of memory. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 39, 3–27.Google Scholar
Gerstadt, C., Hong, Y., & Diamond, A. (1994). The relationship between cognition and action: Performance of children 3 ½–7 years old on a Stroop-like day-night test. Cognition, 53, 129–153.Google Scholar
Gottlieb, G. (1992). Individual Development and Evolution: The Genesis of Novel Behavior. New York: Oxford University Press.Google Scholar
Groppe, K., & Elsner, B. (2014). Executive function and food approach behavior in middle childhood. Frontiers in Psychology, 5, 477.Google Scholar
Hackman, D. A., Gallop, R., Evans, G. W., & Farah, M. J. (2015). Socioeconomic status and executive function: Developmental trajectories and mediation. Developmental Science, 18, 686–702.Google Scholar
Hadley, L. V., Acluche, F., & Chevalier, N. (2019). Encouraging performance monitoring promotes proactive control in children. Developmental Science, e12861.Google Scholar
Hammond, S. I., Müller, U., Carpendale, J. I. M., Bobok, M. B., & Liebermann-Finestone, D. P. (2012). The effects of parental scaffolding on preschoolers’ executive function. Developmental Psychology, 48, 271–281.Google Scholar
Hanson, J. L., Chung, M. K., Avants, B. B., Shirtcliff, E. A., Gee, J. C., Davidson, R. J., et al. (2010). Early stress is associated with alterations in the orbitofrontal cortex: A tensor-based morphometry investigation of brain structure and behavioral risk. Journal of Neuroscience, 30, 7466–7472.Google Scholar
Happaney, K., Zelazo, P. D., & Stuss, D. T. (2004). Development of orbitofrontal function: Current themes and future directions. Brain and Cognition, 55, 1–10.Google Scholar
Hart, B., & Risley, T. (1995). Meaningful Differences in the Everyday Experience of Young American Children. Baltimore, MD: Brookes.Google Scholar
Hinson, J. M., Jameson, T. L., & Whitney, P. (2003). Impulsive decision making and working memory. Journal of Experimental Psychology; Learning Memory and Cognition, 29, 298–306.Google Scholar
Hongwanishkul, D., Happaney, K. R., Lee, W., & Zelazo, P. D. (2005). Hot and cool executive function: Age-related changes and individual differences. Developmental Neuropsychology, 28, 617–644.Google Scholar
Hostinar, C. E., Sullivan, R. M., & Gunnar, M. R. (2014). Psychobiological mechanisms underlying the social buffering of the hypothalamic-pituitary-adrenocortical axis: A review of animal models and human studies across development. Psychological Bulletin, 140, 256–282.Google Scholar
Hughes, C., & Ensor, R. (2009). How do families help or hinder the emergence of early executive function? New Directions in Child and Adolescent Development, 123, 35–50.Google Scholar
Hunter, W. S. (1917). The delayed reaction in a child. Psychological Review, 24, 74–87.Google Scholar
Jacobsen, C. F. (1936). Studies of cerebral function in primates. I. The functions of the frontal association areas in primates. Comparative Psychology Monographs, 13, 1–60.Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences (USA), 105, 6829–6833.Google Scholar
Jester, J. M., Nigg, J. T., Puttler, L. I., Long, J. C., Fitzgerald, H. E., & Zucke, R. A. (2009). Intergenerational transmission of neuropsychological executive functioning. Brain and Cognition, 70, 145–153.CrossRefGoogle ScholarPubMed
Joensson, M., Thomsen, K. R., Andersen, L. M., Gross, J., Mouridsen, K., Sandberg, K., et al. (2015). Making sense: Dopamine activates conscious self-monitoring through medial prefrontal cortex. Human Brain Mapping, 36, 1866–1877.Google Scholar
Johnson, M. H. (2011). Interactive specialization: A domain-general framework for human functional brain development? Developmental Cognitive Neuroscience, 1, 7–21.Google Scholar
Kaller, M. S., Lazari, A., Blanco-Duque, C., Sampaio-Baptista, C., & Johansen-Berg, H. (2017). Myelin plasticity and behaviour: Connecting the dots. Current Opinion in Neurobiology, 47, 86–92.Google Scholar
Kerr, A., & Zelazo, P. D. (2004). Development of “hot” executive function: The Children’s Gambling Task. Brain and Cognition, 55, 148–157.Google Scholar
Kesek, A., Cunningham, W. A., Packer, D. J., & Zelazo, P. D. (2011). Indirect goal priming is more powerful than explicit instruction in children. Developmental Science, 14, 944–948.Google Scholar
Kim, S., Nordling, J. K., Yoon, J. E., Boldt, L. J., & Kochanska, G. (2013). Effortful control in “hot” and “cool” tasks differentially predicts children’s behavior problems and academic performance. Journal of Abnormal Child Psychology, 41, 43–56.Google Scholar
Kishiyama, M. M., Boyce, W. T., Jimenez, A. M., Perry, L. M., & Knight, R. T. (2009). Socioeconomic disparities affect prefrontal function in children. Journal of Cognitive Neuroscience, 21, 1106-1115.Google Scholar
Kolb, B., Harker, L., de Melo, S., & Gibb, R. (2017). Stress and pre-frontal cortical plasticity in the developing brain. Cognitive Development, 42, 15–26.Google Scholar
Korucu, I., Rolan, E., Napoli, A. R., Purpura, D. J., & Schmitt, S. A. (2019). Development of the Home Executive Function Environment (HEFE) scale: Assessing its relation to preschoolers’ executive function. Early Childhood Research Quarterly, 47, 9–19.Google Scholar
Koss, K. J., & Gunnar, M. R. (2018). Annual research review: Early adversity, the hypothalamic-pituitary-adrenocortical axis, and child psychopathology. Journal of Child Psychology and Psychiatry, 59, 327–346.Google Scholar
Kross, E., Duckworth, A., Ayduk, O., Tsukayama, E., & Mischel, W. (2011). The effect of self-distancing adaptive versus maladaptive self-reflection in children. Emotion, 11, 1032–1039.Google Scholar
Lahey, B. B., Krueger, R. F., Rathouz, P. J., Waldman, I. D., & Zald, D. H. (2017). A hierarchical causal taxonomy of psychopathology across the life span. Psychological Bulletin, 143, 142–186.Google Scholar
Lee, K., Bull, R., & Ho, R.M. (2013). Developmental changes in executive functioning. Child Development, 84, 1933–1953.Google Scholar
Lehto, J. E., Juujarvi, P., Kooistra, L., & Pulkkinen, L. (2003). Dimensions of executive functioning: Evidence from children. British Journal of Developmental Psychology, 21, 59–80.Google Scholar
Lengua, L. J., Honorado, E., & Bush, N. R. (2007). Contextual risk and parenting as predictors of effortful control and social competence in preschool children. Journal of Applied Developmental Psychology, 28, 40–55.Google Scholar
Lerner, M. D., & Lonigan, C. J. (2014). Executive function among preschool children: Unitary versus distinct abilities. Journal of Psychopathology and Behavioral Assessment, 36, 626–639.Google Scholar
Levin, H. S., Culhane, K. A., Hartmann, J., Evankovich, K., Mattson, A. J., Harward, H., et al. (1991). Developmental-changes in performance on tests of purported frontal-lobe functioning. Developmental Neuropsychology, 7, 377–395.Google Scholar
Lhermitte, F. (1983). “Utilization behavior” and its relation to lesions to the frontal lobes. Brain, 106, 237–255.Google Scholar
Liston, C., McEwen, B. S., & Casey, B. J. (2009). Psychosocial stress reversibly disrupts prefrontal processing and attentional control. Proceedings of the National Academy of Science (USA), 106, 912–917.Google Scholar
Logue, S. F., & Gould, T. J. (2014). The neural and genetic basis of executive function: Attention, cognitive flexibility, and response inhibition. Pharmacology Biochemistry and Behavior, 123, 45–54.Google Scholar
Luciana, M., & Nelson, C. A. (1998). The functional emergence of prefrontally-guided working memory systems in four-to-eight-year-old children. Neuropsychologia, 36. 273–293.Google Scholar
Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10, 434–445.Google Scholar
Luria, A. R. (1966). Higher Cortical Functions in Man (2nd ed.). New York: Basic Books.Google Scholar
Luria, A. R., & Vinogradova, O. S. (1959). An objective investigation of the dynamics of semantic systems. British Journal of Psychology, 50, 89–105.Google Scholar
Lyons, K. E., & Zelazo, P. D. (2011). Monitoring, metacognition, and executive function: Elucidating the role of self-reflection in the development of self-regulation. Advances in Child Development and Behavior, 40, 379–412.Google Scholar
Maccoby, E. E. (1980). Social Development. San Diego, CA: Harcourt Brace Jovanovich.Google Scholar
Mackey, A. P., Hill, S. S., Stone, S. I., & Bunge, S. A. (2011). Differential effects of reasoning and speed training in children. Developmental Science, 14, 582–590.Google Scholar
Manes, F., Sahakian, B., Clark, L., Rogers, R., Antoun, N., Aitken, M., et al. (2002). Decision-making processes following damage to prefrontal cortex. Brain, 125, 624–639.Google Scholar
Mann, T. D., Hund, A. M., Hesson-McInnis, M. S., & Roman, Z. J. (2017). Pathways to school readiness: Executive functioning predicts academic and social-emotional aspects of school readiness. Mind, Brain, and Education, 11, 21–31.Google Scholar
Martel, M. M., Pan, P. M, Hoffmann, M. S., Gadelha, A., do Rosário, M. C., Jair, J., et al. (2017). A general psychopathology factor (p factor) in children: Structural model analysis and external validation through familial risk and child global executive function. Journal of Abnormal Psychology, 126, 137–148.Google Scholar
Marulis, L., Baker, S., & Whitebread, D. (2020). Integrating metacognition and executive function to enhance young children’s perception of and agency in their learning. Early Childhood Research Quarterly, 50, 46–54.Google Scholar
Masten, A. S., Herbers, J. E., Desjardins, C. D., Cutuli, J. J., McCormick, C. M., Sapienza, J. K., et al. (2012). Executive function skills and school success in young children experiencing homelessness. Educational Researcher, 41, 373–384.Google Scholar
Matheny, A., Jr., Wachs, T. D., Ludwig, J., & Phillips, K. (1995). Bringing order out of chaos: Psychometric characteristics of the Confusion, Hub-bub, and Order Scale. Journal of Applied Developmental Psychology, 16, 429–444.Google Scholar
Matte-Gagne, C., & Bernier, A. (2011). Prospective relations between maternal autonomy support and child executive functioning: Investigating the mediating role of child language ability. Journal of Experimental Child Psychology, 110, 611–625.Google Scholar
McAuley, T., & White, D. A. (2011). A latent variables examination of processing speed, response inhibition, and working memory during typical development. Journal of Experimental Child Psychology, 108, 453–468.Google Scholar
McClelland, M. M., Cameron, C. E., Duncan, R., Bowles, R. P., Acock, A. C., Miao, A., et al. (2014). Predictors of early growth in academic achievement: The Head-Toes-Knees-Shoulders task. Frontiers in Psychology, 5, 599.Google Scholar
McLaughlin, K. A. (2016). Future directions in childhood adversity and youth psychopathology. Journal of Clinical Child & Adolescent Psychology, 45, 361–382.Google Scholar
McLoyd, V. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53, 185–204.Google Scholar
Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science, 11, 512–534.Google Scholar
Meuwissen, A. S., & Carlson, S. M. (2018). An experimental study of the effects of autonomy support on preschoolers’ self-regulation. Journal of Applied Developmental Psychology, 60, 11–23.Google Scholar
Mezzacappa, E., Buckner, J. C., & Earls, F. (2011). Prenatal cigarette exposure and infant learning stimulation as predictors of cognitive control in childhood. Developmental Science, 14, 881–891.Google Scholar
Micalizzi, L., Brick, L. A., Flom, M., Ganiban, J. M., & Saudino, K. J. (2019). Effects of socioeconomic status and executive function on school readiness across levels of household chaos. Early Childhood Research Quarterly, 47, 331–340.Google Scholar
Miller, M. R., Giesbrecht, G. F., Muller, U., McInerney, R. J., & Kerns, K. A. (2012). A latent variable approach to determining the structure of executive function in preschool children. Journal of Cognition and Development, 13, 395–423.Google Scholar
Milner, B. (1963). Effects of different brain lesions on card sorting. Archives of Neurology, 9, 90–100.Google Scholar
Mischel, W., Shoda, Y., & Rodriguez, M. L. (1989). Delay of gratification in children. Science, 244, 933–938.Google Scholar
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current Directions in Psychology, 21, 8–14.Google Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). the unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100.Google Scholar
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., et al. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences (USA), 108, 2693–2698.Google Scholar
Montroy, J. J., Merz, C., Williams, J. M., Landry, S. H., Johnson, U. Y., Zucker, T. A., et al. (2019). Hot and cool dimensionality of executive function: Model invariance across age and maternal education in preschool children. Early Childhood Research Quarterly, 49, 188–201.Google Scholar
Moriguchi, Y., Sakata, Y., Ishibashi, M., & Ishikawa, Y. (2015) Teaching others rule-use improves executive function and prefrontal activations in young children. Frontiers in Psychology, 6, 894.Google Scholar
Moriguchi, Y., & Shinahara, I. (2019). Less Is More activation: The involvement of the lateral prefrontal regions in a “Less Is More” task. Developmental Neuropsychology, 44, 273–281.Google Scholar
Moritz, S., Andreou, C., Schneider, B. C., Wittekind, C. E., Menon, M., Balzan, R. P., et al. (2014). Sowing the seeds of doubt: A narrative review on metacognitive training in schizophrenia. Clinical Psychology Review, 34, 358–366.Google Scholar
Mulder, H., Hoofs, H., Verhagen, J., van der Veen, I., & Leseman, P. P. (2014). Psychometric properties and convergent and predictive validity of an executive function test battery for two-year-olds. Frontiers in Psychology, 5, 733.Google Scholar
Mungas, D., Widaman, K., Zelazo, P.D., Tulsky, D., Heaton, R. K., Slotkin, J., et al. (2013). VII. NIH toolbox Cognition Battery (CB): Factor structure for 3- to 15-year-olds. Monographs of the Society for Research in Child Development, 78, 103–118.Google Scholar
Nejati, V., Salehinejad, M. A., & Nitsche, M. A. (2018). Interaction of the left dorsolateral prefrontal cortex (L-dlPFC) and right orbitofrontal cortex (OFC) in hot and cold executive functions: Evidence from transcranial direct current stimulation (tDCS). Neuroscience, 369(Suppl C), 109–123.Google Scholar
Nelson, T. D., Kidwell, K. M., Nelson, J. M., Tomaso, C. C., Hankey, M., & Espy, K. A. (2018). Preschool executive control and internalizing symptoms in elementary school. Journal of Abnormal Child Psychology, 46, 1509–1520.Google Scholar
Nesbitt, K. T., Baker-Ward, L., & Willoughby, M. T. (2013). Executive function mediates socio-economic and racial differences in early academic achievement. Early Childhood Research Quarterly, 28, 774–783.Google Scholar
Noble, K. G., Houston, S. M, Brito, N. H., Bartsch, H., Kan., E., Kuperman, J. M., et al. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18, 773–778.Google Scholar
Noble, K. G., McCandliss, B. D., & Farah, M. J. (2007). Socioeconomic gradients predict individual differences in neurocognitive abilities. Developmental Science, 10, 464–480.Google Scholar
