Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T02:20:27.690Z Has data issue: false hasContentIssue false

Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 767 - 1162
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Aslin, R. N., Saffran, J. R., & Newport, E. L. (1998). Computation of conditional probability statistics by 8 month old infants. Psychological Science, 9 (4), 321–324.CrossRefGoogle 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, 109 (9), 3253–3258.CrossRefGoogle ScholarPubMed
Berthiaume, V. G., Shultz, T. R., & Onishi, K. H. (2013). A constructivist connectionist model of transitions on false-belief tasks. Cognition, 126 (3), 441–458.Google Scholar
Berthouze, L., & Metta, G. (2005). Epigenetic robotics: modelling cognitive development in robotic systems. Cognitive Systems Research, 6 (3), 189–192.CrossRefGoogle 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 (3), 322–330.CrossRefGoogle ScholarPubMed
Boom, J., & ter Laak, J. (2007). Classes in the balance: latent class analysis and the balance scale task. Developmental Review, 27 (1), 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 (3), 331–340.Google Scholar
Bulf, H., Johnson, S. P., & Valenza, E. (2011). Visual statistical learning in the newborn infant. Cognition, 121 (1), 127–132.CrossRefGoogle ScholarPubMed
Buss, A. T., & Spencer, J. P. (2014). The emergent executive: a dynamic neural field theory of the development of executive function. Monographs of the Society for Research in Child Development, 79, 1–104.Google ScholarPubMed
Cangelosi, A., & Schlesinger, M. (2015). Developmental Robotics: From Babies to Robots. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Cassidy, K. W. (1998). Three- and four-year-old children’s ability to use desire- and belief-based reasoning. Cognition, 66 (1), B1.Google Scholar
Dandurand, F., & Shultz, T. R. (2010). Automatic detection and quantification of growth spurts. Behavior Research Methods, 42 (3), 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 (2), 243–249.CrossRefGoogle ScholarPubMed
Denison, S., & Xu, F. (2010). Twelve- to 14-month-old infants can predict single-event probability with large set sizes. Developmental Science, 13 (5), 798–803.Google Scholar
Denison, S., & Xu, F. (2014). The origins of probabilistic inference in human infants. Cognition, 130 (3), 335–347.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 (6), 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 (3), 382–397.CrossRefGoogle ScholarPubMed
Friedman, O., & Leslie, A. M. (2005). Processing demands in belief-desire reasoning: inhibition or general difficulty? Developmental Science, 8 (3), 218–225.CrossRefGoogle ScholarPubMed
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 Sun, R. (Ed.), 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 (1), 110.Google Scholar
Gopnik, A., & Bonawitz, E. (2015). Bayesian models of child development. Wiley Interdisciplinary Reviews Cognitive Science, 6 (2), 75–86.Google Scholar
Gurteen, P. M., Horne, P. J., & Erjavec, M. (2011). Rapid word learning in 13- and 17-month-olds in a naturalistic two-word procedure: looking versus reaching measures. Journal of Experimental Child Psychology, 109 (2), 201–217.CrossRefGoogle Scholar
Halford, G. S. (1984). Can young children integrate premises in transitivity and serial order tasks? Cognitive Psychology, 16 (1), 65–93.CrossRefGoogle Scholar
Halford, G. S., Andrews, G., Wilson, W. H., & Phillips, S. (2012). Computational models of relational processes in cognitive development. Cognitive Development, 27 (4), 481–499.Google Scholar
Helfer, P., & Shultz, T. R. (2018). Coupled feedback loops maintain synaptic long-term potentiation: a computational model of PKMzeta synthesis and AMPA receptor trafficking. PLoS Computational Biology, 14 (5), 1–31.Google Scholar
Helfer, P., & Shultz, T. R. (2019). A computational model of systems memory consolidation and reconsolidation. Hippocampus, hipo.23187. https://doi.org/10.1002/hipo.23187Google Scholar
Hingston, P. (2012). Believable Bots: Can Computers Play Like People? New York, NY: Spinger.Google Scholar
Justesen, N., Bontrager, P., Togelius, J., & Risi, S. (2019). Deep learning for video game playing. IEEE Transactions on Games, 12 (1), 1–20.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 (2), 4–5.Google Scholar
Kovack-Lesh, K. A., Oakes, L. M., & McMurray, B. (2012). Contributions of attentional style and previous experience to 4-month-old infants’ categorization. Infancy, 17 (3), 324–338.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 (1711), 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), 1–31.Google Scholar
McCrink, K., & Wynn, K. (2007). Ratio abstraction by 6-month-old infants. Psychological Science, 18 (8), 740–745.Google Scholar
McGeer, T. (1990). Passive walking with knees. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 1640–1645).CrossRefGoogle Scholar
Metta, G., Natale, L., Nori, F., et al. (2010). The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Networks, 23 (8–9), 1125–1134.CrossRefGoogle ScholarPubMed
Nobandegani, A., & Shultz, T. (2017). Converting cascade-correlation neural nets into probabilistic generative models. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. J. (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1029–1034). Austin, TX: Cognitive Science Society.Google Scholar
Nobandegani, A., & Shultz, T. (2018). Example generation under constraints using cascade correlation neural nets. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society (pp. 2388–2393). Austin, TX: Cognitive Science Society.Google Scholar
O’Loughlin, C., & Thagard, P. (2000). Autism and coherence: a computational model. Mind and Language, 15 (4), 375–392.Google Scholar
Onishi, K., & Baillargeon, R. (2005). Do 15-month-old infants understand false beliefs? Science, 308 (5719), 255–258.Google Scholar
Oudeyer, P. Y. (2017). What do we learn about development from baby robots? Wiley Interdisciplinary Reviews Cognitive Science, 8 (1–2), 1–7.Google Scholar
Perfors, A., Tenenbaum, J., Griffiths, T., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120 (3), 302–321.CrossRefGoogle ScholarPubMed
Perone, S., Molitor, S. J., Buss, A. T., Spencer, J. P., & Samuelson, L. K. (2015). Enhancing the executive functions of 3-year-olds in the dimensional change card sort task. Child Development, 86 (3), 812–827.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 (3), 413–459.Google Scholar
Quinn, P. C., & Johnson, M. H. (2000). Global-before-basic object categorization in connectionist networks and 2-month-old infants. Infancy, 1 (1), 31–46.CrossRefGoogle ScholarPubMed
Restle, F. (1962). The selection of strategies in cue learning. Psychological Review, 69 (4), 329–343.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274 (5294), 1926–1928.Google Scholar
Segler, M., Preuss, M., & Waller, M. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555, 604–610.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 (4), 341–351.Google Scholar
Shultz, T. R. (2003). Computational Developmental Psychology. Cambridge, MA: MIT Press.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, NY: Oxford University Press.Google Scholar
Shultz, T. R., & Cohen, L. B. (2004). Modeling age differences in infant category learning. Infancy, 5 (2), 153–171.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 (1), 57–86.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, Canada: Cognitive Science Society.Google Scholar
Shultz, T., & Nobandegani, A. (2021). A computational model of infant learning and reasoning with probabilities. Psychological Review. https://doi.org/http://dx.doi.org/10.1037/rev0000322Google Scholar
Shultz, T. R., & Rivest, F. (2001). Knowledge-based cascade-correlation: using knowledge to speed learning. Connection Science, 13 (1), 43–72.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, NY: Cambridge University Press.Google Scholar
Siegler, R. S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481–520.Google Scholar
Siegler, R. S. (1996). Emerging Minds: The Process of Change in Children’s Thinking. New York, NY: Oxford University Press.Google Scholar
Silver, D., Huang, A., Maddison, C., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529 (7587), 484–489.CrossRefGoogle ScholarPubMed
Spencer, J., Thomas, M., & McClelland, J. (2009). Toward a Unified Theory of Development: Connectionism and Dynamic Systems Theory Re-considered. Oxford: Oxford University Press.CrossRefGoogle Scholar
Sun, R. (1995). Robust reasoning: integrating rule-based and similarity-based reasoning. Artificial Intelligence, 75, 241–295.Google Scholar
Teglas, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J., & Bonatti, L. (2011). Pure reasoning in 12-month-old infants as probabilistic inference. Science, 332 (6033), 1054–1059.CrossRefGoogle ScholarPubMed
Thompson, V. A., Prowse Turner, J. A., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63 (3), 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. In Proceedings of the 2019 Annual Conference of the Cognitive Science Society (p. 1045).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 (5), e12474.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 (3), 655–684.Google Scholar
Westermann, G., & Mareschal, D. (2014). From perceptual to language-mediated categorization. Philosophical Transactions of the Royal Society B: Biological Sciences, 369 (1634), Article 20120391.Google Scholar
Wynn, K., Bloom, P., & Chiang, W. C. (2002). Enumeration of collective entities by 5-month-old infants. Cognition, 83 (3), B55–B62.Google Scholar
Xu, F., & Garcia, V. (2008). Intuitive statistics by 8-month-old infants. Proceedings of the National Academy of Sciences, 105 (13), 5012–5015.Google Scholar
Xu, F., & Spelke, E. S. (2000). Large number discrimination in human infants. Cognition, 74, B1–B11.Google Scholar
Younger, B. A., & Cohen, L. B. (1986). Developmental change in infants’ perception of correlations among attributes. Child Development, 57 (3), 803–815.CrossRefGoogle ScholarPubMed
Zelazo, P. D. (2006). The Dimensional Change Card Sort (DCCS): a method of assessing executive function in children. Nature Protocols, 1 (1), 297–301.Google Scholar

References

Abelson, R. P. (1963). Computer simulation of “hot cognition.” In Tomkins, S. S. & Messick, S. (Eds.), Computer Simulation of Personality (pp. 277–298). New York, NY: Wiley.Google Scholar
Abelson, R. P. (1968). Simulation of social behavior. In Lindzey, G. & Aronson, E. (Eds.), Handbook of Social Psychology (revised ed.). Cambridge, MA: Addison-Wesley.Google Scholar
Abelson, R. P. (1973). The structure of belief systems. In Schank, R. C. & Colby, K. (Eds.), Computer Models of Thought and Language (pp. 287–339). San Francisco, CA: Freeman.Google Scholar
Abelson, R. P., & Bernstein, A. (1963). A computer simulation model of community referendum controversies. Public Opinion Quarterly, 27 (1), 93. https://doi.org/10.1086/267152Google Scholar
Abelson, R. P., & Carroll, J. (1965). Computer simulation of individual belief systems. American Behavioral Scientist, 8 , 24–30.Google Scholar
Abelson, R. P., Aronson, E., McGuire, W. J., Newcomb, T. M., Rosenberg, M. J., & Tannenbaum, P. H. (Eds.). (1968). Theories of Cognitive Consistency: A Sourcebook. Chicago, IL: Rand-McNally.Google Scholar
Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Anderson, J. R., & Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Asch, S. E. (1946). Forming impressions of personality. Journal of Abnormal and Social Psychology, 41, 258–290.CrossRefGoogle ScholarPubMed
Atkinson, J. W., & Birch, D. (1970). The Dynamics of Action. New York, NY: John Wiley.Google Scholar
Ballew, C. C., & Todorov, A. (2007). Predicting political elections from rapid and unreflective face judgments. Proceedings of the National Academy of Sciences, 104 , 17948–17953.Google Scholar
Bechara, A., & Naqvi, N. (2004). Listening to your heart: interoceptive awareness as a gateway to feeling. Nature Neuroscience, 7 (2), 102–103.Google Scholar
Berridge, K. C. (2012). From prediction error to incentive salience: mesolimbic computation of reward motivation. European Journal of Neuroscience, 35 (7), 1124–1143. https://doi.org/10.1111/j.1460-9568.2012.07990.xGoogle Scholar
Berridge, K. C., & O’Doherty, J. P. (2013). From experienced utility to decision utility. In Glimcher, P. W. & Fehr, E. (Eds.), Neuroeconomics: Decision Making and the Brain (2nd ed., pp. 325–341). New York, NY: Academic Press.Google Scholar
Brehm, J. W. (1956). Postdecision changes in the desirability of alternatives. The Journal of Abnormal and Social Psychology, 52 (3), 384–389. https://doi.org/10.1037/h0041006Google Scholar
Brewer, M. B. (1988). A dual process model of impression formation. In Srull, T. K. & Wyer, R. S., Jr. (Eds.), Advances in Social Cognition (pp. 1–36). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Brewer, M. B. (1991). The social self: on being the same and different at the same time. Personality and Social Psychology Bulletin, 17 (5), 475–482. https://doi.org/10.1177/0146167291175001Google Scholar
Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1997). Beyond bipolar conceptualizations and measures: the case of attitudes and evaluative space. Personality and Social Psychology Review, 1 (1), 3–25. https://doi.org/10.1037/0022-3514.76.5.839Google Scholar
Carlston, D. E., Skowronski, J. J., & Sparks, C. (1995). Savings in relearning: II. On the formation of behaviour-based trait associations and inferences. Journal of Personality and Social Psychology, 69 (3), 420–436.Google Scholar
Centola, D., Willer, R., & Macy, M. (2005). The emperor’s dilemma: a computational model of self-enforcing norms. American Journal of Sociology, 110 (4), 1009–1040.Google Scholar
Colby, K. M. (1975). Artificial Paranoia: A Computer Simulation of Paranoid Processes. New York, NY: Pergamon Press.Google Scholar
Colby, K. M. (1981). Modeling a paranoid mind. The Behavioral and Brain Sciences, 4, 515–560.Google Scholar
Conrey, F. R., & Smith, E. (2005). Multi-agent simulation of men’s and women’s mate choice: Sex differences in mate characteristics need not reflect sex differences in mate preferences. Unpublished manuscript, Indiana University.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
Dalege, J., Borsboom, D., van Harreveld, F., & van der Maas, H. L. J. (2018). The attitudinal entropy (ae) framework as a general theory of individual attitudes. Psychological Inquiry, 29 (4), 175–193. https://doi.org/10.1080/1047840X.2018.1537246CrossRefGoogle Scholar
Dalege, J., Borsboom, D., van Harreveld, F., van den Berg, H., Conner, M., & van der Maas, H. L. J. (2016). Toward a formalized account of attitudes: the Causal Attitude Network (CAN) model. Psychological Review, 123 (1), 2–22. https://doi.org/10.1037/a0039802Google Scholar
Dawson, C. K., O’Reilly, R. C., & McClelland, J. L. (2003). The PDP++ Software User’s Manual, version 3.0. Pittsburgh, PA: Carnegie-Mellon University.Google Scholar
Ehret, P. J., Monroe, B. M., & Read, S. J. (2015). Modeling the dynamics of evaluation: a multilevel neural network implementation of the iterative reprocessing model. Personality and Social Psychology Review, 19 (2), 148–176.Google Scholar
Eiser, J. R., Fazio, R. H., Stafford, T., & Prescott, T. J. (2003). Connectionist simulation of attitude learning: asymmetries in the acquisition of positive and negative evaluations. Personality and Social Psychology Bulletin, 29 (10), 1221–1235. https://doi.org/10.1177/0146167203254605Google Scholar
Eiser, J. R., Stafford, T., & Fázio, R. H. (2008). Expectancy confirmation in attitude learning: a connectionist account. European Journal of Social Psychology, 38 (6), 1023–1032. https://doi.org/10.1002/ejsp.530Google Scholar
Eiser, J. R., Stafford, T., & Fazio, R. H. (2009). Prejudiced learning: a connectionist account. British Journal of Psychology, 100 (2), 399–413. https://doi.org/10.1348/000712608X357849Google Scholar
Festinger, L. (1957). A Theory of Cognitive Dissonance. Evanston, IL: Row, Peterson.CrossRefGoogle Scholar
Fiedler, K. (1996). Explaining and simulating judgment biases as an aggregation phenomenon in probabilistic, multiple-cue environments. Psychological Review, 103 (1), 193–214.Google Scholar
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Cambridge, MA: Addison-Wesley.Google Scholar
Fiske, S. T., & Neuberg, S. L. (1990). A continuum of impression formation, from category based to individuating processes: influences of information and motivation on attention and interpretation. In Zanna, M. (Ed.), Advances in Experimental Social Psychology (pp. 1–74). San Diego, CA: Academic Press.Google Scholar
Flache, A., Mäs, M., Feliciani, T., et al. (2017). Models of social influence: towards the next frontiers. Journal of Artificial Societies and Social Simulation, 20 (4), 2. https://doi.org/10.18564/jasss.3521Google Scholar
Fleeson, W. (2004). Moving personality beyond the person-situation debate: the challenge and the opportunity of within-person variability. Current Directions in Psychological Science, 13 (2), 83–87.Google Scholar
Fleeson, W. (2007). Situation-based contingencies underlying trait-content manifestation in behavior. Journal of Personality, 75 (4), 825–862. https://doi.org/10.1111/j.1467-6494.2007.00458.xGoogle Scholar
Freedman, J. L. (1965). Long-term behavioral effects of cognitive dissonance. Journal of Experimental Social Psychology, 1 , 145–155. http://dx.doi.org/10.1016/0022-1031(65)90042-9Google Scholar
Freeman, J. B., & Ambady, N. (2011). A dynamic interactive theory of person construal. Psychological Review, 118 (2), 247–279. https://doi.org/10.1037/a0022327.Google Scholar
Gerard, H. B., & Mathewson, G. C. (1966). The effects of severity of initiation on liking for a group: a replication. Journal of Experimental Social Psychology, 2 , 278–287. http://dx.doi.org/10.1016/0022-1031(66)90084-9Google Scholar
Gray, J. A. (1987a). The neuropsychology of emotion and personality. In Gray, A., Stahl, S. M., Iverson, S. D., & Goodman, E. C. (Eds.), Cognitive Neurochemistry (pp. 171–190). Oxford: Oxford University Press.Google Scholar
Gray, J. A. (1987b). The Psychology of Fear and Stress (2nd ed.). New York, NY: Cambridge University Press.Google Scholar
Gray, J. A. (1991). The neuropsychology of temperament. In Strelau, J. & Angleitner, A. (Eds.), Explorations in Temperament: International Perspectives on Theory and Measurement. Perspectives on Individual Differences (pp. 105–128). New York, NY: Plenum Press.Google Scholar
Gray, J. A., & McNaughton, N. (2000). The Neuropsychology of Anxiety: An Enquiry into the Functions of the Septo-Hippocampal System (2nd ed.). Oxford: Oxford University Press.Google Scholar
Gray, K., Rand, D. G., Ert, E., Lewis, K., Hershman, S., & Norton, M. I. (2014). The emergence of “us and them” in 80 lines of code: modeling group genesis in homogeneous populations. Psychological Science, 25 (4), 982–990. https://doi.org/10.1177/0956797614521816Google Scholar
Greenwald, A. G., & Banaji, M. R. (1989). The self as a memory system: powerful, but ordinary. Journal of Personality and Social Psychology, 57 (1), 41–54.Google Scholar
Gullahorn, J., & Gullahorn, J. E. (1963). A computer model of elementary social behavior. Behavioral Science, 8, 354–362.Google Scholar
Hastie, R. (1988). A computer simulation model of person memory. Journal of Experimental Social Psychology, 24 (5), 423–447.Google Scholar
Heider, F. (1958). The Psychology of Interpersonal Relations. New York, NY: Wiley.Google Scholar
Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple trace memory model. Psychological Review, 95, 528–551.CrossRefGoogle Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79, 2554–2558.Google Scholar
Hopfield, J. J. (1984). Neurons with graded responses have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81, 3088–3092.CrossRefGoogle ScholarPubMed
Ising, E. (1925). Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Physik, 31 (1), 253–258. https://doi.org/10.1007/BF02980577Google Scholar
Kalick, S. M., & Hamilton, T. E. (1986). The matching hypothesis reexamined. Journal of Personality and Social Psychology, 51 (4), 673–682.Google Scholar
Kashima, Y., Woolcock, J., & Kashima, E. S. (2000). Group impressions as dynamic configurations: the tensor product model of group impression formation and change. Psychological Review, 107 (4), 914–942.Google Scholar
Kashima, Y., Woolcock, J., & King, D. (1998). The dynamics of group impression formation: the tensor product model of exemplar-based social category learning. In Read, S. J. & Miller, L. C. (Eds.), Connectionist Models of Social Reasoning and Social Behavior (pp. 71–109). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Kenrick, D. T., Li, N. P., & Butner, J. (2003). Dynamical evolutionary psychology: individual decision rules and emergent social norms. Psychological Review, 110 (1), 3–28.Google Scholar
Klapper, A., Dotsch, R., van Rooij, I., & Wigboldus, D. H. J. (2018). Social categorization in connectionist models: a conceptual integration. Social Cognition, 36 (2), 221–246. https://doi.org/10.1521/soco.2018.36.2.221Google Scholar
Kunda, Z., & Thagard, P. (1996). Forming impressions from stereotypes, traits, and behaviors: a parallel-constraint-satisfaction theory. Psychological Review, 103 (2), 284–308. https://doi.org/10.1037/0033-295X.103.2.284Google Scholar
Latané, B. (1996). Strength from weakness: the fate of opinion minorities in spatially distributed groups. In Witte, E. H. & Davis, J. H. (Eds.), Understanding Group Behavior, Vol. 1: Consensual Action by Small Groups (pp. 193–219). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Latané, B. (2000). Pressures to uniformity and the evolution of cultural norms: modeling dynamic social impact. In Ilgen, D. R. & Hulin, C. H. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 189–220). Washington, DC: American Psychological Association.Google Scholar
Latané, B., & Bourgeois, M. J. (2001). Successfully simulating dynamic social impact: three levels of prediction. In Forgaz, J. P. & Williams, K. D. (Eds.), Social Influence: Direct and Indirect Processes (pp. 61–76). New York, NY: Psychology Press.Google Scholar
Latané, B., Nowak, A., & Liu, J. H. (1994). Measuring emergent social phenomena: dynamism, polarization, and clustering as order parameters of social systems. Behavioral Science, 39 (1), 1–24.Google Scholar
Leonardelli, G. J., Pickett, C. L., & Brewer, M. B. (2010). Optimal distinctiveness theory. In Advances in Experimental Social Psychology (Vol. 43, pp. 63–113). London: Elsevier. https://doi.org/10.1016/S0065-2601(10)43002-6Google Scholar
Lewin, K. (1935). A Dynamic Theory of Personality. New York, NY: McGraw-Hill.Google Scholar
Lewin, K. (1947a). Frontiers in group dynamics: I. Human Relations, 1, 2–38.Google Scholar
Lewin, K. (1947b). Frontiers in group dynamics: II. Human Relations, 1, 143–153.Google Scholar
Linder, D. E., Cooper, J., & Jones, E. E. (1967). Decision freedom as a determinant of the role of incentive magnitude in attitude change. Journal of Personality and Social Psychology, 6, 245–254. http://dx.doi.org/10.1037/h0021220Google Scholar
Linville, P. W., Fischer, G. W., & Salovey, P. (1989). Perceived distributions of the characteristics of in-group and out-group members: empirical evidence and a computer simulation. Journal of Personality and Social Psychology, 57 , 165–188.Google Scholar
Loehlin, J. C. (1968). Computer Models of Personality. New York, NY: Random House.Google Scholar
MacCoun, R. J. (2012). The burden of social proof: shared thresholds and social influence. Psychological Review, 119 (2), 345–372. https://doi.org/10.1037/a0027121Google Scholar
MacCoun, R. J. (2015). Balancing evidence and norms in cultural evolution. Organizational Behavior and Human Decision Processes, 129, 93–104. https://doi.org/10.1016/j.obhdp.2014.09.009Google Scholar
MacCoun, R. J. (2017). Computational models of social influence and collective behavior. In Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.), Computational Social Psychology (pp. 258–280). London: Routledge.Google Scholar
Mason, W. A., Conrey, F. R., & Smith, E. R. (2007). Situating social influence processes: dynamic, multidirectional flows of influence within social networks. Personality and Social Psychology Review, 11 (3), 279–300. https://doi.org/10.1177/1088868307301032Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part I. An account of basic findings. Psychological Review, 88, 375–407.Google Scholar
McClelland, J. L., & Rumelhart, D. E. (Eds.). (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 2: Psychological and Biological Models. Cambridge, MA: MIT Press/Bradford Books.Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1988). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. Cambridge, MA: MIT Press/Bradford Books.Google Scholar
Miller, N. (1959). Liberalization of basic S-R concepts: extensions to conflict behavior, motivation and social learning. In Koch, S. (Ed.), Psychology: A Study of a Science, Study 1 (Vol. 2, pp. 196–292). London: McGraw-Hill.Google Scholar
Mischel, W., & Shoda, Y. (1995). A cognitive affective system theory of personality: reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102 (2), 246–268.Google Scholar
Monroe, B. M., & Read, S. J. (2008). A general connectionist model of attitude structure and change: the ACS (Attitudes as Constraint Satisfaction) model. Psychological Review, 115 (3), 733–759.Google Scholar
Monroe, B. M., Laine, T., Gupta, S., & Farber, I. (2017). Using connectionist models to capture the distinctive psychological structure of impression formation. In Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.), Computational Social Psychology (pp. 38–60). London: Routledge.Google Scholar
Montoya, J. A., & Read, S. J. (1998). A constraint satisfaction model of the correspondence bias: the role of accessibility and applicability of explanations. In Gernsbacher, M. A., & Derry, S. J. (Eds.), The Proceedings of the Twentieth Annual Cognitive Science Society Conference (pp. 722–727). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Mullen, B. (1983). Operationalizing the effect of the group on the individual: a self-attention perspective. Journal of Experimental Social Psychology, 19 , 295–322. https://doi.org/10.1016/0022-1031(83)90025-2Google Scholar
Muthukrishna, M., & Schaller, M. (2020). Are collectivistic cultures more prone to rapid transformation? Computational models of cross-cultural differences, social network structure, dynamic social influence, and cultural change. Personality and Social Psychology Review, 24 (2), 103–120. https://doi.org/10.1177/1088868319855783Google Scholar
Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108.Google Scholar
Nowak, A., & Vallacher, R. R. (1998). Toward computational social psychology: cellular automata and neural network models of interpersonal dynamics. In Read, S. J. & Miller, L. C. (Eds.), Connectionist Models of Social Reasoning and Social Behavior (pp. 277–311). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Nowak, A., Gelfand, M. J., Borkowski, W., Cohen, D., & Hernandez, I. (2016). The evolutionary basis of honor cultures. Psychological Science, 27 (1), 12–24. https://doi.org/10.1177/0956797615602860Google Scholar
Nowak, A., Szamrej, J., & Latané, B. (1990). From private attitude to public opinion: a dynamic theory of social impact. Psychological Review, 97 (3), 362–376.Google Scholar
Nowak, A., Vallacher, R. R., & Zochowski, M. (2002). The emergence of personality: personal stability through interpersonal synchronization. In Cervone, D. & Mischel, W. (Eds.), Advances in Personality Science (Vol. 1, pp. 292–331). New York, NY: Guilford Press.Google Scholar
Nowak, A., Vallacher, R. R., Tesser, A., & Borkowski, W. (2000). Society of self: the emergence of collective properties in self-structure. Psychological Review, 107 (1), 39–61.Google Scholar
O’Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience. Cambridge, MA: MIT Press.Google Scholar
Orghian, D., Garcia-Marques, L., Uleman, J. S., & Heinke, D. (2015). A connectionist model of spontaneous trait inference and spontaneous trait transference: do they have the same underlying processes? Social Cognition, 33 (1), 20–66.Google Scholar
Orr, M. G., & Chen, D. (2017). Computational modeling of health behavior. In Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.), Computational Social Psychology (pp. 81–102). London: Routledge.Google Scholar
Orr, M. G., Thrush, R., & Plaut, D. C. (2013). The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior. PLoS ONE, 8 (5), e62490. https://doi.org/10.1371/journal.pone.0062490Google Scholar
Pickering, A. D. (2008). Formal and computational models of reinforcement sensitivity theory. In Corr, P. J. (Ed.), The Reinforcement Sensitivity Theory of Personality (pp. 453–481). Cambridge: Cambridge University Press.Google Scholar
Queller, S., & Smith, E. R. (2002). Subtyping versus bookkeeping in stereotype learning and change: connectionist simulations and empirical findings. Journal of Personality and Social Psychology, 82 (3), 300–313.Google Scholar
Read, S. J., Brown, A. D., Wang, P., & Miller, L. C. (2021). Neural networks and virtual personalities: capturing the structure and dynamics of personality. In Rauthmann, J. F. (Ed.), The Handbook of Personality Dynamics and Processes. London: Elsevier.Google Scholar
Read, S. J., Droutman, V., & Miller, L. C. (2017). Virtual personalities: a neural network model of the structure and dynamics of personality. In Vallacher, R. R., Read, S. J., & Nowak, A. (Eds.), Computational Social Psychology. (pp. 15–37). London: Routledge.Google Scholar
Read, S. J., & Marcus-Newhall, A. (1993). Explanatory coherence in social explanations: a parallel distributed processing account. Journal of Personality and Social Psychology, 65 , 429–447.Google Scholar
Read, S. J., & Miller, L. C. (1993). Rapist or “regular guy”: explanatory coherence in the construction of mental models of others. Personality and Social Psychology Bulletin, 19, 526–540.Google Scholar
Read, S. J., & Miller, L. C. (1994). Dissonance and balance in belief systems: the promise of parallel constraint satisfaction processes and connectionist modeling approaches. In Schank, R. C. & Langer, E. (Eds.), Beliefs, Reasoning, and Decision-making: Psycho-logic in Honor of Bob Abelson. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Read, S. J., & Miller, L. C. (2002). Virtual personalities: a neural network model of personality. Personality and Social Psychology Review, 6 (4), 357–369.Google Scholar
Read, S. J., & Miller, L. C. (2021). Neural network models of personality structure and dynamics. In Wood, D., Harms, P., Read, S. J., & Slaughter, A., (Eds.), Measuring and Modeling Persons and Situations. Cambridge, MA: Elsevier.Google Scholar
Read, S. J., Monroe, B. M., Brownstein, A. L., Yang, Y., Chopra, G., & Miller, L. C. (2010). A neural network model of the structure and dynamics of human personality. Psychological Review, 117 (1), 61–92. https://doi.org/10.1037/a0018131.Google Scholar
Read, S. J., & Monroe, B. M. (2019). Modeling cognitive dissonance as a parallel constraint satisfaction network with learning. In Harmon-Jones, E. (Ed.), Cognitive Dissonance: Reexamining a Pivotal Theory in Psychology (2nd ed., pp. 197–226). Washington, DC: American Psychological Association. https://doi.org/10.1037/0000135-010Google Scholar
Read, S. J., & Montoya, J. A. (1999a). An auto associative model of causal learning and causal reasoning. Journal of Personality and Social Psychology, 76, 728–742.Google Scholar
Read, S. J., & Montoya, J. A. (1999b). A feedback neural network model of causal learning and causal reasoning. In Hahn, M. & Stoness, S. C. (Eds.), The Proceedings of the Twenty-first Annual Cognitive Science Society Conference (pp. 578–583). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Read, S. J., Smith, B., Droutman, V., & Miller, L. C. (2017). Virtual personalities: using computational modeling to understand within-person variability. Journal of Research in Personality, 69, 237–249. http://dx.doi.org/10.1016/j.jrp.2016.10.005Google Scholar
Read, S. J., & Urada, D. I. (2003). A neural network simulation of the outgroup homogeneity effect. Personality and Social Psychology Review, 7 (2), 146–159.Google Scholar
Read, S. J., Vanman, E. J., & Miller, L. C. (1997). Connectionism, parallel constraint satisfaction processes, and gestalt principles: (Re) introducing cognitive dynamics to social psychology. Personality and Social Psychology Review, 1, 26–53.Google Scholar
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and non-reinforcement. In Black, A. H. & Prokasy, W. F. (Eds.), Classical Conditioning II: Current Research and Theory. New York, NY: Appleton-Century-Crofts.Google Scholar
Revelle, W., & Condon, D. M. (2015). A model for personality at three levels. Journal of Research in Personality, 56, 70–81. https://doi.org/10.1016/j.jrp.2014.12.006Google Scholar
Rumelhart, D. E., & McClelland, J. L. (Eds.). (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Vol. 1: Foundations. Cambridge, MA: MIT Press.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Shanks, D. R. (1991). Categorization by a connectionist network. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17 , 433–443.Google Scholar
Shoda, Y., LeeTiernan, S., & Mischel, W. (2002). Personality as a dynamical system: emergency of stability and distinctiveness from intra- and interpersonal interactions. Personality and Social Psychology Review, 6 (4), 316–325.Google Scholar
Shultz, T. R., & Lepper, M. R. (1996). Cognitive dissonance reduction as constraint satisfaction. Psychological Review, 103 (2), 219–240.Google Scholar
Shultz, T. R., & Lepper, M. R. (1998). The consonance model of dissonance reduction. In Read, S. J. & Miller, L. C. (Eds.), Connectionist Models of Social Reasoning and Social Behavior (pp. 211–244). Hillsdale, NJ: Erlbaum.Google Scholar
Shultz, T. R., Leveille, E., & Lepper, M. R. (1999). Free choice and cognitive dissonance revisited: choosing “lesser evils” versus “greater goods.” Personality and Social Psychology Bulletin, 25 (1), 40–48.Google Scholar
Skowronski, J. J., Carlston, D. E., Mae, L., & Crawford, M. T. (1998). Spontaneous trait transference: communicators take on the qualities they describe in others. Journal of Personality and Social Psychology, 74 (4), 837–848.Google Scholar
Smaldino, P. E., & Epstein, J. M. (2015). Social conformity despite individual preferences for distinctiveness. Royal Society Open Science, 2 (3), 140437. https://doi.org/10.1098/rsos.140437Google Scholar
Smaldino, P., Pickett, C., Sherman, J., & Schank, J. (2012). An agent-based model of social identity dynamics. Journal of Artificial Societies and Social Simulation, 15 (4), 7. https://doi.org/10.18564/jasss.2030Google Scholar
Smillie, L. D., Pickering, A. D., & Jackson, C. J. (2006). The new reinforcement sensitivity theory: implications for personality measurement. Personality and Social Psychology Review, 10 (4), 320–335. https://doi.org/10.1207/s15327957pspr1004_3Google Scholar
Smith, E. R. (1991). Illusory correlation in a simulated exemplar-based memory. Journal of Experimental Social Psychology, 27 (2), 107–123.Google Scholar
Smith, E. R. (2014). Evil acts and malicious gossip: a multiagent model of the effects of gossip in socially distributed person perception. Personality and Social Psychology Review, 18 (4), 311–325. https://doi.org/10.1177/1088868314530515Google Scholar
Smith, E. R., Coats, S., & Walling, D. (1999). Overlapping mental representations of self, in-group, and partner: further response time evidence and a connectionist model. Personality and Social Psychology Bulletin, 25 (7), 873–882.Google Scholar
Smith, E. R., & Collins, E. C. (2009). Contextualizing person perception: distributed social cognition. Psychological Review, 116 (2), 343–364. https://doi.org/10.1037/a0015072Google Scholar
Smith, E. R., & DeCoster, J. (1998). Knowledge acquisition, accessibility, and use in person perception and stereotyping: simulation with a recurrent connectionist network. Journal of Personality and Social Psychology, 74 (1), 21–35.Google Scholar
Smith, E. R., & Zárate, M. A. (1992). Exemplar-based model of social judgment. Psychological Review, 99 (1), 3–21. https://doi.org/10.1037/0033-295X.99.1.3Google Scholar
Sorrentino, R. M., Smithson, M., Hodson, G., Roney, C. J. R., & Walker, A. M. (2003). The theory of uncertainty orientation: a mathematical reformulation. Journal of Mathematical Psychology, 47 (2), 132–149.Google Scholar
Spellman, B. A., Ullman, J. B., & Holyoak, K. J. (1993). A coherence model of cognitive consistency: dynamics of attitude change during the Persian Gulf War. Journal of Social Issues, 49 (4), 147–165.Google Scholar
Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychological Review, 112, 159–192.Google Scholar
Tanford, S., & Penrod, S. (1983). Computer modeling of influence in the jury: the role of the consistent juror. Social Psychology Quarterly, 46 , 200–212. https://doi.org/10.2307/3033791Google Scholar
Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences, 12 (3), 435–502.Google Scholar
Thagard, P. (2000). Probabilistic networks and explanatory coherence. Cognitive Science Quarterly, 1 (1), 91–114.Google Scholar
Thagard, P. (2003). Why wasn’t O. J. convicted: emotional coherence in legal inference. Cognition and Emotion, 17, 361–383.Google Scholar
Todorov, A., & Uleman, J. S. (2002). Spontaneous trait inferences are bound to actors’ faces: evidence from a false recognition paradigm. Journal of Personality and Social Psychology, 83 (5), 1051–1065.Google Scholar
Todorov, A., & Uleman, J. S. (2003). The efficiency of binding spontaneous trait inferences to actors’ faces. Journal of Experimental Social Psychology, 39 (6), 549–562.Google Scholar
Todorov, A., & Uleman, J. S. (2004). The person reference process in spontaneous trait inferences. Journal of Personality and Social Psychology, 87 (4), 482–493.Google Scholar
Tomkins, S. S., & Messick, S. (Eds.). (1963). Computer Simulations of Personality. New York, NY: Wiley.Google Scholar
Turner, M. A., & Smaldino, P. E. (2018). Paths to polarization: how extreme views, miscommunication, and random chance drive opinion dynamics. Complexity, 2018, 1–17. https://doi.org/10.1155/2018/2740959Google Scholar
Uleman, J. S., Newman, L. S., & Moskowitz, G. B. (1996). People as flexible interpreters: evidence and issues from spontaneous trait inference. Advances in Experimental Social Psychology, 28, 211–279.Google Scholar
Van Overwalle, F. (1998). Causal explanation as constraint satisfaction: a critique and a feedforward connectionist alternative. Journal of Personality and Social Psychology, 74, 312–328.Google Scholar
Van Overwalle, F., & Heylighen, F. (2006). Talking nets: a multiagent connectionist approach to communication and trust between individuals. Psychological Review, 113 (3), 606–627. https://doi.org/10.1037/0033-295X.113.3.606Google Scholar
Van Overwalle, F., & Jordens, K. (2002). An adaptive connectionist model of cognitive dissonance. Personality and Social Psychology Review, 6 (3), 204–231.Google Scholar
Van Overwalle, F., & Labiouse, C. (2004). A recurrent connectionist model of person impression formation. Personality and Social Psychology Review, 8 (1), 28–61.Google Scholar
Van Overwalle, F., & Siebler, F. (2005). A connectionist model of attitude formation and change. Personality and Social Psychology Review, 9 (3), 231–274.Google Scholar
Van Overwalle, F., & Van Rooy, D. (1998). A connectionist approach to causal attribution. In Read, S. J. & Miller, L. C. (Eds.), Connectionist Models of Social Reasoning and Social Behavior (pp. 143–171). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Van Overwalle, F., & Van Rooy, D. (2001). How one cause discounts or augments another: a connectionist account of causal competition. Personality and Social Psychology Bulletin, 27 (12), 1613–1626.Google Scholar
Van Rooy, D., Van Overwalle, F., Vanhoomissen, T., Labiouse, C., & French, R. (2003). A recurrent connectionist model of group biases. Psychological Review, 110 (3), 536–563.Google Scholar
Widrow, G., & Hoff, M. E. (1960). Adaptive switching circuits. In Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record (Part 4, pp. 96–104).Google Scholar
Zebrowitz, L. A., Fellous, J., Mignault, A., & Andreoletti, C.(2003).Trait impressions as overgeneralized responses to adaptively significant facial qualities: evidence from connectionist modeling. Personality and Social Psychology Review, 7 (3), 194–215.Google Scholar
Zebrowitz, L. A., Kikuchi, M., & Fellous, J.-M. (2007). Are effects of emotion expression on trait impressions mediated by babyfaceness? Evidence from connectionist modeling. Personality and Social Psychology Bulletin, 33 (5), 648–662. https://doi.org/10.1177/0146167206297399Google Scholar