Normann, N., & Morina, N. (2018). The efficacy of metacognitive therapy: A systematic review and meta-analysis. Frontiers in Psychology, 9, 2211.Google Scholar
O’Hearn, K., Osato, M., Ordaz, S., & Luna, B. (2008). Neurodevelopment and executive function in autism. Development and Psychopathology, 20, 1103–1132.Google Scholar
Overman, W. H., Bachevalier, J., Schumann, E., & Ryan, P. (1996). Cognitive gender differences in very young children parallel biologically based cognitive gender differences in monkeys. Behavioral Neuroscience, 110, 673–684.Google Scholar
Passler, M. A., Isaac, W., & Hynd, G. W. (1985). Neuropsychological development of behavior attributed to frontal lobe functioning in children. Developmental Neuropsychology, 1, 349–370.Google Scholar
Peterson, E., & Welsh, M. C. (2014). The development of hot and cool executive functions in childhood and adolescence: Are we getting warmer? In Goldstein, S., & Naglieri, J. (eds.), Executive Functioning Handbook (pp. 45–65). New York: Springer.Google Scholar
Petrides, M., & Milner, B. (1982). Deficits on subject-ordered tasks after frontal-and temporal-lobe lesions in man. Neuropsychologia, 20, 249–262.Google Scholar
Pickering, S. J., & Gathercole, S. E. (2001). Working Memory Test Battery for Children. London: Psychological Corp.Google Scholar
Pietrefesa, A. S., & Evans, D. W. (2007). Affective and neuropsychological correlates of children’s rituals and compulsive-like behaviors: Continuities and discontinuities with Obsessive-Compulsive Disorder. Brain and Cognition, 65, 36–46.Google Scholar
Plamondon, A., Akbari, E., Atkinson, L., Steiner, M., Meaney, M. J., & Fleming, A. S. (2015). Spatial working memory and attention skills are predicted by maternal stress during pregnancy. Early Human Development, 91, 23–29.Google Scholar
Plessow, F., Fischer, R., Kirschbaum, C., & Goschke, T. (2011). Inflexibly focused under stress: Acute psychosocial stress increases shielding of action goals at the expense of reduced cognitive flexibility with increasing time lag to the stressor. Journal of Cognitive Neuroscience, 23, 3218–3227.Google Scholar
Pozuelos, J. P., Combita, L. M., Abundis, A., Paz-Alonso, P. M., Conejero, A., Guerra, S., et al. (2019). Metacognitive scaffolding boosts cognitive and neural benefits following executive attention training in children. Developmental Science, 22, e12756.Google Scholar
Prencipe, A., Kesek, A., Cohen, J., Lamm, C., & Zelazo, P. D. (2011). Development of hot and cool executive function during the transition to adolescence. Journal of Experimental Child Psychology, 108, 621–637.Google Scholar
Prencipe, A., & Zelazo, P. D. (2005). Development of affective decision-making for self and other: Evidence for the integration of first- and third-person perspectives. Psychological Science, 16, 501–505.Google Scholar
Pribram, K. H. (1973). The primate frontal cortex: Executive of the brain. In Pribram, K. H., & Luria, A. R. (eds.), Psychophysiology of the Frontal Kobes (pp. 293–314). New York: Academic Press.Google Scholar
Rhoades, R. D., Greenberg, M. C., & Domitrovich, T. (2009). The contribution of inhibitory control to preschoolers’ social–emotional competence. Journal of Applied Developmental Psychology, 30, 310–320.Google Scholar
Riggs, N. R., Greenberg, M. T., Kusché, C. A., & Pentz, M. A. (2006). The mediational role of neurocognition in the behavioral outcomes of a social-emotional prevention program in elementary school students: Effects of the PATHS curriculum. Prevention Science, 7, 91–102.Google Scholar
Robbins, T. W., & Arnsten, A. F. (2009). The neuropsychopharmacology of fronto-executive function: Monoaminergic modulation. Annual Review of Neuroscience, 32, 267–287.Google Scholar
Robbins, T. W., James, M., Owen, A. M., Sahakian, B. J., McInnes, L., & Rabbitt, P. (1994). Cambridge Neuropsychological Test Automated Battery (CANTAB): A factor analytic study of a large sample of normal elderly volunteer. Dementia, 5, 266–281.Google Scholar
Roebers, C. (2017). Executive function and metacognition: Towards a unifying framework of cognitive self-regulation. Developmental Review, 45, 31–51.Google Scholar
Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain and Cognition, 55, 11–29.Google Scholar
Rubia, K. (2011). “Cool” inferior frontostriatal dysfunction in attention-deficit/hyperactivity disorder versus “hot” ventromedial orbitofrontal-limbic dysfunction in conduct disorder: A review. Biological Psychiatry, 69, e69–e87.Google Scholar
Sameroff, A. J. (1983). Developmental systems: Contexts and evolution. In Kessen, W. (Series ed.) & Mussen, P. H. (Vol ed.), Handbook of Child Psychology: Vol. 1. History, Theories, and Methods (pp. 238–294). New York: Wiley.Google Scholar
Saver, J. L., & Damasio, A. R. (1991). Preserved access and processing of social knowledge in a patient with acquired sociopathy due to ventromedial frontal damage. Neuropsychologia, 29, 1241–1249.Google Scholar
Schmitt, S. A., Simpson, A. M., & Friend, M. (2011). A longitudinal assessment of the home literacy environment and early language. Infant and Child Development, 20, 409–431.Google Scholar
Schoemaker, K., Bunte, T., Wiebe, S. A., Espy, K. A., Deković, M., & Matthys, W. (2012). Executive function deficits in preschool children with ADHD and DBD. Journal of Child Psychology and Psychiatry, 53, 111–119.Google Scholar
Sheppes, G., Suri, G., & Gross, J. J. (2015). Emotion regulation and psychopathology. Annual Review of Clinical Psychology, 11, 379–405.Google Scholar
Sheridan, M. A., Peverill, M., Finn, A. S., & McLaughlin, K. A. (2017). Dimensions of childhood adversity have distinct associations with neural systems underlying executive functioning. Development and Psychopathology, 29, 1777–1794.Google Scholar
Sheridan, M. A., Sarsour, K., Jutte, D., D’Esposito, M., & Boyce, W. T. (2012). The impact of social disparity on prefrontal function in childhood. PLoS ONE, 7, e35744.Google Scholar
Shi, R., Sharpe, L., & Abbott, M. (2017). A meta-analysis of the relationship between anxiety and attentional control. Clinical Psychology Review, 72, 101754.Google Scholar
Shields, G. S., Sazma, M. A., & Yonelinas, A. P. (2016). The effects of acute stress on core executive functions: A meta-analysis and comparison with cortisol. Neuroscience & Biobehavioral Review, 68, 651–668.Google Scholar
Shinaver, C. S., Entwistle, P. C., & Söderqvist, S. (2014). Cogmed WM training: Reviewing the reviews. Applied Neuropsychology: Child, 3, 163–172.Google Scholar
Shing, Y.L., Lindenberger, U., Diamond, A., Li, S.C., & Davidson, M. C. (2010). Memory maintenance and inhibitory control differentiate from early childhood to adolescence. Developmental Neuropsychology, 35, 679–697.Google Scholar
Shoda, Y., Mischel, W., & Peake, P. K. (1990). Predicting adolescent cognitive and self-regulatory competencies from preschool delay of gratification: Identifying diagnostic conditions. Developmental Psychology, 26, 978–986.Google Scholar
Shonkoff, J. P. (2011). Protecting brains, not simply stimulating minds. Science, 333, 982–983.Google Scholar
Shonkoff, J. P., Garner, A. S., Siegel, B. S., Dobbins, M. I., Earls, M. F., Garner, A. S., et al. (2012). The lifelong effects of early childhood adversity and toxic stress. Pediatrics, 129, e232–e246.Google Scholar
Smith, S. M. (1982). Enhancement of recall using multiple environmental contexts during learning. Memory & Cognition, 10, 405–412.Google Scholar
Smith-Donald, R., Raver, C. C., Hayes, T., & Richardson, B. (2007). Preliminary construct and concurrent validity of the preschool self-regulation assessment (PSRA) for field-based research. Early Childhood Research Quarterly, 22, 173–187.Google Scholar
Sonuga-Barke, E. J. S. (2003). The dual pathway model of AD/HD: An elaboration of neuro-developmental characteristics. Neuroscience and Biobehavioral Reviews, 27, 593–604.Google Scholar
Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development. 55, 17–29.Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.Google Scholar
Stuss, D. T., & Benson, D. F. (1986). The Frontal Lobes. New York: Raven Press.Google Scholar
Thorell, L. B. (2007). Do delay aversion and executive function deficits make distinct contributions to the functional impact of ADHD symptoms? A study of early academic skill deficits. Journal of Child Psychology and Psychiatry, 48, 1061–1070.Google Scholar
Toll, S. W., Van der Ven, S. H., Kroesbergen, E. H., & Van Luit, J. E. (2011). Executive functions as predictors of math learning disabilities. Journal of Learning Disabilities, 44, 521–532.Google Scholar
Travers-Hill, E., Dunn, B., Hoppitt, L., Hitchcock, C., & Dalgleish, T. (2017). Beneficial effects of training in self-distancing and perspective broadening for people with a history of recurrent depression. Behaviour Research and Therapy, 95, 19–28.Google Scholar
Usai, M. C., Viterbori, P., Traverso, L., & De Franchis, V. (2014). Latent structure of executive function in 5- and 6-year-old children: A longitudinal study. European Journal of Developmental Psychology, 11, 447–462.Google Scholar
Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E., & Buckner, R. L. (2008). Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology, 100, 3328–3342.Google Scholar
Wass, S. V., Smith, C. G., Daubney, K. R., Suata, Z. M., Clackson, K., Begum, A., et al. (2019). Influences of environmental stressors on autonomic function in 12-month-old infants: Understanding early common pathways to atypical emotion regulation and cognitive performance. Journal of Child Psychology and Psychiatry, 60, 1323–1333.CrossRefGoogle ScholarPubMed
Wechsler, D. (1992). Wechsler Intelligence Scale for Children – Third Edition. London: Psychological Corporation.Google Scholar
Welsh, M. C., & Pennington, B. F. (1988). Assessing frontal lobe functioning in children: Views from developmental psychology. Developmental Neuropsychology, 4, 199–230.Google Scholar
Welsh, M. C., Pennington, B. F., & Groisser, D. B. (1991). A normative-developmental study of executive function: A window on prefrontal function in children. Developmental Neuropsychology, 7, 131–149.CrossRefGoogle Scholar
Wiebe, S. A., Espy, K. A., & Charak, D. (2008). Using confirmatory factor analysis to understand executive control in preschool children: I. Latent structure. Developmental Psychology, 44, 575–587.CrossRefGoogle ScholarPubMed
Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A. C., Chevalier, N., & Espy, K. A. (2011). The structure of executive function in 3-year-olds. Journal of Experimental Child Psychology, 108, 436–452.Google Scholar
Willoughby, M., Kupersmidt, J., Voegler-Lee, M., & Bryant, D. (2011). Contributions of hot and cool self-regulation to preschool disruptive behavior and academic achievement. Developmental Neuropsychology, 36, 162–180.Google Scholar
Xu, F., Han, Y., Sabbagh, M.A., Wang, T., Ren, X., & Li, C. (2013). Developmental differences in the structure of executive function in middle childhood and adolescence. PLoS ONE, 8, e77770.Google Scholar
Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2013). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nature Neuroscience, 15, 528–536.Google Scholar
Zelazo, P. D. (2006). The dimensional change card sort: A method of assessing executive function in children. Nature Protocols, 1, 297–301.Google Scholar
Zelazo, P. D. (2015). Executive function: Reflection, iterative reprocessing, complexity, and the developing brain. Developmental Review, 38, 55–68.Google Scholar
Zelazo, P. D. (2020). Executive function and psychopathology: A neurodevelopmental perspective. Annual Review of Clinical Psychology, 16, 14.1–14.24.Google Scholar
Zelazo, P. D., Anderson, J. E., Richler, J., Wallner-Allen, K., Beaumont, J. L., Conway, K. P., et al. (2014). NIH Toolbox Cognition Battery (CB): Validation of executive function measures in adults. Journal of the International Neuropsychological Society, 20, 620–629.Google Scholar
Zelazo, P. D., Anderson, J. E., Richler, J., Wallner-Allen, K., Beamont, J. L., & Weintraub, S. (2013). NIH Toolbox Cognition Battery (CB): Measuring executive function and attention. Monographs of the Society for Research in Child Development, 78, 16–33.Google Scholar
Zelazo, P. D., Blair, C. B., & Willoughby, M. T. (2016). Executive function: Implications for education. US Department of Education, 1–148. Available from https://ies.ed.gov/ncer/pubs/20172000/pdf/20172000.pdf. Last accessed August 4, 2021.Google Scholar
Zelazo, P. D., Carter, A., Reznick, J. S., & Frye, D. (1997). Early development of executive function: A problem-solving framework. Review of General Psychology, 1, 198–226.Google Scholar
Zelazo, P. D., & Cunningham, W. (2007). Executive function: Mechanisms underlying emotion regulation. In Gross, J. (ed.), Handbook of Emotion Regulation (pp. 135–158). New York: Guilford.Google Scholar
Zelazo, P. D., Forston, J. L., Masten, A. S., & Carlson, S. M. (2018). Mindfulness plus reflection training: Effects on executive function in early childhood. Frontiers in Psychology, 9, 1–12.Google Scholar
Zelazo, P. D., Frye, D., & Rapus, T. (1996). An age-related dissociation between knowing rules and using them. Cognitive Development, 11, 37–63.Google Scholar
Zelazo, P. D., & Jacques, S. (1996). Children’s rule use: Representation, reflection, and cognitive control. Annals of Child Development, 12, 119–176.Google Scholar
Zelazo, P. D., & Müller, U. (2002). Executive function in typical and atypical development. In Goswami, U. (ed.), Handbook of Childhood Cognitive Development (pp. 445–469). Oxford: Blackwell.Google Scholar
Zelazo, P. D., Müller, U., Frye, D., & Marcovitch, S. (2003). The development of executive function in early childhood. Monographs of the Society for Research on Child Development, 68, vii–137.Google Scholar
Zelazo, P. D., & Reznick, J. S. (1991). Age related asynchrony of knowledge and action. Child Development, 62, 719–735.Google Scholar

References

Altmann, E. M., & Trafton, J. G. (2002). Memory for goals: An activation-based model. Cognitive Science, 26, 39–83.Google Scholar
Ambrosi, S., Lemaire, P., & Blaye, A. (2016). Do young children modulate their cognitive control?: Sequential congruency effects across three conflict tasks in 5-to-6 year-olds. Experimental Psychology, 63, 117–126.Google Scholar
Ambrosi, S., Servant, M., Blaye, A., & Burle, B. (2019). Conflict processing in kindergarten children: New evidence from distribution analyses reveals the dynamics of incorrect response activation and suppression. Journal of Experimental Child Psychology, 177, 36–52.Google Scholar
Ambrosi, S., Śmigasiewicz, K., Burle, B., & Blaye, A. (2020). The dynamics of interference control across childhood and adolescence: Distribution analyses in three conflict tasks and ten age groups. Developmental Psychology, 56, 2262–2280.Google Scholar
Andrews-Hanna, J. R., Mackiewicz Seghete, K. L., Claus, E. D., Burgess, G. C., Ruzic, L., & Banich, M. T. (2011). Cognitive control in adolescence: Neural underpinnings and relation to self-report behaviors. PLoS ONE, 6, e21598.Google Scholar
Aron, A. R., Fletcher, P. C., Bullmore, E. T., Sahakian, B. J., & Robbins, T. W. (2003). Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nature Neuroscience, 6, 115–116.Google Scholar
Atas, A., Desender, K., Gevers, W., & Cleeremans, A. (2016). Dissociating perception from action during conscious and unconscious conflict adaptation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42, 866–881.Google Scholar
Barber, A. D., Caffo, B. S., Pekar, J. J., & Mostofsky, S. H. (2013). Developmental changes in within- and between-network connectivity between late childhood and adulthood. Neuropsychologia, 51, 156–167.Google Scholar
Barnes, J. J., Nobre, A. C., Woolrich, M. W., Baker, K., & Astle, D. E. (2016). Training working memory in childhood enhances coupling between frontoparietal control network and task-related regions. Journal of Neuroscience, 36, 9001–9011.CrossRefGoogle ScholarPubMed
Blackwell, K. A., & Munakata, Y. (2014). Costs and benefits linked to developments in cognitive control. Developmental Science, 17, 203–211.Google Scholar
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663.Google Scholar
Blaye, A., Ambrosi, S., Lucenet, J., & Burle, B. (2018). The development of within and between-trials dynamics of inhibitory processes across childhood and adolescence. Paper presented to the 48th Annual meeting of the Jean Piaget Society, 31 May–2 June, Amsterdam, the Netherlands.Google Scholar
Blaye, A., & Chevalier, N. (2011). The role of goal representation in preschoolers’ flexibility and inhibition. Journal of Experimental Child Psychology, 108, 469–483.Google Scholar
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652.Google Scholar
Braver, T. S. (2012). The variable nature of cognitive control: A dual mechanisms framework. Trends in Cognitive Sciences, 16, 106–113.Google Scholar
Braver, T. S., & Burgess, G. C. (2007). Explaining the many varieties of working memory variation: Dual mechanisms of cognitive control. In Conway, A., Jarrold, C., Kane, M., Miyake, A., & Towse, J. (eds.), Variation in Working Memory (pp. 76–106). Oxford: Oxford University Press.Google Scholar
Brinums, M., Imuta, K., & Suddendorf, T. (2018). Practicing for the future: Deliberate practice in early childhood. Child Development, 86, 2051–2058.Google Scholar
Buss, A. T., & Spencer, J. P. (2018). Changes in frontal and posterior cortical activity underlie the early emergence of executive function. Developmental Science, 21, e12602.Google Scholar
Camos, V., & Barrouillet, P. (2011). Developmental change in working memory strategies: From passive maintenance to active refreshing. Developmental Psychology, 47, 898–904.Google Scholar
Carp, J., & Compton, R. J. (2009). Alpha power is influenced by performance errors. Psychophysiology, 46, 336–343.Google Scholar
Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18, 414–421.Google Scholar
Chatham, C. H., Frank, M. J., & Munakata, Y. (2009). Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control. Proceedings of the National Academy of Sciences (USA), 106, 5529–5533.Google Scholar
Chein, J. M., & Schneider, W. (2012). The brain’s learning and control architecture. Current Directions in Psychological Science, 21, 78–84.Google Scholar
Chevalier, N. (2015). The development of executive function: Toward more optimal coordination of control with age. Child Development Perspectives, 9, 239–244.Google Scholar
Chevalier, N. (2018). Willing to think hard? The subjective value of cognitive effort in children. Child Development, 89, 1283–1295.Google Scholar
Chevalier, N., & Blaye, A. (2009). Setting goals to switch between tasks: Effect of cue transparency on children’s cognitive flexibility. Developmental Psychology, 45, 782–797.Google Scholar
Chevalier, N., & Blaye, A. (2016). Metacognitive monitoring of executive control engagement during childhood. Child Development, 87, 1264–1276.Google Scholar
Chevalier, N., Dauvier, B., & Blaye, A. (2009). Preschoolers’ use of feedback for flexible behavior: Insights from a computational model. Journal of Experimental Child Psychology, 103, 251–267.Google Scholar
Chevalier, N., Huber, K. L., Wiebe, S. A., & Espy, K. A. (2013). Qualitative change in executive control during childhood and adulthood. Cognition, 128, 1–12.Google Scholar
Chevalier, N., Jackson, J., Revueltas Roux, A., Moriguchi, Y., & Auyeung, B. (2019). Differentiation in prefrontal cortex recruitment during childhood: Evidence from cognitive control demands and social contexts. Developmental Cognitive Neuroscience, 36, 100629.Google Scholar
Chevalier, N., James, T. D., Wiebe, S. A., Nelson, J. M., & Espy, K. A. (2014). Contribution of reactive and proactive control to children’s working memory performance: Insight from item recall durations in response sequence planning. Developmental Psychology, 50, 1999–2008.Google Scholar
Chevalier, N., Martis, S. B., Curran, T., & Munakata, Y. (2015). Metacognitive processes in executive control development: The case of reactive and proactive control. Journal of Cognitive Neuroscience, 27, 1125–1136.Google Scholar
Chevalier, N., Wiebe, S. A., Huber, K. L., & Espy, K. A. (2011). Switch detection in preschoolers’ cognitive flexibility. Journal of Experimental Child Psychology, 109, 353–370.Google Scholar
Christ, S. E., Kanne, S. M., & Reiersen, A. M. (2010). Executive function in individuals with subthreshold autism traits. Neuropsychology, 24, 590–598.Google Scholar
Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences (USA), 113, 8664–8668.Google Scholar
Cragg, L. (2016). The development of stimulus and response interference control in mid-childhood. Developmental Psychology, 52, 242–252.Google Scholar
Cragg, L., & Nation, K. (2010). Language and the development of cognitive control. Topics in Cognitive Science, 2, 631–642.Google Scholar
Crone, E. A. (2009). Executive functions in adolescence: Inferences from brain and behavior. Developmental Science, 12, 825–830.Google Scholar
Crone, E. A., Donohue, S. E., Honomichl, R., Wendelken, C., & Bunge, S. A. (2006). Brain regions mediating flexible rule use during development. The Journal of Neuroscience, 26, 11239–11247.Google Scholar
Crump, M. J. C., Vaquero, J. M. M., & Milliken, B. (2008). Context-specific learning and control: The roles of awareness, task relevance, and relative salience. Consciousness and Cognition, 17, 22–36.Google Scholar
Daly, M., Delaney, L., Egan, M., & Baumeister, R. F. (2015). Childhood self-control and unemployment throughout the life span: Evidence from two British cohort studies. Psychological Science, 26, 709–723.Google Scholar
Dauvier, B., Chevalier, N., & Blaye, A. (2012). Using finite mixture of GLMs to explore variability in children’s flexibility in a task-switching paradigm. Cognitive Development, 27, 440–454.Google Scholar
Davis, E. P., Bruce, J., Snyder, K., & Nelson, C. A. (2003). The X-trials: Neural correlates of an inhibitory control task in children and adults. Journal of Cognitive Neuroscience, 15, 432–443.Google Scholar
Destan, N., Hembacher, E., Ghetti, S., & Roebers, C. M. (2014). Early metacognitive abilities: The interplay of monitoring and control processes in 5- to 7-year-old children. Journal of Experimental Child Psychology, 126, 213–228.Google Scholar
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168.CrossRefGoogle ScholarPubMed
Doebel, S., Barker, J. E., Chevalier, N., Michaelson, L. E., Fisher, V., & Munakata, Y. (2017). Getting ready to use control: Advances in the measurement of young children’s use of proactive control. PLoS ONE, 12, e0175072.Google Scholar
Doebel, S., & Zelazo, P. D. (2015). A meta-analysis of the dimensional change card sort: Implications for developmental theories and the measurement of executive function in children. Developmental Review, 38, 241–268.Google Scholar
DuPuis, D., Ram, N., Willner, C. J., Karalunas, S., Segalowitz, S. J., & Gatzke-Kopp, L. M. (2015). Implications of ongoing neural development for the measurement of the error-related negativity in childhood. Developmental Science, 18, 452–468.Google Scholar
Durston, S., Davidson, M. C., Tottenham, N., Galvan, A., Spicer, J., Fossella, J. A., & Casey, B. J. (2006). A shift from diffuse to focal cortical activity with development. Developmental Science, 9, 1–8.Google Scholar
Durston, S., Thomas, K. M., Yang, Y., Ulug, A. M., Zimmerman, R. D., & Casey, B. J. (2002). A neural basis for the development of inhibitory control. Developmental Science, 5, F9–F16.Google Scholar
Duthoo, W., Abrahamse, E. L., Braem, S., Boehler, C. N., & Notebaert, W. (2014). The heterogeneous world of congruency sequence effects: An update. Frontiers in Psychology, 5, 1–9.Google Scholar
Egner, T. (2007). Congruency sequence effects and cognitive control. Cognitive, Affective, & Behavioral Neuroscience, 7, 380–390.Google Scholar
Elke, S., & Wiebe, S. A. (2017). Proactive control in early and middle childhood: An ERP study. Developmental Cognitive Neuroscience, 26, 28–38.Google Scholar
Erb, C. D., & Marcovitch, S. (2019). Tracking the within-trial, cross-trial, and developmental dynamics of cognitive control: Evidence from the Simon task. Child Development, 90, e831–e848.Google Scholar
Erb, C. D., Moher, J., Sobel, D. M., & Song, J. H. (2016). Reach tracking reveals dissociable processes underlying cognitive control. Cognition, 152, 114–126.CrossRefGoogle ScholarPubMed
Ezekiel, F., Bosma, R., & Morton, J. B. (2013). Dimensional change card sort performance associated with age-related differences in functional connectivity of lateral prefrontal cortex. Developmental Cognitive Neuroscience, 5, 40–50.Google Scholar
Fair, D. A., Dosenbach, N. U. F., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., … Schlaggar, B. L. (2007). Development of distinct control networks through segregation and integration. Proceedings of the National Academy of Sciences (USA), 104, 13507–13512.Google Scholar
Fair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., … Milham, M. P. (2013). Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Frontiers in Systems Neuroscience, 6, 1–31.Google Scholar
Falkenstein, M., Hohnsbein, J., Hoormann, J., & Blanke, L. (1991). Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. Electroencephalography and Clinical Neurophysiology, 78, 447–455.Google Scholar
Fatzer, S. T., & Roebers, C. M. (2012). Language and executive functions: The effect of articulatory suppression on executive functioning in children. Journal of Cognition and Development, 13, 454–472.Google Scholar
Ferdinand, N. K., & Kray, J. (2014). Developmental changes in performance monitoring: How electrophysiological data can enhance our understanding of error and feedback processing in childhood and adolescence. Behavioural Brain Research, 263, 122–132.Google Scholar
Finn, A. S., Minas, J. E., Leonard, J. A., Mackey, A. P., Salvatore, J., Goetz, C., … Gabrieli, J. D. E. (2017). Functional brain organization of working memory in adolescents varies in relation to family income and academic achievement. Developmental Science, 20, e12450.Google Scholar
Fjell, A. M., Walhovd, K. B., Brown, T. T., Kuperman, J. M., Chung, Y., Hagler, D. J., … Dale, A. M. (2012). Multimodal imaging of the self-regulating developing brain. Proceedings of the National Academy of Sciences (USA), 109, 19620–19625.Google Scholar
Gehring, W. J., Goss, B., Coles, M. G. H., David, E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4, 385–390.Google Scholar
Gehring, W. J., & Knight, R. T. (2000). Prefrontal–cingulate interactions in action monitoring. Nature Neuroscience, 3, 516–520.Google Scholar
Geurts, H. M., Verté, S., Oosterlaan, J., Roeyers, H., & Sergeant, J. A. (2004). How specific are executive functioning deficits in attention deficit hyperactivity disorder and autism? Journal of Child Psychology and Psychiatry, and Allied Disciplines, 45, 836–854.Google Scholar
Gold, J. M., Kool, W., Botvinick, M. M., Hubzin, L., August, S., & Waltz, J. A. (2015). Cognitive effort avoidance and detection in people with schizophrenia. Cognitive, Affective & Behavioral Neuroscience, 15, 145–154.Google Scholar
Gonthier, C., Ambrosi, S., & Blaye, A. (2021). Learning-based before intentional cognitive control: Developmental evidence for a dissociation between implicit and explicit control. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publicationGoogle Scholar
Gonthier, C., Zira, M., Colé, P., & Blaye, A. (2019). Evidencing the developmental shift from reactive to proactive control in early childhood and its relationship to working memory. Journal of Experimental Child Psychology, 177, 1–16.Google Scholar
Gratton, G., Coles, M. G., & Donchin, E. (1992). Optimizing the use of information: Strategic control of activation of responses. Journal of Experimental Psychology: General, 121, 480–506.CrossRefGoogle ScholarPubMed
Grayson, D. S., & Fair, D. A. (2017). Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. NeuroImage, 160, 15–31.Google Scholar
Gupta, R., Kar, B. R., & Srinivasan, N. (2009). Development of task switching and post-error-slowing in children. Behavioral and Brain Functions: BBF, 5, 38.Google Scholar
Hadley, L. V., Acluche, F., & Chevalier, N. (2020). Encouraging performance monitoring promotes proactive control in children. Developmental Science, 23, e12861.Google Scholar
Haimovitz, K., & Dweck, C. S. (2017). The origins of children’s growth and fixed mindsets: New research and a new proposal. Child Development, 88, 1849–1859.CrossRefGoogle Scholar
Helfrich, R. F., & Knight, R. T. (2016). Oscillatory dynamics of prefrontal cognitive control. Trends in Cognitive Sciences, 20, 916–930.Google Scholar
Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679–709.Google Scholar
Hommel, B., Proctor, R. W., & Vu, K.-P. L. (2004). A feature-integration account of sequential effects in the Simon task. Psychological Research, 68, 1–17.Google Scholar
Hwang, K., Velanova, K., & Luna, B. (2010). Strengthening of top-down frontal cognitive control networks underlying the development of inhibitory control: A functional magnetic resonance imaging effective connectivity study. Journal of Neuroscience, 30, 15535–15545.Google Scholar
Iani, C., Stella, G., & Rubichi, S. (2014). Response inhibition and adaptations to response conflict in 6- to 8-year-old children: Evidence from the Simon effect. Attention, Perception & Psychophysics, 76, 1234–1241.Google Scholar
Jiang, J., Correa, C. M., Geerts, J., & van Gaal, S. (2018). The relationship between conflict awareness and behavioral and oscillatory signatures of immediate and delayed cognitive control. NeuroImage, 177, 11–19.Google Scholar
Johnson, M. H. (2011). Interactive specialization: A domain-general framework for human functional brain development? Developmental Cognitive Neuroscience, 1, 7–21.Google Scholar
Jones, L. B., Rothbart, M. K., & Posner, M. I. (2003). Development of executive attention in preschool children. Developmental Science, 6, 498–504.CrossRefGoogle Scholar
Karr, J. E., Areshenkoff, C. N., Rast, P., Hofer, S. M., Iverson, G. L., & Garcia-Barrera, M. A. (2018). The unity and diversity of executive functions: A systematic review and re-analysis of latent variable studies. Psychological Bulletin, 144(11), 1147–1185.Google Scholar