References

Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.Google Scholar
Anderson, J. A. (1995). An Introduction to Neural Networks. Cambridge, MA: MIT Press.Google Scholar
Antonakis, J. (2017). On doing better science: from thrill of discovery to policy implications. The Leadership Quarterly, 28 , 5–21.Google Scholar
Ashford, S. J., & Cummings, L. L. (1983). Feedback as an individual resource: personal strategies of creating information. Organizational Behavior and Human Performance, 32 (3), 370–398.Google Scholar
Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: structure, process, and content. Psychological Bulletin, 120 (3), 338.Google Scholar
Ballard, T., Vancouver, J. B., & Neal, A. (2018). On the pursuit of multiple goals with different deadlines. Journal of Applied Psychology, 103, 1242–1264.Google Scholar
Ballard, T., Vancouver, J. B., Yeo, G., & Neal, A. (2017). The dynamics of approach and avoidance goal striving: a formal model. Motivation and Emotion, 41, 698–707.Google Scholar
Ballard, T., Yeo, G., Loft, S., Vancouver, J. B., & Neal, A. (2016). An integrative formal model of motivation and decision making: the MGPM*. Journal of Applied Psychology, 101 , 1240–1265.Google Scholar
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287.Google Scholar
Barling, J., Christie, A., & Hoption, C. (2011). Leadership. In Zedeck, S. (Ed.), APA Handbook of Industrial and Organizational Psychology, Vol. 1: Building and Developing the Organization (pp. 183–240). Washington, DC: American Psychological Association.Google Scholar
Bauer, T. N., Bodner, T., Erdogan, B., Truxillo, D. M., & Tucker, J. S. (2007). Newcomer adjustment during organizational socialization: a meta-analytic review of antecedents, outcomes, and methods. Journal of Applied Psychology, 92 (3), 707.CrossRefGoogle ScholarPubMed
Bauer, T. N., & Green, S. G. (1998). Testing the combined effects of newcomer information seeking and manager behavior on socialization. Journal of Applied Psychology, 83 (1), 72.Google Scholar
Beehr, T. A., & Gupta, N. (1978). A note on the structure of employee withdrawal. Organizational Behavior and Human Performance, 21 (1), 73–79.Google Scholar
Busemeyer, J., & Diederich, A. (2010). Cognitive Modeling. Thousand Oaks, CA: Sage.Google Scholar
Carver, C. S., & Scheier, M. F. (1998). On the Self-regulation of Behavior. New York, NY: Cambridge University Press.Google Scholar
Cronin, M., & Vancouver, J. B. (2020). The only constant is change: expanding theory by incorporating dynamic properties into one’s models. In Humphrey, S. E. & LeBreton, J. M. (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis. Washington, DC: American Psychological Association.Google Scholar
Cronin, M. A., Gonzalez, C., & Sterman, J. D. (2009). Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organizational Behavior and Human Decision Processes, 108 , 116–130.Google Scholar
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. The Academy of Management Review, 32 , 480–499.Google Scholar
Dionne, S. D., & Dionne, P. J. (2008). Levels-based leadership and hierarchical group decision optimization: a simulation. The Leadership Quarterly, 19 (2), 212–234.Google Scholar
Dionne, S. D., Sayama, H., Hao, C., & Bush, B. J. (2010). The role of leadership in shared mental model convergence and team performance improvement: an agent-based computational model. The Leadership Quarterly, 21 (6), 1035–1049.Google Scholar
Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51 (4), 380–417.Google Scholar
Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., & Apkarian, A. V. (2005). Scale-free brain functional networks. Physical Review Letters, 94 (1), 018102.Google Scholar
Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19 , 329–335.Google Scholar
Gibson, F. P., Fichman, M., & Plaut, D. C. (1997). Learning in dynamic decision tasks: computational model and empirical evidence. Organizational Behavior and Human Decision Processes, 71 (1), 1–35.Google Scholar
Glebbeek, A. C., & Bax, E. H. (2004). Is high employee turnover really harmful? An empirical test using company records. Academy of Management Journal, 47 (2), 277–286.Google Scholar
Graen, G. B., & Uhl-Bien, M. (1995 ). Relationship-based approach to leadership: development of leader-member exchange (LMX) theory of leadership over 25 years: applying a multi-level multi-domain perspective. The Leadership Quarterly, 6 (2), 219–247.Google Scholar
Grand, J. A. (2017). Brain drain? An examination of stereotype threat effects during training on knowledge acquisition and organizational effectiveness. Journal of Applied Psychology, 102 (2), 115.Google Scholar
Grand, J. A., Braun, M. T., Kuljanin, G., Kozlowski, S. W., & Chao, G. T. (2016). The dynamics of team cognition: a process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101 , 1353–1385.Google Scholar
Griffith, R. L., Chmielowski, T., & Yoshita, Y. (2007). Do applicants fake? An examination of the frequency of applicant faking behavior. Personnel Review, 36, 341–355.Google Scholar
Hanisch, K. A. (2000). The impact of organizational interventions on behaviors: an examination of models of withdrawal. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 33–68). Washington, DC: American Psychological Association.Google Scholar
Hanisch, K. A., Hulin, C. L., & Seitz, S. T. (1996). Mathematical/computational modeling of organizational withdrawal processes: benefits, methods, and results. Research in Personnel and Human Resources Management, 14, 91–142.Google Scholar
Hardy III, J. H. (2014). Dynamics in the self-efficacy–performance relationship following failure. Personality and Individual Differences, 71, 151–158.Google Scholar
Hardy III, J., Day, E. A., & ArthurJr, W. (2018). Exploration-exploitation tradeoffs and information-knowledge gaps in self-regulated learning: implications for training and development. Unpublished manuscript.Google Scholar
Harrison, J., & Carroll, G. (1991). Keeping the faith: a model of cultural transmission in formal organizations. Administrative Science Quarterly, 36 (4), 552–582.Google Scholar
Hill, J. M. M., & Trist, E. L. (1955). Changes in accidents and other absences with length of service: a further study of their incidence and relation to each other in an iron and steel works. Human Relations, 8 (2), 121–152.Google Scholar
Hough, L. M., & Furnham, A. (2003). Use of personality variables in work settings. In Hough, L. M. & Furnham, A. (Eds.), Handbook of Psychology: Industrial and Organizational Psychology (Vol. 12, pp. 131–169). New York, NY: John Wiley & Sons.Google Scholar
Jagacinski, R. J., & Flach, J. M. (2003). Control Theory for Humans: Quantitative Approaches to Modeling Performance. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Kennedy, D. M., & McComb, S. A. (2014). When teams shift among processes: insights from simulation and optimization. Journal of Applied Psychology, 99 (5), 784.Google Scholar
Kerr, N. L. (2000). Getting tangled in one’s own (Petri) net: on the promises and perils of computational modeling. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 183–188). Washington, DC: American Psychological Association.Google Scholar
Lewin, K. (1951). Field Theory in Social Science: Selected Theoretical Papers. Oxford: Harpers.Google Scholar
Li, X. (2017). Dynamic goal choice when environment demands exceed individual’s capacity: scaling up the multiple-goal pursuit model. Ohio University.Google Scholar
Locke, E. (1997). The motivation to work: what we know. In Maehr, M. & Pintrich, P. (Eds.), Advances in Motivation and Achievement (Vol. 10, pp. 375–412). Greenwich, CT: JAI Press.Google Scholar
Locke, E. A., & Latham, G. P. (1990). A Theory of Goal Setting and Task Performance. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Lomi, A., & Larsen, E. R. (2001). Dynamics of Organizations: Computational Modeling and Organization Theories. Cambirdge, MA: MIT Press.Google Scholar
Lord, R. G., & Levy, P. E. (1994). Moving from cognition to action: a control theory perspective. Applied Psychology, 43 , 335–367.Google Scholar
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2 (1), 71–87.Google Scholar
March, J. G., & Simon, H. A. (1958). Organizations. New York: Wiley.Google Scholar
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26 (3), 356–376.Google Scholar
Martell, R. F., Lane, D. M., & Emrich, C. (1996). Male-female differences: a computer simulation. American Psychologist, 51, 157–158.Google Scholar
McGrath, J. E. (1962). The influence of positive interpersonal relations on adjustment and effectiveness in rifle teams. The Journal of Abnormal and Social Psychology, 65 (6), 365.Google Scholar
McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1 (1), 30.Google Scholar
McHugh, K. A., Yammarino, F. J., Dionne, S. D., Serban, A., Sayama, H., & Chatterjee, S. (2016). Collective decision making, leadership, and collective intelligence: tests with agent-based simulations and a field study. The Leadership Quarterly, 27 (2), 218–241.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. (1960). Plans and the Structure of Behavior. New York, NY: Holt.Google Scholar
Muller, P. (2006). Reputation, trust and the dynamics of leadership in communities of practice. Journal of Management and Governance, 10 , 381–400.Google Scholar
Neal, A., Ballard, T., & Vancouver, J. B. (2017). Dynamic self-regulation and multiple-goal pursuit. Annual Review of Organizational Psychology and Organizational Behavior, 4, 401–423.Google Scholar
O’BoyleJr, E., & Aguinis, H. (2012). The best and the rest: revisiting the norm of normality of individual performance. Personnel Psychology, 65 (1), 79–119.Google Scholar
Oh, W., Moon, J. Y., Hahn, J., & Kim, T. (2016). Research note – Leader influence on sustained participation in online collaborative work communities: a simulation-based approach. Information Systems Research, 27 (2), 383–402.Google Scholar
Peters, L. H., O’Connor, E. J., Pooyan, A., & Quick, J. C. (1984). The relationship between time pressure and performance: a field test of Parkinson’s Law. Journal of Occupational Behaviour, 5 , 293–299.Google Scholar
Phelps, K. C., & Hubler, A. W. (2006). Toward an understanding of membership and leadership in youth organizations: sudden changes in average participation due to the behavior of one individual. Emergence: Complexity & Organization, 8, 28–55.Google Scholar
Powers, W. T. (1973). Behavior: The Control of Perception. Chicago, IL: Aldine.Google Scholar
Rees, J., & Koehler, G. J. (2000). Leadership and group search in group decision support systems. Decision Support Systems, 30 (1), 73–82.Google Scholar
Richardson, G. P. (1991). Feedback Thought: In Social Science and Systems Theory. Philadelphia, PA: University of Pennsylvania Press.Google Scholar
Rosenblueth, A., Wiener, N., & Bigelow, J. (1943). Behavior, purpose and teleology. Philosophy of Science, 10 (1), 18–24.Google Scholar
Scherbaum, C. A., & Vancouver, J. B. (2010). If we produce discrepancies, then how? Testing a computational process model of positive goal revision. Journal of Applied Social Psychology, 40, 2201–2231.Google Scholar
Schmidt, A. M., Beck, J. W., & Gillespie, J. Z. (2013). Motivation. In Schmitt, N. W. & Highhouse, S. (Eds.), Handbook of Psychology (2nd ed., Vol. 12, pp. 311–340). Hoboken, NJ: Wiley.Google Scholar
Schmidt, A. M., & DeShon, R. P. (2007). What to do? The effects of discrepancies, incentives, and time on dynamic goal prioritization. Journal of Applied Psychology, 92 (4), 928.Google Scholar
Schmidt, A. M., & DeShon, R. P. (2010). The moderating effects of performance ambiguity on the relationship between self-efficacy and performance. Journal of Applied Psychology, 95 , 572–581.Google Scholar
Senge, P. M. (1990). Catalyzing systems thinking within organizations. In Masaryk, F. (Ed.), Advances in Organization Development (Vol. 1, pp. 197–246). Westport, CT: Ablex Publishing.Google Scholar
Serban, A., Yammarino, F. J., Dionne, S. D., et al. (2015). Leadership emergence in face-to-face and virtual teams: a multi-level model with agent-based simulations, quasi-experimental and experimental tests. The Leadership Quarterly, 26 (3), 402–418.Google Scholar
Simon, H. A. (1969). The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Sitzmann, T., & Yeo, G. (2013). A meta-analytic investigation of the within-person self-efficacy domain: is self-efficacy a product of past performance or a driver of future performance? Personnel Psychology, 66, 531–568.Google Scholar
Steel, P., & König, C. J. (2006). Integrating theories of motivation. Academy of Management Review, 31 , 889–913.Google Scholar
Steel, P., & Weinhardt, J. M. (2018). The building blocks of motivation: goal phase system. In Ones, D. S., Anderson, N., Viswesvaran, C., & Sinangil, H. K. (Eds.), The SAGE Handbook of Industrial, Work & Organizational Psychology: Organizational Psychology (pp. 69–96). London: Sage Reference.Google Scholar
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69 (5), 797.Google Scholar
Sterman, J. D. (1989). Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes, 43 (3), 301–335.Google Scholar
Sun, R. (2016). Anatomy of the Mind. New York, NY: Oxford University Press.Google Scholar
Thomas, M. S. C., & McClelland, J. L. (2008). Connectionist models of cognition. In Sun, R. (Ed.), Cambridge Handbook of Computational Psychology (pp. 23–58). New York, NY: Cambridge University Press.Google Scholar
Vancouver, J. B., & Purl, J. D. (2017). A computational model of self-efficacy’s various effects on performance: moving the debate forward. Journal of Applied Psychology, 102 , 599–616.Google Scholar
Vancouver, J. B., Putka, D. J., & Scherbaum, C. A. (2005). Testing a computational model of the goal-level effect: an example of a neglected methodology. Organizational Research Methods, 8 , 100–127.Google Scholar
Vancouver, J. B., & Scherbaum, C. A. (2008). Do we self-regulate actions or perceptions? A test of two computational models. Computational and Mathematical Organizational Theory, 14 , 1–22.Google Scholar
Vancouver, J. B., Li, X., Weinhardt, J. M., Purl, J. D., & Steel, P. (2016). Using a computational model to understand possible sources of skews in distributions of job performance. Personnel Psychology, 69, 931–974.Google Scholar
Vancouver, J. B., More, K. M., & Yoder, R. J. (2008). Self-efficacy and resource allocation: support for a nonmonotonic, discontinuous model. Journal of Applied Psychology, 93 , 35v47.Google Scholar
Vancouver, J. B., Tamanini, K. B., & Yoder, R. J. (2010). Using dynamic computational models to reconnect theory and research: socialization by the proactive newcomer example. Journal of Management, 36 , 764–793.Google Scholar
Vancouver, J. B., Weinhardt, J. M., & Schmidt, A. M. (2010). A formal, computational theory of multiple-goal pursuit: integrating goal-choice and goal-striving processes. Journal of Applied Psychology, 95 , 985–1008.Google Scholar
Vancouver, J. B., Weinhardt, J. M., & Vigo, R. (2014). Change one can believe in: adding learning to computational models of self-regulation. Organizational Behavior and Human Decision Processes, 124, 56–74.Google Scholar
Vroom, V. R. (1964). Work and Motivation. New York, NY: Wiley.Google Scholar
Weinhardt, J. M., & Vancouver, J. B. (2012). Computational models and organizational psychology: opportunities abound. Organizational Psychology Review, 2 , 267–292.Google Scholar
Weiss, H. M. (1990). Learning theory and industrial and organizational psychology. In Dunnette, M. D. & Hough, L. M. (Eds.), Handbook of Industrial and Organizational Psychology (Vol. 1, 1st ed., pp. 171–221). Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Wellman, N., Applegate, J. M., Harlow, J., & Johnston, E. W. (2020). Beyond the pyramid: alternative formal hierarchical structures and team performance. Academy of Management Journal, 63 (4), 997–1027.Google Scholar
Will, T. E. (2016). Flock leadership: understanding and influencing emergent collective behavior. The Leadership Quarterly, 27 (2), 261–279.Google Scholar
Zhou, L., Wang, M., & Vancouver, J. B. (2019). A formal model of leadership goal striving: development of core process mechanisms and extensions to action team context. Journal of Applied Psychology, 104 , 388–410.Google Scholar
Zickar, M. J. (2000). Modeling faking on personality tests. In Ilgen, D. R. & Hulin, C. L. (Eds.), Computational Modeling of Behavior in Organizations: The Third Scientific Discipline (pp. 95–113). Washington, DC: American Psychological Association.Google Scholar