Kelly, A. M. C., Di Martino, A., Uddin, L. Q., Shehzad, Z., Gee, D. G., Reiss, P. T., … Milham, M. P. (2009). Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cerebral Cortex, 19, 640–657.Google Scholar
Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology. General, 139, 665–682.Google Scholar
Kray, J., Karbach, J., & Blaye, A. (2012). The influence of stimulus-set size on developmental changes in cognitive control and conflict adaptation. Acta Psychologica, 140, 119–128.Google Scholar
Larson, M. J., Clawson, A., Clayson, P. E., & South, M. (2012). Cognitive control and conflict adaptation similarities in children and adults. Developmental Neuropsychology, 37, 343–357.CrossRefGoogle ScholarPubMed
Lee, K., Bull, R., & Ho, R. M. H. (2013). Developmental changes in executive functioning. Child Development, 84, 1933–1953.Google Scholar
Linzarini, A., Houdé, O., & Borst, G. (2017). Cognitive control outside of conscious awareness. Consciousness and Cognition, 53, 185–193.Google Scholar
Lo, S. L. (2018). A meta-analytic review of the event-related potentials (ERN and N2) in childhood and adolescence: Providing a developmental perspective on the conflict monitoring theory. Developmental Review, 48, 82–112.Google Scholar
Lucenet, J., & Blaye, A. (2014). Age-related changes in the temporal dynamics of executive control: A study in 5- and 6-year-old children. Frontiers in Psychology, 5, 1–11.CrossRefGoogle Scholar
Luna, B., Marek, S., Larsen, B., Tervo-Clemmens, B., & Chahal, R. (2015). An integrative model of the maturation of cognitive control. Annual Review of Neuroscience, 38, 151–170.Google Scholar
Luna, B., Padmanabhan, A., & O’Hearn, K. (2010). What has fMRI told us about the development of cognitive control through adolescence? Brain and Cognition, 72, 101–113.CrossRefGoogle ScholarPubMed
Mai, X., Tardif, T., Doan, S. N., Liu, C., Gehring, W. J., & Luo, Y.-J. (2011). Brain activity elicited by positive and negative feedback in preschool-aged children. PLoS ONE, 6, e18774.Google Scholar
Marcovitch, S., & Zelazo, P. D. (1999). The A-not-B error: Results from a logistic meta-analysis. Child Development, 70, 1297–1313.Google Scholar
Marek, S., Hwang, K., Foran, W., Hallquist, M. N., & Luna, B. (2015). The contribution of network organization and integration to the development of cognitive control. PLoS Biology, 13, 1–25.Google Scholar
Marklund, P., & Persson, J. (2012). Context-dependent switching between proactive and reactive working memory control mechanisms in the right inferior frontal gyrus. NeuroImage, 63, 1552–1560.Google Scholar
Marsh, R., Zhu, H., Schultz, R. T., Quackenbush, G., Royal, J., Skudlarski, P., & Peterson, B. S. (2006). A developmental fMRI study of self-regulatory control. Human Brain Mapping, 27, 848–863.Google Scholar
Mayr, U., Awh, E., & Laurey, P. (2003). Conflict adaptation effects in the absence of executive control. Nature Neuroscience, 6, 450–452.Google Scholar
McGuire, J. T., & Botvinick, M. M. (2010). Prefrontal cortex, cognitive control, and the registration of decision costs. Proceedings of the National Academy of Sciences (USA), 107, 7922–7926.Google Scholar
Meyer, A., Hajcak, G., Torpey, D. C., Kujawa, A., Kim, J., Bufferd, S., … Klein, D. N. (2013). Increased error-related brain activity in six-year-old children with clinical anxiety. Journal of Abnormal Child Psychology, 41, 1257–1266.CrossRefGoogle ScholarPubMed
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current Directions in Psychological Science, 21, 8–14.Google Scholar
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex ‘Frontal Lobe’ tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100.Google Scholar
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., … Caspi, A. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences (USA), 108, 2693–2698.Google Scholar
Morey, C. C., Mareva, S., Lelonkiewicz, J. R., & Chevalier, N. (2018). Gaze-based rehearsal in children under 7: A developmental investigation of eye movements during a serial spatial memory task. Developmental Science, 21, e12559.Google Scholar
Moriguchi, Y., & Hiraki, K. (2011). Longitudinal development of prefrontal function during early childhood. Developmental Cognitive Neuroscience, 1, 153–162.Google Scholar
Muhle-Karbe, P. S., Jiang, J., & Egner, T. (2018). Causal evidence for learning-dependent frontal-lobe contributions to cognitive control. The Journal of Neuroscience, 38, 962–973.Google Scholar
Munakata, Y., Snyder, H. R., & Chatham, C. H. (2012). Developing cognitive control: Three key transitions. Current Directions in Psychological Science, 21, 71–77.Google Scholar
Niebaum, J. C., Chevalier, N., Guild, R. M., & Munakata, Y. (2019). Adaptive control and the avoidance of cognitive control demands across development. Neurospychologia, 123, 152–158.Google Scholar
Nieuwenhuis, S., Stins, J., Posthuma, D., Polderman, T. C., Boomsma, D., & Geus, E. (2006). Accounting for sequential trial effects in the flanker task: Conflict adaptation or associative priming? Memory & Cognition, 34, 1260–1272.Google Scholar
Noble, K. G., McCandliss, B. D., & Farah, M. J. (2007). Socioeconomic gradients predict individual differences in neurocognitive abilities. Developmental Science, 10, 464–480.Google Scholar
O’Leary, A. P., & Sloutsky, V. M. (2017). Carving metacognition at its joints: Protracted development of component processes. Child Development, 88, 1015–1032.Google Scholar
Ordaz, S. J., Foran, W., Velanova, K., & Luna, B. (2013). Longitudinal growth curves of brain function underlying inhibitory control through adolescence. Journal of Neuroscience, 33, 18109–18124.Google Scholar
Peters, S., Koolschijn, P. C. M. P., Crone, E. A., Van Duijvenvoorde, A. C. K., & Raijmakers, M. E. J. (2014). Strategies influence neural activity for feedback learning across child and adolescent development. Neuropsychologia, 62, 365–374.Google Scholar
Polizzotto, N. R., Hill-Jarrett, T., Walker, C., & Cho, Y. (2018). Normal development of context processing using the AXCPT paradigm. PLoS ONE, 13, e0197812.CrossRefGoogle ScholarPubMed
Pozuelos, J. P., Combita, L. M., Abundis, A., Paz-Alonsa, P. M., Conejero, Á., Guerra, S., & Rueda, M. R. (2019). Metacognitive scaffolding boosts cognitive and neural benefits following executive attention training in children. Developmental Science, 22, e12756.Google Scholar
Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., … Giedd, J. N. (2011). How does your cortex grow? The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 31, 7174–7177.Google Scholar
Ridderinkhof, K. R., van den Wildenberg, W. P. M., Segalowitz, S. J., & Carter, C. S. (2004). Neurocognitive mechanisms of cognitive control: The role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain and Cognition, 56, 129–140.Google Scholar
Roebers, C. M. (2017). Executive function and metacognition: Towards a unifying framework of cognitive self-regulation. Developmental Review, 45, 31–51.Google Scholar
Schachar, R. J., Chen, S., Logan, G. D., Ornstein, T. J., Crosbie, J., Ickowicz, A., & Pakulak, A. (2004). Evidence for an error monitoring deficit in attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 32, 285–293.Google Scholar
Schmidt, J. R. (2013). Questioning conflict adaptation: Proportion congruent and Gratton effects reconsidered. Psychonomic Bulletin & Review, 20, 615–630.Google Scholar
Schmidt, J. R. (2019). Evidence against conflict monitoring and adaptation: An updated review. Psychonomic Bulletin & Review, 26, 753–771.Google Scholar
Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., … Wise, S. P. (2008). Neurodevelopmental trajectories of the human cerebral cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 28, 3586–3594.Google Scholar
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron, 79, 217–240.Google Scholar
Sherman, L. E., Rudie, J. D., Pfeifer, J. H., Masten, C. L., McNealy, K., & Dapretto, M. (2014). Development of the default mode and central executive networks across early adolescence: A longitudinal study. Developmental Cognitive Neuroscience, 10, 148–159.Google Scholar
Smulders, S. F. A., Soetens, E., & van der Molen, M. W. (2016). What happens when children encounter an error? Brain and Cognition, 104, 34–47.Google Scholar
Smulders, S. F. A., Soetens, E. L. L., & van der Molen, M. W. (2018). How do children deal with conflict? A developmental study of sequential conflict modulation. Frontiers in Psychology, 9, 766.Google Scholar
Stins, J. F., Polderman, J. C. T., Boomsma, D. I., & de Geus, E. J. C. (2007). Conditional accuracy in response interference tasks: Evidence from the Eriksen flanker task and the spatial conflict task. Advances in Cognitive Psychology, 3, 409–417.Google Scholar
Strang, N. M., & Pollak, S. D. (2014). Developmental continuity in reward-related enhancement of cognitive control. Developmental Cognitive Neuroscience, 10C, 34–43.Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643.CrossRefGoogle Scholar
Tamm, L., Menon, V., & Reiss, A. L. (2002). Maturation of brain function associated with response inhibition. Journal of American Academy of Child and Adolescent Psychiatry, 41, 1231–1238.Google Scholar
Tamnes, C. K., Walhovd, K. B., Torstveit, M., Sells, V. T., & Fjell, A. M. (2013). Performance monitoring in children and adolescents: A review of developmental changes in the error-related negativity and brain maturation. Developmental Cognitive Neuroscience, 6, 1–13.Google Scholar
Torpey, D. C., Hajcak, G., Kim, J., Kujawa, A., & Klein, D. N. (2012). Electrocortical and behavioral measures of response monitoring in young children during a Go/No-Go task. Developmental Psychobiology, 54, 139–150.Google Scholar
Tsujii, T., Yamamoto, E., Masuda, S., & Watanabe, S. (2009). Longitudinal study of spatial working memory development in young children. NeuroReport, 20, 759–763.Google Scholar
Uddin, L. Q., Supekar, K. S., Ryali, S., & Menon, V. (2011). Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 31, 18578–18589.Google Scholar
van de Laar, M. C., van den Wildenberg, W. P. M., van Boxtel, G. J. M., & van der Molen, M. W. (2011). Lifespan changes in global and selective stopping and performance adjustments. Frontiers in Psychology, 2, 357.Google Scholar
van Gaal, S., Lamme, V. A. F., & Ridderinkhof, K. R. (2010). Unconsciously triggered conflict adaptation. PLoS ONE, 5, 6.Google Scholar
Velanova, K., Wheeler, M. E., & Luna, B. (2008). Maturational changes in anterior cingulate and frontoparietal recruitment support the development of error processing and inhibitory control. Cerebral Cortex, 18, 2505–2522.CrossRefGoogle ScholarPubMed
Verguts, T. (2017). Binding by random bursts: A computational model of cognitive control. Journal of Cognitive Neuroscience, 29, 1103–1118.Google Scholar
Voigt, B., Mahy, C. E. V, Ellis, J., Schnitzspahn, K., Krause, I., Altgassen, M., & Kliegel, M. (2014). The development of time-based prospective memory in childhood: The role of working memory updating. Developmental Psychology, 50, 2393.Google Scholar
Wiebe, S. A., Espy, K. A., & Charak, D. (2008). Using confirmatory factor analysis to understand executive control in preschool children: I. Latent structure. Developmental Psychology, 44(2), 575–587.Google Scholar
Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A. C., Chevalier, N., & Espy, K. A. (2011). The structure of executive function in 3-year-olds. Journal of Experimental Child Psychology, 108, 436–452.Google Scholar
Wiersema, J. R., van der Meere, J. J., & Roeyers, H. (2007). Developmental changes in error monitoring: An event-related potential study. Neuropsychologia, 45, 1649–1657.Google Scholar
Wilk, H. A., & Morton, J. B. (2012). Developmental changes in patterns of brain activity associated with moment-to-moment adjustments in control. NeuroImage, 63, 475–484.Google Scholar
Willoughby, M. T., Blair, C. B., Wirth, R. J., & Greenberg, M. (2012). The measurement of executive function at age 5: Psychometric properties and relationship to academic achievement. Psychological Assessment, 24, 226–239.Google Scholar
Yordanova, J., Kolev, V., Albrecht, B., Uebel, H., & Banaschewski, T. (2011). May posterior performance be a critical factor for behavioral deficits in attention-deficit/hyperactivity disorder? Biological Psychiatry, 70, 246–254.Google Scholar
Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function in childhood and adolescence: Development and plasticity. Child Development Perspectives, 6, 354–360.CrossRefGoogle Scholar

References

Bago, B., & De Neys, W. (2017). Fast logic?: Examining the time course assumption of dual process theory. Cognition, 158, 90–109.Google Scholar
Bago, B., & De Neys, W. (2020a). Advancing the specification of dual process models of higher cognition: A critical test of the hybrid model view. Thinking & Reasoning, 26, 1–30.Google Scholar
Bago, B., & De Neys, W. (2020b). The smart system 1: Evidence for the intuitive nature of correct responding on the bat-and-ball problem. Thinking & Reasoning, 26, 1–30.Google Scholar
Ball, L., Thompson, V., & Stupple, E. (2017). Conflict and dual process theory: The case of belief bias. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 100–120). Oxford: Routledge.Google Scholar
Banks, A. (2017). Comparing dual process theories: Evidence from event-related potentials. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 66–81). Oxford: Routledge.Google Scholar
Barrouillet, P. (2011). Dual process theories of reasoning: The test of development. Developmental Review, 31, 151–179.Google Scholar
Brainerd, C. J., & Reyna, V. F. (2001). Fuzzy-trace theory: Dual processes in memory, reasoning, and cognitive neuroscience. In Reese, H. W., & Kail, R. (eds.), Advances in Child Development and Behavior (Vol. 28, pp. 41–100). San Diego, CA: Academic Press.Google Scholar
Brainerd, C. J., Reyna, V. F., & Ceci, S. J. (2008). Developmental reversals in false memory: A review of data and theory. Psychological Bulletin, 134, 343– 382.Google Scholar
Davidson, D. (1995). The representativeness heuristic and the conjunction fallacy effect in children’s decision making. Merrill-Palmer Quarterly, 41, 328–346.Google Scholar
De Neys, W. (2006). Dual processing in reasoning: Two systems but one reasoner. Psychological Science, 17, 428–433.Google Scholar
De Neys, W. (2012). Bias and conflict a case for logical intuitions. Perspectives on Psychological Science, 7, 28–38.Google Scholar
De Neys, W. (2013). Heuristics, biases, and the development of conflict detection during reasoning. In Markovits, H. (ed.), The Developmental Psychology of Reasoning and Decision Making (pp. 130–147). Hove: Psychology Press.Google Scholar
De Neys, W. (2017). Bias, conflict, and fast logic: Towards a hybrid dual process future? In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 47–65). Oxford: Routledge.Google Scholar
De Neys, W., & Feremans, V. (2013). Development of heuristic bias detection in elementary school. Developmental Psychology, 49, 258–269.Google Scholar
De Neys, W., & Glumicic, T. (2008). Conflict monitoring in dual process theories of thinking. Cognition, 106, 1248–1299.Google Scholar
De Neys, W., Rossi, S., & Houdé, O. (2013). Bats, balls, and substitution sensitivity: Cognitive misers are no happy fools. Psychonomic Bulletin & Review, 20, 269–273.Google Scholar
De Neys, W., & Vanderputte, K. (2011). When less is not always more: Stereotype knowledge and reasoning development. Developmental Psychology, 47, 432–441.Google Scholar
De Neys, W., Vartanian, O., & Goel, V. (2008). Smarter than we think when our brains detect that we are biased. Psychological Science, 19, 483–489.Google Scholar
Evans, J. St. B. T. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7, 454–459.Google Scholar
Evans, J. St. B. T. (2008). Dual-processing accounts of reasoning, judgement and social cognition. Annual Review of Psychology, 59, 255–278.Google Scholar
Evans, J. St. B. T. (2010). Intuition and reasoning: A dual process perspective. Psychological Inquiry, 21, 313–326.Google Scholar
Evans, J. St. B. T. (2017). Dual process theories: Perspectives and problems. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 137–155). Oxford: Routledge.Google Scholar
Evans, J. St. B. T., & Over, D. E. (1996). Rationality and Reasoning. Hove: Psychology Press.Google Scholar
Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition advancing the debate. Perspectives on Psychological Science, 8, 223–241.Google Scholar
Franssens, S., & De Neys, W. (2009). The effortless nature of conflict detection during thinking. Thinking & Reasoning, 15, 105–128.Google Scholar
Gangemi, A., Bourgeois-Gironde, S., & Mancini, F. (2015). Feelings of error in reasoning – in search of a phenomenon. Thinking & Reasoning, 21, 383–396.Google Scholar
Houdé, O. (1997). Rationality in reasoning: The problem of deductive competence and the inhibitory control of cognition. Current Psychology of Cognition, 16, 108–113.Google Scholar
Houdé, O. (2000). Inhibition and cognitive development: Object, number, categorization, and reasoning. Cognitive Development, 15, 63–73.Google Scholar
Houdé, O. (2007). First insights on neuropedagogy of reasoning. Thinking & Reasoning, 13, 81–89.Google Scholar
Houdé, O., & Borst, G. (2014). Measuring inhibitory control in children and adults: Brain imaging and mental chronometry. Frontiers in Psychology, 5, 616.Google Scholar
Jacobs, J. E., & Klaczynski, P. A. (2002). The development of decision making during childhood and adolescence. Current Directions in Psychological Science, 4, 145–149.Google Scholar
Jacobs, J. E., & Potenza, M. (1991). The use of judgment heuristics to make social and object decisions: A developmental perspective. Child Development, 62, 166–178.Google Scholar
Johnson, E. D., Tubau, E., & De Neys, W. (2016). The doubting system 1: Evidence for automatic substitution sensitivity. Acta Psychologica, 164, 56–64.Google Scholar
Kahneman, D. (2002, December). Maps of bounded rationality: A perspective on intuitive judgement and choice. Nobel Prize Lecture. Available from http://nobelprize.org/nobel_prizes/economics/laureates/2002/kahnemann-lecture.pdf, Last accessed January 11, 2006.Google Scholar
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.Google Scholar
Kahneman, D. & Frederick, S. (2005). A model of heuristic judgement. In Holyoak, K. J., & Morrison, R. G. (eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 267–293). Cambridge, MA: Cambridge University Press.Google Scholar
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.Google Scholar
Klaczynski, P. A., Byrnes, J. B., & Jacobs, J. E. (2001). Introduction: Special issue on decision making. Journal of Applied Developmental Psychology, 22, 225–236.Google Scholar
Klaczynski, P. A., & Narashimham, G. (1998). Representations as mediators of adolescent deductive reasoning. Developmental Psychology, 5, 865–881.Google Scholar
Kokis, J. V., Macpherson, R., Toplak, M. E., West, R. F., & Stanovich, K. E. (2002). Heuristic and analytic processing: Age trends and associations with cognitive ability and cognitive styles. Journal of Experimental Child Psychology, 83, 26–52.Google Scholar
Markovits, H., & Barrouillet, P. (2004). Why is understanding the development of reasoning important? Thinking and Reasoning, 10, 113–121.Google Scholar
Mevel, K., Poirel, N., Rossi, S., Cassotti, M., Simon, G., Houdé, O., & Neys, W. D. (2015). Bias detection: Response confidence evidence for conflict sensitivity in the ratio bias task. Journal of Cognitive Psychology, 27, 227–237.Google Scholar
Newman, I., Gibb, M., & Thompson, V. A. (2017). Rule-based reasoning is fast and belief-based reasoning can be slow: Challenging current explanations of belief -bias and base-rate neglect. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43, 1154–1170.Google Scholar
Pennycook, G. (2017). A perspective on the theoretical foundation of dual process models. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 5–27). Oxford: Routledge.Google Scholar
Pennycook, G., Cheyne, J. A., Barr, N., Koehler, D. J., & Fugelsang, J. A. (2014a). Cognitive style and religiosity: The role of conflict detection. Memory & Cognition, 42, 1–10.Google Scholar
Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2012). Are we good at detecting conflict during reasoning. Cognition, 124, 101–106.Google Scholar
Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2015). What makes us think? A three-stage dual-process model of analytic engagement. Cognitive Psychology, 80, 34–72.Google Scholar
Pennycook, G., Trippas, D., Handley, S. J., & Thompson, V. A. (2014b). Base rates: Both neglected and intuitive. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 544–554.Google Scholar
Piaget, J. (1952/1941). The Child’s Conception of Number. New York: Routledge & Kegan Paul.Google Scholar
Raoelison, M., Boissin, E., Borst, G., & De Neys, W. (2021). From slow to fast logic: The development of logical intuitions. Thinking & Reasoning, 1–25, online doi.org/10.1080/13546783.2021.1885488.Google Scholar
Raoelison, M., Thompson, V., & De Neys, W. (2020). The smart intuitor: Cognitive capacity predicts intuitive rather than deliberate thinking. Cognition, 204, 104381.Google Scholar
Reyna, V. F. (2004). How people make decisions that involve risk: A dual-processes approach. Current Directions in Psychological Science, 13, 60–66.Google Scholar
Reyna, V. F., & Brainerd, C. J. (1994). The origins of probability judgment: A review of data and theories. In Wright, G., & Ayton, P. (eds.), Subjective Probability (pp. 239–272). New York: Wiley.Google Scholar
Reyna, V. F., & Farley, F. (2006). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy. Psychological Science in the Public Interest, 7, 1–44.Google Scholar
Reyna, V. F., Lloyd, F. J., & Brainerd, C. J. (2003). Memory, development, and rationality: An integrative theory of judgement and decision-making. In Schneider, S., & Shanteau, J. (eds.), Emerging Perspectives on Judgment and Decision Research (pp. 201–245). New York: Cambridge University Press.Google Scholar
Simon, G., Lubin, A., Houdé, O., & De Neys, W. (2015). Anterior cingulate cortex and intuitive bias detection during number conservation. Cognitive Neuroscience, 6, 158–168.Google Scholar
Stanovich, K. E. (2011). Rationality and the Reflective Mind. Oxford: Oxford University Press.Google Scholar
Stanovich, K. E. (2018). Miserliness in human cognition: The interaction of detection, override and mindware. Thinking & Reasoning, 24, 423–444.Google Scholar
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral and Brain Sciences, 23, 645–665.Google Scholar
Stanovich, K. E., West, R. F., & Toplak, M. E. (2011). The complexity of developmental predictions from dual process models. Developmental Review, 31, 103–118.Google Scholar
Stupple, E. J., Ball, L. J., Evans, J. S. B., & Kamal-Smith, E. (2011). When logic and belief collide: Individual differences in reasoning times support a selective processing model. Journal of Cognitive Psychology, 23, 931–941.Google Scholar
Thompson, V. A., & Johnson, S. C. (2014). Conflict, metacognition, and analytic thinking. Thinking & Reasoning, 20, 215–244.Google Scholar
Thompson, V. A., & Newman, I. (2017). Logical intuitions and other conundra for dual process theories. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 121–136). Oxford: Routledge.Google Scholar
Thompson, V. A., Pennycook, G., Trippas, D., & Evans, J. S. B. (2018). Do smart people have better intuitions? Journal of Experimental Psychology: General, 147, 945.Google Scholar
Thompson, V. A., Turner, J. A. P., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63, 107–140.Google Scholar
Trippas, D., & Handley, S. (2017). The parallel processing model of belief bias: Review and extensions. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 28–46). Oxford: Routledge.Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.Google Scholar
Vartanian, O., Beatty, E. L., Smith, I., Blackler, K., Lam, Q., Forbes, S., & De Neys, W. (2018). The reflective mind: Examining individual differences in susceptibility to base rate neglect with FMRI. Journal of Cognitive Neuroscience, 30, 1011–1022.Google Scholar

References

Aboud, F. E. (1988). Children and Prejudice. New York: Blackwell.Google Scholar
Abrams, D., & Rutland, A. (2008). The development of subjective group dynamics. In Levy, S. R., & Killen, M. (eds.), Intergroup Attitudes and Relations in Childhood through Adulthood: Studies in Crime and Public Policy (pp. 47–65). Oxford: Oxford University Press.Google Scholar
Abrams, D., Rutland, A., & Cameron, L. (2003). The development of subjective group dynamics: Children’s judgments of normative and deviant in-group and out-group individuals. Child Development, 74, 1840–1856.Google Scholar
Abrams, D., Rutland, A., Pelletier, J., & Ferrell, J. M. (2009). Children’s group nous: Understanding and applying peer exclusion within and between groups. Child Development, 80, 224–243.Google Scholar
Allport, G. (1954). The Nature of Prejudice. Cambridge: Addison Wesley.Google Scholar
Astuti, R., Solomon, G. E., & Carey, S. (2004). Constraints on conceptual development: A case study of the acquisition of folkbiological and folksociological knowledge in Madagascar. Monographs of the Society for Research in Child Development, 69, 1–135.Google Scholar
Atran, S. (1990). Cognitive Foundations of Natural History. New York: Cambridge University Press.Google Scholar
Bar-Haim, Y., Ziv, T., Lamy, D., & Hodes, R. M. (2006). Nature and nurture in own-race face processing. Psychological Science, 17, 159–163.Google Scholar
Baron, A. S., & Banaji, M. R. (2006). The development of implicit attitudes evidence of race evaluations from ages 6 and 10 and adulthood. Psychological Science, 17, 53–58.Google Scholar
Baron, A. S., & Dunham, Y. (2015). Representing “us” and “them”: Building blocks of intergroup cognition. Journal of Cognition and Development, 16, 780–801.Google Scholar
Barrett, M. (2007). Children’s Knowledge, Beliefs and Feelings about Nations and National Groups. Hove: Psychology Press.Google Scholar
Batson, C. D., Polycarpou, M. P., Harmon-Jones, E., Imhoff, H. J., Mitchener, E. C., Bednar, L. L., Klein, T. R., & Highberger, L. (1997). Empathy and attitudes: Can feeling for a member of a stigmatized group improve feelings toward the group? Journal of Personality and Social Psychology, 72, 105–118.Google Scholar
Baumeister, R. F., & Leary, M. F. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117, 497–529.Google Scholar
Bennett, M., Lyons, E., Sani, F., & Barrett, M. (1998). Children’s subjective identification with the group and in-group favoritism. Developmental Psychology, 34, 902–909.Google Scholar
Bennett, M., & Sani, F. (2008a). Children’s subjective identification with social groups: A self-stereotyping approach. Developmental Science, 11, 69–75.Google Scholar
Bennett, M., & Sani, F. (2008b). The effect of comparative context upon stereotype content: Children’s judgments of ingroup behavior. Scandinavian Journal of Psychology, 49, 141–146.Google Scholar
Benozio, A., & Diesendruck, G. (2015). From effort to value: Preschool children’s alternative to effort justification. Psychological Science, 26, 1423–1429.Google Scholar
Berndt, T. J., & Heller, K. A. (1986). Gender stereotypes and social inferences: A developmental study. Journal of Personality and Social Psychology, 50, 889–898.Google Scholar
Bian, L., Leslie, S. J., & Cimpian, A. (2017). Gender stereotypes about intellectual ability emerge early and influence children’s interests. Science, 355, 389–391.Google Scholar
Bian, L., Sloane, S., & Baillargeon, R. (2018). Infants expect ingroup support to override fairness when resources are limited. Proceedings of the National Academy of Sciences (USA), 115, 2705–2710.Google Scholar
Biernat, M. (1991). Gender stereotypes and the relationship between masculinity and femininity: A developmental analysis. Journal of Personality and Social Psychology, 61, 351–365.Google Scholar
Bigler, R. S., & Liben, L. S. (1993). A cognitive-developmental approach to racial stereotyping and reconstructive memory in Euro-American children. Child Development, 64, 1507–1518.Google Scholar
Bigler, R. S., & Liben, L. S. (2006). A developmental intergroup theory of social stereotypes and prejudice. Advances in Child Development and Behavior, 34, 39–89.Google Scholar
Binder, J., Zagefka, H., Brown, R., Funke, F., Kessler, T., Mummendey, A., Maquil, A., Demoulin, S., & Leyens, J. P. (2009). Does contact reduce prejudice or does prejudice reduce contact? A longitudinal test of the contact hypothesis among majority and minority groups in three European countries. Journal of Personality and Social Psychology, 96, 843–856.Google Scholar
Birnbaum, D., Deeb, I., Segall, G., Ben-Eliyahu, A., & Diesendruck, G. (2010). The development of social essentialism: The case of Israeli children’s inferences about Jews and Arabs. Child Development, 81, 757–777.Google Scholar
Boyd, R., & Richerson, P. J. (2009). Culture and the evolution of human cooperation. Philosophical Transactions of the Royal Society B: Biological Sciences, 364, 3281–3288.Google Scholar
Brown, D. E. (2004). Human universals, human nature & human culture. Daedalus, 133, 47–54.Google Scholar
Butler, L. P., & Walton, G. M. (2013). The opportunity to collaborate increases preschoolers’ motivation for challenging tasks. Journal of Experimental Child Psychology, 116, 953–961.Google Scholar
Buttelmann, D., & Bohm, R. (2014). The ontogeny of the motivation that underlies in-group bias. Psychological Science, 25, 921–927.Google Scholar
Buttelmann, D., Zmyj, N., Daum, M., & Carpenter, M. (2013). Selective imitation of in-group over out-group members in 14-month-old infants. Child Development, 64, 422–428.Google Scholar
Cameron, L., & Rutland, A. (2006). Extended contact through story reading in school: Reducing children’s prejudice toward the disabled. Journal of Social Issues, 62, 469–488.Google Scholar
Cameron, L., Rutland, A., & Brown, R. (2007). Promoting children’s positive intergroup attitudes towards stigmatized groups: Extended contact and multiple classification skills training. International Journal of Behavioral Development, 31, 454–466.Google Scholar
Cameron, L., Rutland, A., Brown, R., & Douch, R. (2006). Changing children’s intergroup attitudes toward refugees: Testing different models of extended contact. Child Development, 77, 1208–1219.Google Scholar