References

Adams, C. D., & Dickinson, A. (1981). Instrumental responding following reinforcer devaluation. The Quarterly Journal of Experimental Psychology Section B, 33 (2b), 109–121.Google Scholar
Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in Psychiatry, 4, 47.Google Scholar
Bach, D. R. (2015). Anxiety-like behavioural inhibition is normative under environmental threat-reward correlations. PLoS Computational Biology, 11 (12), e1004646.Google Scholar
Beck, A. T., Emery, G., & Greenberg, R. L. (2005). Anxiety Disorders and Phobias: A Cognitive Perspective. New York, NY: Basic Books.Google Scholar
Bergstrom, D., Carlson, J., Chase, T., Braun, A., et al. (1987). D1 dopamine receptor activation required for postsynaptic expression of d2 agonist effects. Science, 236 (4802), 719–722.Google Scholar
Berns, G. S., & Sejnowski, T. J. (1998). A computational model of how the basal ganglia produce sequences. Journal of Cognitive Neuroscience, 10 (1), 108–121.Google Scholar
Blanchard, D. C., & Blanchard, R. J. (2008). Four defensive behaviors, fear, and anxiety. Handbook of Behavioral Neuroscience, 17, 63–79.Google Scholar
Borsboom, D., Cramer, A. O., & Kalis, A. (2019). Brain disorders? Not really: why network structures block reductionism in psychopathology research. Behavioral and Brain Sciences, 42, e2.Google Scholar
Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3 (3), 223–230.Google Scholar
Chekroud, A. M. (2015). Unifying treatments for depression: an application of the free energy principle. Frontiers in Psychology, 6, 153.Google Scholar
Clayton, N. S., Bussey, T. J., & Dickinson, A. (2003). Can animals recall the past and plan for the future? Nature Reviews Neuroscience, 4 (8), 685.Google Scholar
Conceicao, V. A., Dias, A., Farinha, A. C., & Maia, T. V. (2017). Premonitory urges and tics in Tourette syndrome: computational mechanisms and neural correlates. Current Opinion in Neurobiology, 46, 187–199.Google Scholar
Contopoulos-Ioannidis, D. G., Alexiou, G. A., Gouvias, T. C., & Ioannidis, J. P. (2008). Life cycle of translational research for medical interventions. Science, 321 (5894), 1298–1299.Google Scholar
Daw, N. D., Kakade, S., & Dayan, P. (2002). Opponent interactions between serotonin and dopamine. Neural Networks, 15 (4–6), 603–616.Google Scholar
Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006). Cortical substrates for exploratory decisions in humans. Nature, 441 (7095), 876.Google Scholar
Dayan, P., Abbott, L. F., & Abbott, L. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press.Google Scholar
Dayan, P., & Huys, Q. J. (2008). Serotonin, inhibition, and negative mood. PLoS Computational Biology, 4 (2), e4.Google Scholar
Dayan, P., & Huys, Q. J. (2009). Serotonin in affective control. Annual Review of Neuroscience, 32, 95–126.Google Scholar
Declercq, M., De Houwer, J., & Baeyens, F. (2008). Evidence for an expectancy-based theory of avoidance behaviour. Quarterly Journal of Experimental Psychology, 61 (12), 1803–1812.Google Scholar
Dougherty, D. D., Brennan, B. P., Stewart, S. E., Wilhelm, S., Widge, A. S., & Rauch, S. L. (2018). Neuroscientifically informed formulation and treatment planning for patients with obsessive-compulsive disorder: a review. JAMA Psychiatry, 75 (10), 1081–1087.Google Scholar
Ehlers, A., Margraf, J., Roth, W. T., Taylor, C. B., & Birbaumer, N. (1988). Anxiety induced by false heart rate feedback in patients with panic disorder. Behaviour Research and Therapy, 26 (1), 1–11.Google Scholar
Flagel, S., Pine, D., Ahmari, S., et al. (2016). A Novel Framework for Improving Psychiatric Diagnostic Nosology. Cambridge, MA: MIT Press.Google Scholar
Frank, M. J., Santamaria, A., O’Reilly, R. C., & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder. Neuropsychopharmacology, 32 (7), 1583.Google Scholar
Freeman, J., Garcia, A., Benito, K., et al. (2012). The Pediatric Obsessive Compulsive Disorder Treatment Study for young children (POTS jr): developmental considerations in the rationale, design, and methods. Journal of Obsessive-Compulsive and Related Disorders, 1 (4), 294–300.Google Scholar
Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11 (2), 127.Google Scholar
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. Lancet Psychiatry, 1 (2), 148–158.Google Scholar
Frith, U. (2003). Autism: Explaining the Enigma. Oxford: Blackwell Publishing.Google Scholar
Garfinkel, S. N., Tiley, C., O’Keeffe, S., Harrison, N. A., Seth, A. K., & Critchley, H. D. (2016). Discrepancies between dimensions of interoception in autism: implications for emotion and anxiety. Biological Psychology, 114, 117–126.Google Scholar
Gazzaniga, M. S., Bogen, J. E., & Sperry, R. W. (1965). Observations on visual perception after disconnexion of the cerebral hemispheres in man. Brain, 88 (2), 221–236.Google Scholar
George, M. S., Trimble, M. R., Ring, H. A., Sallee, F., & Robertson, M. M. (1993). Obsessions in obsessive-compulsive disorder with and without Gilles de la Tourette’s syndrome. The American Journal of Psychiatry, 150 (1), 93–97.Google Scholar
Gilbert, D. L., Budman, C. L., Singer, H. S., Kurlan, R., & Chipkin, R. E. (2014). A D1 receptor antagonist, ecopipam, for treatment of tics in Tourette syndrome. Clinical Neuropharmacology, 37 (1), 26–30.Google Scholar
Gillan, C. M., Papmeyer, M., Morein-Zamir, S., et al. (2011). Disruption in the balance between goal-directed behavior and habit learning in obsessive-compulsive disorder. American Journal of Psychiatry, 168 (7), 718–726.Google Scholar
Gillan, C. M., & Robbins, T. W. (2014). Goal-directed learning and obsessive–compulsive disorder. Philosophical Transactions of the Royal Society B: Biological Sciences, 369 (1655), 20130475.Google Scholar
Gómez, C., Lizier, J. T., Schaum, M., et al. (2014). Reduced predictable information in brain signals in autism spectrum disorder. Frontiers in Neuroinformatics, 8, 9.Google Scholar
Gradin, V. B., Kumar, P., Waiter, G., et al. (2011). Expected value and prediction error abnormalities in depression and schizophrenia. Brain, 134 (6), 1751–1764.Google Scholar
Gray, J. A. (1982). Précis of the neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system. Behavioral and Brain Sciences, 5 (3), 469–484.Google Scholar
Graybiel, A. M. (1995). Building action repertoires: memory and learning functions of the basal ganglia. Current Opinion in Neurobiology, 5 (6), 733–741.Google Scholar
Graybiel, A. M., & Rauch, S. L. (2000). Toward a neurobiology of obsessive-compulsive disorder. Neuron, 28 (2), 343–347.Google Scholar
Happé, F., & Frith, U. (2006). The weak coherence account: detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36 (1), 5–25.Google Scholar
Hassabis, D., Kumaran, D., Vann, S. D., & Maguire, E. A. (2007). Patients with hippocampal amnesia cannot imagine new experiences. Proceedings of the National Academy of Sciences, 104 (5), 1726–1731.Google Scholar
Hauser, T. U., Fiore, V. G., Moutoussis, M., & Dolan, R. J. (2016). Computational psychiatry of ADHD: neural gain impairments across marrian levels of analysis. Trends in Neurosciences, 39 (2), 63–73.Google Scholar
Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S. (2014). Role of the medial prefrontal cortex in impaired decision making in juvenile attention-deficit/hyperactivity disorder. JAMA Psychiatry, 71 (10), 1165–1173.Google Scholar
Hebb, D. (1957). The Organization of Behavior. New York, NY: Wiley.Google Scholar
Hertz, J., Krogh, A., Palmer, R. G., & Horner, H. (1991). Introduction to the theory of neural computation. Physics Today, 44, 70.Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79 (8), 2554–2558.Google Scholar
Huang, Y., & Rao, R. P. (2011). Predictive coding. Wiley Interdisciplinary Reviews Cognitive Science, 2 (5), 580–593.Google Scholar
Huys, Q. J., Daw, N. D., & Dayan, P. (2015). Depression: a decision-theoretic analysis. Annual Review of Neuroscience, 38, 1–23.Google Scholar
Huys, Q. J., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Computational Biology, 8 (3), e1002410.Google Scholar
Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19 (3), 404.Google Scholar
Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013). Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biology of Mood & Anxiety Disorders, 3 (1), 12.Google Scholar
Ito, R., & Lee, A. C. (2016). The role of the hippocampus in approach-avoidance conflict decision-making: evidence from rodent and human studies. Behavioural Brain Research, 313, 345–357.Google Scholar
Johnson, A., & Redish, A. D. (2007). Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. Journal of Neuroscience, 27 (45), 12176–12189.Google Scholar
Kahneman, D. (2011). Thinking, Fast and Slow. Oxford: Macmillan.Google Scholar
Kalanithi, P. S., Zheng, W., Kataoka, Y., et al. (2005). Altered parvalbumin-positive neuron distribution in basal ganglia of individuals with Tourette syndrome. Proceedings of the National Academy of Sciences, 102 (37), 13307–13312.Google Scholar
Kim, E. J., Park, M., Kong, M.-S., Park, S. G., Cho, J., & Kim, J. J. (2015). Alterations of hippocampal place cells in foraging rats facing a “predatory” threat. Current Biology, 25 (10), 1362–1367.Google Scholar
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27 (12), 712–719.Google Scholar
Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35 (1), 217.Google Scholar
Laplane, D., Levasseur, M., Pillon, B., et al. (1989). Obsessive-compulsive and other behavioural changes with bilateral basal ganglia lesions: a neuropsychological, magnetic resonance imaging and positron tomography study. Brain, 112 (3), 699–725.Google Scholar
Lashley, K. S. (1951). The Problem of Serial Order in Behavior, Vol. 21. Indianapolis, IN: Bobbs-Merrill.Google Scholar
Lee, D. K., Itti, L., Koch, C., & Braun, J. (1999). Attention activates winner-take-all competition among visual filters. Nature Neuroscience, 2 (4), 375.Google Scholar
Lieberman, J. A. (2015). Shrinks: The Untold Story of Psychiatry. London: Hachette.Google Scholar
Loh, M., Rolls, E. T., & Deco, G. (2007). A dynamical systems hypothesis of schizophrenia. PLoS Computational Biology, 3 (11), e228.Google Scholar
Lovibond, P. F., Saunders, J. C., Weidemann, G., & Mitchell, C. J. (2008). Evidence for expectancy as a mediator of avoidance and anxiety in a laboratory model of human avoidance learning. The Quarterly Journal of Experimental Psychology, 61 (8), 1199–1216.Google Scholar
Lynn, C. W., & Bassett, D. S. (2019). The physics of brain network structure, function and control. Nature Reviews Physics, 1 (5), 318–332.Google Scholar
MacDonald, A. W., Zick, J. L., Chafee, M. V., & Netoff, T. I. (2016). Integrating insults: using fault tree analysis to guide schizophrenia research across levels of analysis. Frontiers in Human Neuroscience, 9, 698.Google Scholar
MacLeod, A. K., & Byrne, A. (1996). Anxiety, depression, and the anticipation of future positive and negative experiences. Journal of Abnormal Psychology, 105 (2), 286.Google Scholar
Maia, T. V., & Conceicao, V. A. (2017). The roles of phasic and tonic dopamine in tic learning and expression. Biological Psychiatry, 82 (6), 401–412.Google Scholar
Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14 (2), 154.Google Scholar
Maia, T. V., & McClelland, J. L. (2012). A neurocomputational approach to obsessive-compulsive disorder. Trends in Cognitive Sciences, 16 (1), 14–15.Google Scholar
Mathys, C. (2016). How could we get nosology from computation? In Redish, A. D. & Gordon, J. A. (Eds.), Computational Psychiatry: New Perspectives on Mental Illness. Strüngmann Forum Reports, Vol. 20. Cambridge, MA: MIT Press.Google Scholar
Miloyan, B., Bulley, A., & Suddendorf, T. (2016). Episodic foresight and anxiety: proximate and ultimate perspectives. British Journal of Clinical Psychology, 55 (1), 4–22.Google Scholar
Mobbs, D., Petrovic, P., Marchant, J. L., et al. (2007). When fear is near: threat imminence elicits prefrontal-periaqueductal gray shifts in humans. Science, 317 (5841), 1079–1083.Google Scholar
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16 (1), 72–80.Google Scholar
Moutoussis, M., Shahar, N., Hauser, T. U., & Dolan, R. J. (2018). Computation in psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry, 2, 50–73.Google Scholar
Niedenthal, P. M. (2007). Embodying emotion. Science, 316 (5827), 1002–1005.Google Scholar
NIMH. (2019a). National Institute of Mental Health: Anxiety disorders. Available at: www.nimh.nih.gov/health/topics/anxiety-disorders/index.shtml [last accessed July 22, 2022].Google Scholar
NIMH. (2019b). National Institute of Mental Health: Research domain criteria. Available at: www.nimh.nih.gov/research/research-funded-by-nimh/rdoc/index.shtml [last accessed July 22, 2022].Google Scholar
Nolen-Hoeksema, S. (2000). The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. Journal of Abnormal Psychology, 109 (3), 504.Google Scholar
O’Keefe, J., & Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford: Clarendon Press.Google Scholar
Paulus, M. P., & Yu, A. J. (2012). Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends in Cognitive Sciences, 16 (9), 476–483.Google Scholar
Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16 (10), 504–510.Google Scholar
Perusini, J. N., & Fanselow, M. S. (2015). Neurobehavioral perspectives on the distinction between fear and anxiety. Learning & Memory, 22 (9), 417–425.Google Scholar
Peterson, B. S., Skudlarski, P., Anderson, A. W., et al. (1998). A functional magnetic resonance imaging study of tic suppression in Tourette syndrome. Archives of General Psychiatry, 55 (4), 326–333.Google Scholar
Ramachandran, V. S., Blakeslee, S., & Shah, N. (1998). Phantoms in the Brain: Probing the Mysteries of the Human Mind. New York, NY: William Morrow.Google Scholar
Raymond, J. G., Steele, J. D., & Seriés, P. (2017). Modeling trait anxiety: from computational processes to personality. Frontiers in Psychiatry, 8, 1.Google Scholar
Redish, A. D. (1999). Beyond the Cognitive Map: From Place Cells to Episodic Memory. Cambridge, MA: MIT Press.Google Scholar
Redish, A. D. (2004). Addiction as a computational process gone awry. Science, 306 (5703), 1944–1947.Google Scholar
Redish, A. D. (2013). The Mind Within the Brain: How We Make Decisions and How Those Decisions Go Wrong. Oxford: Oxford University Press.Google Scholar
Redish, A. D. (2016). Vicarious trial and error. Nature Reviews Neuroscience, 17 (3), 147.Google Scholar
Redish, A. D., & Gordon, J. A. (2016). Computational Psychiatry: New Perspectives on Mental Illness, Vol. 20. Cambridge, MA: MIT Press.Google Scholar
Redish, A. D., Jensen, S., & Johnson, A. (2008). Addiction as vulnerabilities in the decision process. Behavioral and Brain Sciences, 31 (4), 461–487.Google Scholar
Redish, A. D., Kummerfeld, E., Morris, R. L., & Love, A. C. (2018). Opinion: reproducibility failures are essential to scientific inquiry. Proceedings of the National Academy of Sciences, 115 (20), 5042–5046.Google Scholar
Reynolds, J. H., & Heeger, D. J. (2009). The normalization model of attention. Neuron, 61 (2), 168–185.Google Scholar
Robinson, T. E., & Berridge, K. C. (2001). Incentive-sensitization and addiction. Addiction, 96 (1), 103–114.Google Scholar
Rolls, E. T., Loh, M., & Deco, G. (2008). An attractor hypothesis of obsessive–compulsive disorder. European Journal of Neuroscience, 28 (4), 782–793.Google Scholar
Sagvolden, T., & Sergeant, J. A. (1998). Attention Deficit/Hyperactivity Disorder: From Brain Dysfunctions to Behaviour. London: Routledge.Google Scholar
Saint-Cyr, J. A., Taylor, A., & Nicholson, K. (1995). Behavior and the basal ganglia. Advances in Neurology, 65, 1–28.Google Scholar
Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events: concepts, data, and applications. Annals of the New York Academy of Sciences, 1124 (1), 39–60.Google Scholar
Schmitz, T. W., & Duncan, J. (2018). Normalization and the cholinergic microcircuit: a unified basis for attention. Trends in Cognitive Sciences, 22 (5), 422–437.Google Scholar
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275 (5306), 1593–1599.Google Scholar
Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20 (1), 11.Google Scholar
Seamans, J. K., & Yang, C. R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 74 (1), 1–58.Google Scholar
Seligman, M. E. (1972). Learned helplessness. Annual Review of Medicine, 23 (1), 407–412.Google Scholar
Seneca, L. A. (65 ce). Letters from a Stoic. London: HarperCollins.Google Scholar
Seymour, B., Daw, N. D., Roiser, J. P., Dayan, P., & Dolan, R. (2012). Serotonin selectively modulates reward value in human decision-making. Journal of Neuroscience, 32 (17), 5833–5842.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27 (3), 379–423.Google Scholar
Smith, A., Li, M., Becker, S., & Kapur, S. (2006). Dopamine, prediction error and associative learning: a model-based account. Network: Computation in Neural Systems, 17 (1), 61–84.Google Scholar
Stephan, K. E., Bach, D. R., Fletcher, P. C., et al. (2016). Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. Lancet Psychiatry, 3 (1), 77–83.Google Scholar
Suddendorf, T. (2013). The Gap: The Science of What Separates Us from Other Animals. Baltimore: Constellation.Google Scholar
Sutton, R. S., & Barto, A. G. (1998). Introduction to Reinforcement Learning. Cambridge, MA: MIT Press.Google Scholar
Swain, J. E., Scahill, L., Lombroso, P. J., King, R. A., & Leckman, J. F. (2007). Tourette syndrome and tic disorders: a decade of progress. Journal of the American Academy of Child & Adolescent Psychiatry, 46 (8), 947–968.Google Scholar
Tripp, G., & Wickens, J. R. (2008). Research review: dopamine transfer deficit: a neurobiological theory of altered reinforcement mechanisms in ADHD. Journal of Child Psychology and Psychiatry, 49 (7), 691–704.Google Scholar
Tsibulsky, V. L., & Norman, A. B. (1999). Satiety threshold: a quantitative model of maintained cocaine self-administration. Brain Research, 839 (1), 85–93.Google Scholar
Van Boxtel, J. J., & Lu, H. (2013). A predictive coding perspective on autism spectrum disorders. Frontiers in Psychology, 4, 19.Google Scholar
Verduzco-Flores, S., Ermentrout, B., & Bodner, M. (2012). Modeling neuropathologies as disruption of normal sequence generation in working memory networks. Neural Networks, 27, 21–31.Google Scholar
Vinogradov, S. (2017). The golden age of computational psychiatry is within sight. Nature Human Behaviour, 1, 0047.Google Scholar
Walters, C. J., Jubran, J., Sheehan, A., Erickson, M. T., & Redish, A. D. (2019). Avoid-approach conflict behaviors differentially affected by anxiolytics: implications for a computational model of risky decision-making. Neuroscience, 236 (8), 2513–2525.Google Scholar
Walters, C. J., & Redish, A. D. (2018). A case study in computational psychiatry: addiction as failure modes of the decision-making system. In Anticevic, A. & Murray, J. D. (Eds.), Computational Psychiatry: Mathematical Modeling of Mental Illness (Chapter 8, pp. 199–217). Cambridge, MA: Academic Press.Google Scholar
Williams, J., & Dayan, P. (2005). Dopamine, learning, and impulsivity: a biological account of attention-deficit/hyperactivity disorder. Journal of Child & Adolescent Psychopharmacology, 15 (2), 160–179.Google Scholar
Wu, J. Q., Szpunar, K. K., Godovich, S. A., Schacter, D. L., & Hofmann, S. G. (2015). Episodic future thinking in generalized anxiety disorder. Journal of Anxiety Disorders, 36, 1–8.Google Scholar
Yeung, M., Treit, D., & Dickson, C. T. (2012). A critical test of the hippocampal theta model of anxiolytic drug action. Neuropharmacology, 62 (1), 155–160.Google Scholar
Zick, J. L., Blackman, R. K., Crowe, D. A., et al. (2018). Blocking NMDAR disrupts spike timing and decouples monkey prefrontal circuits: implications for activity-dependent disconnection in schizophrenia. Neuron, 98 (6), 1243–1255.Google Scholar

References

Alishahi, A. (2010). Computational Modeling of Human Language Acquisition. San Rafael, CA: Morgan & Claypool.Google Scholar
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409–429. https://doi.org/10.1037/0033-295X.98.3.409Google Scholar
Aurnhammer, C., & Frank, S. L. (2019). Comparing gated and simple recurrent neural network architectures as models of human sentence processing. In Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 112–118). Austin, TX: Cognitive Science Society.Google Scholar
Baggio, G., & Hagoort, P. (2011). The balance between memory and unification in semantics: a dynamic account of the N400. Language and Cognitive Processes, 26, 1338–1367.Google Scholar
Bever, T. G. (1970). The cognitive basis for linguistic structures. In Hayes, J. R. (Ed.), Cognition and the Development of Language (pp. 279–352). New York, NY: Wiley.Google Scholar
Boston, M. F., Hale, J., Kliegl, R., Patil, U., & Vasishth, S. (2008). Parsing costs as predictors of reading difficulty: an evaluation using the Potsdam Sentence Corpus. Journal of Eye Movement Research, 2, 1–12.Google Scholar
Bowman, S. R., Rastogi, A., Gupta, R., Manning, C. D., & Potts, C. (2016). A fast unified model for parsing and sentence understanding. In Proceedings of the Association for Computational Linguistics (pp. 1466–1477).Google Scholar
Brennan, J. R., & Hale, J. T. (2019). Hierarchical structure guides rapid linguistic predictions during naturalistic listening. PLoS One, 14 (1), e0207741. https://doi.org/10.1371/journal.pone.0207741Google Scholar
Brennan, J. R., Kuncoro, A., Dyer, C., & Hale, J. T. (2020). Localizing syntactic predictions using recurrent neural network grammars. Neuropsychologia 146, 1074–1079. https://doi.org/10.1016/j.neuropsychologia.2020.107479Google Scholar
Brouwer, H., Crocker, M. W., Venhuizen, N. J., & Hoeks, J. C. J. (2017). A neurocomputational model of the N400 and the P600 in language processing. Cognitive Science, 41 (S6), 1318–1352. https://doi.org/10.1111/cogs.12461Google Scholar
Brouwer, H., Delogu, F., Venhuizen, N. J., & Crocker, M. W. (2021). Neurobehavioral correlates of surprisal in language comprehension: a neurocomputational model. Frontiers in Psychology, 12, 110. https://doi.org/10.3389/fpsyg.2021.615538Google Scholar
Chater, N., Crocker, M. W., & Pickering, M. J. (1998). The rational analysis of inquiry: the case for parsing. In Chater, N. & Oaksford, M. (Eds.), Rational Analysis of Cognition (pp. 441–468). Oxford: Oxford University Press.Google Scholar
Crocker, M. W. (1996). Computational Psycholinguistics: An Interdisciplinary Approach to the Study of Language. Dordrecht: Kluwer.Google Scholar
Crocker, M. W. (1999). Mechanisms for sentence processing. In Garrod, S. & Pickering, M. J. (Eds.), Language Processing (pp. 191–232). London: Psychology Press.Google Scholar
Crocker, M. W. (2005). Rational models of comprehension: addressing the performance paradox. In Cutler, A. (Ed.), Twenty-First Century Psycholinguistics: Four Cornerstones (pp. 363–380). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Crocker, M. W., & Brants, T. (2000). Wide coverage probabilistic sentence processing. Journal of Psycholinguistic Research, 29 (6), 647–669.Google Scholar
Crocker, M. W., Knoeferle, P., & Mayberry, M. R. (2010). Situated sentence processing: the coordinated interplay account and a neurobehavioral model. Brain and Language, 112, 189–201. https://doi.org/10.1016/j.bandl.2009.03.004Google Scholar
Dell, G. S., & Cholin, J. (2012). Language production: computational models. In Spivey, M. J., McRae, K., & Joanisse, M. F. (Eds.), The Cambridge Handbook of Psycholinguistics (pp. 426–442). Cambridge: Cambridge University Press.Google Scholar
Delogu, F., Brouwer, H., & Crocker, M. W. (2019). Event-related potentials index lexical retrieval (N400) and integration (P600) during language comprehension. Brain and Cognition (online), 135. https://doi.org/10.1016/j.bandc.2019.05.007Google Scholar
Delogu, F., Brouwer, H., & Crocker, M. W. (2021). When components collide: spatiotemporal overlap of the N400 and P600 in language comprehension. Brain Research (online), 1766. https://doi.org/10.1016/j.brainres.2021.147514Google Scholar
Delogu, F., Crocker, M. W., & Drenhaus, H. (2017). Teasing apart coercion and surprisal: evidence from ERPs and eye-movements. Cognition, 161, 46–59.Google Scholar
Demberg, V., & Keller, F. (2008). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109 (2), 193–210.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14 (2), 179–211. https://doi.org/10.1207/s15516709cog1402_1Google Scholar
Ferreira, F. (2003). The misinterpretation of noncanonical sentences. Cognitive Psychology, 47, 164–203.Google Scholar
Ferreira, F., Ferraro, V., & Bailey, K. G. D. (2002). Good-enough representations in language comprehension. Current Directions in Psychological Science, 11, 11–15.Google Scholar
Ferreira, F., & Patson, N. (2007). The ‘good enough’ approach to language comprehension. Language and Linguistics Compass, 1 (1–2), 71–83.Google Scholar
Fitz, H., & Chang, F. (2019). Language ERPs reflect learning through prediction error propagation. Cognitive Psychology, 111, 15–52. https://doi.org/10.1016/j.cogpsych.2019.03.002Google Scholar
Fodor, J. A. (1983). The Modularity of Mind: An Essay on Faculty Psychology. Cambridge, MA: MIT Press.Google Scholar
Frank, S. L., Haselager, W. F., & van Rooij, I. (2009). Connectionist semantic systematicity. Cognition, 110 (3), 358–379. https://doi.org/10.1016/j.cognition.2008.11.013Google Scholar
Frank, S. L., Koppen, M., Noordman, L. G., & Vonk, W. (2003). Modeling knowledge-based inferences in story comprehension. Cognitive Science, 27 (6), 875–910. https://doi.org/10.1207/s15516709cog2706_3Google Scholar
Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G. (2015). The ERP response to the amount of information conveyed by words in sentences. Brain and Language, 140, 1–11.Google Scholar
Frazier, L. (1979). On comprehending sentences: syntactic parsing strategies. Ph.D. thesis, University of Connecticut, Connecticut.Google Scholar
Gibson, E. A. (1998). Linguistic complexity: locality of syntactic dependencies. Cognition, 68, 1–76.Google Scholar
Gibson, E., Bergen, L., & Piantadosi, S. T. (2013). Rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proceedings of the National Academy of Sciences, 110 (20), 8051–8056.Google Scholar
Gibson, E., Tan, C., Futrell, R., et al. (2017). Don’t underestimate the benefits of being misunderstood. Psychological Science, 28 (6), 703–712. https://doi.org/10.1177/0956797617690277Google Scholar
Gouvea, A. C., Phillips, C., Kazanina, N., & Poeppel, D. (2010). The linguistic processes underlying the P600. Language and Cognitive Processes, 25, 149–188.Google Scholar
Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of North American Association for Computational Linguistics (Vol. 2, pp. 159–166).Google Scholar
Hoeks, J. C. J., Stowe, L. A., & Doedens, G. (2004). Seeing words in context: the interaction of lexical and sentence level information during reading. Cognitive Brain Research, 19 (1), 59–73.Google Scholar
Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press.Google Scholar
Jurafsky, D. (1996). A probabilistic model of lexical and syntactic access and disambiguation. Cognitive Science, 20, 137–194.Google Scholar
Kim, A., & Osterhout, L. (2005). The independence of combinatory semantic processing: evidence from event-related potentials. Journal of Memory and Language, 52 (2), 205–225.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
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: brain potentials reflect semantic incongruity. Science, 207 (4427), 203–205.Google Scholar
Laszlo, S., & Plaut, D. C. (2012). A neurally plausible Parallel Distributed Processing model of event-related potential word reading data. Brain and Language, 120, 271–281. https://doi.org/10.1016/j.bandl.2011.09.001Google Scholar
Lenci, A. (2018). Distributional models of word meaning. Annual Review of Linguistics, 4 (1), 151–171.Google Scholar
Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106 (3), 1126–1177. https://doi.org/10.1016/j.cognition.2007.05.006Google Scholar
Lewis, R. L., & Vasishth, S. (2005). An activation‐based model of sentence processing as skilled memory retrieval. Cognitive Science, 29, 375–419. https://doi.org/10.1207/s15516709cog0000_25Google Scholar
Linzen, T., & Baroni, M. (2021). Syntactic structure from deep learning. Annual Reviews of Linguistics, 7, 195–212.Google Scholar
Lopopolo, A., & Rabovsky, M. (2021). Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society.Google Scholar
MacDonald, M. C., Pearlmutter, N. J., & Seidenberg, M. S. (1994). The lexical nature of syntactic ambiguity resolution. Psychological Review, 101 (4), 676–703. https://doi.org/10.1037/0033-295X.101.4.676Google Scholar
Magnuson, J. S., Mirman, D., & Harris, H. D. (2012). Computational models of spoken word recognition. In Spivey, M., McRae, K., & Joanisse, M. (Eds.), The Cambridge Handbook of Psycholinguistics (pp. 76–103). Cambridge: Cambridge University Press.Google Scholar
Marcus, M. P. (1980). A Theory of Syntactic Recognition for Natural Language. Cambridge, MA: MIT Press.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: W. H. Freeman.Google Scholar
Mayberry, M. R., Crocker, M. W., & Knoeferle, P. (2009). Learning to attend: a connectionist model of situated language comprehension. Cognitive Science, 33 (3), 449–496.Google Scholar
McClelland, J. L., St. John, M. F., & Taraban, R. (1989). Sentence comprehension: a parallel distributed processing approach. Language and Cognitive Processes, 4, 287–336.Google Scholar
McRae, K., Spivey-Knowlton, M. J., & Tanenhaus, M. K. (1998). Modeling the influence of thematic fit (and other constraints) in on-line sentence comprehension. Journal of Memory and Language, 38 (3), 283–312.Google Scholar
Michaelov, J., & Bergen, B. (2020). How well does surprisal explain N400 amplitude under different experimental conditions? In Proceedings of the 24th Conference on Computational Natural Language Learning.Google Scholar
Newell, A. (1973). You can’t play 20 questions with nature and win: projective comments on the papers of this symposium. In Chase, W. G. (Ed.), Visual Information Processing: Proceedings of the Eighth Annual Carnegie Symposium on Cognition. New York, NY: Academic Press.Google Scholar
Pado, U., Crocker, M. W., & Keller, F. (2009). A probabilistic model of semantic plausibility in sentence processing. Cognitive Science, 33, 794–838.Google Scholar
Pereira, F. C. N. (1985). A new characterization of attachment preferences. In Dowty, D., Karttunen, L., & Zwicky, A. (Eds.), Natural Language Parsing: Psychological, Computational, and Theoretical Perspectives. Cambridge: Cambridge University Press.Google Scholar
Pritchett, B. L. (1988). Garden path phenomena and the grammatical basis of language processing. Language, 64 , 539–576.Google Scholar
Rabovsky, M., & McRae, K. (2014). Simulating the N400 ERP component as semantic network error: insights from a feature-based connectionist attractor model of word meaning. Cognition, 132, 68–89. https://doi.org/10.1016/j.cognition.2014.03.010.Google Scholar
Rabovsky, M., Hansen, S. S., & McClelland, J. L. (2018). Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behavior, 2, 693–705. https://doi.org/10.1038/s41562-018-0406-4Google Scholar
Rabovsky, M., & McClelland, J. L. (2019). Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension. Philosophical Transactions of the Royal Society B: Biological Sciences, 375 (1791). https://doi.org/10.1098/rstb.2019.0313Google Scholar
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124 (3), 372–422.Google Scholar
Rayner, K., Carlson, M., & Frazier, L. (1983). The interaction of syntax and semantics during sentence processing. Journal of Verbal Learning and Verbal Behavior, 22, 358–374.Google Scholar
Rayner, K., & Well, A. D. (1996). Effects of contextual constraint on eye movements in reading: a further examination. Psychonomic Bulletin & Review, 3, 504–509.Google Scholar
Roark, B., Bachrach, A., Cardenas, C., & Pallier, C. (2009). Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (pp. 324–333).Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323 (6088), 533–536.Google Scholar
Sanford, A. J., Leuthold, H., Bohan, J., & Sanford, A. J. S. (2011). Anomalies at the borderline of awareness: an ERP study. Journal of Cognitive Neuroscience, 23 (3), 514–523.Google Scholar
Sanford, A. J., & Sturt, P. (2002). Depth of processing in language comprehension: not noticing the evidence. Trends in Cognitive Sciences, 6 (9), 382–386.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27 (3), 379–423.Google Scholar
Smith, N. J., & Levy, R. (2013). The effect of word predictability on reading time is logarithmic. Cognition, 128 (3), 302–319.Google Scholar
Spivey, M., McRae, K., & Joanisse, M. (Eds.). (2012). The Cambridge Handbook of Psycholinguistics. Cambridge: Cambridge University Press.Google Scholar
Staudte, M., Ankener, C., Drenhaus, H., & Crocker, M. W. (2021). Graded expectations in visually situated comprehension: costs and benefits as indexed by the N400. Psychonomic Bulletin & Review, 28, 624–631.Google Scholar
Stevenson, S. (1994). Competition and recency in a hybrid network model of syntactic disambiguation. Journal of Psycholinguistic Research, 23 (4), 295–322.Google Scholar
Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M., & Sedivy, J. C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268 (5217), 1632–1634. https://doi.org/10.1126/science.7777863Google Scholar
Tanenhaus, M. K., Trueswell, J. C., & Hanna, J. E. (2000). Modeling thematic and discourse context effects with a multiple constraints approach: implications for the architecture of the language comprehension system. In Crocker, M. W., Pickering, M. J., & Clifton, C. (Eds.), Architectures and Mechanism for Language Processing (pp. 90–118). Cambridge: Cambridge University Press.Google Scholar
Taylor, W. L. (1953). “Cloze procedure”: a new tool for measuring readability. Journalism Quarterly, 30, 415–433.Google Scholar
Townsend, D., & Bever, T. G. (2001). Sentence Comprehension: The Integration of Habits and Rules. Cambridge, MA: MIT Press.Google Scholar
Trueswell, J. C., Tanenhaus, M. K., & Garnsey, S. M. (1994). Semantic influences on parsing: use of thematic role information in syntactic ambiguity resolution. Journal of Memory and Language, 33, 285–318.Google Scholar
van Dijk, T. A., & Kintsch, W. (1983). Strategies of Discourse Comprehension. New York, NY: Academic Press.Google Scholar
van Herten, M., Kolk, H. H. J., & Chwilla, D. J. (2005). An ERP study of P600 effects elicited by semantic anomalies. Cognitive Brain Research, 22 (2), 241–255.Google Scholar
Venhuizen, N. J., Crocker, M. W., & Brouwer, H. (2019). Expectation-based comprehension: modeling the interaction of world knowledge and linguistic experience. Discourse Processes, 56 (3), 229–255. https://doi.org/10.1080/0163853X.2018.1448677Google Scholar
Venhuizen, N. J., Hendriks, P., Crocker, M. W., & Brouwer, H. (2022). Distributional formal semantics. Information and Computation, 287, 104763. https://doi.org/10.1016/j.ic.2021.104763Google Scholar
Warren, T., & Dickey, M. W. (2021). The use of linguistic and world knowledge in language processing. Language and Linguistics Compass, 15, e12411. https://doi.org/10.1111/lnc3.12411Google Scholar
Wehbe, L., Murphy, B., Talukdar, P., Fyshe, A., Ramdas, A., & Mitchell, T. (2014). Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses. PLoS One, 9 (11), e112575.Google Scholar
Zwaan, R. A., & Radvansky, G. A. (1998). Situation models in language comprehension and memory. Psychological Bulletin, 123 (2), 162–185. https://doi.org/10.1037/0033-2909.123.2.162Google Scholar