Castelli, L., De Amicis, L., & Sherman, S.J. (2007). The loyal member effect: On the preference for ingroup members who engage in exclusive relations with the ingroup. Developmental Psychology, 43, 1347–1359.Google Scholar
Chalik, L., Leslie, S. J., & Rhodes, M. (2017). Cultural context shapes essentialist beliefs about religion. Developmental Psychology, 53, 1178–1187.Google Scholar
Chalik, L., & Rhodes, M. (2014). Preschoolers use social allegiances to predict behavior. Journal of Cognition and Development, 15, 136–160.Google Scholar
Chalik, L., & Rhodes, M. (2018). Learning about social category-based obligations. Cognitive Development, 48, 117–124.Google Scholar
Chalik, L., Rivera, C., & Rhodes, M. (2014). Children’s use of categories and mental states to predict social behavior. Developmental Psychology, 50, 2360–2367.Google Scholar
Chen, E. E., Corriveau, K. H., & Harris, P. L. (2013). Children trust a consensus composed of outgroup members - but do not retain that trust. Child Development, 84, 269–282.Google Scholar
Corenblum, B. (2003). What children remember about ingroup and outgroup peers: Effects of stereotypes on children’s processing of information about group members. Journal of Experimental Child Psychology, 86, 32–66.Google Scholar
Corriveau, K. H., & Harris, P. L. (2010). Preschoolers (sometimes) defer to the majority in making simple perceptual judgments. Developmental Psychology, 46, 437–445.Google Scholar
Corriveau, K. H., Kinzler, K. D., & Harris, P. L. (2013). Accuracy trumps accent in children’s endorsement of object labels. Developmental Psychology, 49, 470–479.Google Scholar
Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math-gender stereotypes in elementary school children. Child Development, 82, 766–779.Google Scholar
Degner, J., & Wentura, D. (2010). Automatic prejudice in childhood and early adolescence. Journal of Personality and Social Psychology, 98, 356.Google Scholar
DeJesus, J. M., Rhodes, M., & Kinzler, K. D. (2014). Evaluations versus expectations: Children’s divergent beliefs about resource distribution. Cognitive Science, 38, 178–193.Google Scholar
del Río, M. F., & Strasser, K. (2011). Chilean children’s essentialist reasoning about poverty. British Journal of Developmental Psychology, 29, 722–743.Google Scholar
Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18.Google Scholar
Diesendruck, G., & Haber, L. (2009). God’s categories: The effect of religiosity on children’s teleological and essentialist beliefs about categories. Cognition, 110, 100–114.Google Scholar
Diesendruck, G., & HaLevi, H. (2006). The role of language, appearance, and culture in children’s social category-based induction. Child Development, 77, 539–553.Google Scholar
Dunham, Y. (2018). Mere membership. Trends in Cognitive Sciences, 22, 780–793.Google Scholar
Dunham, Y., Baron, A. S., & Banaji, M. R. (2006). From American city to Japanese village: A cross-cultural investigation of implicit race attitudes. Child Development, 77, 1268–1281Google Scholar
Dunham, Y., Baron, A. S., & Banaji, M. R. (2007). Children and social groups: A developmental analysis of implicit consistency in Hispanic Americans. Self and Identity, 6, 238–255.Google Scholar
Dunham, Y., Baron, A. S., & Banaji, M. R. (2008). The development of implicit intergroup cognition. Trends in Cognitive Sciences, 12, 248–253.Google Scholar
Dunham, Y., Baron, A. S., & Banaji, M. R. (2015). The development of implicit gender attitudes. Developmental Science, 18, 469–483.Google Scholar
Dunham, Y., Baron, A. S., & Carey, S. (2011). Consequences of “minimal” group affiliations in children. Child Development, 82, 793–811.Google Scholar
Dunham, Y., & Degner, J. (2013). From categories to exemplars (and back again). In Banaji, M. R., & Gelman, S. A. (eds.), Navigating the Social World: What Infants, Children, and Other Species Can Teach Us (pp. 275–280). New York: Oxford University Press.Google Scholar
Dunham, Y., & Emory, J. (2014). Of affect and ambiguity: The emergence of preference for arbitrary ingroups. Journal of Social Issues, 70, 81–98.Google Scholar
Dunham, Y., Newheiser, A. K., Hoosain, L., Merrill, A., & Olson, K. R. (2014a). From a different vantage: Intergroup attitudes among children from low- and intermediate-status racial groups. Social Cognition, 32, 1–21.Google Scholar
Dunham, Y., Srinivasan, M., Dorsch, R., & Barner, D. (2014b). Religion insulates ingroup evaluations: The development of intergroup attitudes in India. Developmental Science, 17, 311–319.Google Scholar
Engelmann, J. M., Herrmann, E., Rapp, D. J., & Tomasello, M. (2016). Young children (sometimes) do the right thing even when their peers do not. Cognitive Development, 39, 86–92.Google Scholar
Engelmann, J. M., Over, H., Herrmann, E., & Tomasello, M. (2013). Young children care more about their reputation with ingroup members and potential reciprocators. Developmental Science, 16, 952–958.Google Scholar
Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature, 454, 1079–1083.Google Scholar
Finlay, K. A., & Stephan, W. G. (2000). Improving intergroup relations: The effects of empathy on racial attitudes. Journal of Applied Social Psychology, 30, 1720–1737.Google Scholar
Gaias, L. M., Gal, D., Abry, T., Granger, K. L., & Taylor, M. (2018). Diversity exposure in preschool: Longitudinal implications for cross-race friendships and racial bias. Journal of Applied Developmental Psychology, 59, 5–15.Google Scholar
Gelman, S. A. (2003). The Essential Child: Origins of Essentialism in Everyday Thought. Oxford: Oxford University Press.Google Scholar
Gelman, S., Collman, P., & Maccoby, E. (1986). Inferring properties from categories versus inferring categories from properties: The case of gender. Child Development, 57, 396–404.Google Scholar
Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin, 138, 1085–1108.Google Scholar
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464.Google Scholar
Griffiths, J., & Nesdale, D. (2006). Ingroup and outgroup attitudes of ethnic majority and minority children. International Journal of Intercultural Relations, 30, 735–749.Google Scholar
Halim, M. L. D., Ruble, D. N., Tamis-LeMonda, C. S., Shrout, P. E., & Amodio, D. M. (2017). Gender attitudes in early childhood: Behavioral Consequences and cognitive antecedents. Child Development, 88, 882–899.Google Scholar
Haslam, N., Rothschild, L., & Ernst, D. (2000). Essentialist beliefs about social categories. British Journal of Social Psychology, 39, 113–127.Google Scholar
Haun, D. B., & Tomasello, M. (2011). Conformity to peer pressure in preschool children. Child Development, 82, 1759–1767.Google Scholar
Heiphetz, L., Spelke, E. S., & Banaji, M. R. (2013). Patterns of implicit and explicit attitudes in children and adults: Tests in the domain of religion. Journal of Experimental Psychology: General, 142, 864–879.Google Scholar
Hetherington, C., Hendrickson, C., & Koenig, M. (2014). Reducing an in-group bias in preschool children: The impact of moral behavior. Developmental Science, 17, 1042–1049.Google Scholar
Hilliard, L. J., & Liben, L. S. (2010). Differing levels of gender salience in preschool classrooms: Effects on children’s gender attitudes and intergroup bias. Child Development, 81, 1787–1798.Google Scholar
Hirschfeld, L. A. (1996). Race in the Making. Cambridge, MA: MIT Press.Google Scholar
Howard, L. H., Henderson, A. M., Carrazza, C., & Woodward, A. L. (2015). Infants’ and young children’s imitation of linguistic in-group and out-group informants. Child Development, 86, 259–275.Google Scholar
James, J. D. (2001). The role of cognitive development and socialization in the initial development of team loyalty. Leisure Sciences, 23, 233–261.Google Scholar
Jin, K., & Baillargeon, R. (2017). Infants possess an abstract expectation of ingroup support. Proceedings of the National Academy of Sciences (USA), 114, 8199–8204.Google Scholar
Jordan, J. J., McAuliffe, K., & Warneken, F. (2014). Development of ingroup favoritism in children’s third-party punishment of selfishness. Proceedings of the National Academy of Sciences (USA), 111, 12710–12715.Google Scholar
Kalish, C. W., & Lawson, C. A. (2008). Development of social category representations: Early appreciation of roles and deontic relations. Child Development, 79, 577–593.Google Scholar
Keller, J. (2005). In genes we trust: The biological component of psychological essentialism and its relationship to mechanisms of motivated social cognition. Journal of Personality and Social Psychology, 88, 686–702.Google Scholar
Kelly, D. J., Quinn, P. C., Slater, A. M., Lee, K., Gibson, A., Smith, M., Ge, L., & Pascalis, O. (2005). Three-month-olds but not newborns prefer own-race faces. Developmental Science, 8, F31–F36.Google Scholar
Kinzler, K. D., & Dautel, J. (2012). Children’s essentialist reasoning about language and race. Developmental Science, 15, 131–138.Google Scholar
Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2007). The native language of social cognition. Proceedings of the National Academy of Sciences (USA), 104, 12577–12580.Google Scholar
Kinzler, K. D., Dupoux, E., & Spelke, E. S. (2012). “Native” objects and collaborators: Infants’ object choices and acts of giving reflect favor for native over foreign speakers. Journal of Cognition and Development, 13, 67–81.Google Scholar
Kinzler, K. D., Shutts, K., DeJesus, J., & Spelke, E. S. (2009). Accent trumps race in guiding children’s social preferences. Social Cognition, 27, 623–634.Google Scholar
Kinzler, K. D., & Spelke, E. S. (2011). Do infants show social preferences for people differing in race? Cognition, 119, 1–9.Google Scholar
Lee, K., Quinn, P. C., & Pascalis, O. (2017). Face race processing and racial bias in early development: A perceptual-social linkage. Current Directions in Psychological Science, 26, 256–262.Google Scholar
Liberman, Z., Kinzler, K. D., & Woodward, A. L. (2014). Friends or foes: Infants use shared evaluations to infer others’ social relationships. Journal of Experimental Psychology: General, 143, 966–971.Google Scholar
Liberman, Z., Woodward, A. L., & Kinzler, K. D. (2017). Preverbal infants infer third-party social relationships based on language. Cognitive Science, 41, 622–634.Google Scholar
Mahajan, N., & Wynn, K. (2012). Origins of “us” versus “them”: Prelinguistic infants prefer similar others. Cognition, 124, 227–233.Google Scholar
Mahalingam, R. (2003). Essentialism, culture, and power: Representations of social class. Journal of Social Issues, 59, 733–749.Google Scholar
Marques, J. M., Yzerbyt, V. Y., & Leyens, J. (1988). “The black sheep effect”: Extremity of judgments toward ingroup members as a function of group identification. European Journal of Social Psychology, 18, 1–16.Google Scholar
Master, A., Cheryan, S., & Meltzoff, A. N. (2017). Social group membership increases STEM engagement among preschoolers. Developmental Psychology, 53, 201–209.Google Scholar
Master, A. & Walton, G. M. (2012). Minimal groups increase young children’s motivation and learning on group-relevant tasks. Child Development, 84, 737–751.Google Scholar
McAuliffe, K., & Dunham, Y. (2017). Fairness overrides group bias in children’s second-party punishment. Journal of Experimental Psychology: General, 146, 485–494.Google Scholar
McGlothlin, H., & Killen, M. (2010). How social experience is related to children’s intergroup attitudes. European Journal of Social Psychology, 40, 625–634.Google Scholar
McLoughlin, N., & Over, H. (2017). The developmental origins of dehumanization. Advances in Child Development and Behavior, 54, 153–178.Google Scholar
McLoughlin, N., Tipper, S. P., & Over, H. (2018). Young children perceive less humanness in outgroup faces. Developmental Science, 21, e12539.Google Scholar
Medin, D. L., & Ortony, A. (1989). Psychological essentialism. In Vosnaidou, S., & Ortony, A. (eds.), Similarity and Analogical Reasoning (pp. 179–196). Cambridge, MA: Cambridge University Press.Google Scholar
Misch, A., & Dunham, Y. (2021). (Peer) group influence on children's prosocial and antisocial behavior. Journal of Experimental Child Psychology, 201, 104994.Google Scholar
Misch, A., Over, H., & Carpenter, M. (2014). Stick with your group: Young children’s attitudes about group loyalty. Journal of Experimental Child Psychology, 126, 19–36.Google Scholar
Misch, A., Over, H., & Carpenter, M. (2016). I won’t tell: Young children show loyalty to their group by keeping group secrets. Journal of Experimental Child Psychology, 142, 96–106.Google Scholar
Misch, A., Over, H., & Carpenter, M. (2018). The whistleblower’s dilemma in young children: When loyalty trumps other moral concerns. Frontiers in Psychology, 9, 250.Google Scholar
Muzzatti, B., & Agnoli, F. (2007). Gender and mathematics: Attitudes and stereotype threat susceptibility in Italian children. Developmental Psychology, 43, 747–759.Google Scholar
Nesdale, D., & Flesser, D. (2001). Social identity and the development of children’s group attitudes. Child Development, 72, 506–517Google Scholar
Newheiser, A. K., & Olson, K. R. (2012). White and black American children’s implicit intergroup bias. Journal of Experimental Social Psychology, 48, 264–270.Google Scholar
Noyes, A., & Dunham, Y. (2017). Mutual intentions as a causal framework for social groups. Cognition, 162, 133–142.Google Scholar
Olson, K. R., & Dunham, Y. (2010). The development of implicit social cognition. In Gawronski, B., & Payne, B. K. (eds.), Handbook of Implicit Social Cognition: Measurement, Theory, and Applications (pp. 241–254). New York: Guilford Press.Google Scholar
Oostenbroek, J., & Over, H. (2015). Young children contrast their behavior to that of out-group members. Journal of Experimental Child Psychology, 139, 234–241.Google Scholar
Over, H., & Carpenter, M. (2009). Eighteen-month-old infants show increased helping following priming with affiliation. Psychological Science, 20, 1189–1193.Google Scholar
Over, H., Eggleston, A., Bell, J., & Dunham, Y. (2017). Young children seek out biased information about social groups. Developmental Science, 21, 1–12.Google Scholar
Over, H., Vaish, A., & Tomasello, M. (2016). Do young children accept responsibility for the negative actions of ingroup members? Cognitive Development, 40, 24–32.Google Scholar
Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90, 751–783.Google Scholar
Powell, L. J., & Spelke, E. S. (2013). Preverbal infants expect members of social groups to act alike. Proceedings of the National Academy of Sciences (USA), 110, E3965–E3972.Google Scholar
Prentice, D. A., & Miller, D. T. (2007). Psychological essentialism of human categories. Current Directions in Psychological Science, 16, 202–206.Google Scholar
Qian, M. K., Quinn, P. C., Heyman, G. D., Pascalis, O., Fu, G., & Lee, K. (2017). Perceptual individuation training (but not mere exposure) reduces implicit racial bias in preschool children. Developmental Psychology, 53, 845–859.Google Scholar
Quinn, P. C., Yahr, J., Kuhn, A., Slater, A. M., & Pascalis, O. (2002). Representation of the gender of human faces by infants: A preference for female. Perception, 31, 1109–1121.Google Scholar