References

Bickerton, D. (1990). Language and Species. Chicago, IL: University of Chicago Press.Google Scholar
Bratman, M. E. (1987). Intentions, Plans, and Practical Reason. Cambridge, MA: Harvard University Press.Google Scholar
Cantrell, R., Schermerhorn, P., & Scheutz, M. (2011). Learning actions from human-robot dialogues. In Proceedings of the 2011 IEEE Symposium on Robot and Human Interactive Communication (pp. 125–130). IEEE Press.Google Scholar
Church, K. (2011). A pendulum swung too far. Linguistic Issues in Language Technology, 6 , 1–27.Google Scholar
Culicover, P. W., & Jackendoff, R. (2005). Simpler Syntax. Oxford: Oxford University Press.Google Scholar
Demberg, V., Keller, F., & Koller, A. (2013). Incremental, predictive parsing with psycholinguistically motivated tree-adjoining grammar. Computational Linguistics, 39 (4), 1025–1066.Google Scholar
English, J., & Nirenburg, S. (2020). OntoAgent: implementing content-centric cognitive models. In Proceedings of the 2020 Conference on Advances in Cognitive Systems.Google Scholar
Fillmore, C. J., & Baker, C. F. (2012). A frames approach to semantic analysis. In Heine, B. & Narrog, H. (Eds.),The Oxford Handbook of Linguistic Analysis (Chapter 13, pp. 313–340). Oxford: Oxford University Press.Google Scholar
Fox, J. J. (1977). Roman Jakobson and the comparative study of parallelism. In van Schooneveld, C. H. & Armstrong, D. (Eds.), Roman Jakobson: Echoes of His Scholarship (pp. 59–90). The Hague: Peter de Ridder Press.Google Scholar
Goodall, G. (1987). Parallel Structures in Syntax: Coordination, Causatives and Restructuring. Cambridge: Cambridge University Press.Google Scholar
Hobbs, J., & Kehler, A. (1997). A theory of parallelism and the case of VP ellipsis. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL-98) (pp. 394–401).Google Scholar
Hoffman, T., & Trousdale, G. (Eds.) (2013). The Oxford Handbook of Construction Grammar. Oxford: Oxford University Press.Google Scholar
Jackendoff, R. (2002). Foundations of Language: Brain, Meaning, Grammar, Evolution. Oxford: Oxford University Press.Google Scholar
Jackendoff, R., & Wittenberg, E. (2014). What you can say without syntax: a hierarchy of grammatical complexity. In Newmeyer, F. & Preston, L. (Eds.), Measuring Linguistic Complexity (pp. 65–82). Oxford: Oxford University Press.Google Scholar
Jackendoff, R., & Wittenberg, E. (2017). Linear grammar as a possible stepping-stone in the evolution of language. Psychonomic Bulletin & Review, 24, 219–224.Google Scholar
Jakobson, R., & Vine, B. (1985). Poetry of grammar and grammar of poetry. In Pomorska, K. & Rudy, S. (Eds.), Verbal Art, Verbal Sign, Verbal Time (pp. 37–46). Minneapolis, MN: University of Minnesota Press.Google Scholar
Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing (2nd ed.). Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Kahneman, D. (2011). Thinking: Fast and Slow. New York, NY: Farrar, Straus & Giroux.Google Scholar
Kempson, R., Meyer-Viol, W., & Gabbay, D. (2001). Dynamic Syntax: The Flow of Language Understanding. Oxford: Wiley-Blackwell.Google Scholar
Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: research issues and challenges. Cognitive Systems Research, 10, 141–160.Google Scholar
Lee, H., Chang, A., Peirsman, Y., Chambers, N., Surdeanu, M., & Jurafsky, D. (2013). Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics, 39 (4), 885–916.Google Scholar
Lenat, D. B., & Guha, R. (1990). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project (1st ed.). Boston, MA: Addison-Wesley Longman.Google Scholar
Lenat, D. B., Guha, R. V., Pittman, K., Pratt, D., & Shepherd, M. (1990). Cyc: toward programs with common sense. Communications of ACM, 33 (8), 30–49.Google Scholar
Lepore, E., & Stone, M. (2010). Against metaphorical meaning. Topoi, 29 (2),165–180.Google Scholar
Lieto, A., Lebiere, C., & Oltramari, A. (2018). The knowledge level in cognitive architectures: current limitations and possible developments. Cognitive Systems Research, 48 , 39–55.Google Scholar
Lindes, P., & Laird, J. E. (2016). Toward integrating cognitive linguistics and cognitive language processing. In Reitter, D. & Ritter, F. E. (Eds.), Proceedings of the 14th International Conference on Cognitive Modeling (pp. 86–92).Google Scholar
Marcus, G. (2020). The next decade in AI: four steps towards robust artificial intelligence. arXiv: 2002.06177.Google Scholar
Marr, D. (1982). Vision: A Computational Approach. New York, NY: W. H. Freeman.Google Scholar
McShane, M. (2009). Reference resolution challenges for an intelligent agent: the need for knowledge. IEEE Intelligent Systems, 24 (4), 47–58.Google Scholar
McShane, M. (2018). Typical event sequences as licensors of direct object ellipsis in Russian. Lingvisticæ Investigationes, 41 (2), 179–212.Google Scholar
McShane, M., & Leon, I. (2021). Language generation for broad-coverage, explainable cognitive systems. In Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems.Google Scholar
McShane, M., & Nirenburg, S. (2012). A knowledge representation language for natural language processing, simulation and reasoning. International Journal of Semantic Computing, 6 (1), 3–23.Google Scholar
McShane, M., & Nirenburg, S. (2021). Linguistics for the Age of AI. Cambridge: MIT Press. https://direct.mit.edu/books/book/5042/Linguistics-for-the-Age-of-AI.Google Scholar
Newell, A. (1982). The knowledge level. Artificial Intelligence, 18, 87–127.Google Scholar
Newmeyer, F. J., & Preston, L. B. (2014). Measuring Grammatical Complexity. Oxford: Oxford University Press.Google Scholar
Nirenburg, S., McShane, M., Beale, S., et al. (2018). Toward human-like robot learning. In Natural Language Processing and Information Systems, Proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems (NLDB 2018) (pp. 73–82). Springer.Google Scholar
Nirenburg, S., McShane, M., & English, J. (2020). Content-centric computational cognitive modeling. In Proceedings of the 2020 Conference on Advances in Cognitive Systems.Google Scholar
Nirenburg, S., Oates, T., & English, J. (2007). Learning by reading by learning to read. In Proceedings of the International Conference on Semantic Computing (pp. 694–701). IEEE Press.Google Scholar
Nirenburg, S., & Raskin, V. (2004). Ontological Semantics. Cambridge: MIT Press.Google Scholar
Nirenburg, S., & Wood, P. (2017). Toward human-style learning in robots. In Proceedings of the AAAI Fall Symposium “Natural Communication for Human-Robot Collaboration.” The AAAI Press.Google Scholar
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021 ). A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32 (2), 604–624. https://doi.org/10.1109/tnnls.2020.2979670Google Scholar
Purver, M., Eshghi, A., & Hough, J. (2011). Incremental semantic construction in a dialogue system. In Bos, J. & Pulman, S. (Eds.), Proceedings of the 9th International Conference on Computational Semantics (pp. 365–369). The Association for Computational Linguistics.Google Scholar
Rueschemeyer, S.-A., & Gaskell, M. G. (Eds.) (2018). The Oxford Handbook of Psycholinguistics (2nd ed.). Oxford: Oxford University Press.Google Scholar
Schank, R. C. (1982). Dynamic Memory. Cambridge: Cambridge University Press.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Scheutz, M., Krause, E., Oosterveld, B., Frasca, T., & Platt, R. (2017). Spoken instruction-based one-shot object and action learning in a cognitive robotic architecture. In Das, S., Durfee, E., Larson, K., & Winikoff, M. (Eds.), Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017).Google Scholar
Spivey, M. J., McRae, K., & Joanisse, M. F. (Eds.) (2012). The Cambridge Handbook of Psycholinguistics. Cambridge: Cambridge University Press.Google Scholar
Zubicaray, G. I. de, & Schiller, N. O. (2019). The Oxford Handbook of Neurolinguistics. Oxford: Oxford University Press.Google Scholar

References

Al-Rifaie, M. M., & Bishop, M. (2015). Weak and strong computational creativity. In Besold, T. R., Schorlemmer, M., & Smaill, A. (Eds.), Computational Creativity Research: Towards Creative Machines (pp. 37–49). Paris, France: Springer.Google Scholar
Ashby, F. G., & Hélie, S. (2011). A tutorial on computational cognitive neuroscience: modeling the neurodynamics of cognition. Journal of Mathematical Psychology, 55, 273–289.Google Scholar
Augello, A., Infantino, I., Pilato, G., Rizzo, R., & Vella, F. (2015). Creativity evaluation in a cognitive architecture. Biologically Inspired Cognitive Architectures, 11, 29–37.Google Scholar
Barsalou, L. (2003). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London, 358, 1177–1187.Google Scholar
Barsalou, L., & Wiemer-Hastings, K. (2005). Situating abstract concepts. In Pecher, D. & Zwaan, R. (Eds.), Grounding Cognition: The Role of Perception and Action in Memory, Language, and Thought (pp. 129–163). New York, NY: Cambridge University Press.Google Scholar
Boden, M. A. (2004). The Creative Mind: Myths and Mechanisms (2nd ed.). London: Routledge.Google Scholar
Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate problems. Behavior Research Methods, Instruments, & Computers, 35 (4), 634–639.Google Scholar
Brewer, W. F., & Treyens, J. C. (1981). Role of schemata in memory for places. Cognitive Psychology, 13 (2), 207–230.Google Scholar
Calic, G., & Hélie, S. (2018). Creative sparks or paralysis traps? The effects of contradictions on creative processing and creative products. Frontiers in Psychology, 9, 1489.Google Scholar
Calic, G., Hélie, S., Bontis, N., & Mosakowski, E. (2019). Creativity from paradoxical experience: a theory of how individuals achieve creativity while adopting paradoxical frames. Journal of Knowledge Management, 23, 397–418.Google Scholar
Calic, G., Mosakowski, E., Bontis, N., & Hélie, S. (2022). Is maximizing creativity good? The importance of elaboration and internal confidence in producing creative ideas. Knowledge Management Research & Practice, 20 , 776–791.Google Scholar
Campbell, D. T. (1960). Blind variation and selective retention in creative thought as in other knowledge processes. Psychological Review, 67, 380–400.Google Scholar
Chartier, S., & Proulx, R. (2005). NDRAM: a nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns. IEEE Transactions on Neural Networks, 16, 1393–1400.Google Scholar
Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82, 407–428.Google Scholar
Csikszentmihalyi, M. (1996). Creativity: Flow and the Psychology of Discovery and Invention. New York, NY: HarperCollins.Google Scholar
Duch, W. (2006). Computational creativity. In Proceedings of the International Joint Conference on Neural Networks (pp. 435–442). Vancouver, BC: IEEE Press.Google Scholar
Duncker, K. (1945). On problem solving. Psychological Monographs, 58, i–113.Google Scholar
Durso, F. T., Rea, C. B., & Dayton, T. (1994). Graph-theoretic confirmation of restructuring during insight. Psychological Science, 5, 94–98.Google Scholar
Eppe, M., Maclean, E., Confalonieri, R., et al. (2018). A computational framework for conceptual blending. Artificial Intelligence, 256, 105–129.Google Scholar
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41 (1), 1–63.Google Scholar
Fedor, A., Zachar, I., Szilagyi, A., Ollinger, M., de Vladar, H. P., & Szathmary, E. (2017). Cognitive architecture with evolutionary dynamics solves insight problem. Frontiers in Psychology, 8, 427.Google Scholar
Finke, R. A., Ward, T. B., & Smith, S. M. (1992). Creative Cognition: Theory, Research, and Applications. Cambridge, MA: MIT Press.Google Scholar
Gabora, L. (2005). Creative thought as a non-Darwinian evolutionary process. The Journal of Creative Behavior, 39, 262–283.Google Scholar
Gärdenfors, P. (2004). Conceptual Spaces: The Geometry of Thought. Cambridge, MA: MIT Press.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7 (2), 155–170.Google Scholar
Gilhooly, K. J., Fioratou, E., Anthony, S. H., & Wynn, V. (2007). Divergent thinking: strategies and executive involvement in generating novel uses for familiar objects. British Journal of Psychology, 98 (4), 611–625.Google Scholar
Gray, K., Anderson, S., Chen, E. E., et al. (2019). “Forward flow”: a new measure to quantify free thought and predict creativity. American Psychologist, 74, 539–554.Google Scholar
Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53 (4), 267–293.Google Scholar
Guilford, J. P. (1967). The Nature of Human Intelligence. New York, NY: McGraw-Hill.Google Scholar
Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley.Google Scholar
Hélie, S., & Cousineau, D. (2014). The cognitive neuroscience of automaticity: behavioral and brain signatures. In Sun, M.-K. (Ed.), Advances in Cognitive and Behavioral Sciences (pp. 141–159). New York, NY: Nova Science Publishers.Google Scholar
Hélie, S., Ell, S.W., & Ashby, F.G. (2015). Learning robust cortico-frontal associations with the basal ganglia: an integrative review. Cortex, 64, 123–135.Google Scholar
Hélie, S., Proulx, R., & Lefebvre, B. (2011). Bottom-up learning of explicit knowledge using a Bayesian algorithm and a new Hebbian learning rule. Neural Networks, 24, 219–232.Google Scholar
Hélie, S., Shamloo, F., Novak, K., & Foti, D. (2017). The roles of valuation and reward processing in cognitive function and psychiatric disorders. Annals of the New York Academy of Sciences, 1395, 33–48.Google Scholar
Hélie, S., & Sun, R. (2008). Knowledge integration in creative problem solving. In Love, B. C., McRae, K., & Sloutsky, V. M. (Eds.) Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 1681–1686). Austin, TX: Cognitive Science Society.Google Scholar
Hélie, S., & Sun, R. (2009). Simulating incubation effects using the Explicit-Implicit Interaction with Bayes factor (EII-BF) model. In Proceedings of the International Joint Conference on Neural Networks (pp. 1199–1205). Atlanta, GA: IEEE Press.Google Scholar
Hélie, S., & Sun, R. (2010). Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychological Review, 117 (3), 994–1024.Google Scholar
Hofstadter, D. R., & Mitchell, M. (1994). The copycat project: a model of mental fluidity and analogy making. In Holyoak, K. & Barnden, J. (Eds.), Advances in Connectionist and Neural Computation Theory: Vol. 2. Analogical Connections (pp. 31–112). Norwood, NJ: Ablex Publishing.Google Scholar
Indurkhya, B. (1999). An algebraic approach to modeling creativity of metaphor. In Nehaniv, C. L. (Ed.), Computation for Metaphors, Analogy, and Agents (pp. 292–306). Cham: Springer.Google Scholar
Jennings, K. E. (2010). Developing creativity: artificial barriers in artificial intelligence. Minds and Machines, 20, 489–501.Google Scholar
Johnson-Laird, P. N. (1988). Freedom and constraint in creativity. In Sternberg, R. J. (Ed.), The Nature of Creativity (pp. 202–219). New York, NY: Cambridge University Press.Google Scholar
Jordanous, A. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science, 28, 194–216.Google Scholar
Kaufman, A. B., & Kaufman, J. C. (Eds.). (2015). Animal Creativity and Innovation. Oxford: Elsevier.Google Scholar
Kenett, Y. N. (2018). Investigating creativity from a semantic network perspective. In Kapoula, Z., Volle, E., Renoult, J., & Andreatta, M. (Eds.), Exploring Transdisciplinarity in Art and Sciences (pp. 49–76). Cham: Springer.Google Scholar
Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, 407.Google Scholar
Kenett, Y. N., & Faust, M. (2019). A semantic network cartography of the creative mind. Trends in Cognitive Sciences, 23, 271–274.Google Scholar
Kenett, Y. N., Levy, O., Kenett, D. Y., Stanley, H. E., Faust, M., & Havlin, S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Sciences, 115, 867–872.Google Scholar
Kim, K. H. (2006). Can we trust creativity tests? A review of the Torrance Tests of Creative Thinking (TTCT). Creativity Research Journal, 18 (1), 3–14.Google Scholar
Koestler, A. (1964). The Act of Creation. New York, NY: Macmillan.Google Scholar
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43 (1), 59–69.Google Scholar
Kosko, B. (1988). Bidirectional associative memories. IEEE Transactions on Systems, Man, and Cybernetics, 18 (1), 49–60.Google Scholar
Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago, IL: University of Chicago Press.Google Scholar
Lakoff, G., & Johnson, M. (1999). Philosophy in the Flesh: The Embodied Mind and its Challenge to Western Thought. New York, NY: Basic Books.Google Scholar
Langley, P., & Jones, R. (1988). A computational model of scientific insight. In Sternberg, R. J. (Ed.), The Nature of Creativity (pp. 177–201). New York, NY: Cambridge University Press.Google Scholar
Lubart, T. I. (2001). Models of the creative process: past, present and future. Creativity Research Journal, 13, 295–308.Google Scholar
MacGregor, J. N., & Cunningham, J. B. (2009). The effects of number and level of restructuring in insight problem solving. Journal of Problem Solving, 2 (2), 130–141.Google Scholar
Maier, N. R. (1931). Reasoning in humans. ii. The solution of a problem and its appearance in consciousness. Journal of Comparative Psychology, 12 (2), 181–194.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: Freeman.Google Scholar
Martindale, C. (1995). Creativity and connectionism. In Smith, S. M., Ward, T. B., & Finke, R. A. (Eds.), The Creative Cognition Approach (pp. 249–268). Cambridge, MA: MIT Press.Google Scholar
Marupaka, N., Iyer, L. R., & Minai, A. A. (2012). Connectivity and thought: the influence of semantic network structure in a neurodynamical model of thinking. Neural Networks, 32, 147–158.Google Scholar
Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69, 220–232.Google Scholar
Mednick, S. A., & Mednick, M. (1971). Remote Associates Test: Examiner’s Manual. Boston, MA: Houghton Mifflin.Google Scholar
Minsky, M. (1975). A framework for representing knowledge. In Winston, P. (Ed.), The Psychology of Computer Vision (pp. 211–277). New York, NY: McGraw-Hill.Google Scholar
Miron-Spektor, E., Gino, F., & Argote, L. (2011). Paradoxical frames and creative sparks: enhancing individual creativity through conflict and integration. Organizational Behavior and Human Decision Processes, 116, 229–240.Google Scholar
Nersessian, N. (2008). Creating Scientific Concepts. Cambridge, MA: MIT Press.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1962). The processes of creative thinking. In Gruber, H. E., Terrell, G., & Wertheimer, M. (Eds.), Contemporary Approaches to Creative Thinking (pp. 63–119). New York, NY: Atherton Press.Google Scholar
Ohlsson, S. (1984). Restructuring revisited: I. Summary and critique of the Gestalt theory of problem solving. Scandinavian Journal of Psychology, 25, 65–78.Google Scholar
Oltețeanu, A. M. (2014). Two general classes in creative problem-solving? An account based on the cognitive processes involved in the problem structure – representation structure relationship. In Proceedings of the Workshop “Computational Creativity, Concept Invention, and General Intelligence”, Osnabrück, Germany.Google Scholar
Oltețeanu, A. M. (2016a). From simple machines to eureka in four not-so-easy steps. Towards creative visuospatial intelligence. In Müller, V. C. (Ed.), Fundamental Issues of Artificial Intelligence (vol. 376, pp. 159–180). London: Synthese Library.Google Scholar
Oltețeanu, A. M. (2016b). Towards an approach for the computationally assisted creation of insight problems in the practical object domain. In Besold, T., Kutz, O., & Leon, C. (Eds.), Proceedings of the 5th International Workshop on “Computational Creativity, Concept Invention, and General Intelligence,” Osnabruck, Germany.Google Scholar
Olteţeanu, A. M., & Falomir, Z. (2015). ComRAT-C: a computational compound Remote Associates Test solver based on language data and its comparison to human performance. Pattern Recognition Letters, 67, 81–90.Google Scholar
Olteţeanu, A. M., & Falomir, Z. (2016). Object replacement and object composition in a creative cognitive system: towards a computational solver of the Alternative Uses Test. Cognitive Systems Research, 39, 15–32.Google Scholar
Oltețeanu, A. M., Falomir, Z., & Freksa, C. (2018). Artificial cognitive systems that can answer human creativity tests: an approach and two case studies. IEEE Transactions on Cognitive and Developmental Systems, 10, 469–475.Google Scholar
Oltețeanu, A. M., Gautam, B., & Falomir, Z. (2015). Towards a Visual Remote Associates Test and its computational solver. In Proceedings of the International Workshop on Artificial Intelligence and Cognition – AIC 2015 (CEUR-Ws Vol. 1510).Google Scholar
Olteţeanu, A. M., & Indurkhya, B. (Eds.) (2019). Re-representation in cognitive systems. A special issue. Frontiers in Cognitive Science. Special issue.Google Scholar
Olteţeanu, A. M., Schöttner, M., & Schuberth, S. (2019). Computationally resurrecting the functional remote associates test using cognitive word associates and principles from a computational solver. Knowledge-Based Systems, 168, 1–9.Google Scholar
Oltețeanu, A. M., & Schultheis, H. (2019). What determines creative association? Revealing two factors which separately influence the creative process when solving the Remote Associates Test. Journal of Creative Behavior, 53, 389–395.Google Scholar
Olteţeanu, A. M., Schultheis, H., & Dyer, J. B. (2018). Computationally constructing a repository of compound remote associates test items in American English with comRAT-G. Behavior Research Methods, 50 (5), 1971–1980.Google Scholar
Perlovsky, L., & Levine, D. (2012). The drive for creativity and the escape from creativity: neurocognitive mechanisms. Cognitive Computation, 4, 292–305.Google Scholar
Qiu, J., Li, H., Yang, D., et al. (2008). The neural basis of insight problem solving: an event-related potential study. Brain and Cognition, 68 (1), 100–106.Google Scholar
Rumelhart, D. E. (1984). Schemata and the cognitive system. Handbook of Social Cognition, 1, 161–188.Google Scholar
Saugstad, P., & Raaheim, K. (1957). Problem-solving and availability of functions. Acta Psychologica, 13, 263–278.Google Scholar
Saunders, R. (2012). Towards autonomous creative systems: a computational approach. Cognitive Computation, 4, 216–225.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Erlbaum.Google Scholar
Schooler, J. W., & Melcher, J. (1995). The ineffability of insight. In Ward, T. & Finke, R. (Eds.), The Creative Cognition Approach (pp. 249–268). Cambridge, MA: MIT Press.Google Scholar
Schooler, J. W., Ohlsson, S., & Brooks, K. (1993). Thoughts beyond words: when language overshadows insight. Journal of Experimental Psychology: General, 122, 166–183.Google Scholar
Searle, J. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417–457.Google Scholar
Siew, C., Wulff, D., Beckage, N., & Kenett, Y. (2019). Cognitive Network Science: a review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 2108423.Google Scholar
Simonton, D. K. (2013). Creative thought as blind variation and selective retention: why creativity is inversely related to sightedness. Journal of Theoretical and Philosophical Psychology, 33 (4), 253–266.Google Scholar
Smith, S. M., & Vela, E. (1991). Incubated reminiscence effects. Memory & Cognition, 19, 168–176.Google Scholar
Sowa, J. (1992). Semantic networks. In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence (2nd ed., pp. 1493–1511). New York, NY: Wiley.Google Scholar
Sun, R. (1994). Integrating Rules and Connectionism for Robust Commonsense Reasoning. New York, NY: John Wiley & Sons.Google Scholar
Sun, R. (2002). Duality of the Mind: A Bottom-up Approach Toward Cognition. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25, 203–244.Google Scholar
Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychological Review, 112, 159–192.Google Scholar
Threadgold, E., Marsh, J. E., & Ball, L. J. (2018). Normative data for 84 english rebus puzzles. Frontiers in Psychology, 9, 2513.Google Scholar
Toivonen, H., & Gross, O. (2015). Data mining and machine learning in computational creativity. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5, 265–275.Google Scholar
Wallach, M. A., & Kogan, N. (1965). Modes of Thinking in Young Children: A Study of the Creativity-Intelligence Distinction. Saint Louis, MO: Holt, Rinehart & Winston.Google Scholar
Wallas, G. (1926). The Art of Thought. New York, NY: Franklin Watts.Google Scholar
Whitt, J. K., & Prentice, N. M. (1977). Cognitive processes in the development of children’s enjoyment and comprehension of joking riddles. Developmental Psychology, 13 (2), 129–136.Google Scholar
Worthen, B. R., & Clark, P. M. (1971). Toward an improved measure of remote associational ability. Journal of Educational Measurement, 8 (2), 113–123.Google Scholar
Yaniv, I., & Meyer, D. E. (1987). Activation and metacognition of inaccessible stored information: potential bases for incubation effects in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 187–205.Google Scholar

References

Adam, C., Herzig, A., & Longin, D. (2009). A logical formalization of the OCC theory of emotions. Synthese, 168 (2), 201–248.Google Scholar
Alfonso, B., Vivancos, E., & Botti, V. J. (2014). An open architecture for affective traits in a BDI agent. Paper presented at the 6th ECTA (the 6th IJCCI).Google Scholar
Alfonso, B., Vivancos, E., & Botti, V. (2017). Toward formal modeling of affective agents in a BDI architecture. ACM Transactions on Internet Technology (TOIT), 17, Article 5. JCR 0.705 – 71/106 Q3 T2.Google Scholar
Andre, E., Klesen, M., Gebhard, P., Allen, S., & Rist, T. (2000). Exploiting models of personality and emotions to control the behavior of animated interactive agents. In Proceedings of IWAI, Siena, Italy.Google Scholar
Arbib, M. A. (2005). Beware the passionate robot. In Fellous, J.-M. & Arbib, M. A. (Eds.), Who Needs Emotions? The Brain Meets the Robot (pp. 333–383). New York, NY: Oxford University Press.Google Scholar
Arnold, M. B. (1960). Emotion and Personality. New York, NY: Columbia University Press.Google Scholar
Averill, J. R. (1994). I Feel, Therefore I Am – I Think. In Ekman, P. & Davidson, R. J. (Eds.), The Nature of Emotion: Fundamental Questions. Oxford: Oxford University Press.Google Scholar
Aylett, R., Louchart, S., Dias, J., Paiva, A., & Vala, M. (2005). Fearnot! – an experiment in emergent narrative. Paper presented at Intelligent Virtual Agents 2005.Google Scholar
Aylett, R. S. (2004). Agents and affect: why embodied agents need affective systems. Paper presented at the 3rd Hellenic Conference on AI, Samos, Greece.Google Scholar
Bach, J. (2009). Principles of Synthetic Intelligence: Psi: An Architecture of Motivated Cognition. New York, NY: Oxford University Press.Google Scholar
Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2007). Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychological Bulletin, 133, 1–24.Google Scholar
Barrett, L. F. (2014). The conceptual act theory: a précis. Emotion Review, 6 (4), 292–297. https://doi.org/10.1177/1754073914534479er.sagepub.comGoogle Scholar
Barrett, L. F. (2017). How Emotions Are Made: The Secret Life of the Brain. New York, NY: Houghton Mifflin Harcourt.Google Scholar
Barrett, L. F., Lewis, M., & Haviland-Jones, J. M. (2016). Handbook of Emotions (4th ed.). New York, NY: Guilford.Google Scholar
Bates, J., Loyall, A. B., & Reilly, W. S. (1992). Integrating reactivity, goals, and emotion in a broad agent. In Proceedings of the 14th Meeting of the Cognitive Science Society.Google Scholar
Beaudoin, L., Pudlo, M., & Hyniewska, S. (2020). Mental perturbance: an integrative design-oriented concept for understanding repetitive thought, emotions and related phenomena involving a loss of control of executive functions. SFU Educational Review, 13 (1), 29–58.Google Scholar
Becker-Asano, C. (2008). WASABI: Affect Simulation for Agents with Believable Interactivity. Clifton, VA: IOS Press.Google Scholar
Becker-Asano, C. (2013). WASABI for affect simulation in human-computer interaction Architecture description and example applications. Ph.D. Thesis, Bielefeld University.Google Scholar
Becker-Asano, C., Kopp, S., Pfeiffer-Leßmann, N., & Wachsmuth, I. (2008). Virtual humans growing up: from primary toward secondary emotions. KI – Künstliche Intelligenz, 1, 23–27.Google Scholar
Becker-Asano, C., Stahl, P., Ragni, M., Courgeon, M., Martin, J.-C., & Nebel, B. (2013). An affective virtual agent providing embodied feedback in the paired associate task: system design and evaluation. Paper presented at IVA 2013.Google Scholar
Becker-Asano, C., Meneses, E., Riesterer, N., Hue, J., Dornhege, C., & Nebel, B. (2014). The hybrid Agent MARCO: a multimodal autonomous robotic chess opponent. Paper presented at the 2nd International Conference on Human-Agent Interaction, Tsukuba, Japan.Google Scholar
Becker-Asano, C., & Wachsmuth, I. (2009). Affective computing with primary and secondary emotions in a virtual human. Paper presented at the Autonomous Agents and Multi-Agent Systems.Google Scholar
Becker-Asano, C., & Wachsmuth, I. (2010). Affective computing with primary and secondary emotions in a virtual human. Autonomous Agents and Multi-Agent Systems, 20, 32–49.Google Scholar
Belavkin, R. V., & Ritter, F. E. (2004). OPTIMIST: a new conflict resolution algorithm for ACT-R. In Proceedings of the Sixth International Conference on Cognitive Modeling, Pittsburgh, PA.Google Scholar
Blaney, P. H. (1986). Affect and memory. Psychological Bulletin, 99 (2), 229–246.Google Scholar
Bless, H., & Fiedler, K. (2006). Mood and the regulation of information processing and behavior. In Forgas, J. P. (Ed.), Hearts and Minds: Affective Influences on Social Cognition and Behaviour (pp. 65–84). New York, NY: Psychology Press.Google Scholar
Bosse, T. (2017). On computational models of emotion regulation and their applications within HCI. In Jeon, M. (Ed.), Emotions and Affect in Human Factors and Human-Computer Interaction (pp. 311–337). London: Academic Press/Elsevier.Google Scholar
Bosse, T., Broekens, J., Dias, J., & van der Zwaan, J. (2014). Emotion Modeling: Towards Pragmatic Computational Models of Affective Processes (LNAI 8750). Cham: Springer International Publishing.Google Scholar
Bosse, T., Duell, R., Memon, Z. A., Treur, J., & Wal, C. N. v. d. (2015). Agent-based modeling of emotion contagion in groups. Cognitive Computation, 7, 111–136. https://doi.org/10.1007/s12559-014-9277-9Google Scholar
Bosse, T., Gerritsen, C., & Man, J. d. (2014). Agent-based simulation as a tool for the design of a virtual training environment. Paper presented at the 14th International Conference on Intelligent Agent Technology (IAT’14).Google Scholar
Bosse, T., & Zwanenburg, E. (2014). Do prospect-based emotions enhance believability of game characters? A case study in the context of a dice game. IEEE Transactions on Affective Computing, 5 (1), 17–31. https://doi.org/10.1109/T-AFFC.2013.30Google Scholar
Boukricha, H., Wachsmuth, I., Carminati, M., & Knoeferle, P. (2013). A computational model of empathy: empirical evaluation. Paper presented at the Affective Computing and Intelligent Interaction (ACII).Google Scholar
Boukricha, H., & Wachsmuth, I. (2011). Empathy-based emotional alignment for a virtual human: a three-step approach. KI – Künstliche Intelligenz, 25 (3), 195–204.Google Scholar
Bower, G. H. (1981). Mood and memory. American Psychologist, 36, 129–148.Google Scholar
Bower, G. H. (1992). How might emotions affect memory? In Christianson, S. A. (Ed.), Handbook of Emotion and Memory. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Breazeal, C. L. (2003). Emotion and sociable humanoid robots. International Journal of Human Computer Studies, 59 (1–2), 119–155.Google Scholar
Breazeal, C., & Brooks, R. (2005). Robot emotion: a functional perspective. In Fellous, J.-M. & Arbib, M. A. (Eds.), Who Needs Emotions? New York, NY: Oxford University Press.Google Scholar
Broekens, J. (2010). Modelling the experience of emotion. International Journal of Synthetic Emotions, 1 (1), 1–17.Google Scholar
Broekens, J. (2011). Computational affective science. International Journal of Synthetic Emotions, 2 (2), 73–75.Google Scholar
Broekens, J., Bosse, T., & Marsella, S. (2013). Challenges in computational modeling of affective processes. IEEE Transactions on Affective Computing, 4 (3), 242–245. https://doi.org/10.1109/T-AFFC.2013.23Google Scholar
Broekens, J., & Dai, L. (2019). A TDRL model for the emotion of regret. Paper presented at the 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK.Google Scholar
Broekens, J., DeGroot, D., & Kosters, W. A. (2008). Formal models of appraisal: theory, specification, and computational model. Cognitive Systems Research, 9 (3), 173–197.Google Scholar
Broekens, J., Hudlicka, E., & Bidarra, R. (2016). Emotional appraisal engines for games. In Karpouzis, C. & Yannakakis, G. (Eds.), Emotion in Games (Vol. 4, pp. 215–232). Cham: Springer International Publishing.Google Scholar
Broekens, J., Jacobs, E., & Jonker, C. M. (2015). A reinforcement learning model of joy, distress, hope and fear. Connection Science, 27 (4), 1–19.Google Scholar
Brosch, T. (2013). Comment: on the role of appraisal processes in the construction of emotion. Emotion Review, 5 (4), 369–373. https://doi.org/10.1177/1754073913489752Google Scholar
Busemeyer, J. R., Dimperio, E., & Jessup, R. K. (2007). Integrating emotional processes into decision-making models. In Gray, W. (Ed.), Integrated Models of Cognitive Systems. New York, NY: Oxford University Press.Google Scholar
Cañamero, L. (1997). A hormonal model of emotions for behavior control. Paper presented at the 4th European Conference on Artificial Life (ECAL ‘97), Brighton, UK.Google Scholar
Cañamero, L. D. (2001). Building emotional artifacts in social worlds: challenges and perspectives. Paper presented at the AAAI Fall Symposium “Emotional and Intelligent II: The Tangled Knot of Social Cognition,” Cape Cod, MA.Google Scholar
Cañamero, L., & Avila-Gracia, O. (2007). A bottom-up investigation of emotional modulation in competitive scenarios. Paper presented at the Affective Computing and Intelligent Interaction.Google Scholar
Cannon, W. B. (1927). The James-Lange theory of emotions: a critical examination and an alternative theory. American Journal of Psychology, 39, 106–124.Google Scholar
Castelfranchi, C., & Miceli, M. (2009). The cognitive-motivational compound of emotional experience. Emotion Review, 1 (3), 223–231.Google Scholar
Castellanos, S., Rodriguez, L.-F., & Gutierrez-Garcia, J. O. (2019). A mechanism for biasing the appraisal process in affective agents. Cognitive Systems Research, 58, 351–365.Google Scholar
Clore, G. L. (1994). Why emotions are felt? In Ekman, P. & Davidson, R. J. (Eds.), The Nature of Emotion: Fundamental Questions. Oxford: Oxford University Press.Google Scholar
Clore, G. L., & Ortony, A. (2013). Psychological construction in the OCC model of emotion. Emotion Review, 5 (4), 335–343. https://doi.org/10.1177/1754073913489751er.sagepub.comGoogle Scholar
Coenen, R., & Broekens, J. (2012). Modeling emotional contagion based on experimental evidence for moderating factors. Paper presented at the Workshop on Emotional and Empathic Agents, at the 11th International Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain.Google Scholar
Costa, P. T., & McCrae, R. R. (1992). Four ways five factors are basic. Personality & Individual Differences, 13, 653–665.Google Scholar
Damasio, A., & Carvalho, G. B. (2013). The nature of feelings: evolutionary and neurobiological origins. Nature Reviews Neuroscience, 14 (2), 143–152. https://doi.org/10.1038/nrn3403Google Scholar
Damasio, A. R. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. New York, NY: Putnam.Google Scholar
Dancy, C. L. (2013). ACT-RΦ: a cognitive architecture with physiology and affect. Biologically Inspired Cognitive Architectures, 6 (1), 40–45.Google Scholar
Dastani, M., & Lorini, E. (2012). A logic of emotions: from appraisal to coping. Paper presented at the 11th International Conference on Autonomous Agents and Multiagent Systems.Google Scholar
Dastani, M., & Pankov, A. (2017). Other-condemning moral emotions: anger, contempt and disgust. ACM Transactions on Internet Technologies, 17 (1), 1–24.Google Scholar
Davidson, R., Scherer, K., & Goldsmith, H. H. (2003). Handbook of Affective Sciences. New York, NY: Oxford University Press.Google Scholar
de Rosis, F., Pelachaud, C., Poggi, I., Carofiglio, V., & De Carolis, B. (2003). From Greta’s mind to her face: modelling the dynamics of affective states in a conversational embodied agent. International Journal of Human-Computer Studies, 59 (1–2), 81–118.Google Scholar
Derryberry, D. (1988). Emotional influences on evaluative judgments: roles of arousal, attention, and spreading activation. Motivation and Emotion, 12 (1), 23–55.Google Scholar
Derryberry, D., & Reed, M. A. (2002). Anxiety-related attentional biases and their regulation by attentional control. Journal of Abnormal Psychology, 111, 225–236.Google Scholar
Dias, J., Mascarenhas, S., & Paiva, A. (2014). FAtiMA modular: towards an agent architecture with a generic appraisal framework. In Bosse, T., Broekens, J., Dias, J., & van der Zwaan, J. (Eds.), Towards Pragmatic Computational Models of Affective Processes. Cham: Springer.Google Scholar
Dias, J., & Paiva, A. (2005). Feeling and reasoning: a computational model for emotional agents. In Proceedings of the 12th Portuguese Conference on Artificial Intelligence (EPIA).Google Scholar
Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6 (3–4), 169–200.Google Scholar
Ekman, P. (1994). All emotions are basic. In Ekman, P. & Davidson, R. J. (Eds.), The Nature of Emotions: Fundamental Questions (pp. 15–19). New York, NY: Oxford University Press.Google Scholar
Ekman, P., & Cordaro, D. (2011). What is meant by calling emotions basic. Emotion Review, 3 (4), 364–370.Google Scholar
Ekman, P., & Davidson, R. J. (1994). The Nature of Emotion: Fundamental Questions. New York, NY: Oxford University Press.Google Scholar
Elliot, C. (1992). The affective reasoner: a process model of emotions in a multiagent system. Ph.D. Thesis, Northwestern University, Evanston.Google Scholar
Ellsworth, P. C., & Scherer, K. R. (2003). Appraisal processes in emotion. In Davidson, R. J., Scherer, K. R., & Goldsmith, H. H. (Eds.), Handbook of Affective Sciences. New York, NY: Oxford University Press.Google Scholar
El-Nasr, M. S., Yen, J., & Ioerger, T. R. (2000). FLAME – Fuzzy logic adaptive model of emotions. Autonomous Agents and Multi-Agent Systems, 3 (3), 219–257.Google Scholar
Fellous, J. M. (2004). From human emotions to robot emotions. In Proceedings of the AAAI Spring Symposium 2004: Architectures for Modeling Emotion, Stanford University, Palo Alto, CA.Google Scholar
Fellous, J. M., & Arbib, M. A. (2005). Who Needs Emotions? New York, NY: Oxford University Press.Google Scholar
Fitrianie, S., Bruijnes, M., Richards, D., Abdulrahman, A., & Brinkman, W.-P. (2019). What are we measuring anyway? A literature survey of questionnaires used in studies reported in the intelligent virtual agent conferences. Paper presented at Intelligent Virtual Agent Conference (IVA), Paris, France.Google Scholar
Fontaine, J. R. J., Scherer, K., Roesch, E. B., & Ellsworth, P. C. (2007). The world of emotions is not two-dimensional. Psychological Science, 18 (12), 1050–1057. https://doi.org/10.1111/j.1467-9280.2007.02024.xGoogle Scholar
Forgas, J. (1995). Mood and judgment: the affect infusion model (AIM). Psychological Bulletin, 117 (1), 39–66.Google Scholar
Forgas, J. (2003). Affective influences on attitudes and judgments. In Davidson, K. R. S. R. J. & Goldsmith, H. H. (Eds.), Handbook of Affective Sciences. New York, NY: Oxford University Press.Google Scholar
Forgas, J. P. (2017). Mood effects on cognition: affective influences on the content and process of information processing and behavior. In Jeon, M. (Ed.), Emotions and Affect in Human Factors and Human-Computer Interaction (pp. 89–122). London: Academic Press/Elsevier.Google Scholar
Fox, A. S., Lapate, R. C., Shackman, A. J., & Davidson, R. J. (Eds.). (2018). The Nature of Emotion: Fundamental Questions. New York, NY: Oxford University Press.Google Scholar
Franklin, S., Madl, T., D’Mello, S., & Snaider, J. (2014). LIDA: a systems-level architecture for cognition, emotion, and learning. IEEE Transactions on Autonomous Mental Development, 6 (6), 19–41. https://doi.org/10.1109/TAMD.2013.2277589Google Scholar
Frederickson, B., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19 (3), 313–332. https://doi.org/10.1080/02699930441000238Google Scholar
Frijda, N. (1993). Moods, emotion episodes, and emotions. In Lewis, M. & Haviland-Jones, J. M. (Eds.), Handbook of Emotions. New York, NY: The Guilford Press.Google Scholar
Frijda, N. (2008). The psychologists’ point of view. In Lewis, M., Haviland-Jones, J. M., & Barrett, L. F. (Eds.), Handbook of Emotions (3rd ed.). New York, NY: The Guilford Press.Google Scholar
Frijda, N. H. (1986). The Emotions. Cambridge: Cambridge University Press.Google Scholar
Frijda, N. H., & Swagerman, J. (1987). Can computers feel? Theory and design of an emotional system. Cognition and Emotion, 1 (3), 235–257.Google Scholar
Frijda, N. H., & Scherer, K. R. (2009). Emotion definition (psychological perspectives). In Sander, D. & Scherer, K. R. (Eds.), Oxford Companion to Emotion and the Affective Sciences (pp. 142–143). Oxford: Oxford University Press.Google Scholar
Gasper, K., & Clore, G. L. (2002). Attending to the big picture: mood and global versus local processing of visual information. Psychological Science, 13 (1), 34–40.Google Scholar
Gephard, P. (2005). ALMA – a layered model of affect. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems.Google Scholar
Gluz, J., & Jaques, P. A. (2017). A probabilistic formalization of the appraisal for the OCC event-based emotions. Journal of Artificial Intelligence Research, 58 (1), 627–664.Google Scholar
Gratch, J., & Marsella, S. (2004). A domain-independent framework for modeling emotion. Journal of Cognitive Systems Research, 5 (4), 269–306.Google Scholar
Gratch, J., & Marsella, S. (2015). Appraisal models. In Calvo, R. A., D’Mello, S., Gratch, J., & Kappas, A. (Eds.), The Oxford Handbook of Affective Computing. New York, NY: Oxford University Press.Google Scholar
Gratch, J., Marsella, S., Wang, N., & Stankovic, B. (2009). Assessing the validity of appraisal-based models of emotion. In Proceedings of the 3rd Affective Computing and Intelligent Interaction (ACII).Google Scholar
Gray, J. R., Schaefer, A., Braver, T. S., & Most, S. B. (2005). Affect and the resolution of cognitive control dilemmas. In Feldman-Barrett, L., Niedenthal, P. M., & Winkielman, P. (Eds.), Emotion and Consciousness. New York, NY: The Guilford Press.Google Scholar
Hamann, S. (2012). Mapping discrete and dimensional emotions onto the brain: controversies and consensus. Trends in Cognitive Science, 16 (9), 458–466. https://doi.org/10.1016/j.tics.2012.07.006Google Scholar
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021). Deeply felt affect: the emergence of valence in deep active inference. Neural Computation, 33, 1–49. https://doi.org/10.1162/neco_a_01341Google Scholar
Hindriks, K. V., & Broekens, J. (2011). Comparing formal cognitive emotion theories. Paper presented at the Standards in Emotion Modeling, Leiden, Netherlands.Google Scholar
Hoemann, K., Devlin, M., & Barrett, L. F. (2019). Comment: emotions are abstract, conceptual categories that are learned by a predicting brain. Emotion Review, 12 (4), 253–255.Google Scholar
Hudlicka, E. (1998). Modeling emotion in symbolic cognitive architectures. In Proceedings of the AAAI Fall Symposium: Emotional and Intelligent I, Orlando, FL.Google Scholar
Hudlicka, E. (2002). This time with feeling: integrated model of trait and state effects on cognition and behavior. Applied Artificial Intelligence, 16, 1–31.Google Scholar
Hudlicka, E. (2003). Modeling effects of behavior moderators on performance: evaluation of the MAMID methodology and architecture. In Proceedings of BRIMS-12, Phoenix, AZ.Google Scholar
Hudlicka, E. (2004). Two sides of appraisal: implementing appraisal and its consequences within a cognitive architecture. In Proceedings of the AAAI Spring Symposium: Architectures for Modeling Emotion, Stanford University, Palo Alto, CA.Google Scholar
Hudlicka, E. (2007). Reasons for emotions. In Gray, W. (Ed.), Advances in Cognitive Models and Cognitive Architectures. New York, NY: Oxford University Press.Google Scholar
Hudlicka, E. (2008a). What are we modeling when we model emotion?. In AAAI Spring Symposium: Emotion, Personality, and Social Behavior (Vol. Technical Report SS-08-04, pp. 52–59), Stanford University, CA. Menlo Park, CA: AAAI Press.Google Scholar
Hudlicka, E. (2008b). Modeling the mechanisms of emotion effects on cognition. Paper presented at the AAAI Fall Symposium on Biologically Inspired Cognitive Architectures, Arlington, VA.Google Scholar
Hudlicka, E. (2012). Guidelines for designing computational models of emotions. International Journal of Synthetic Emotions (IJSE), 2 (1), 26–79.Google Scholar
Hudlicka, E. (2014a). From habits to standards: towards systematic design of emotion models and affective architectures. In Tibor Bosse, J. B., Dias, J., & van der Zwaan, J. (Eds.), Towards Pragmatic Computational Models of Affective Processes (pp. 1–21). Cham: Springer.Google Scholar
Hudlicka, E. (2014b). Affective BICA: challenges and open questions. Biologically Inspired Cognitive Architectures, 7, 98–125.Google Scholar
Hudlicka, E. (2014c). From cognitive biases to panic: modeling the mechanisms of anxiety disorders. Paper presented at the Workshop on “Computational Modeling of Cognition-Emotion Interactions: Relevance to Mechanisms of Affective Disorders,” in conjunction with CogSci, Quebec City, Quebec, Canada.Google Scholar
Hudlicka, E. (2016). Virtual companions, coaches, and therapeutic games in psychotherapy. In Luxton, D. D. (Ed.), Artificial Intelligence in Mental Healthcare. Waltham, MA: Academic Press/Elsevier.Google Scholar
Hudlicka, E. (2017). Computational modeling of cognition‐emotion interactions: theoretical and practical relevance for behavioral healthcare. In Jeon, M. (Ed.), Emotions and Affect in Human Factors and Human-Computer Interaction (pp. 383–436). London: Academic Press/Elsevier.Google Scholar
Hudlicka, E. (2019a). Modeling cognition–emotion interactions in symbolic agent architectures: examples of research and applied models. In Aldinhas Ferreira, M. I., Silva Sequeira, J., & Ventura, R. (Eds.), Cognitive Architectures (Vol. 94). Cham: Springer.Google Scholar
Hudlicka, E. (2019b). Cognitive-affective architectures as clinical case formulations. Paper presented at the ISRE, Amsterdam, Netherlands.Google Scholar
Hudlicka, E. (2020). The case for cognitive-affective architectures as affective user models in behavioral health technologies. In Schmorrow, D. & C. Fidopiastis, (Eds.), Augmented Cognition. Human Cognition and Behavior (Vol. 12197). Cham: Springer.Google Scholar
Isen, A. M. (1993). Positive affect and decision making. In Haviland, J. M. & Lewis, M. (Eds.), Handbook of Emotions. New York, NY: The Guilford Press.Google Scholar
Izard, C. E. (1977). Human Emotions. New York, NY: Plenum.Google Scholar
Izard, C. E. (1993). Four systems for emotion activation: cognitive and noncognitive processes. Psychological Review, 100 (1), 68–90.Google Scholar
Izard, C. E. (2009). Emotion theory and research: highlights, unanswered questions, and emerging issues. Annual Review of Psychology, 60, 1–25.Google Scholar
Jack, R., Garrod, O. G. B., & Schyns, P. (2014). Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Current Biology, 24, 187–192. https://doi.org/10.1016/j.cub.2013.11.064Google Scholar
Jack, R., Garrod, O. G. B., Yu, H., Caldara, R., & Schyns, P. G. (2012). Facial expressions of emotion are not culturally universal. Proceedings of the National Academy of Sciences (online). www.pnas.org/cgi/doi/10.1073/pnas.1200155109 [last accessed July 25, 2022].Google Scholar
Jiang, H., Vidal, J. M., & Huhns, M. N. (2007). EBDI: an architecture for emotional agents. Paper presented at the 6th International Joint Conference on Autonomous Agents and Multiagent Systems.Google Scholar
Jones, H., Saunier, J., & Lourdeaux, D. (2009). Personality, emotions and physiology in a BDI agent architecture: the PEP→BDI model. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.Google Scholar
Junge, M., & Reisenzein, R. (2013). Indirect scaling methods for testing quantitative emotion theories. Cognition and Emotion, 27 (7), 1247–1275.Google Scholar
Kaptein, F., Broekens, J., Hindriks, K. V., & Neerincx, M. (2016). CAAF: a cognitive affective agent programming framework. Paper presented at IVA 2016.Google Scholar