Raabe, T., & Beelmann, A. (2011). Development of ethnic, racial, and national prejudice in childhood and adolescence: A multinational meta-analysis of age differences. Child Development, 82, 1715–1737.Google Scholar
Renno, M. P., & Shutts, K. (2015). Children’s social category-based giving and its correlates: Expectations and preferences. Developmental Psychology, 51, 533–543.Google Scholar
Rhodes, M. (2012). Naïve theories of social groups. Child Development, 83, 1900–1916.Google Scholar
Rhodes, M., & Chalik, L. (2013). Social categories as markers of intrinsic interpersonal obligations. Psychological Science, 24, 999–1006.Google Scholar
Rhodes, M., & Gelman, S. A. (2009). A developmental examination of the conceptual structure of animal, artifact, and human social categories across two cultural contexts. Cognitive Psychology, 59, 244–274.Google Scholar
Rhodes, M., Leslie, S. J., Saunders, K., Dunham, Y., & Cimpian, A. (2017). How does social essentialism affect the development of inter-group relations? Developmental Science, 21, 1–15.Google Scholar
Rhodes, M., Leslie, S. J., & Tworek, C. M. (2012). Cultural transmission of social essentialism. Proceedings of the National Academy of Sciences (USA), 109, 13526–13531.Google Scholar
Rotenberg, K. J., & Cerda, C. (1994). Racially based trust expectancies of Native American and Caucasian children. Journal of Social Psychology, 134, 621–631.Google Scholar
Rothbart, M., & Taylor, M. (1992). Category labels and social reality: Do we view social categories as natural kinds? In Semin, G. R., & Fiedler, K. (eds.), Language, Interaction and Social Cognition (pp. 11–36). Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
Rutland, A., Cameron, L., Bennett, L., & Ferrell, J. (2005a). Interracial contact and racial constancy: A multi-site study of racial intergroup bias in 3–5 year old Anglo-British children. Journal of Applied Developmental Psychology, 26, 699–713.Google Scholar
Rutland, A., Cameron, L., Milne, A., & McGeorge, P. (2005b). Social norms and self‐presentation: Children's implicit and explicit intergroup attitudes. Child Development, 76, 451–466.Google Scholar
Rutland, A., & Killen, M. (2015). A developmental science approach to reducing prejudice and social exclusion: Intergroup processes, social-cognitive development, and moral reasoning: A developmental science approach to reducing prejudice and social exclusion. Social Issues and Policy Review, 9, 121–154.Google Scholar
Rutland, A., Killen, M., & Abrams, D. (2010). A new social-cognitive developmental perspective on prejudice: The interplay between morality and group identity. Perspectives on Psychological Science, 5, 279–291.Google Scholar
Salomon, E., & Cimpian, A. (2014). The inherence heuristic as a source of essentialist thought. Personality and Social Psychology Bulletin, 40, 1297–1315.Google Scholar
Sani, F., & Bennett, M. (2009). Children’s inclusion of the group in the self: Evidence from a self-ingroup confusion paradigm. Developmental Psychology, 45, 503–510.Google Scholar
Schmidt, M. F., Rakoczy, H., & Tomasello, M. (2012). Young children enforce social norms selectively depending on the violator’s group affiliation. Cognition, 124, 325–333.Google Scholar
Schug, M. G., Shusterman, A., Barth, H., & Palatano, A. L. (2013). Minimal-group membership influences children’s responses to novel experience with group members. Developmental Science, 16, 47–55.Google Scholar
Shutts, K. (2015). Young children’s preferences: Gender, race, and social status. Child Development Perspectives, 9, 262–266.Google Scholar
Shutts, K., Banaji, M. R., & Spelke, E. S. (2010). Social categories guide young children’s preferences for novel objects. Developmental Science, 13, 599–610.Google Scholar
Sierksma, J., Thijs, J. T., & Verkuyten, M. (2015). In-group bias in children’s intention to help can be overpowered by inducing empathy. British Journal of Developmental Psychology, 33, 45–56.Google Scholar
Singarajah, A., Chanley, J., Gutierrez, Y., Cordon, Y., Nguyen, B., Burakowski, L., & Johnson, S. P. (2017). Infant attention to same- and other-race faces. Cognition, 159, 76–84.Google Scholar
Song, R., Over, H., & Carpenter, M. (2015). Children draw more affiliative pictures following priming with third-party ostracism. Developmental Psychology, 51, 831–840.Google Scholar
Sousa, P., Atran, S., & Medin, D. (2002). Essentialism and folkbiology: Evidence from Brazil. Journal of Cognition and Culture, 2, 195–223.Google Scholar
Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In Austin, W. G., & Worchel, S. (eds.), The Social Psychology of Inter-group Relations (pp. 33–47). Monterey, CA: Brooks/Cole.Google Scholar
Taylor, M. G. (1996). The development of children’s beliefs about social and biological aspects of gender differences, Child Development, 67, 1555–1571.Google Scholar
Taylor, M. G., Rhodes, M., & Gelman, S. (2009). Boys will be boys; cows will be cows: Children’s essentialist reasoning about gender categories and animal species. Child Development, 80, 461–481.Google Scholar
Watson-Jones, R. E., Whitehouse, H., & Legare, C. H. (2016). In-group ostracism increases high-fidelity imitation in early childhood. Psychological Science, 27, 34–42.Google Scholar
Waxman, S. R. (2010). Names will never hurt me? Naming and the development of racial and gender categories in preschool-aged children. European Journal of Social Psychology, 40, 593–610.Google Scholar
Waxman, S. R., & Grace, A. D. (2012). Developing gender- and race-based categories in infants: Evidence from 7- and 11-month-olds. In Hayes, G., & Bryant, M. (eds.), Psychology of Culture (pp. 159–175). Hauppauge, NY: Nova Science Publishers.Google Scholar
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337–375.Google Scholar
Williams, M. J., & Eberhardt, J. L. (2008). Biological conceptions of race and the motivation to cross racial boundaries. Journal of Personality and Social Psychology, 94, 1033–1047.Google Scholar
Wilks, M., & Nielsen, M. (2018). Children disassociate from antisocial in-group members. Journal of Experimental Child Psychology, 165, 37–50.Google Scholar
Witt, S. D. (1997). Parental influence on children’s socialization to gender roles. Adolescence, 32, 253–259.Google Scholar
Wright, S. C., Aron, A., McLaughlin-Volpe, T., & Ropp, S. A. (1997). The extended contact effect: Knowledge of cross-group friendships and prejudice. Journal of Personality and Social Psychology, 73, 73–90.Google Scholar
Yang, F., Choi, Y., Misch, A., Yang, X., & Dunham, Y. (2018). In defense of the commons: Young children negatively evaluate and sanction free-riders. Psychological Science, 29, 1598–1611.Google Scholar

References

Achterberg, M., Peper, J. S., Van Duijvenvoorde, A. C., Mandl, R. C., & Crone, E. A. (2016a). Fronto-striatal white matter integrity predicts development in delay of gratification: A longitudinal study. Journal of Neuroscience, 36, 1954–1961.Google Scholar
Achterberg, M., van Duijvenvoorde, A. C., Bakermans-Kranenburg, M. J., & Crone, E. A. (2016b). Control your anger! The neural basis of aggression regulation in response to negative social feedback. Social Cognitive and Affective Neuroscience, 11, 712–720.Google Scholar
Achterberg, M., van Duijvenvoorde, A. C. K., van der Meulen, M., Bakermans-Kranenburg, M. J., & Crone, E. A. (2018). Heritability of aggression following social evaluation in middle childhood: An fMRI study. Human Brain Mapping, 39, 2828–2841.Google Scholar
Achterberg, M., van Duijvenvoorde, A. C. K., van der Meulen, M., Euser, S., Bakermans-Kranenburg, M. J., & Crone, E. A. (2017). The neural and behavioral correlates of social evaluation in childhood. Developmental Cognitive Neuroscience, 24, 107–117.Google Scholar
Blankenstein, N. E., Crone, E. A., van den Bos, W., & van Duijvenvoorde, A. C. K. (2016). Dealing with uncertainty: Testing risk- and ambiguity-attitude across adolescence. Developmental Neuropsychology, 41, 1–16.Google Scholar
Blankenstein, N. E., Schreuders, E., Peper, J. S., Crone, E. A., & van Duijvenvoorde, A. C. K. (2018). Individual differences in risk-taking tendencies modulate the neural processing of risky and ambiguous decision-making in adolescence. NeuroImage, 172, 663–673.Google Scholar
Braams, B. R., Peper, J. S., van der Heide, D., Peters, S., & Crone, E. A. (2016). Nucleus accumbens response to rewards and testosterone levels are related to alcohol use in adolescents and young adults. Developmental Cognitive Neuroscience, 17, 83–93.Google Scholar
Braams, B. R., Peters, S., Peper, J. S., Guroglu, B., & Crone, E. A. (2014). Gambling for self, friends, and antagonists: Differential contributions of affective and social brain regions on adolescent reward processing. NeuroImage, 100, 281–289.Google Scholar
Braams, B. R., van Duijvenvoorde, A. C. K., Peper, J. S., & Crone, E. A. (2015). Longitudinal changes in adolescent risk-taking: A comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior. The Journal of Neuroscience, 35, 7226–7238.Google Scholar
Carlson, S. M., Shoda, Y., Ayduk, O., Aber, L., Schaefer, C., Sethi, A., Wilson, N., Peake, P. K., & Mischel, W. (2018). Cohort effects in children’s delay of gratification. Developmental Psychology Journal, 54, 1395–1407.Google Scholar
Casey, B., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., & Garavan, H. (2018). The adolescent brain cognitive development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54.Google Scholar
Casey, B. J., Galvan, A., & Somerville, L. H. (2016). Beyond simple models of adolescence to an integrated circuit-based account: A commentary. Developmental Cognitive Neuroscience, 17, 128–130.Google Scholar
Casey, B. J., Somerville, L. H., Gotlib, I. H., Ayduk, O., Franklin, N. T., Askren, M. K., Jonides, J., Berman, M. G., Wilson, N. L., Teslovich, T., Glover, G., Zayas, V., Mischel, W., & Shoda, Y. (2011). Behavioral and neural correlates of delay of gratification 40 years later. Proceedings of the National Academy of Sciences (USA), 108, 14998–15003.Google Scholar
Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science, 14, F1–10.Google Scholar
Crone, E. A., & Dahl, R. E. (2012). Understanding adolescence as a period of social-affective engagement and goal flexibility. Nature Reviews Neuroscience, 13, 636–650.Google Scholar
Crone, E. A., & Steinbeis, N. (2017). Neural perspectives on cognitive control development during childhood and adolescence. Trends in Cognitive Science, 21, 205–215.Google Scholar
Dahl, R. E., Allen, N. B., Wilbrecht, L., & Suleiman, A. B. (2018). Importance of investing in adolescence from a developmental science perspective. Nature, 554, 441.Google Scholar
DeWall, C. N., & Bushman, B. J. (2011). Social acceptance and rejection: The sweet and the bitter. Current Directions in Psychological Science, 20, 256–260.Google Scholar
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168.Google Scholar
Do, K. T., Guassi Moreira, J. F., & Telzer, E. H. (2017). But is helping you worth the risk? Defining prosocial risk taking in adolescence. Developmental Cognitive Neuroscience, 25, 260–271.Google Scholar
Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics, 75, 643–669.Google Scholar
Figner, B., Mackinlay, R. J., Wilkening, F., & Weber, E. U. (2009). Affective and deliberative processes in risky choice: Age differences in risk taking in the Columbia Card Task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 709.Google Scholar
Figner, B., & Weber, E. U. (2011). Who takes risks when and why? Determinants of risk taking. Current Directions in Psychological Science, 20, 211–216.Google Scholar
Frey, R., Pedroni, A., Mata, R., Rieskamp, J., & Hertwig, R. (2017). Risk preference shares the psychometric structure of major psychological traits. Science Advances, 3, e1701381.Google Scholar
Galvan, A., Hare, T., Voss, H., Glover, G., & Casey, B. (2007). Risk‐taking and the adolescent brain: Who is at risk? Developmental Science, 10, F8–F14.Google Scholar
Genc, S., Smith, R. E., Malpas, C. B., Anderson, V., Nicholson, J. M., Efron, D., Sciberras, E., Seal, M. L., & Silk, T. J. (2018). Development of white matter fibre density and morphology over childhood: A longitudinal fixel-based analysis. Neuroimage, 183, 666–676.Google Scholar
Gianotti, L. R. R., Knoch, D., Faber, P. L., Lehmann, D., Pascual-Marqui, R. D., Diezi, C., Schoch, C., Eisenegger, C., & Fehr, E. (2009). Tonic activity level in the right prefrontal cortex predicts individuals’ risk taking. Psychological Science, 20, 33–38.Google Scholar
Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 306, 447–452.Google Scholar
Gunther Moor, B., Van Leijenhorst, L., Rombouts, S. A., Crone, E. A., & Van der Molen, M. W. (2010). Do you like me? Neural correlates of social evaluation and developmental trajectories. Social Neuroscience, 5, 461–482.Google Scholar
Harden, K. P., Kretsch, N., Mann, F. D., Herzhoff, K., Tackett, J. L., Steinberg, L., & Tucker-Drob, E. M. (2016). Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking. Developmental Cognitive Neuroscience, 25, 221–234.Google Scholar
Herting, M. M., Gautam, P., Spielberg, J. M., Dahl, R. E., & Sowell, E. R. (2015). A longitudinal study: Changes in cortical thickness and surface area during pubertal maturation. PLoS ONE, 10, e0119774.Google Scholar
Herting, M. M., & Sowell, E. R. (2017). Puberty and structural brain development in humans. Frontiers in Neuroendocrinology, 44, 122–137.Google Scholar
Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Science, 16, 174–180.Google Scholar
Huttenlocher, P. R. (1979). Synaptic density in human frontal cortex-developmental changes and effects of aging. Brain Research, 163, 195–205.Google Scholar
Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. The Journal of Comparative Neurology, 387, 167–178.Google Scholar
Koenis, M. M. G., Brouwer, R. M., Swagerman, S. C., van Soelen, I. L. C., Boomsma, D. I., & Hulshoff Pol, H. E. (2018). Association between structural brain network efficiency and intelligence increases during adolescence. Human Brain Mapping, 39, 822–836.Google Scholar
Kray, J., Schmitt, H., Lorenz, C., & Ferdinand, N. K. (2018). The influence of different kinds of incentives on decision-making and cognitive control in adolescent development: A review of behavioral and neuroscientific studies. Frontiers in Psychology, 9, 768.Google Scholar
Luna, B., Marek, S., Larsen, B., Tervo-Clemmens, B., & Chahal, R. (2015). An integrative model of the maturation of cognitive control. Annual Review of Neuroscience, 38, 151–170.Google Scholar
Ma, I., van Duijvenvoorde, A., & Scheres, A. (2016). The interaction between reinforcement and inhibitory control in ADHD: A review and research guidelines. Clinical Psychology Review, 44, 94–111.Google Scholar
Mamerow, L., Frey, R., & Mata, R. (2016). Risk taking across the life span: A comparison of self-report and behavioral measures of risk taking. Psychology and Aging, 31, 711–723.Google Scholar
Matzke, D., Hughes, M., Badcock, J. C., Michie, P., & Heathcote, A. (2017). Failures of cognitive control or attention? The case of stop-signal deficits in schizophrenia. Attention, Perception, & Psychophysics, 79, 1078–1086.Google Scholar