Kleinginna, P. R., & Kleinginna, A. M. (1981). A categorized list of emotion definitions with suggestions for a consensual definition. Motivation and Emotion, 5, 345–379.Google Scholar
Klug, M., & Zell, A. (2013). Emotion-based human-robot-interaction. Paper presented at the IEEE 9th International Conference on Computational Cybernetics (ICCC), Tihany, Hungary.Google Scholar
Kragel, P. A., & LaBar, K. S. (2016). Decoding the nature of emotion in the brain. Trends in Cognitive Science, 20 (6), 444–455. https://doi.org/10.1016/j.tics.2016.03.011Google Scholar
Kramer, N., Kopp, S., Becker-Asano, C., & Sommer, N. (2013). Smile and the world will smile with you – the effects of a virtual agent’s smile on users’ evaluation and behavior. International Journal of Human-Computer Studies, 71 (3), 335–349.Google Scholar
Lazarus, R. S. (1984). On the primacy of cognition. American Psychologist, 39 (2), 124–129.Google Scholar
LeDoux, J. E. (2000). Cognitive-emotional interactions: listen to the brain. In Lane, R. D. & Nadel, L. (Eds.), Cognitive Neuroscience of Emotion. New York, NY: Oxford University Press.Google Scholar
Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and Decision Making. Annual Review of Psychology, 66, 799–823 https://doi.org/10.1146/annurev-psych-010213-115043Google Scholar
Lerner, J. S., & Tiedens, L. Z. (2006). Portrait of the angry decision maker: how appraisal tendencies shape anger’s influence on cognition. Journal of Behavioral Decision Making, 19, 115–137.Google Scholar
Leventhal, H., & Scherer, K. R. (1987). The relationship of emotion to cognition. Cognition and Emotion, 1, 3–28.Google Scholar
Lewis, M. D. (2005). Bridging emotion theory and neurobiology through dynamic systems modeling. Behavioral and Brain Sciences, 28 (2), 194–245. https://doi.org/10.1017/s0140525x0500004xGoogle Scholar
Lewis, M., & Canamero, L. (2016). Hedonic quality or reward? A study of basic pleasure in homeostasis and decision making of a motivated autonomous robot. Adaptive Behavior, 24, 267–291. https://doi.org/10.1177/1059712316666331Google Scholar
Lewis, M., & Canamero, L. (2014). An affective autonomous robot toddler to support the development of self-efficacy in diabetic children. In Proceedings of the 23rd Annual IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN 2014), Edinburgh, Scotland, UK.Google Scholar
Lewis, M., & Canamero, L. (2017). Robin: an autonomous robot for diabetic children. Paper presented at the UK-RAS Conference on “Robots Working for and Among Us.”Google Scholar
Lewis, M., & Canamero, L. (2019). A robot model of stress-induced compulsive behavior. Paper presented at the 8th ACII, Cambridge, UK.Google Scholar
Lewis, M., Haviland-Jones, J. M., & Barrett, L. F. (2008). Handbook of Emotions (3rd ed.). New York, NY: The Guilford Press.Google Scholar
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Feldman-Barrett, L. (2012). The brain basis of emotion: a meta-analytic review. Behavioral Brain Sciences, 35 (3), 121–143. https://doi.org/10.1017/S0140525X11000446Google Scholar
Lisetti, C., & Gmytrasiewicz, P. (2002). Can rational agents afford to be affectless? Applied Artificial Intelligence, 16(7–8), 577–609.Google Scholar
Lowe, R., Almer, A., & Balkenius, C. (2019). Bridging connectionism and relational cognition through bi-directional affective-associative processing. Open Information Science, 3, 235–260. https://doi.org/10.1515/opis-2019-0017Google Scholar
Lowe, R., & Billing, E. (2017). Affective-associative two-process theory: a neural network investigation of adaptive behaviour in differential outcomes training. Adaptive Behavior, 25 (1), 5–23.Google Scholar
Lowe, R., Dodig-Crnkovic, G., & Almer, A. (2017). Predictive regulation in affective and adaptive behaviour: an allostatic-cybernetics perspective. In Advanced Research on Biologically Inspired Cognitive Architectures (pp. 149–176). Clifton, VA: IGI Global.Google Scholar
Lowe, R., & Kyriazov, K. (2014). Utilizing emotions in autonomous robots: an enactive approach. In Bosse, T., Broekens, J., Dias, J., & van der Zwaan, J. (Eds.), Emotion Modeling: Towards Pragmatic Computational Models of Affective Processes (Vol. 8750, pp. 76–98). Cham: Springer International Publishing.Google Scholar
Loyall, A. B. (1997). Believable agents: building interactive personalities. Ph.D. Thesis, CMU, Pittsburgh.Google Scholar
Luxton, D. D., & Hudlicka, E. (2021). Intelligent virtual agents in healthcare: ethics and application considerations. In Jotterand, F. I., Ienca, M., & Liang, M. (Eds.), Ethics of Artificial Intelligence in Brain and Mental Health. Cham: Springer Nature.Google Scholar
Macedo, L., Cardoso, A., Reisenzein, R., Lorini, E., & Castelfranchi, C. (2009). Artificial surprise. In Vallverdú, J. & Casacuberta, D. (Eds.), Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence. Hershey, PA: IGI Global.Google Scholar
MacLeod, C., & Mathews, A. (2012). Cognitive bias modification approaches to anxiety. Annual Review of Clinical Psychology, 8, 189–217.Google Scholar
Mandler, G. (1984). Mind and Body: The Psychology of Emotion and Stress. New York, NY: Norton.Google Scholar
Marinier, R., & Laird, J. (2004). Toward a comprehensive computational model of emotions and feelings. In Proceedings of International Conference on Cognitive Modeling, Pittsburgh, PA.Google Scholar
Marinier, R. P., & Laird, J. E. (2006). A cognitive architecture theory of comprehension and appraisal. Paper presented at ACE 2006, Vienna, Austria.Google Scholar
Marinier, R. P., Laird, J., & Lewis, R. L. (2009). A computational unification of cognitive behavior and emotion. Cognitive Systems Research, 10 (1), 48–69.Google Scholar
Martinez-Miranda, J., Breso, A., & Garcia-Gomez, J. M. (2014). Modelling two emotion regulation strategies as key features of therapeutic empathy. In Bosse, T., Broekens, J., Dias, J., & van der Zwaan, J. (Eds.), Emotion Modeling: Towards Pragmatic Computational Models of Affective Processes (Vol. 8750, pp. 115–133). Cham: Springer International Publishing.Google Scholar
Matthews, G. A., & Harley, T. A. (1993). Effects of extraversion and self-report arousal on semantic priming: a connectionist approach. Journal of Personality and Social Psychology, 65 (4), 735–756.Google Scholar
McCarthy, J. (1995). Making robots conscious of their mental states. Paper presented at the AAAI Spring Symposium, Stanford University, Palo Alto, CA.Google Scholar
McQuiggan, S. W., & Lester, J. C. (2007). Modeling and evaluating empathy in embodied companion agents. International Journal of Human-Computer Studies, 65 (4), 348–360. https://doi.org/10.1016/j.ijhcs.2006.11.015Google Scholar
Mehrabian, A. (1995). Framework for a comprehensive description and measurement of emotional states. Genetic, Social, and General Psychology Monographs, 121, 339–361.Google Scholar
Mellers, B. A., Ho, K., & Ritov, I. (1997). Decision affect theory: emotional reactions to the outcomes of risky options. Psychological Science, 8, 423–429. https://doi.org/10.1111/j.1467-9280.1997.tb00455Google Scholar
Meyer, J. J. C. (2006). Reasoning about emotional agents. International Journal of Intelligent Systems, 21 (6), 601–619.Google Scholar
Mineka, S., Rafael, E., & Yovel, I. (2003). Cognitive biases in emotional disorders: information processing and social-cognitive perspectives. In Davidson, R. J., Scherer, K. R., & Goldsmith, H. H. (Eds.), Handbook of Affective Science. New York, NY: Oxford University Press.Google Scholar
Minsky, M. (1986). The Society of Mind. Cambridge, MA: MIT Press.Google Scholar
Minsky, M. (2006). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York, NY: Simon & Schuster.Google Scholar
Mobbs, D., Adolphs, R., Fanselow, M. S., et al. (2019). Viewpoints: approaches to defining and investigating fear. Nature Neuroscience, 22 (8), 1205–1216. https://doi.org/10.1038/s41593-019-0456-6Google Scholar
Moutoussis, M., Shahar, N., Hauser, T. U., & Dolan, R. J. (2017). Computation in psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry, 2, 50–73. https://doi.org/10.1162/cpsy_a_00014Google Scholar
Neto, A. F. B., & da Silva, F. S. C. (2012). A computer architecture for intelligent agents with personality and emotions. In Zacarias, M. & Oliveira, J. V. (Eds.), Human-Computer Interaction: The Agency Perspective (pp. 263–285). Berlin and Heidelberg: Springer.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Oatley, K., & Johnson-Laird, P. (1987). Towards a cognitive theory of emotion. Cognition and Emotion, 1, 51–58.Google Scholar
Ochs, M., Sadek, D., & Pelachaud, C. (2012). A formal model of emotions for an empathic rational dialog agent. Autonomous Agents and Multi-Agent Systems, 24, 410–440. https://doi.org/10.1007/s10458–010-9156-zCrossRefGoogle Scholar
Ojha, S., Vitale, J., & Williams, M.-A. (2020). Computational emotion models: a thematic review. International Journal of Social Robotics (online). https://doi.org/10.1007/s12369-020-00713-1Google Scholar
Ortony, A., & Turner, T. J. (1990). What’s basic about basic emotions? Psychological Review, 97 (3), 315–331.Google Scholar
Ortony, A., Clore, G. L., & Collins, A. (1988). The Cognitive Structure of Emotions. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Ortony, A., Norman, D., & Revelle, W. (2005). Affect and proto-affect in effective functioning. In Fellous, J.-M. & Arbib, M. A. (Eds.), Who Needs Emotions? New York, NY: Oxford University Press.Google Scholar
Osuna, E., Rodriguez, L.-F., Gutierrez-Garcia, J. O., & Castro, L. A. (2020). Development of computational models of emotions: a software engineering perspective. Cognitive Systems Research, 60, 1–19. https://doi.org/10.1016/j.cogsys.2019.11.001CrossRefGoogle Scholar
Paiva, A., Dias, J., Sobral, D., et al. (2004). Caring for agents and agents that care: building empathic relations with synthetic agents. Paper presented at the International Joint Conference on Autonomous Agents and Multiagent Systems, New York.Google Scholar
Panskepp, J. (1998). Affective Neuroscience: The Foundations of Human and Animal Emotions. New York, NY: Oxford University Press.Google Scholar
Panskepp, J., & Watt, D. (2011). What is basic about basic emotions? Lasting lessons from affective neuroscience. Emotion Review, 3 (4), 387–396. https://doi.org/10.1177/1754073911410741Google Scholar
Pessoa, L., & McMenamin, B. (2017). Dynamic networks in the emotional brain. Neuroscientist, 23 (4), 383–396. https://doi.org/10.1177/1073858416671936Google Scholar
Pfeifer, R. (1994). The fungus eater approach to emotion: a view from artificial intelligence. Cognitive Studies, 1, 42–57.Google Scholar
Phelps, E. (2006). The interaction of emotion and cognition: the relation between the human amygdala and cognitive awareness. In Hassin, R. R., Uleman, J. S., & Bargh, J. A. (Eds.), The New Unconscious (pp. 60–76). New York, NY: Oxford University Press.Google Scholar
Picard, R. (1997). Affective Computing. Cambridge, MA: MIT Press.Google Scholar
Plutchik, R. (1984). Emotions: a general psychoevolutionary theory. In Scherer, K. R. & Ekman, P. (Eds.), Approaches to Emotion. Hillsdale, NJ: Erlbaum.Google Scholar
Popescu, A., Broekens, J., & Someren, M. v. (2014). GAMYGDALA: an emotion engine for games. IEEE Transactions on Affective Computing, 5 (1), 32–44.CrossRefGoogle Scholar
Prendinger, H., & Ishizuka, M. (2004). Life-Like Characters: Tools, Affective Functions, and Application. New York, NY: Springer.Google Scholar
Prinz, J. (2004). Gut Reactions: A Perceptual Theory of Emotion. Oxford: Oxford University Press.Google Scholar
Rao, A. (2009). AgentSpeak(L): BDI agents speak out in a logical computable language. Paper presented at the European Workshop on Modelling Autonomous Agents in a Multi-Agent World.Google Scholar
Rao, A. S., & Georgeoff, M. P. (1995). BDI agents: from theory to practice. In Proceedings of the 1st International Conference on Multi-Agent Systems (ICMAS).Google Scholar
Reilly, W. S. N. (2006). Modeling what happens between emotional antecedents and emotional consequents. In Proceedings of ACE 2006, Vienna, Austria.Google Scholar
Reilly, W. S. R. (1996). Believable social and emotional agents. Ph.D. Thesis, CMU, Pittsburgh.Google Scholar
Reisenzein, R. (2001). Appraisal processes conceptualized from a schema-theoretic perspective: contributions to a process analysis of emotions. In Scherer, K. R., Schorr, A., & Johnstone, T. (Eds.), Appraisal Processes in Emotion: Theory, Methods, Research. New York, NY: Oxford University Press.Google Scholar
Reisenzein, R. (2009). A theory of emotions as metarepresentational states of mind. Cognitive Systems Research, 10, 6–20. https://doi.org/10.1016/j.cogsys.2008.03.001Google Scholar
Reisenzein, R. (2012). What is an emotion in the belief-desire theory of emotion? In Paglieri, F., Tummolini, L., Falcone, R., & Miceli, M. (Eds.), The Goals of Cognition: Essays in Honor of Cristiano Castelfranchi. London: College Publications.Google Scholar
Reisenzein, R. (2019). Cognition and emotion: a plea for theory. Cognition and Emotion, 33 (1), 109–118. https://doi.org/10.1080/02699931.2019.1568968CrossRefGoogle ScholarPubMed
Reisenzein, R., Hildebrandt, A., & Weber, H. (2020). Personality and emotion. In Corr, P. J. & Matthews, G. (Eds.), Cambridge Handbook of Personality Psychology (2nd ed.) (pp. 81–99). Cambridge: Cambridge University Press.Google Scholar
Reisenzein, R., Hudlicka, E., Dastani, M., et al. (2013). Computational modeling of emotion: toward improving the inter- and intradisciplinary exchange. IEEE Transactions on Affective Computing, 4 (3), 246–266.Google Scholar
Reisenzein, R., & Junge, M. (2006). Uberraschung, Enttauschung und Erleichterung: Emotionsintensitat als Funktion von subjektiver Wahrscheinlichkeit und Erwunschtheit [Surprise, disappointment and relief: emotion intensity as function of subjective probability and desirability]. Paper presented at the 45th Congress of the German Psychological Association, Nuremburg, Germany.Google Scholar
Reisenzein, R., & Junge, M. (2012). Language and emotion from the perspective of the computational belief-desire theory of emotion. Dynamicity in Emotion Concepts, 27, 37–59.Google Scholar
Reisenzein, R., & Stephan, A. (2014). More on James and the physical basis of emotion. Emotion Review, 6 (1), 35–46.Google Scholar
Ritter, F. E., & Avramides, M. N. (2000). Steps Towards Including Behavior Moderators in Human Performance Models in Synthetic Environments. Technical Report No. ACS 2000-1. May 19, 2000. School of information sciences and technology, The Pennsylvania State University.Google Scholar
Ritter, F. E., Reifers, A. L., Klein, L. C., & Schoelles, M. J. (2007). Lessons from defining theories of stress for cognitive architectures. In Gray, W. (Ed.), Advances in Cognitive Models and Cognitive Architectures. New York, NY: Oxford University Press.Google Scholar
Rodriguez, L.-F., & Ramos, F. (2014). Development of computational models of emotions for autonomous agents: a review. Cognitive Computation, 6, 351–375. https://doi.org/10.1007/s12559–013-9244-xGoogle Scholar
Roseman, I. J., & Smith, C. A. (2001). Appraisal theory: overview, assumptions, varieties, controversies. In Scherer, A. S. K. R. & Johnstone, T. (Eds.), Appraisal Processes in Emotion: Theory, Methods, Research. New York, NY: Oxford University Press.Google Scholar
Russell, J. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110 (1), 145–172.Google Scholar
Russell, J., & Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. Journal of Personality and Social Psychology, 76 (5), 805–819.Google Scholar
Russell, J., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research on Personality, 11, 273–294.Google Scholar
Samsonovich, A. V. (2020). Socially emotional brain-inspired cognitive architecture framework for artificial intelligence. Cognitive Systems Research, 60, 57–76. https://doi.org/10.1016/j.cogsys.2019.12.002Google Scholar
Sanchez-Lopez, Y., & Cerezo, E. (2019). Designing emotional BDI agents: good practices and open questions. The Knowledge Engineering Review, 34 (26), 1–33.Google Scholar
Sander, D., & Scherer, S. (Eds.). (2009). Oxford Companion to Emotion and the Affective Sciences. New York, NY: Oxford University Press.Google Scholar
Scarantino, A. (2018). Are LeDoux’s survival circuits basic emotions under a different name? Current Opinion in Behavioral Sciences, 24, 75–82. https://doi.org/10.1016/j.cobeha.2018.06.001Google Scholar
Scarantino, A. (2021). Handbook of Emotion Theory. Abingdon: Routledge.Google Scholar
Scarantino, A., & de Sousa, R. (2018). Emotion. In The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2018/entries/emotion/ [last accessed July 25, 2022].Google Scholar
Scarantino, A., & Griffiths, P. (2011). Don’t give up on basic emotions. Emotion Review, 3 (4), 444–454. https://doi.org/10.1177/1754073911410745Google Scholar
Schachter, S., & Singer, J. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review, 69, 379–399.Google Scholar
Scherer, K. (2009). Emotions are emergent processes: they require a dynamic computational architecture. Philosophical Transactions of the Royal Society B, 364, 3459–3474. https://doi.org/10.1098/rstb.2009.0141Google Scholar
Scherer, K. (2012). Neuroscience findings are consistent with appraisal theories of emotion; but does the brain “respect” constructionism? Behavioral Brain Sciences, 35 (3), 163–164. https://doi.org/10.1017/S0140525X11001750Google Scholar
Scherer, K. R. (1984). On the nature and function of emotion: a component process approach. In Scherer, K. R. & Ekman, P. (Eds.), Approaches to Emotion (pp. 293–318). Hillsdale, NJ: Erlbaum.Google Scholar
Scherer, K. R. (2001a). Appraisal considered as a process of multilevel sequential checking. In Scherer, K. R., Schorr, A., & Johnstone, T. (Eds.), Appraisal Processes in Emotion: Theory, Methods, Research. New York, NY: Oxford University Press.Google Scholar
Scherer, K. R. (2001b). The nature and study of appraisal: a review of the issues. In Scherer, K. R., Schorr, A., & Johnstone, T. (Eds.), Appraisal Processes in Emotion: Theory, Methods, Research. New York, NY: Oxford University Press.Google Scholar
Scherer, K. R. (2005). Unconscious process in emotion: the bulk of the iceberg. In Barrett, L. F., Niedenthal, P. M., & Winkielman, P. (Eds.), Emotion and Consciousness. New York, NY: The Guilford Press.Google Scholar
Scherer, K. R., & Ellgring, H. (2007). Are facial expressions of emotion produced by categorical affect programs or dynamically driven by appraisal? Emotion, 7 (1), 113–130.Google Scholar
Scherer, K., & Moors, A. (2019). The emotion process: event appraisal and component differentiation. Annual Review of Psychology, 70, 719–745. https://doi.org/10.1146/annurev- psych-122216-011854Google Scholar
Scherer, K. R., Schorr, A., & Johnstone, T. (2001). Appraisal Processes in Emotion: Theory, Methods, Research. New York, NY: Oxford University Press.Google Scholar
Scheutz, M., & Sloman, A. (2001). Affect and agent control: experiments with simple affective states. In Proceedings of IAT-01.Google Scholar
Schwarz, N., & Clore, G. L. (1988). How do I feel about it? The information function of affective states. In Fiedler, K. & Forgas, J. P. (Eds.), Affect, Cognition, and Social Behavior (pp. 44–62). Toronto: Hogrefe.Google Scholar
Schwarz, N., & Clore, G. L. (2003). Mood as information: 20 years later. Psychological Inquiry, 14 (3–4), 296–303.Google Scholar
Sloman, A. (2001). Beyond shallow models of emotion. Cognitive Processing, 2 (1), 177–198.Google Scholar
Sloman, A. (2004). What are emotion theories about? In AAAI Spring Symposium: Architectures for Modeling Emotion. Stanford University, CA: AAAI Press.Google Scholar
Sloman, A., Chrisley, R., & Scheutz, M. (2005). The architectural basis of affective states and processes. In Fellous, J.-M. & Arbib, M. A. (Eds.), Who Needs Emotions? New York, NY: Oxford University Press.Google Scholar
Sloman, A., & Croucher, M. (1981). Why robots will have emotions? Paper presented at the 7th International Conference on Artificial Intelligence (IJCAI).Google Scholar
Sloman, A., & Logan, B. (1999). Building cognitively rich agents using the Sim_agent toolkit. Communications of the Association for Computing Machinery, 43 (2), 71–77.Google Scholar
Smith, C. A., & Kirby, L. (2000). Consequences require antecedents: toward a process model of emotion elicitation. In Forgas, J. P. (Ed.), Feeling and Thinking: The Role of Affect in Social Cognition. New York, NY: Cambridge University Press.Google Scholar
Smith, C. A., & Kirby, L. D. (2001). Toward delivering on the promise of appraisal theory. In Scherer, A. S. K. R. & Johnstone, T. (Eds.), Appraisal Processes in Emotion. New York, NY: Oxford University Press.Google Scholar
Smith, R., Lane, R. D., Nadel, L., & Moutoussis, M. (2020). A computational neuroscience perspective on the change process in psychotherapy. In Lane, R. D. & Nadel, L. (Eds.), Neuroscience of Enduring Change. New York, NY: Oxford University Press.Google Scholar
Smith, R., Parr, T., & Friston, K. J. (2019). Simulating emotions: an active inference model of emotional state inference and emotion concept learning. Frontiers in Psychology, 10 , 2844. https://doi.org/10.3389/fpsyg.2019.02844Google Scholar
Staller, A., & Petta, P. (1998). Towards a tractable appraisal-based architecture for situated cognizers. Paper presented at the Grounding Emotions in Adaptive Systems Workshop, at the 5th International Conference of the Society for Adaptive Behaviour (SAB’98). Zurich, Switzerland.Google Scholar
Steunebrink, B. R., Dastani, M., & Meyer, J.-J. C. (2009). The OCC model revisited. Paper presented at the 4th Workshop on Emotion and Computing: Current Research and Future Impact, Paderborn, Germany.Google Scholar
Steunebrink, B. R., Dastani, M., & Meyer, J. J. C. (2012). A formal model of emotion triggers: an approach for BDI Agents. Synthese, 185 (1), 83–129.Google Scholar
Sun, R., Wilson, N., & Lynch, M. (2016). Emotion: a unified mechanistic interpretation from a cognitive architecture. Cognitive Computation, 8 (8), 1–14.Google Scholar
Tomkins, S. S., & McCarter, R. (1964). What and where are the primary affects? Some evidence for a theory. Perceptual and Motor Skills, 18, 119–158.Google Scholar
Trappl, R., Petta, P., & Payr, S. (2003). Emotions in Humans and Artifacts. Cambridge, MA: MIT Press.Google Scholar
Tsai, J., Bowring, E., Marsella, S., & Tambe, M. (2013). Empirical evaluation of computational fear contagion models in crowd dispersions. Autonomous Agents and Multi-Agent Systems, 27, 200–217. https://doi.org/10.1007/s10458-013-9220-6Google Scholar
Turner, T. J., & Ortony, A. (1992). Basic emotions: can conflicting criteria converge? Psychological Review, 99 (3), 566–571.Google Scholar
Turrini, P., Meyer, J.-J. C., & Castelfranchi, C. (2007). Rational agents that blush. In Paiva, A., Prada, R., & Picard, R. (Eds.), Affective Computing and Intelligent Interaction. Berlin: Springer.Google Scholar
Velasquez, J. D. (1997). Modeling emotions and other motivations in synthetic agents. In Proceedings of AAAI-97 (pp. 10–15).Google Scholar
Velásquez, J. D. (1999). An emotion-based approach to robotics. In Proceedings of IROS.Google Scholar
Vernon, D., Lowe, R., Thill, S., & Ziemke, T. (2015). Embodied cognition and circular causality: on the role of constitutive autonomy in the reciprocal coupling of perception and action. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01660Google Scholar
Wright, I., Sloman, A., & Beaudoin, L. (1995). Towards a design-based analysis of emotional episodes. Philosophy, Psychiatry & Psychology, 3 (2), 101–126.Google Scholar
Zajonc, R. B. (1984). On the primacy of affect. American Psychologist, 39 (2), 117–123.Google Scholar

References

Aarts, H., & Dijksterhuis, A. (2003). The silence of the library: environment, situational norm, and social behavior. Journal of Personality and Social Psychology, 84 (1), 18–28. https://doi.org/10.1037/0022-3514.84.1.18Google Scholar
Abel, D., MacGlashan, J., & Littman, M. L. (2016). Reinforcement learning as a framework for ethical decision making. In AAAI Workshop: AI, Ethics, and Society, Volume WS-16-02 of 13th AAAI Workshops.Google Scholar
Alexander, J. C. (1987). The Micro-Macro Link. Oakland, CA: University of California Press.Google Scholar
Alicke, M. D. (2000). Culpable control and the psychology of blame. Psychological Bulletin, 126 (4), 556–574. https://doi.org/10.1037//0033-2909.126.4.556Google Scholar
Anderson, M., & Anderson, S. L. (2006). MedEthEx: a prototype medical ethics advisor. Paper presented at the 18th Conference on Innovative Applications of Artificial Intelligence.Google Scholar
Anderson, M., Anderson, S. L., & Armen, C. (2006). An approach to computing ethics. IEEE Intelligent Systems, 21 (4), 56–63. https://doi.org/10.1109/MIS.2006.64Google Scholar
Andrighetto, G., Brandts, J., Conte, R., Sabater-Mir, J., Solaz, H., & Villatoro, D. (2013). Punish and voice: punishment enhances cooperation when combined with norm-signalling. PLoS One, 8(6). https://doi.org/10.1371/journal.pone.0064941Google Scholar
Andrighetto, G., Castelfranchi, C., Mayor, E., McBreen, J., Lopez-Sanchez, M., & Parsons, S. (2013). (Social) norm dynamics. In Andrighetto, G., Governatori, G., Noriega, P., & van der Torre, L. W. N. (Eds.), Normative Multi-Agent Systems (Vol. 4, pp. 135–170). Wadern: Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik. https://doi.org/10.4230/DFU.Vol4.12111.135Google Scholar
Andrighetto, G., Villatoro, D., & Conte, R. (2010). Norm internalization in artificial societies. AI Communications, 23 (4), 325–339.Google Scholar
Aquinas, T. (2003). On Law, Morality and Politics (Baumgarth, W. P., Ed.; R. J. Regan, Trans.; 2nd ed.). Indianapolis, IN: Hackett Publishing.Google Scholar
Arkin, R. C., & Ulam, P. (2009). An ethical adaptor: behavioral modification derived from moral emotions. In Proceedings of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation – (CIRA) (pp. 381–387). https://doi.org/10.1109/CIRA.2009.5423177Google Scholar
Arnold, T., Kasenberg, D., & Scheutz, M. (2017). Value alignment or misalignment – what will keep systems accountable? In The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence: Technical Reports, WS-17-02: AI, Ethics, and Society (pp. 81–88). Palo Alto, CA: The AAAI Press.Google Scholar
Ayars, A. (2016). Can model-free reinforcement learning explain deontological moral judgments? Cognition, 150, 232–242. https://doi.org/10.1016/j.cognition.2016.02.002Google Scholar
Balafoutas, L., Nikiforakis, N., & Rockenbach, B. (2014). Direct and indirect punishment among strangers in the field. Proceedings of the National Academy of Sciences, 111 (45), 15924–15927. https://doi.org/10.1073/pnas.1413170111Google Scholar
Bartels, D. M., Bauman, C. W., Cushman, F. A., Pizarro, D. A., & McGraw, A. P. (2015). Moral judgment and decision making. In Koehler, D. J. & Harvey, N. (Eds.), The Wiley Blackwell Handbook of Judgment and Decision Making (pp. 478–515). Oxford: John Wiley & Sons. https://doi.org/10.1002/9781118468333.ch17Google Scholar
Battaglino, C., Damiano, R., & Lesmo, L. (2013). Emotional range in value-sensitive deliberation. In Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems (pp. 769–776).Google Scholar
Battaglino, C., Damiano, R., & Lombardo, V. (2014). Moral values in narrative characters: an experiment in the generation of moral emotions. In Mitchell, A., Fernández-Vara, C., & Thue, D. (Eds.), Interactive Storytelling (pp. 212–215). Cham: Springer International Publishing.Google Scholar
Bauer, W. A. (2020). Virtuous vs. utilitarian artificial moral agents. AI & Society, 35 (1), 263–271. https://doi.org/10.1007/s00146-018-0871-3Google Scholar
Benzmüller, C. (2019). Universal (meta-)logical reasoning: recent successes. Science of Computer Programming, 172, 48–62. https://doi.org/10.1016/j.scico.2018.10.008Google Scholar
Bicchieri, C. (2006). The Grammar of Society: The Nature and Dynamics of Social Norms. Cambridge: Cambridge University Press.Google Scholar
Bordini, R. H., Hübner, J. F., & Wooldridge, M. (2007). Programming Multi-Agent Systems in Agentspeak Using Jason. Oxford: John Wiley & Sons.Google Scholar
Bratman, M. E. (1987). Intention, Plans, and Practical Reason. Cambridge, MA: Harvard University Press.Google Scholar
Brennan, G., Eriksson, L., Goodin, R. E., & Southwood, N. (2013). Explaining Norms. Oxford: Oxford University Press.Google Scholar
Bretz, S., & Sun, R. (2018). Two models of moral judgment. Cognitive Science, 42, 4–37. https://doi.org/10.1111/cogs.12517Google Scholar
Bringsjord, S., & Taylor, J. (2012). The divine-command approach to robot ethics. In Lin, P., Abney, K., & Bekey, G. A. (Eds.), Robot Ethics: The Ethical and Social Implications of Robotics (pp. 85–108). Cambridge, MA: MIT Press.Google Scholar
Broeders, R., van den Bos, K., Müller, P. A., & Ham, J. (2011). Should I save or should I not kill? How people solve moral dilemmas depends on which rule is most accessible. Journal of Experimental Social Psychology, 47 (5), 923–934. https://doi.org/10.1016/j.jesp.2011.03.018Google Scholar
Brundage, M. (2014). Limitations and risks of machine ethics. Journal of Experimental & Theoretical Artificial Intelligence, 26 (3), 355–372.Google Scholar
Buckholtz, J. W., Martin, J. W., Treadway, M. T., et al. (2015). From blame to punishment: disrupting prefrontal cortex activity reveals norm enforcement mechanisms. Neuron, 87 (6), 1369–1380. https://doi.org/10.1016/j.neuron.2015.08.023Google Scholar
Carmo, J., & Jones, A. J. I. (2002). Deontic logic and contrary-to-duties. In Gabbay, D. M. & Guenthner, F. (Eds.), Handbook of Philosophical Logic (Vol. 8, pp. 265–343). Cham: Springer. https://doi.org/10.1007/978-94-010-0387-2_4Google Scholar
Castelfranchi, C., Dignum, F., Jonker, C. M., & Treur, J. (2000). Deliberative normative agents: principles and architecture. In Jennings, N. R. & Lespérance, Y. (Eds.), Intelligent Agents VI. Agent Theories, Architectures, and Languages (pp. 364–378). Cham: Springer. https://doi.org/10.1007/10719619_27Google Scholar
Cerulo, K. A., & Ruane, J. M. (2014). Apologies of the rich and famous: cultural, cognitive, and social explanations of why we care and why we forgive. Social Psychology Quarterly, 77 (2), 123–149.CrossRefGoogle Scholar
Cervantes, J.-A., Rodríguez, L.-F., López, S., Ramos, F., & Robles, F. (2016). Autonomous agents and ethical decision-making. Cognitive Computation, 8 (2), 278–296. https://doi.org/10.1007/s12559-015-9362-8Google Scholar
Christensen, J. F., & Gomila, A. (2012). Moral dilemmas in cognitive neuroscience of moral decision-making: a principled review. Neuroscience & Biobehavioral Reviews, 36 (4), 1249–1264. https://doi.org/10.1016/j.neubiorev.2012.02.008CrossRefGoogle ScholarPubMed
Cialdini, R. B., Kallgren, C. A., & Reno, R. R. (1991). A focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior. In Zanna, M. P. (Ed.), Advances in Experimental Social Psychology (Vol. 24, pp. 201–234). New York, NY: Academic Press.Google Scholar
Conitzer, V., Sinnott-Armstrong, W., Borg, J. S., Deng, Y., & Kramer, M. (2017). Moral decision making frameworks for artificial intelligence. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (pp. 4831–4835). AAAI Press.Google Scholar
Conte, R., Andrighetto, G., & Campenni, M. (2013). Minding Norms: Mechanisms and Dynamics of Social Order in Agent Societies. New York, NY: Oxford University Press.Google Scholar
Coricelli, G., Rusconi, E., & Villeval, M. C. (2014). Tax evasion and emotions: an empirical test of re-integrative shaming theory. Journal of Economic Psychology, 40, 49–61. https://doi.org/10.1016/j.joep.2012.12.002CrossRefGoogle Scholar
Crockett, M. J. (2013). Models of morality. Trends in Cognitive Sciences, 17 (8), 363–366. https://doi.org/10.1016/j.tics.2013.06.005Google Scholar
Curry, O. S., Mullins, D. A., & Whitehouse, H. (2019). Is it good to cooperate? Testing the theory of morality-as-cooperation in 60 societies. Current Anthropology, 60 (1), 47–69. https://doi.org/10.1086/701478Google Scholar
Cushman, F. (2008). Crime and punishment: distinguishing the roles of causal and intentional analyses in moral judgment. Cognition, 108 (2), 353–380. https://doi.org/10.1016/j.cognition.2008.03.006Google Scholar
Cushman, F. (2013). Action, outcome, and value: a dual-system framework for morality. Personality and Social Psychology Review, 17 (3), 273–292. https://doi.org/10.1177/1088868313495594Google Scholar
Cushman, F., Young, L., & Greene, J. D. (2010). Multi-system moral psychology. In Doris, J. M. (Ed.), The Moral Psychology Handbook (pp. 47–71). Oxford: Oxford University Press.Google Scholar
Danaher, J. (2020). Robot betrayal: a guide to the ethics of robotic deception. Ethics and Information Technology, 22 (2), 117–128. https://doi.org/10.1007/s10676-019-09520-3CrossRefGoogle Scholar
Dancy, J. (2009). Moral particularism. In Zalta, E. N. (Ed.), Stanford Encyclopedia of Philosophy. Center for the Study of Language and Information, Stanford University. https://plato.stanford.edu/entries/moral-particularism/ [last accessed July 27, 2022].Google Scholar
Dastani, M. (2008). 2APL: a practical agent programming language. Autonomous Agents and Multi-Agent Systems, 16 (3), 214–248. https://doi.org/10.1007/s10458-008-9036-yGoogle Scholar
Dennis, L., Fisher, M., Slavkovik, M., & Webster, M. (2016). Formal verification of ethical choices in autonomous systems. Robotics and Autonomous Systems, 77, 1–14. https://doi.org/10.1016/j.robot.2015.11.012Google Scholar
D’Inverno, M., Luck, M., Georgeff, M., Kinny, D., & Wooldridge, M. (2004). The dMARS architecture: a specification of the distributed multi-agent reasoning system. Autonomous Agents and Multi-Agent Systems, 9 (1), 5–53. https://doi.org/10.1023/B:AGNT.0000019688.11109.19CrossRefGoogle Scholar
Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77 (2), 321–357. https://doi.org/10.1016/0004-3702(94)00041-XGoogle Scholar
Eisenberg, N. (2000). Emotion, regulation, and moral development. Annual Review of Psychology, 51, 665–697.Google Scholar
Fehr, E., & Fischbacher, U. (2004). Social norms and human cooperation. Trends in Cognitive Sciences, 8 (4), 185–190. https://doi.org/10.1016/j.tics.2004.02.007Google Scholar
Ferreira, N., Mascarenhas, S., Paiva, A., et al. (2013). An agent model for the appraisal of normative events based in in-group and out-group relations. In AAAI Conference on Artificial Intelligence.Google Scholar
Foot, P. (1967). The problem of abortion and the doctrine of double effect. Oxford Review, 5, 5–15.Google Scholar
Forbus, K. D., Gentner, D., & Law, K. (1995). MAC/FAC: a model of similarity-based retrieval. Cognitive Science, 19 (2), 141–205. https://doi.org/10.1207/s15516709cog1902_1Google Scholar
Francis, K. B., Howard, C., Howard, I. S., et al. (2016). Virtual morality: transitioning from moral judgment to moral action? PLoS One, 11 (10), e0164374. https://doi.org/10.1371/journal.pone.0164374Google Scholar
Gibbs, J. P. (1965). Norms: the problem of definition and classification. American Journal of Sociology, 70 (5), 586–594. https://doi.org/10.1086/223933Google Scholar
Goble, L. (2003). Preference semantics for deontic logic. Part I – simple models. Logique et Analyse, 46 (183/184), 383–418.Google Scholar
Gold, N., Pulford, B. D., & Colman, A. M. (2015). Do as I Say, Don’t Do as I Do: differences in moral judgments do not translate into differences in decisions in real-life trolley problems. Journal of Economic Psychology, 47, 50–61. https://doi.org/10.1016/j.joep.2015.01.001Google Scholar
Govindarajulu, N. S., & Bringsjord, S. (2017). On automating the doctrine of double effect. In Proceedings of the International Joint Conference on AI (IJCAI 2017) (pp. 4722–4730).Google Scholar
Govindarajulu, N. S., Bringsjord, S., & Peveler, M. (2019). On quantified modal theorem proving for modeling ethics. Electronic Proceedings in Theoretical Computer Science, 311, 43–49. https://doi.org/10.4204/EPTCS.311.7Google Scholar
Greene, J. D. (2007). Why are VMPFC patients more utilitarian? A dual-process theory of moral judgment explains. Trends in Cognitive Sciences, 11 (8), 322–323. https://doi.org/10.1016/j.tics.2007.06.004Google Scholar
Greene, J. D., Cushman, F. A., Stewart, L. E., Lowenberg, K., Nystrom, L. E., & Cohen, J. D. (2009). Pushing moral buttons: the interaction between personal force and intention in moral judgment. Cognition, 111 (3), 364–371. https://doi.org/10.1016/j.cognition.2009.02.001Google Scholar
Greene, J. D., Morelli, S. A., Lowenberg, K., Nystrom, L. E., & Cohen, J. D. (2008). Cognitive load selectively interferes with utilitarian moral judgment. Cognition, 107 (3), 1144–1154. https://doi.org/10.1016/j.cognition.2007.11.004Google Scholar
Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science, 293 (5537), 2105–2108. https://doi.org/10.1126/science.1062872CrossRefGoogle ScholarPubMed
Guala, F. (2012). Reciprocity: weak or strong? What punishment experiments do (and do not) demonstrate. Behavioral and Brain Sciences, 35 (1), 1–15. https://doi.org/10.1017/S0140525X11000069Google Scholar
Guarini, M. (2007). Computation, coherence, and ethical reasoning. Minds and Machines, 17 (1), 27–46. https://doi.org/10.1007/s11023-007-9056-4Google Scholar
Guarini, M. (2010). Particularism, analogy, and moral cognition. Minds and Machines, 20 (3), 385–422. https://doi.org/10.1007/s11023-010-9200-4Google Scholar
Guglielmo, S. (2015). Moral judgment as information processing: an integrative review. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01637Google Scholar
Gürçay, B., & Baron, J. (2017). Challenges for the sequential two-system model of moral judgement. Thinking & Reasoning, 23 (1), 49–80. https://doi.org/10.1080/13546783.2016.1216011Google Scholar
Haas, J. (2020). Moral gridworlds: a theoretical proposal for modeling artificial moral cognition. Minds and Machines, 30 (2), 219–246. https://doi.org/10.1007/s11023-020-09524-9Google Scholar
Hadfield-Menell, D., Russell, S. J., Abbeel, P., & Dragan, A. (2016). Cooperative inverse reinforcement learning. In Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I., & Garnett, R. (Eds.), Advances in Neural Information Processing Systems 29 (pp. 3909–3917). Red Hook, NY: Curran Associates.Google Scholar
Haidt, J. (2001). The emotional dog and its rational tail: a social intuitionist approach to moral judgment. Psychological Review, 108 (4), 814–834. https://doi.org/10.1037/0033-295X.108.4.814Google Scholar
Halpern, J. Y., & Kleiman-Weiner, M. (2018). Towards formal definitions of blameworthiness, intention, and moral responsibility. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
Hechter, M., & Opp, K.-D. (Eds.). (2001). Social Norms. New York, NY: Russell Sage Foundation.Google Scholar
Heider, F. (1958). The Psychology of Interpersonal Relations. Oxford: Wiley.Google Scholar
Holyoak, K. J., & Powell, D. (2016). Deontological coherence: a framework for commonsense moral reasoning. Psychological Bulletin, 142 (11), 1179–1203. https://doi.org/10.1037/bul0000075Google Scholar
Howard, D., & Muntean, I. (2017). Artificial moral cognition: moral functionalism and autonomous moral agency. In Powers, T. (Ed.), Philosophy and Computing (pp. 121–159). Cham: Springer. https://doi.org/10.1007/978-3-319-61043-6_7Google Scholar
Kasenberg, D., Roque, A., Thielstrom, R., Chita-Tegmark, M., & Scheutz, M. (2019). Generating justifications for norm-related agent decisions. In 12th International Conference on Natural Language Generation (INLG), Tokyo, Japan.Google Scholar
Kasenberg, D., & Scheutz, M. (2018). Norm conflict resolution in stochastic domains. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (pp. 85–92).Google Scholar
Kleiman-Weiner, M., Gerstenberg, T., Levine, S., & Tenenbaum, J. B. (2015). Inference of intention and permissibility in moral decision making. In Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 1123–1128). Cognitive Science Society.Google Scholar
Kohlberg, L. (1984). The Psychology of Moral Development: The Nature and Validity of Moral Stages. New York, NY: Harper & Row.Google Scholar
Kowalczuk, Z., & Czubenko, M. (2016). Computational approaches to modeling artificial emotion – an overview of the proposed solutions. Frontiers in Robotics and AI, 3. https://doi.org/10.3389/frobt.2016.00021Google Scholar
Laurent, S. M., Nuñez, N. L., & Schweitzer, K. A. (2016). Unintended, but still blameworthy: the roles of awareness, desire, and anger in negligence, restitution, and punishment. Cognition & Emotion, 30 (7), 1271–1288. https://doi.org/10.1080/02699931.2015.1058242Google Scholar
Leben, D. (2017). A Rawlsian algorithm for autonomous vehicles. Ethics and Information Technology, 19 (2), 107–115.Google Scholar
Levine, S., Kleiman-Weiner, M., Schulz, L., Tenenbaum, J. B., & Cushman, F. A. (2020). The logic of universalization guides moral judgment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/p7e6hGoogle Scholar
Levine, S., Leslie, A. M., & Mikhail, J. (2018). The mental representation of human action. Cognitive Science, 42 (4), 1229–1264. https://doi.org/10.1111/cogs.12608Google Scholar
Lindenberg, S. (2013). How cues in the environment affect normative behaviour. In Steg, L., van den Berg, A. E., & de Groot, J. I. M. (Eds.), Environmental Psychology: An Introduction (pp. 119–128). Oxford: BPS/Blackwell.Google Scholar
Malle, B. F. (2020). Graded representations of norm strength. In Denison, S., Mack, M., Xu, Y., & Armstrong, B. C. (Eds.), Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (pp. 3342–3348). Cognitive Science Society.Google Scholar
Malle, B. F. (2021). Moral judgments. Annual Review of Psychology, 72. https://doi.org/10.1146/annurev-psych-072220-104358Google Scholar
Malle, B. F., Guglielmo, S., & Monroe, A. E. (2014). A theory of blame. Psychological Inquiry, 25 (2), 147–186. https://doi.org/10.1080/1047840X.2014.877340Google Scholar
Malle, B. F., Rosen, E., Chi, V. B., Berg, M., & Haas, P. (2020). A general methodology for teaching norms to social robots. In Proceedings of the 29th International Conference on Robot & Human Interactive Communication.Google Scholar
Malle, B. F., Scheutz, M., & Austerweil, J. L. (2017). Networks of social and moral norms in human and robot agents. In Aldinhas Ferreira, M. I., Silva Sequeira, J., Tokhi, M. O., Kadar, E. E., & Virk, G. S. (Eds.), A World with Robots: International Conference on Robot Ethics: ICRE 2015 (pp. 3–17). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-46667-5_1Google Scholar
Mao, W., & Gratch, J. (2012). Modeling social causality and responsibility judgment in multi-agent interactions. Journal of Artificial Intelligence Research, 44, 223–273.Google Scholar
Marcus, G., & Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. New York, NY: Pantheon.Google Scholar
McLaren, B. M. (2006). Computational models of ethical reasoning: challenges, initial steps, and future directions. IEEE Intelligent Systems, 21, 29–37.Google Scholar
Meyer, J. J. Ch., Broersen, J. M., & Herzig, A. (2015). BDI Logics. In van Ditmarsch, H., Halpern, J. Y., van der Hoek, W., & Kooi, B. (Eds.), Handbook of Logics of Knowledge and Belief (pp. 453–498). Rickmansworth: College Publications. https://dspace.library.uu.nl/handle/1874/315954Google Scholar
Mikhail, J. (2008). Moral cognition and computational theory. In Sinnott-Armstrong, W. (Ed.), Moral Psychology, Vol. 3: The Neuroscience of Morality (pp. 81–92). Cambridge, MA: MIT Press.Google Scholar
Ohtsubo, Y., Matsunaga, M., Tanaka, H., et al. (2018). Costly apologies communicate conciliatory intention: an fMRI study on forgiveness in response to costly apologies. Evolution and Human Behavior, 39 (2), 249–256. https://doi.org/10.1016/j.evolhumbehav.2018.01.004Google Scholar
Ortony, A., Clore, G. L., & Collins, A. (1988). The Cognitive Structure of Emotions. Cambridge: Cambridge University Press.Google Scholar
Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect (1st ed.). New York, NY: Basic Books.Google Scholar
Pereira, L. M., & Saptawijaya, A. (2017). Counterfactuals, logic programming and agent morality. In Urbaniak, R. & Payette, G. (Eds.), Applications of Formal Philosophy: The Road Less Travelled (pp. 25–53). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-58507-9_3Google Scholar
Powers, T. M. (2006). Prospects for a Kantian machine. IEEE Intelligent Systems, 21 (4), 46–51. https://doi.org/10.1109/MIS.2006.77Google Scholar
Prakken, H., & Sergot, M. (1997). Dyadic deontic logic and contrary-to-duty obligations. In Nute, D. (Ed.), Defeasible Deontic Logic (pp. 223–262). Cham: Springer. https://doi.org/10.1007/978-94-015-8851-5_10Google Scholar
Prinz, J. (2006). The emotional basis of moral judgments. Philosophical Explorations, 9 (1), 29–43. https://doi.org/10.1080/13869790500492466Google Scholar
Quinn, P. L. (1978). Divine Commands and Moral Requirements. Oxford: Clarendon Press.Google Scholar
Rao, A. S. (1996). AgentSpeak(L): BDI agents speak out in a logical computable language. In Van de Velde, W. & Perram, J. W. (Eds.), Agents Breaking Away (pp. 42–55). Cham: Springer.Google Scholar
Rao, A. S., & Georgeff, M. P. (1991). Modeling rational agents within a BDI-architecture. In Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning (pp. 473–484). http://dl.acm.org/citation.cfm?id=3087158.3087205Google Scholar
Realpe-Gómez, J., Andrighetto, G., Nardin, L. G., & Montoya, J. A. (2018). Balancing selfishness and norm conformity can explain human behavior in large-scale prisoner’s dilemma games and can poise human groups near criticality. Physical Review E, 97 (4), 042321. https://doi.org/10.1103/PhysRevE.97.042321CrossRefGoogle ScholarPubMed
Rosales, J.-H., Rodríguez, L.-F., & Ramos, F. (2019). A general theoretical framework for the design of artificial emotion systems in Autonomous Agents. Cognitive Systems Research, 58, 324–341. https://doi.org/10.1016/j.cogsys.2019.08.003Google Scholar
Rosen, E., Hsiung, E., Chi, V. B., & Malle, B. F. (2022). Norm learning with reward models from instructive and evaluative feedback. In Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2022). Piscataway, NJ: IEEE.Google Scholar
Ross, W. D. (1930). The Right and the Good. Oxford: Oxford University Press.Google Scholar
Royzman, E. B., Goodwin, G. P., & Leeman, R. F. (2011). When sentimental rules collide: “norms with feelings” in the dilemmatic context. Cognition, 121 (1), 101–114. https://doi.org/10.1016/j.cognition.2011.06.006Google Scholar
Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. New York, NY: Viking.Google Scholar
Sachdeva, S., Singh, P., & Medin, D. (2011). Culture and the quest for universal principles in moral reasoning. International Journal of Psychology, 46 (3), 161–176. https://doi.org/10.1080/00207594.2011.568486Google Scholar
Santos, J. S., Zahn, J. O., Silvestre, E. A., Silva, V. T., & Vasconcelos, W. W. (2017). Detection and resolution of normative conflicts in multi-agent systems: a literature survey. Autonomous Agents and Multi-Agent Systems, 31 (6), 1236–1282. https://doi.org/10.1007/s10458-017-9362-zGoogle Scholar
Sauer, H. (2012). Morally irrelevant factors: what’s left of the dual process-model of moral cognition? Philosophical Psychology, 25 (6), 783–811. https://doi.org/10.1080/09515089.2011.631997Google Scholar
Scanlon, T. (1998). What We Owe to Each Other (Issue 1, pp. 169–175). Cambridge, MA: Harvard University Press.Google Scholar
Schaich Borg, J., Hynes, C., Van Horn, J., Grafton, S., & Sinnott-Armstrong, W. (2006). Consequences, action, and intention as factors in moral judgments: an fMRI investigation. Journal of Cognitive Neuroscience, 18 (5), 803–817. https://doi.org/10.1162/jocn.2006.18.5.803Google Scholar
Shams, Z., Vos, M. D., Oren, N., & Padget, J. (2020). Argumentation-based reasoning about plans, maintenance goals, and norms. ACM Transactions on Autonomous and Adaptive Systems, 14 (3), 9:1–9:39. https://doi.org/10.1145/3364220Google Scholar
Shaver, K. G. (1985). The Attribution of Blame: Causality, Responsibility, and Blameworthiness. New York, NY: Springer Verlag.Google Scholar
Shoham, Y., & Tennenholtz, M. (1995). On social laws for artificial agent societies: off-line design. Artificial Intelligence, 73 (1–2), 231–252. https://doi.org/10.1016/0004-3702(94)00007-NGoogle Scholar
Shultz, T. R. (1987). A computational model of causation, responsibility, blame, and punishment. Meeting of the Society for Research in Child Development, Baltimore, MD.Google Scholar
Sileno, G., Saillenfest, A., & Dessalles, J.-L. (2017). A computational model of moral and legal responsibility via simplicity theory. In Wyner, A. & Casini, G. (Eds.), Legal Knowledge and Information Systems (pp. 171–176). Clifton, VA: IOS Press. http://ebooks.iospress.nl/publication/48059Google Scholar
Slocum, D., Allan, A., & Allan, M. M. (2011). An emerging theory of apology. Australian Journal of Psychology, 63 (2), 83–92. https://doi.org/10.1111/j.1742-9536.2011.00013.xGoogle Scholar
Sripada, C. S., & Stich, S. (2006). A framework for the psychology of norms. In Carruthers, P., Laurence, S., & Stich, S. (Eds.), The Innate Mind (Vol. 2: Culture and Cognition) (pp. 280–301). Oxford: Oxford University Press.Google Scholar
Stallen, M., Rossi, F., Heijne, A., Smidts, A., De Dreu, C. K. W., & Sanfey, A. G. (2018). Neurobiological mechanisms of responding to injustice. The Journal of Neuroscience, 38 (12), 2944–2954. https://doi.org/10.1523/JNEUROSCI.1242-17.2018Google Scholar
Tangney, J. P., & Dearing, R. L. (2002). Shame and Guilt. New York, NY: Guilford Press.Google Scholar
Thagard, P. (1998). Ethical coherence. Philosophical Psychology, 11 (4), 405–422. https://doi.org/10.1080/09515089808573270Google Scholar
Turiel, E. (2002). The Culture of Morality: Social Development, Context, and Conflict. Cambridge: Cambridge University Press.Google Scholar
Ullmann-Margalit, E. (1977). The Emergence of Norms. Oxford: Clarendon Press.Google Scholar
van der Torre, L. W. N., & Tan, Y.-H. (1997). The many faces of defeasibility in defeasible deontic logic. In Nute, D. (Ed.), Defeasible Deontic Logic (pp. 79–121). Cham: Springer. https://doi.org/10.1007/978-94-015-8851-5_5Google Scholar
Von Wright, G. H. (1951). Deontic logic. Mind, LX (237), 1–15. https://doi.org/10.1093/mind/LX.237.1Google Scholar
Watanabe, S., & Laurent, S. M. (2020). Feeling bad and doing good: forgivability through the lens of uninvolved third parties. Social Psychology, 51 (1), 35–49. https://doi.org/10.1027/1864-9335/a000390Google Scholar
Weiner, B. (2001). Responsibility for social transgressions: an attributional analysis. In Malle, B. F., Moses, L. J., & Baldwin, D. A. (Eds.), Intentions and Intentionality: Foundations of Social Cognition (pp. 331–344). Cambridge, MA: MIT Press.Google Scholar
Zinchenko, O. (2019). Brain responses to social punishment: a meta-analysis. Scientific Reports, 9. https://doi.org/10.1038/s41598-019-49239-1Google Scholar

References

Abdelzaher, T., Han, J., Hao, Y., et al. (2020). Multiscale online media simulation with SocialCube. Computational and Mathematical Organization Theory, 26, 145–174.Google Scholar
Alexander, J., Giesen, B., Munch, R., & Smelser, N. (Eds.). (1987). The Micro-Macro Link. Berkeley, CA: University of California Press.Google Scholar
Allen, J., & Sun, R. (2016). Emotion contagion in a cognitive architecture. In Jin, Y. & Kollias, S. (Eds.), Proceedings of IEEE Symposium Series in Computational Intelligence. Piscataway, NJ: IEEE Press.Google Scholar
Andersen, S. M., & Chen, S. (2002). The relational self: an interpersonal social-cognitive theory. Psychological Review, 109 (4), 619–645.Google Scholar
Anderson, J., & Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Atran, S., & Norenzayan, A. (2004). Religion’s evolutionary landscape: counterintuition, commitment, compassion, and communion. Brain and Behavioral Sciences, 27, 713–770.Google Scholar
Axelrod, R. (1984). The Evolution of Cooperation. New York, NY: Basic Books.Google Scholar
Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In Conte, R., Hegselmann, R., & Terna, P. (Eds.), Simulating Social Phenomena (pp. 21–40). Berlin: Springer.Google Scholar
Axtell, R., Axelrod, J., & Cohen, M. (1996). Aligning simulation models: a case study and results. Computational and Mathematical Organization Theory, 1 (2), 123–141.CrossRefGoogle Scholar
Balke, T., & Gilbert, N. (2014). How do agents make decisions? A survey. Journal of Artificial Societies and Social Simulation, 17(4). http://jasss.soc.surrey.ac.uk/17/4/13.htmlGoogle Scholar
Bourgais, M., Taillandier, P., Vercouter, L., & Adam, C. (2018). Emotion modeling in social simulation: a survey. Journal of Artificial Societies and Social Simulation, 21 (2). http://jasss.soc.surrey.ac.uk/21/2/5.htmlGoogle Scholar
Boyer, P., & Ramble, C. (2001). Cognitive templates for religious concepts: cross-cultural evidence for recall of counter-intuitive representations. Cognitive Science, 25, 535–564.Google Scholar
Brekhus, W., & Ignatow, G. (2019). The Cambridge Handbook of Cognitive Sociology. New York, NY: Oxford University Press.Google Scholar
Bretz, S., & Sun, R. (2018). Two models of moral judgment. Cognitive Science, 42, 4–37.Google Scholar
Brousmiche, K. L., Kant, J. D., Sabouret, N., & Prenot-Guinard, F. (2016). From beliefs to attitudes: Polias, a model of attitude dynamics based on cognitive modeling and field data. Journal of Artificial Societies and Social Simulation, 19 (4). http://jasss.soc.surrey.ac.uk/19/4/2.htmlGoogle Scholar
Camerer, C., Loewenstein, G., & Rabin, M. (Eds.) (2003). Advances in Behavioral Economics. Princeton, NJ: Princeton University Press.Google Scholar
Carley, K., & Newell, A. (1994). The nature of social agent. Journal of Mathematical Sociology, 19 (4), 221–262.Google Scholar
Carley, K., Prietula, M. J., & Lin, Z. (1998). Design versus cognition: the interaction of agent cognition and organizational design on organizational performance. Journal of Artificial Societies and Social Simulation, 1 (3). www.soc.surrey.ac.uk/JASSS/1/3/4.htmlGoogle Scholar
Castelfranchi, C. (2001). The theory of social functions: challenges for computational social science and multi-agent learning. Cognitive Systems Research, 2 (1), 5–38.Google Scholar
Cecconi, F., & Parisi, D. (1998). Individual versus social survival strategies. Journal of Artificial Societies and Social Simulation, 1 (2). www.soc.surrey.ac.uk/JASSS/1/2/1.htmlGoogle Scholar
Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-Process Theories in Social Psychology. New York, NY: Guilford Press.Google Scholar
Cialdini, R. (2009). Influence: Science and Practice. Boston, MA: Pearson Education.Google Scholar
Clancey, W. J., Linde, C., Seah, C., & Shafto, M. (2013). Work Practice Simulation of Complex Human-Automation Systems in Safety Critical Situations: The Brahms Generalized Überlingen Model. NASA Technical Publication 2013-216508, Washington, DC.Google Scholar
Clancey, W., Sierhuis, M., Damer, B., & Brodsky, B. (2006). Cognitive modeling of social behaviors. In Sun, R. (Ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press.Google Scholar
Cole, J., Ghafurian, M., & Reitter, D. (2019). Word adoption in online communities. IEEE Transactions on Computational Social Systems, 6 (1), 178–188. https://doi.org/10.1109/TCSS.2018.2889493Google Scholar
Conte, R., Andrighetto, G., & Campennl, M. (2013). Minding Norms: Mechanisms and Dynamics of Social Order in Agent Societies. New York, NY: Oxford University Press.Google Scholar
Conte, R., & Giardini, F. (2016). Towards computational and behavioral social science. European Psychologist, 21 (2), 131–140.Google Scholar
Conte, R., Hegselmann, R., & Terna, P. (Eds.). (1997). Simulating Social Phenomena. Berlin: Springer.Google Scholar
D’Andrade, R. G., & Strauss, C. (Eds). (1992). Human Motives and Cultural Models. Cambridge: Cambridge University Press.Google Scholar
Dignum, M. V., Tranier, J. F. R., & Dignum, F. P. M. (2010). Simulation of intermediation using rich cognitive agents. Simulation Modelling Practice and Theory, 18, 1526–1536.Google Scholar
DiMaggio, P. (1997). Culture and cognition. Annual Review of Sociology, 23 , 263–288.Google Scholar
Doran, J., Palmer, M., Gilbert, N., & Mellars, P. (1994). The EOS project: modeling upper Palaeolithic social change. In Gilbert, N. & Doran, J. (Eds.), Simulating Societies. London: UCL Press.Google Scholar
Edmonds, B. (2014). Contextual cognition in social simulation. In Brézillon, P. & Gonzalez, A. (Eds.), Context in Computing. New York, NY: Springer.Google Scholar
Edmonds, B. (2020). Co‑developing beliefs and social influence networks—towards understanding socio‑cognitive processes like Brexit. Quality & Quantity, 54, 491–515. https://doi.org/10.1007/s11135-019-00891-9Google Scholar
Elsenbroich, C., & Gilbert, N. (2014). Modelling Norms. Berlin: Springer.Google Scholar
Epstein, J., & Axtell, R. (1996). Growing Artificial Societies. Cambridge, MA: MIT Press.Google Scholar
Erisen, C., Lodge, M., & Taber, C. S. (2014). Affective contagion in effortful political thinking. Political Psychology, 35 (2), 187–206. https://doi.org/10.1111/j.1467-9221.2012.00937.xGoogle Scholar
Estes, W. (1972). Research and theory on the learning of probabilities. Journal of the American Statistical Association, 67, 81–102.Google Scholar
Falk, E. B., & Bassett, D. S. (2017). Brain and social networks: fundamental building blocks of human experience. Trends in Cognitive Sciences, 21 (9), 674–690.Google Scholar
Gilbert, N., & Doran, J. (1994). Simulating Societies: The Computer Simulation of Social Phenomena. London: UCL Press.Google Scholar
Goldspink, C. (2000). Modelling social systems as complex: towards a social simulation meta-model. Journal of Artificial Societies and Social Simulation, 3 (2). www.jasss.org/3/2/1.htmlGoogle Scholar
Gong, T., Shuai, L., & Zhang, M. (2014). Modelling language evolution: examples and predictions. Physics of Life Reviews, 11 (2), 280–302.Google Scholar
Grand, J. A., Braun, M. T., Kuljanin, G., Kozlowski, S. W., & Chao, G. T. (2016). The dynamics of team cognition: a process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101, 1353–1385.Google Scholar
Gratch, J., Mao, W., & Marsella, S. (2006). Modeling social emotions and social attributions. In Sun, R. (Ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press.Google Scholar
Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5 (3). http://jasss.soc.surrey.ac.uk/5/3/2.htmlGoogle Scholar
Helmhout, M. (2006). The social cognitive actor: a multi-actor simulation of organisations. Ph.D Thesis, University of Groningen, Groningen, Netherlands.Google Scholar
Henrich, J., Heine, S., & Norenzayan, A. (2010). The Weirdest People in the World? Behavioral and Brain Sciences, 33, 61–135.Google Scholar
Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and Organizations: Software of the Mind (3rd ed.) New York, NY: McGraw-Hill.Google Scholar
Iyengar, S. S., & Lepper, M. R. (1999). Rethinking the value of choice: a cultural perspective on intrinsic motivation. Journal of Personality and Social Psychology, 76 (3), 349–366.Google Scholar
Jager, W. (2017). Enhancing the realism of simulation (EROS): on implementing and developing psychological theory in social simulation. Journal of Artificial Societies and Social Simulation, 20 (3). http://jasss.soc.surrey.ac.uk/20/3/14.htmlGoogle Scholar
Juvina, I., Lebiere, C., & Gonzalez, C. (2015). Modeling trust dynamics in strategic interaction. Journal of Applied Research in Memory and Cognition, 4 (3), 197–211.Google Scholar
Juvina, I., Lebiere, C., Martin, J. M., & Gonzalez, C. (2011). Intergroup prisoner’s dilemma with intragroup power dynamics. Games, 2, 21–51.Google Scholar
Kahan, J., & Rapoport, A. (1984). Theories of Coalition Formation. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Kaidesoja, T., Sarkia, M., & Hyyryläinen, M. (2019). Arguments for the cognitive social sciences. Journal for the Theory of Social Behaviour, 49 (4), 480–498. https://doi.org/10.1111/jtsb.12226Google Scholar
Kenrick, D., Li, N., & Butner, J. (2003). Dynamical evolutionary psychology: individual decision rules and emergent social norms. Psychological Review, 110 (1), 3–28.Google Scholar
Kim, S., Taber, C. S., & Lodge, M. (2010). A computational model of the citizen as motivated reasoner: modeling the dynamics of the 2000 presidential election. Political Behavior, 32, 1–28.Google Scholar
Kluver, J., Malecki, R., Schmidt, J., & Stoica, C. (2003). Sociocultural evolution and cognitive ontogenesis: a sociocultural-cognitive algorithm. Computational and Mathematical Organization Theory, 9, 255–273.Google Scholar
Kohler, T. A., & Gumerman, G. J. (2000). Dynamics in Human and Primate Societies. New York, NY: Oxford University Press.Google Scholar
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108 (3), 480–498.Google Scholar
Locke, E. A., & Latham, G. P. (2013). New Developments in Goal Setting and Task Performance. New York, NY: Routledge.Google Scholar
Lodge, M., & Taber, C. (2013). The Rationalizing Voter. New York, NY: Cambridge University Press.Google Scholar
Loewenstein, G., Rick, S., & Cohen, J. (2008). Neuroeconomics. Annual Reviews of Psychology, 59, 647–672. https://doi.org/10.1146/annurev.psych.59.103006.093710Google Scholar
Lotem, A., Halpern, J. Y., Edelman, S., & Kolodny, O. (2017). The evolution of cognitive mechanisms in response to cultural innovations. PNAS, 114 (30), 7915–7922.Google Scholar
Mason, W., Conrey, F., & Smith, E. (2007). Situating social influence processes: dynamic, multidirectional flows of influence within social networks. Personality and Social Psychology Review, 11 (3), 279–300.Google Scholar
Medin, D. L., & Atran, S. (2004). The native mind: biological categorization and reasoning in development and across cultures. Psychological Review, 111, 960–983.Google Scholar
Mithen, S. (1996). The Prehistory of the Mind: The Cognitive Origins of Art, Religion, and Science. London: Thames & Hudson.Google Scholar
Moss, S. (1999). Relevance, realism and rigour: a third way for social and economic research. CPM Report No. 99-56. Center for Policy Analysis, Manchester Metropolitan University, Manchester, UK.Google Scholar
Moss, S., & Davidsson, P. (Eds.). (2001). Multi-Agent-Based Simulation. Berlin: Springer.Google Scholar
Muthukrishna, M., & Schaller, M. (2020). Are collectivistic cultures more prone to rapid transformation? Computational models of cross-cultural differences, social network structure, dynamic social influence, and cultural change. Personality and Social Psychology Review, 24 (2), 103–120.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Nisbett, R., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: holistic versus analytic cognition. Psychological Review, 108 (2), 291–310.Google Scholar
Nowak, A., Gelfand, M. J., Borkowski, W., Cohen, D., & Hernandez, I. (2016). The evolutionary basis of honor cultures. Psychological Science, 27 (1), 12–24.Google Scholar
Nyborg, K., Anderies, J. M., Dannenberg, A., et al. (2016). Social norms as solutions: policies may influence large-scale behavioral tipping. Science, 354 (6308), 42–43.Google Scholar
Parunak, H. V. D., Brooks, S. H., Brueckner, S. A., Gupta, R., & Li, L. (2014). Dynamically tracking the real world in an agent-based model. Multi-Agent-Based Simulation, XIV, 3–16.Google Scholar
Pew, R., & Mavor, A. (Eds). (1998). Modeling Human and Organizational Behavior: Application to Military Simulations. Washington, DC: National Academy Press.Google Scholar
Plott, C. R., & Smith, V. L. (2008). Handbook of Experimental Economics Results (Vol. 1). Amsterdam: Elsevier.Google Scholar
Prietula, M., Carley, K., & Gasser, L. (Eds.). (1998). Simulating Organizations: Computational Models of Institutions and Groups. Cambridge, MA: MIT Press.Google Scholar
Red’ko, V. G. (2015). Modeling of cognitive evolution: perspective direction of interdisciplinary investigation. Procedia Computer Science, 71, 215–220.Google Scholar
Reynolds, R. (1994). Learning to co-operate using cultural algorithms. In Gilbert, N. & Doran, J. (Eds.), Simulating Societies: The Computer Simulation of Social Phenomena. London: UCL Press.Google Scholar
Sawyer, R. (2003). Multiagent systems and the micro-macro link in sociological theory. Sociological Methods and Research, 31 (3), 325–363.Google Scholar
Schelling, T. C. (2006). Micromotives and Macrobehavior. New York, NY: W. W. Norton.Google Scholar
Schreiber, D. (2004). A hybrid model of political cognition. Paper presented at Midwestern Political Science Association Annual Meeting, Chicago, USA.Google Scholar
Schultheis, H. (2021). Computational cognitive modeling in the social sciences. In Engel, U., Quan-Haase, A., Liu, S., & Lyberg, L. E. (Eds.), Handbook of Computational Social Science (Vol. 1). London: Routledge.Google Scholar
Shell, D., & Mataric, M. (2006). Behavior-based methods for modeling and structuring control of social robots. In Sun, R. (Ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press.Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550 (7676), 354–359.Google Scholar
Sperber, D., & Hirschfeld, L. (2004). The cognitive foundations of cultural stability and diversity. Trends in Cognitive Sciences, 8 (1), 40–46.Google Scholar
Strandell, J. (2019). Bridging the vocabularies of dual-process models of culture and cognition. In Brekhus, W. & Ignatow, G., (Eds.). The Cambridge Handbook of Cognitive Sociology. New York, NY: Oxford University Press.Google Scholar
Sun, R. (2001). Cognitive science meets multi-agent systems: a prolegomenon. Philosophical Psychology, 14 (1), 5–28.Google Scholar
Sun, R. (Ed.). (2006). Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press.Google Scholar
Sun, R. (Ed.). (2008). The Cambridge Handbook of Computational Psychology. New York, NY: Cambridge University Press.Google Scholar
Sun, R. (Ed.). (2012). Grounding Social Sciences in Cognitive Sciences. Cambridge, MA: MIT Press.Google Scholar
Sun, R. (2016). Anatomy of the Mind. New York, NY: Oxford University Press.Google Scholar
Sun, R. (2018). Cognitive social simulation for policy making. Policy Insights from the Behavioral and Brain Sciences, 5 (2), 240–246.Google Scholar
Sun, R. (2020a). Full human-machine symbiosis and truly intelligent cognitive systems. AI and Society, 35 (1), 17–28. https://doi.org/10.1007/s00146-017-0775-7Google Scholar
Sun, R. (2020b). Exploring culture from the standpoint of a cognitive architecture. Philosophical Psychology, 33 (2), 155–180. https://doi.org/10.1080/09515089.2020.1719054Google Scholar
Sun, R., Coward, A., & Zenzen, M. (2005). On levels of cognitive modeling. Philosophical Psychology, 18 (5), 613–637.Google Scholar
Sun, R., & Fleischer, P. (2012). A cognitive social simulation of tribal survival strategies: the importance of cognitive and motivational factors. Journal of Cognition and Culture, 12 (3–4), 287–321.Google Scholar
Sun, R., & Naveh, I. (2004). Simulating organizational decision making with a cognitive architecture Clarion. Journal of Artificial Society and Social Simulation, 7 (3). http://jasss.soc.surrey.ac.uk/7/3/5.htmlGoogle Scholar
Sun, R., & Naveh, I. (2007). Social institution, cognition, and survival: a cognitive-social simulation. Mind and Society, 6 (2), 115–142.Google Scholar
Sun, R., & Wilson, N. (2014). A model of personality should be a cognitive architecture itself. Cognitive Systems Research, 29–30, 1–30.Google Scholar
Sun, R., Wilson, N., & Lynch, M. (2016). Emotion: a unified mechanistic interpretation from a cognitive architecture. Cognitive Computation, 8 (1), 1–14.Google Scholar
Tani, J. (2016). Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena. New York, NY: Oxford University Press.Google Scholar
Thagard, P. (2019). Mind-Society. New York, NY: Oxford University Press.Google Scholar
Thagard, P., & Kroon, F. W. (2006). Emotional consensus in group decision making. Mind and Society, 5 (1), 85–104.Google Scholar
Thaler, R. H. (2016). Behavioral economics: past, present, and future. American Economic Review, 106 (7), 1577–1600. https://doi.org/10.1257/aer.106.7.1577Google Scholar
Turner, M. (2001). Cognitive Dimensions of Social Science. New York, NY: Oxford University Press.Google Scholar
Van Overwalle, F., & Heylighen, F. (2006). Talking nets: a multiagent connectionist approach to communication and trust between individuals. Psychological Review, 113 (3), 606–627.Google Scholar
Vernon, D. (2014). Artificial Cognitive Systems: A Primer. Cambridge, MA: MIT Press.Google Scholar
Von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behaviour. Princeton, NJ: Princeton University Press.Google Scholar
Vu, T. M., Probst, C., Nielsen, A., et al. (2020). A software architecture for mechanism-based social systems modelling in agent-based simulation models. Journal of Artificial Societies and Social Simulation, 23 (3). http://jasss.soc.surrey.ac.uk/23/3/1.htmlGoogle Scholar
Vygotsky, L. (1962). Thought and Language. Cambridge, MA: MIT Press.Google Scholar
Watts, C., & Gilbert, N. (2014). Simulating Innovation: Computer-Based Tools for Rethinking Innovation. Cheltenham, UK: Edward Elgar.Google Scholar
West, R., Lebiere, C., & Bothell, D. (2006). Cognitive architectures, game playing, and human evolution. In Sun, R. (Ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. New York, NY: Cambridge University Press.Google Scholar
White, J. (2020). The role of robotics and AI in technologically mediated human evolution: a constructive proposal. AI and Society, 35, 177–185. https://doi.org/10.1007/s00146-019-00877-zGoogle Scholar
Wilson, N., & Sun, R. (2021). A mechanistic account of stress-induced performance degradation. Cognitive Computation, 13 (1), 207–227. https://dx.doi.org/10.1007/s12559-020-09725-5Google Scholar
Zerubavel, E. (1997). Social Mindscapes: An Invitation to Cognitive Sociology. Cambridge, MA: Harvard University Press.Google Scholar