McCormick, E. M., & Telzer, E. H. (2017). Failure to retreat: Blunted sensitivity to negative feedback supports risky behavior in adolescents. NeuroImage, 147, 381–389.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.Google Scholar
Mills, K. L., & Tamnes, C. K. (2014). Methods and considerations for longitudinal structural brain imaging analysis across development. Developmental Cognitive Neuroscience, 9, 172–190.Google Scholar
Mischel, W., Ayduk, O., Berman, M. G., Casey, B. J., Gotlib, I. H., Jonides, J., Kross, E., Teslovich, T., Wilson, N. L., Zayas, V., & Shoda, Y. (2011). ‘Willpower’ over the life span: Decomposing self-regulation. Social Cognitive and Affective Neuroscience, 6, 252–256.Google Scholar
Nussey, S., & Whitehead, S. (2001). Endocrinology: An Integrated Approach. Oxford: BIOS Scientific Publishers.Google Scholar
Peper, J. S., Braams, B. R., Blankenstein, N. E., Bos, M. G., & Crone, E. A. (2018). Development of multifaceted risk taking and the relations to sex steroid hormones: A longitudinal study. Child Development, 89, 1887–1907.Google Scholar
Peper, J. S., & Dahl, R. E. (2013). Surging hormones: Brain–behavior interactions during puberty. Current Directions in Psychological Science, 22, 134–139.Google Scholar
Peper, J. S., de Reus, M. A., van den Heuvel, M. P., & Schutter, D. J. (2015). Short fused? associations between white matter connections, sex steroids, and aggression across adolescence. Human Brain Mapping, 36, 1043–1052.Google Scholar
Peper, J. S., Koolschijn, P. C., & Crone, E. A. (2013a). Development of risk taking: Contributions from adolescent testosterone and the orbito-frontal cortex. Journal of Cognitive Neuroscience, 25, 2141–2150.Google Scholar
Peper, J. S., Mandl, R. C., Braams, B. R., de Water, E., Heijboer, A. C., Koolschijn, P. C., & Crone, E. A. (2013b). Delay discounting and frontostriatal fiber tracts: A combined DTI and MTR study on impulsive choices in healthy young adults. Cerebral Cortex, 23, 1695–1702.Google Scholar
Peters, S., Van der Meulen, M., Zanolie, K., & Crone, E. A. (2017). Predicting reading and mathematics from neural activity for feedback learning. Developmental Psychology Journal, 53, 149–159.Google Scholar
Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., Clasen, L., Gogtay, N., & Giedd, J. N. (2011). How does your cortex grow? Journal of Neuroscience, 31, 7174–7177.Google Scholar
Rubia, K. (2018). Cognitive neuroscience of attention deficit hyperactivity disorder (ADHD) and its clinical translation. Frontiers in Human Neuroscience, 12, 100.Google Scholar
Schreuders, E., Braams, B. R., Blankenstein, N. E., Peper, J. S., Güroğlu, B., & Crone, E. A. (2018). Contributions of reward sensitivity to ventral striatum activity across adolescence and early adulthood. Child Development, 89, 797–810.Google Scholar
Schriber, R. A., & Guyer, A. E. (2016). Adolescent neurobiological susceptibility to social context. Developmental Cognitive Neuroscience, 19, 1–18.Google Scholar
Shoda, Y., Mischel, W., & Peake, P. K. (1990). Predicting adolescent cognitive and self-regulatory competences from preschool delay of gratification – Identifying diagnostic conditions. Developmental Psychology, 26, 978–986.Google Scholar
Shulman, E. P., Smith, A. R., Silva, K., Icenogle, G., Duell, N., Chein, J., & Steinberg, L. (2016). The dual systems model: Review, reappraisal, and reaffirmation. Developmental Cognitive Neuroscience, 17, 103–117.Google Scholar
Silverman, M. H., Jedd, K., & Luciana, M. (2015). Neural networks involved in adolescent reward processing: An activation likelihood estimation meta-analysis of functional neuroimaging studies. NeuroImage, 122, 427–439.Google Scholar
Somerville, L. H., & Casey, B. J. (2010). Developmental neurobiology of cognitive control and motivational systems. Current Opinion in Neurobiology, 20, 236–241.Google Scholar
Somerville, L. H., Heatherton, T. F., & Kelley, W. M. (2006). Anterior cingulate cortex responds differentially to expectancy violation and social rejection. Nature Neuroscience, 9, 1007–1008.Google Scholar
Spear, L. P. (2018). Effects of adolescent alcohol consumption on the brain and behaviour. Nature Reviews Neuroscience, 19, 197.Google Scholar
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106.Google Scholar
Tamnes, C. K., Herting, M. M., Goddings, A. L., Meuwes, R., Blakemore, S. J., Dahl, R. E., Guroglu, B., Raznahan, A., Sowell, E. R., Crone, E. A., & Mills, K. L. (2017). Development of the cerebral cortex across adolescence: A multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. Journal of Neuroscience, 37, 3402–3412.Google Scholar
Tan, P. Z., Silk, J. S., Dahl, R. E., Kronhaus, D., & Ladouceur, C. D. (2018). Age-related developmental and individual differences in the influence of social and non-social distractors on cognitive performance. Frontiers in Psychology, 9, 863.Google Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.Google Scholar
Twenge, J. M., Baumeister, R. F., Tice, D. M., & Stucke, T. S. (2001). If you can’t join them, beat them: Effects of social exclusion on aggressive behavior. Journal of Personality and Social Psychology, 81, 1058–1069.Google Scholar
Tymula, A., Rosenberg Belmaker, L. A., Roy, A. K., Ruderman, L., Manson, K., Glimcher, P. W., & Levy, I. (2012). Adolescents’ risk-taking behavior is driven by tolerance to ambiguity. Proceedings of the National Academy of Sciences (USA), 109, 17135–17140.Google Scholar
van den Bos, W., & Hertwig, R. (2017). Adolescents display distinctive tolerance to ambiguity and to uncertainty during risky decision making. Scientific Reports, 7, 40962.Google Scholar
van Duijvenvoorde, A. C. K., Blankenstein, N., Crone, E., & Figner, B. (2017). Towards a better understanding of adolescent risk taking: Contextual moderators and model-based analysis. In Toplak, M. E., & Weller, J. A. (eds.), Individual Differences in Judgment and Decision-Making: A Developmental Perspective (pp. 8–27). Hove: Psychology Press.Google Scholar
van Duijvenvoorde, A. C. K., & Crone, E. A. (2013). The teenage brain a neuroeconomic approach to adolescent decision making. Current Directions in Psychological Science, 22, 108–113.Google Scholar
van Duijvenvoorde, A. C. K., Huizenga, H. M., Somerville, L. H., Delgado, M. R., Powers, A., Weeda, W. D., Casey, B., Weber, E. U., & Figner, B. (2015). Neural correlates of expected risks and returns in risky choice across development. The Journal of Neuroscience, 35, 1549–1560.Google Scholar
van Duijvenvoorde, A. C. K., Peters, S., Braams, B. R., & Crone, E. A. (2016). What motivates adolescents? Neural responses to rewards and their influence on adolescents’ risk taking, learning, and cognitive control. Neuroscience & Biobehavioral Reviews, 70, 135–147.Google Scholar
Van Leijenhorst, L., Moor, B. G., de Macks, Z. A. O., Rombouts, S. A., Westenberg, P. M., & Crone, E. A. (2010). Adolescent risky decision-making: Neurocognitive development of reward and control regions. NeuroImage, 51, 345–355.Google Scholar
van Noordt, S. J. R., & Segalowitz, S. J. (2012). Performance monitoring and the medial prefrontal cortex: A review of individual differences and context effects as a window on self-regulation. Frontiers in Human Neuroscience, 6, 197.Google Scholar
van Timmeren, T., Daams, J. G., van Holst, R. J., & Goudriaan, A. E. (2018). Compulsivity-related neurocognitive performance deficits in gambling disorder: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 84, 204–217.Google Scholar
Vives, M.-L., & FeldmanHall, O. (2018). Tolerance to ambiguous uncertainty predicts prosocial behavior. Nature Communications, 9, 2156.Google Scholar
Von Gaudecker, H.-M., Van Soest, A., & Wengström, E. (2011). Heterogeneity in risky choice behavior in a broad population. The American Economic Review, 101, 664–694.Google Scholar
Watts, T. W., Duncan, G. J., & Quan, H. (2018). Revisiting the Marshmallow Test: A conceptual replication investigating links between early delay of gratification and later outcomes. Psychological Science, 29, 1159–1177.Google Scholar
Wierenga, L. M., Bos, M. G. N., Schreuders, E., Vd Kamp, F., Peper, J. S., Tamnes, C. K., & Crone, E. A. (2018). Unraveling age, puberty and testosterone effects on subcortical brain development across adolescence. Psychoneuroendocrinology, 91, 105–114.Google Scholar
Willoughby, T., Good, M., Adachi, P. J., Hamza, C., & Tavernier, R. (2014). Examining the link between adolescent brain development and risk taking from a social–developmental perspective (reprinted). Brain and Cognition, 89, 70–78.Google Scholar
Yakovlev, P., Lecours, A.-R., Minkowski, A., & Davis, F. (1967). Regional Development of the Brain in Early Life. Oxford: Blackwell Scientific.Google Scholar

References

Balleine, B. W., & Killcross, S. (2006). Parallel incentive processing: An integrated view of amygdala function. Trends in Neuroscience, 29, 272–279.Google Scholar
Balleine, B. W., & O’Doherty, J. P. (2010). Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35, 48–69.Google Scholar
Banich, M. T. (2009). Executive function: The search for an integrated account. Current Directions in Psychological Science, 18, 89–94.Google Scholar
Berridge, K. C., & Kringelbach, M. L. (2013). Neuroscience of affect: Brain mechanisms of pleasure and displeasure. Current Opinions in Neurobiology, 23, 294–303.Google Scholar
Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neuroscience, 26, 507–513.Google Scholar
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: Rewarding, aversive, and alerting. Neuron, 68, 815–834.Google Scholar
Campese, V. D., Sears, R. M., Moscarello, J. M., Diaz-Mataix, L., Cain, C. K., & LeDoux, J. E. (2016). The neural foundations of reaction and action in aversive motivation. In Simpson, E. H., & Balsam, P. D. (eds.), Behavioral Neuroscience of Motivation (pp. 171–195). Cham: Springer International Publishing.Google Scholar
Casey, B. J., Jones, R. M., & Hare, T. A. (2008). The adolescent brain. Annals of the New York Academy of Sciences, 1124, 111–126.Google Scholar
Christoff, K., & Gabrieli, J. D. E. (2000). The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology, 28, 168–186.Google Scholar
Delgado, M. R., Jou, R. L., & Phelps, E. A. (2011). Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers. Frontiers in Neuroscience, 5, 71.Google Scholar
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14, 91–118.Google Scholar
Elliot, A. J., & Covington, M. V. (2001). Approach and avoidance motivation. Educational Psychology Review, 13, 73–92.Google Scholar
Ernst, M. (2014). The triadic model perspective for the study of adolescent motivated behavior. Brain and Cognition, 89, 104–111.Google Scholar
Ernst, M., Daniele, T., & Frantz, K. (2011). New perspectives on adolescent motivated behavior: Attention and conditioning. Developmental Cognitive Neuroscience, 1, 377–389.Google Scholar
Ernst, M., Pine, D. S., & Hardin, M. (2006). Triadic model of the neurobiology of motivated behavior in adolescence. Psychological Medicine, 36, 299–312.Google Scholar
Ernst, M., & Spear, L. P. (2009). Reward systems. In de Haan, M. & Gunnar, M. R. (eds.), Handbook of Developmental Social Neuroscience (pp. 324–341). New York: The Guilford Press.Google Scholar
Gleason, P. M., Boushey, C. J., Harris, J. E., & Zoellner, J. (2015). Publishing nutrition research: A review of multivariate techniques – Part 3: Data reduction methods. Journal of the Academy of Nutrition & Dietetics, 115, 1072–1082.Google Scholar
Goode, T. D., & Maren, S. (2017). Role of the bed nucleus of the stria terminalis in aversive learning and memory. Learning & Memory, 24, 480–491.Google Scholar
Hare, T. A., Tottenham, N., Galvan, A., Voss, H. U., Glover, G. H., & Casey, B. J. (2008). Biological substrates of emotional reactivity and regulation in adolescence during an emotional go–nogo task. Biological Psychiatry, 63, 927–934.Google Scholar
Iniesta, R., Stahl, D., & McGuffin, P. (2016). Machine learning, statistical learning and the future of biological research in psychiatry. Psychological Medicine, 46, 2455–2465.Google Scholar
Jernigan, T. L., & Brown, S. A. (2018). Introduction. Developmental Cognitive Neuroscience, 32, 1–3.Google Scholar
Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nature Neuroscience, 12, 939–945.Google Scholar
Kragel, P. A., & LaBar, K. S. (2016). Decoding the nature of emotion in the brain. Trends in Cognitive Sciences, 20, 444–455.Google Scholar
LeDoux, J., & Daw, N. D. (2018). Surviving threats: Neural circuit and computational implications of a new taxonomy of defensive behaviour. Nature Reviews Neuroscience, 19, 269–282.Google Scholar
Maren, S. (2016). Parsing reward and aversion in the amygdala. Neuron, 90, 209–211.Google Scholar
Mirenowicz, J., & Schultz, W. (1996). Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli. Nature, 379, 449–451.Google Scholar
Murphy, F. C., Nimmo-Smith, I., & Lawrence, A. D. (2003). Functional neuroanatomy of emotions: A meta-analysis. Cognitive, Affective, & Behavioral Neuroscience, 3, 207–233.Google Scholar
Petrides, M. (2005). Lateral prefrontal cortex: Architectonic and functional organization. Philosophical Transactions of the Royal Society B: Biological Sciences, 360, 781–795.Google Scholar
Quevedo, K. M., Benning, S. D., Gunnar, M. R., & Dahl, R. E. (2009). The onset of puberty: Effects on the psychophysiology of defensive and appetitive motivation. Development and Psychopathology, 21, 27–45.Google Scholar
Sanford, C. A., Soden, M. E., Baird, M. A., Miller, S. M., Schulkin, J., Palmiter, R. D., … Zweifel, L. S. (2017). A central amygdala CRF circuit facilitates learning about weak threats. Neuron, 93, 164–178.Google Scholar
Silvers, J. A., McRae, K., Gabrieli, J. D. E., Gross, J. J., Remy, K. A., & Ochsner, K. N. (2012). Age-related differences in emotional reactivity, regulation, and rejection sensitivity in adolescence. Emotion, 12, 1235–1247.Google Scholar
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106.Google Scholar
Stuss, D. T., & Alexander, M. P. (2007). Is there a dysexecutive syndrome? Philosophical Transactions of the Royal Society of London B: Biological Science, 362, 901–915.Google Scholar
Tarca, A. L., Carey, V. J., Chen, X. W., Romero, R., & Drăghici, S. (2007). Machine learning and its applications to biology. PLoS Computational Biology, 3, e116.Google Scholar
Tsutsui-Kimura, I., Bouchekioua, Y., Mimura, M., & Tanaka, K. F. (2017). A new paradigm for evaluating avoidance/escape motivation. International Journal of Neuropsychopharmacology, 20, 593–601.Google Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.Google Scholar
Varoquaux, G., & Craddock, R. C. (2013). Learning and comparing functional connectomes across subjects. Neuroimage, 80, 405–415.Google Scholar
Varoquaux, G., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage, 145, 166–179.Google Scholar
Wassum, K. M., & Izquierdo, A. (2015). The basolateral amygdala in reward learning and addiction. Neuroscience& Biobehavioral Reviews, 57, 271–283.Google Scholar

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