References

Abbate, A. J., & Bass, E. J. (2015). Using computational tree logic methods to analyze reachability in user documentation. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 59, pp. 1481–1485).Google Scholar
Aït-Ameur, Y., & Baron, M. (2006). Formal and experimental validation approaches in HCI systems design based on a shared event B model. International Journal on Software Tools for Technology Transfer, 8 (6), 547–563.Google Scholar
Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Anderson, J. R., Bothell, D., Byrne, M. D., Douglas, S., Lebiere, C., & Quin, Y. (2004). An integrated theory of the mind. Psychological Review, 111 (4), 1036–1060.Google Scholar
Anderson, J. R., Matessa, M., & Lebiere, C. (1997). ACT-R: a theory of higher-level cognition and its relation to visual attention. Human-Computer Interaction, 12 (4), 439–462.Google Scholar
Bainbridge, L. (1983). Ironies of automation. Automatica, 19 (6), 775–780.Google Scholar
Ball, J., Myers, C., Heiberg, A., et al. (2010). The synthetic teammate project. Computational and Mathematical Organization Theory, 16 (3), 271–299.Google Scholar
Ballard, D. H., & Sprague, N. (2007). On the role of embodiment in modeling natural behaviors. In Gray, W. D. (Ed.), Integrated Models of Cognitive Systems. New York, NY: Oxford University Press.Google Scholar
Barbosa, A., Paiva, A. C., & Campos, J. C. (2011). Test case generation from mutated task models. In Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing Systems (pp. 175–184).Google Scholar
Basnyat, S., Palanque, P. A., Bernhaupt, R., & Poupart, E. (2008). Formal modelling of incidents and accidents as a means for enriching training material for satellite control operations. In Proceedings of the Joint ESREL 2008 and 17th SRA-Europe Conference (CD-ROM). London: Taylor & Francis.Google Scholar
Basnyat, S., Palanque, P., Schupp, B., & Wright, P. (2007). Formal socio-technical barrier modelling for safety-critical interactive systems design. Safety Science, 45 (5), 545–565.Google Scholar
Bastide, R., & Basnyat, S. (2007). Error patterns: systematic investigation of deviations in task models. In Task Models and Diagrams for Users Interface Design 5th International Workshop (pp. 109–121). Berlin: Springer.Google Scholar
Basuki, T. A., Cerone, A., Griesmayer, A., & Schlatte, R. (2009). Model-checking user behaviour using interacting components. Formal Aspects of Computing, 1–18.Google Scholar
Bolton, M. L. (2015). Model checking human–human communication protocols using task models and miscommunication generation. Journal of Aerospace Information Systems, 12 (7), 476–489.Google Scholar
Bolton, M. L. (2017a). Novel developments in formal methods for human factors engineering. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (pp. 715–717).Google Scholar
Bolton, M. L. (2017b). A task-based taxonomy of erroneous human behavior. International Journal of Human-Computer Studies, 108, 105–121.Google Scholar
Bolton, M. L., & Bass, E. J. (2010). Formally verifying human-automation interaction as part of a system model: limitations and tradeoffs. Innovations in Systems and Software Engineering: A NASA Journal, 6 (3), 219–231.Google Scholar
Bolton, M. L., & Bass, E. J. (2013). Generating erroneous human behavior from strategic knowledge in task models and evaluating its impact on system safety with model checking. IEEE Transactions on Systems, Man and Cybernetics: Systems, 43 (6), 1314–1327.Google Scholar
Bolton, M. L., & Bass, E. J. (2017). Enhanced operator function model (EOFM): a task analytic modeling formalism for including human behavior in the verification of complex systems. In Weyers, B., Bowen, J., Dix, A., & Palanque, P. (Eds.), The Handbook of Formal Methods in Human-Computer Interaction. Berlin: Springer.Google Scholar
Bolton, M. L., Bass, E. J., & Siminiceanu, R. I. (2012). Generating phenotypical erroneous human behavior to evaluate human–automation interaction using model checking. International Journal of Human-Computer Studies, 70 (11), 888–906.Google Scholar
Bolton, M. L., Bass, E. J., & Siminiceanu, R. I. (2013). Using formal verification to evaluate human-automation interaction in safety critical systems, a review. IEEE Transactions on Systems, Man and Cybernetics: Systems, 43 (3), 488–503.Google Scholar
Bolton, M. L., Molinaro, K. A., & Houser, A. M. (2019). A formal method for assessing the impact of task-based erroneous human behavior on system safety. Reliability Engineering & System Safety, 188, 168–180.Google Scholar
Bolton, M. L., Siminiceanu, R. I., & Bass, E. J. (2011). A systematic approach to model checking human-automation interaction using task-analytic models. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 41(5), 961–976.Google Scholar
Boring, R. L., & Rasmussen, M. (2016). GOMS-HRA: a method for treating subtasks in dynamic human reliability analysis. In Proceedings of the 2016 European Safety and Reliability Conference (pp. 956–963).Google Scholar
Bovair, S., Kieras, D. E., & Polson, P. G. (1990). The acquisition and performance of text-editing skill: a cognitive complexity analysis. Human-Computer Interaction, 5 (1), 1–48.Google Scholar
Byrne, M. D. (2007). Cognitive architecture. In Sears, A. & Jacko, J. A. (Eds.), The Human-Computer Interaction Handbook (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Byrne, M. D., & Anderson, J. R. (1998). Perception and action. In Anderson, J. R. & Lebiѐre, C. (Eds.), The Atomic Components of Thought (pp. 167–200). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Byrne, M. D., & Bovair, S. (1997). A working memory model of a common procedural error. Cognitive Science, 21 (1), 31–61.Google Scholar
Campos, J. C., Fayollas, C., Martinie, C., Navarre, D., Palanque, P., & Pinto, M. (2016). Systematic automation of scenario-based testing of user interfaces. In Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (pp. 138–148).Google Scholar
Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Clarke, E. M., Grumberg, O., & Peled, D. A. (1999). Model Checking. Cambridge, MA: MIT Press.Google Scholar
Curzon, P., & Blandford, A. (2004). Formally justifying user-centered design rules: a case study on post-completion errors. In Proceedings of the 4th International Conference on Integrated Formal Methods (pp. 461–480). Berlin: Springer.Google Scholar
Curzon, P., & Rukšėnas, R. (2017). Modelling the user. In Weyers, B., Bowen, J., Dix, A., & Palanque, P. (Eds.), The Handbook of Formal Methods in Human-Computer Interaction. Berlin: Springer.Google Scholar
Curzon, P., Rukšėnas, R., & Blandford, A. (2007). An approach to formal verification of human–computer interaction. Formal Aspects of Computing, 19 (4), 513–550.Google Scholar
Degani, A. (2004).Taming HAL: Designing Interfaces Beyond 2001. New York, NY: Macmillan.Google Scholar
Degani, A., Heymann, M., & Shafto, M. (1999). Formal aspects of procedures: the problem of sequential correctness. In Proceedings of the 43rd Annual Meeting of the Human Factors and Ergonomics Society (pp. 1113–1117). Los Angeles, CA: SAGE.Google Scholar
Demir, M., McNeese, N. J., Cooke, N. J., Ball, J. T., Myers, C., & Frieman, M. (2015). Synthetic teammate communication and coordination with humans. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (pp. 951–955). Los Angeles, CA: SAGE.Google Scholar
Demir, M., McNeese, N. J., & Cooke, N. J. (2016). Team communication behaviors of the human-automation teaming. In 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA) (pp. 28–34). New York, NY: IEEE.Google Scholar
Emerson, E. A. (1990). Temporal and modal logic. In Formal Models and Semantics (pp. 995–1072). Oxford: Elsevier.Google Scholar
España, S., Pederiva, I., & Panach, J. I. (2007). Integrating model-based and task-based approaches to user interface generation. In Computer-Aided Design of User Interfaces V (pp. 253–260). Amsterdam: Springer.Google Scholar
Fahssi, R., Martinie, C., & Palanque, P. (2015). Enhanced task modelling for systematic identification and explicit representation of human errors. In Human-Computer Interaction – Interact 2015 (pp. 192–212). Cham: Springer International Publishing.Google Scholar
Fields, R. E. (2001). Analysis of erroneous actions in the design of critical systems. Unpublished doctoral dissertation, University of York, York.Google Scholar
Gluck, K. A., Ball, J. T., Gunzelmann, G., Krusmark, M., Lyon, D., & Cooke, N. (2005). A prospective look at a synthetic teammate for UAV applications. In Infotech@ Aerospace. Reston: American Institute of Aeronautics and Astronautics.Google Scholar
Gluck, K. A., Ball, J. T., & Krusmark, M. A. (2007). Cognitive control in a computational model of the predator pilot. In Gray, W. D. (Ed.), Integrated Models of Cognitive Systems (pp. 13–28). New York, NY: Oxford University Press.Google Scholar
Gray, W. D. (2008). Cognitive modeling for cognitive engineering. In Sun, R. (Ed.), The Cambrdge Handbook of Computational Psychology (pp. 565–588). Cambridge: Cambridge University Press.Google Scholar
Gray, W. D. (Ed.). (2007). Integrated Models of Cognitive Systems. New York, NY: Oxford University Press.Google Scholar
Gray, W. D., & Boehm-Davis, D. A. (2000). Milliseconds matter: an introduction to microstrategies and to their use in describing and predicting interactive behavior. Journal of Experimental Psychology: Applied, 6 (4), 322–335.Google Scholar
Gray, W. D., John, B. E., & Atwood, M. E. (1993). Project Ernestine: validating a GOMS analysis for predicting and explaining real-world performance. Human-Computer Interaction, 8 (3), 237–309.Google Scholar
Gunning, D., & Aha, D. W. (2019). DARPA’s explainable artificial intelligence program. AI Magazine, 40 (2), 44–58.Google Scholar
Hollnagel, E. (1993). The phenotype of erroneous actions. International Journal of Man-Machine Studies, 39 (1), 1–32.Google Scholar
Jeong, H., & Liu, Y. (2017). Modeling of stimulus-response secondary tasks with different modalities while driving in a computational cognitive architecture. In Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design (pp. 193–199). Iowa, IA: University of Iowa.Google Scholar
John, B. E. (1988). Contributions to engineering models of human-computer interaction. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
John, B. E. (1996). TYPIST: a theory of performance in skilled typing. Human-Computer Interaction, 11 (4), 321–355.Google Scholar
Kebabjian, R. (2016). Accident statistics. planecrashinfo.com. Retrieved from www.planecrashinfo.com/cause.htm [last accessed July 30, 2022].Google Scholar
Kenny, D. J. (2015). 26th Joseph T. Nall Report: General Aviation Accidents in 2014. Technical Report. Frederick, MD: AOPA Foundation.Google Scholar
Kieras, D. E. (1997). A guide to GOMS model usability evaluation using NGOMSL. In Helander, M., Landauer, T. K., & Prabhu, P. (Eds.), Handbook of Human-Computer Interaction (2nd ed., pp. 733–766). New York, NY: Elsevier.Google Scholar
Kieras, D. E. (2007). Model-based evaluation. In Sears, A. & Jacko, J. A. (Eds.), The Human-Computer Interaction Handbook (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Kieras, D. E., & Bovair, S. (1986). The acquisition of procedures from text: a production-system analysis of transfer of training. Journal of Memory and Language, 25, 507–524.Google Scholar
Kieras, D. E., & Meyer, D. E. (1997). An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction, 12 (4), 391–438.Google Scholar
Kieras, D. E., Wakefield, G. H., Thompson, E. R., Iyer, N., & Simpson, B. D. (2016). Modeling two-channel speech processing with the EPIC cognitive architecture. Topics in Cognitive Science, 8 (1), 291–304.Google Scholar
Kirwan, B., & Ainsworth, L. K. (Eds.). (1992). A Guide to Task Analysis. Washington, DC: Taylor & Francis.Google Scholar
Le Bot, P. (2004). Human reliability data, human error and accident models – illustration through the Three Mile Island accident analysis. Reliability Engineering & System Safety, 83 (2), 153–167.Google Scholar
Li, M., & Bolton, M. L. (2019). Task-based automated test case generation for human-machine interaction. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 63, pp. 807–811).Google Scholar
Li, M., Wei, J., Zheng, X., & Bolton, M. L. (2017). A formal machine learning approach to generating human-machine interfaces from task models. IEEE Transactions of Human Machine Systems, 47 (6), 822–833.Google Scholar
Liu, Y., Feyen, R., & Tsimhoni, O. (2006). Queueing Network-Model Human Processor (QN-MHP): a computational architecture for multitask performance in human-machine systems. ACM Transactions on Computer-Human Interaction (TOCHI), 13 (1), 37–70.Google Scholar
Lovett, M. C., & Anderson, J. R. (1996). History of success and current context in problem solving: combined influences on operator selection. Cognitive Psychology, 31, 168–217.Google Scholar
Luyten, K., Clerckx, T., Coninx, K., & Vanderdonckt, J. (2003). Derivation of a dialog model from a task model by activity chain extraction. In Proceedings of the 10th International Workshop on Interactive Systems. Design, Specification, and Verification (pp. 203–217). Berlin: Springer.Google Scholar
Manning, S. D., Rash, C. E., LeDuc, P. A., Noback, R. K., & McKeon, J. (2004). The Role of human Causal Factors in US Army Unmanned Aerial Vehicle Accidents. Technical Report No. 2004-11. Adelphi, MD: USA Army Research Laboratory.Google Scholar
Makary, M. A., & Daniel, M. (2016). Medical error – the third leading cause of death in the US. BMJ, 353, 5.Google Scholar
Mirman, J. H. (2019). A dynamical systems perspective on driver behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 193–203.Google Scholar
Mirman, J. H., Curry, A. E., & Mirman, D. (2019). Learning to drive: a reconceptualization. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 316–326.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Card, S. K. (1985). The prospects for psychological science in human-computer interaction. Human-Computer Interaction, 1 (3), 209–242.Google Scholar
NHTSA. (2008). National Motor Vehicle Crash Causation Survey: Report to Congress. Technical Report No. DOT HS 811 059. Springfield: National Highway Traffic Safety Administration.Google Scholar
Pan, D., & Bolton, M. L. (2018). Properties for formally assessing the performance level of human-human collaborative procedures with miscommunications and erroneous human behavior. International Journal of Industrial Ergonomics, 63, 75–88.Google Scholar
Paternò, F., & Santoro, C. (2001). Integrating model checking and HCI tools to help designers verify user interface properties. In Proceedings of the 7th International Workshop on the Design, Specification, and Verification of Interactive Systems (pp. 135–150). Berlin: Springer.Google Scholar
Paternò, F., Mancini, C., & Meniconi, S. (1997). ConcurTaskTrees: a diagrammatic notation for specifying task models. In Proceedings of the IFIP TC13 International Conference on Human-Computer Interaction (pp. 362–369). London: Chapman & Hall.Google Scholar
Pew, R. W. (2007). Some history of human performance modeling. In Gray, W. D. (Ed.), Integrated Models of Cognitive Systems. New York, NY: Oxford University Press.Google Scholar
Pritchett, A. R., Feigh, K. M., Kim, S. Y., & Kannan, S. K. (2014). Work models that compute to describe multiagent concepts of operation: part 1. Journal of Aerospace Information Systems, 11 (10), 610–622.Google Scholar
Reason, J. (1990). Human Error. New York, NY: Cambridge University Press.Google Scholar
Rehman, U., Cao, S., & MacGregor, C. (2019). Using an integrated cognitive architecture to model the effect of environmental complexity on drivers’ situation awareness. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (pp. 812–816).Google Scholar
Rhie, Y. L., Lim, J. H., & Yun, M. H. (2018). Queueing network based driver model for varying levels of information processing. IEEE Transactions on Human-Machine Systems, 49 (6), 508–517.Google Scholar
Rodgers, S., Myers, C., Ball, J., & Freiman, M. (2011). The situation model in the synthetic teammate project. In Proceedings of the 20th Annual Conference on Behavior Representation in Modeling and Simulation (pp. 66–73).Google Scholar
Rukšėnas, R., Back, J., Curzon, P., & Blandford, A. (2008). Formal modelling of salience and cognitive load. In Proceedings of the 2nd International Workshop on Formal Methods for Interactive Systems (pp. 57–75). Amsterdam: Elsevier Science Publishers.Google Scholar
Rukšėnas, R., Back, J., Curzon, P., & Blandford, A. (2009). Verification-guided modelling of salience and cognitive load. Formal Aspects of Computing, 21 (6), 541–569.Google Scholar
Rukšėnas, R., Curzon, P., Back, J., & Blandford, A. (2007). Formal modelling of cognitive interpretation. In Proceedings of the 13th International Workshop on the Design, Specification, and Verification of Interactive Systems (pp. 123–136). London: Springer.Google Scholar
Rukšėnas, R., Curzon, P., Blandford, A., & Back, J. (2014). Combining human error verification and timing analysis: a case study on an infusion pump. In Proceedings of the 13th International Workshop on the Design, Specification, and Verification of Interactive Systems (pp. 123–136). London: Springer.Google Scholar
Salvucci, D. D. (2001). Predicting the effects of in-car interface use on driver performance: an integrated model approach. International Journal of Human-Computer Studies, 55 (1), 85–107.Google Scholar
Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48 (2), 362–380.Google Scholar
Salvucci, D. D., & Gray, R. (2004). A two-point visual control model of steering. Perception, 33 (10), 1233–1248.Google Scholar
Salvucci, D. D., & Macuga, K. L. (2002). Predicting the effects of cellular-phone dialing on driver performance. Cognitive Systems Research, 3 (1), 95–102.Google Scholar
Santoro, C. (2005). A Task Model-Based Approach for Design and Evaluation of Innovative User Interfaces. Belgium: Presses universitaires de Louvain.Google Scholar
Schweickert, R., Fisher, D. L., & Proctor, R. W. (2003). Steps toward building mathematical and computer models from cognitive task analyses. Human Factors, 45 (1), 77–103.Google Scholar
Shepherd, A. (1998). HTA as a framework for task analysis. Ergonomics, 41 (11), 1537–1552.Google Scholar
Shepherd, A. (2001). Hierarchical Task Analysis. New York, NY: Taylor & Francis.Google Scholar
Sheridan, T. B., & Parasuraman, R. (2005). Human-automation interaction. Reviews of Human Factors and Ergonomics, 1 (1), 89–129.Google Scholar
Simon, H. A. (1996). The Sciences of the Artificial (3rd ed.). Cambridge, MA: MIT Press.Google Scholar
Strauch, B. (2017). Ironies of automation: still unresolved after all these years. IEEE Transactions on Human-Machine Systems, 48 (5), 419–433.Google Scholar
Thomas, M. (1994). The role of formal methods in achieving dependable software. Reliability Engineering & System Safety, 43 (2), 129–134.Google Scholar
Vieira, M., Leduc, J., Hasling, B., Subramanyan, R., & Kazmeier, J. (2006). Automation of GUI testing using a model-driven approach. In Proceedings of the 2006 International Workshop on Automation of Software Test (pp. 9–14).Google Scholar
Weyers, B., Bowen, J., Dix, A., & Palanque, P. (Eds.). (2017). The Handbook of Formal Methods in Human-Computer Interaction. Berlin: Springer.Google Scholar
Wing, J. M. (1990). A specifier’s introduction to formal methods. Computer, 23 (9), 8–22.Google Scholar
Wu, C., Rothrock, L., & Bolton, M. (2019). Editorial special issue on computational human performance modeling. IEEE Transactions on Human-Machine Systems, 49 (6), 470–473.Google Scholar
Young, R. M., Green, T. R. G., & Simon, T. (1989). Programmable user models for predictive evaluation of interface designs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 15–19). New York: ACM.Google Scholar
Zheng, X., Bolton, M. L., Daly, C., & Biltekoff, E. (2020). The development of a next-generation human reliability analysis: systems analysis for formal pharmaceutical human reliability (SAFPH℞). Reliability Engineering & System Safety, 20. https://doi.org/10.1016/j.ress.2020.106927Google Scholar

References

Adamson, P. (2016). Philosophy in the Islamic World: A History of Philosophy Without Any Gaps. Oxford: Oxford University Press.Google Scholar
Avicenna, . (1973). A Treatise on the Canon of Medicine of Avicenna. Trans. O. Cameron Gruner. New York, NY: AMS Press.Google Scholar
Berkeley, G. (1709). An Essay towards a New Theory of Vision. Dublin: Aaron Rhames.Google Scholar
Cadieu, C. F., Hong, H., Yamins, D. L., et al. (2014). Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput Biol, 10 (12), e1003963.Google Scholar
Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., & Urtasun, R. (2016). Monocular 3D object detection for autonomous driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2147–2156).Google Scholar
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Scientific Reports, 6, 27755.Google Scholar
Cranefield, P. F. (1970). On the origin of the phrase Nihil est in intellectu quod non prius fuerit in sensu. Journal of the History of Medicine, 25 (1), 77–80.Google Scholar
Crick, F. (1989). The recent excitement about neural networks. Nature, 337 (6203), 129–132.Google Scholar
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255).Google Scholar
Descartes, R. (1985). Treatise on man. In The Philosophical Writings of Rene Descartes (Vol. 1, pp. 99–107). Cambridge: Cambridge University Press.Google Scholar
Doerig, A., Schmittwilken, L., Sayim, B., Manassi, M., & Herzog, M. H. (2020a). Capsule networks as recurrent models of grouping and segmentation. PLoS Computational Biology, 16 (7), e1008017.Google Scholar
Doerig, A., Bornet, A., Choung, O. H., & Herzog, M. H. (2020b). Crowding reveals fundamental differences in local vs. global processing in humans and machines. Vision Research, 167, 39–45.Google Scholar
Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In IEEE CVPR Workshop on Generative-Model Based Vision.Google Scholar
Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47.Google Scholar
Finger, S. (1994). Origins of Neuroscience: A History of Explorations into Brain Function (pp. 67–69). Oxford: Oxford University Press.Google Scholar
Fukushima, K. (1980). Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202. https://doi.org/10.1007/BF00344251Google Scholar
Fukushima, K., & Miyake, S. (1982). Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and Cooperation in Neural Nets (pp. 267–285). Berlin and Heidelberg: Springer.Google Scholar
Galen, . (1968). Galen on the Usefulness of the Parts of the Body. Trans. Margaret Tallmadge May. Ithaca, NY: Cornell University Press.Google Scholar
Geirhos, R., Temme, C. R., Rauber, J., Schütt, H. H., Bethge, M., & Wichmann, F. A. (2018a). Generalisation in humans and deep neural networks. In Advances in Neural Information Processing Systems (pp. 7538–7550).Google Scholar
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2018b). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231.Google Scholar
Grüsser, O. J., & Hagner, M. (1990). On the history of deformation phosphenes and the idea of internal light generated in the eye for the purpose of vision. Documenta Ophthalmologica, 74 (1–2), 57–85.Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).Google Scholar
Hinton, G. E., & Sejnowski, T. J. (1983). Optimal perceptual inference. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 448–453). Washington, DC: IEEE Computer Society.Google Scholar
Hochberg, J., & Brooks, V. (1962). Pictorial recognition as an unlearned ability: A study of one child’s performance. The American Journal of Psychology, 75 (4), 624–628.Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79 (8), 2554–2558.Google Scholar
Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst.Google Scholar
Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurones in the cat’s striate cortex. Journal of Physiology, 148, 574–591.Google Scholar
Hubel, D. H., & Wiesel, T. N. (1977). Ferrier Lecture: functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society B: Biological Sciences, 198, 1–59.Google Scholar
Hubel, D. H., & Wiesel, T. N. (1998). Early exploration of the visual cortex. Neuron, 20, 401–412.Google Scholar
Hubel, D. H., & Wiesel, T. N. (2005). Brain and Visual Perception: The Story of a 25-Year Collaboration. New York, NY: Oxford University Press.Google Scholar
Huttenlocher, P. R., de Courten, C., Garey, L. J., & Van der Loos, H. (1982). Synaptogenesis in human visual cortex – evidence for synapse elimination during normal development. Neuroscience Letters, 33, 247–252.Google Scholar
Kant, I. (1781). Critique of Pure Reason (pp. 370–456). Modern Classical Philosophers. Cambridge, MA: Houghton Mifflin.Google Scholar
Khaligh-Razavi, S. M., & Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology, 10 (11), e1003915.Google Scholar
Kheradpisheh, S. R., Ghodrati, M., Ganjtabesh, M., & Masquelier, T. (2016). Deep networks resemble human feed-forward vision in invariant object recognition. arXiv preprint arXiv:1508.03929Google Scholar
Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K., Cichy, R. M., Hauk, O., & Kriegeskorte, N. (2019). Recurrence is required to capture the representational dynamics of the human visual system. Proceedings of the National Academy of Sciences, 116 (43), 21854–21863.Google Scholar
Koffka, K. (1935). Principles of Gestalt Psychology (p. 176). New York, NY: Harcourt, Brace.Google Scholar
Kreiman, G., & Serre, T. (2020). Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences, 1464 (1), 222–241.Google Scholar
Kriegeskorte, N. (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 24 (1), 417–446.Google Scholar
Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis: connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.Google Scholar
Kuffler, S. W. (1953). Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology, 16 (1), 37–68.CrossRefGoogle ScholarPubMed
Lake, B. M., Zaremba, W., Fergus, R., & Gureckis, T. M. (2015). Deep neural networks predict category typicality ratings for images. In Proceedings of the 37th Annual Conference of the Cognitive Science Society.Google Scholar
Land, E. H., & McCann, J. J. (1971). Lightness and retinex theory. Journal of the Optical Society of America, 61 (1), 1–11.Google Scholar
Lappe, M., & Rauschecker, J. P. (1993). A neural network for the processing of optic flow from ego-motion in man and higher mammals. Neural Computation, 5 (3), 374–391.Google Scholar
Lee, W. C., & Reid, R. C. (2011). Specificity and randomness: structure-function relationships in neural circuits. Current Opinion in Neurobiology, 21 (5), 801–807.Google Scholar
Locke, J. (1690). An essay concerning human understanding. In Dennis, W. (Ed.), Readings in the History of Psychology (pp. 55–68). New York, NY: Appleton-Century-Crofts.Google Scholar
Lotter, W., Kreiman, G., & Cox, D. (2020). A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence, 2 (4), 210–219.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: Henry Holt.Google Scholar
Marr, D., & Poggio, T. (1976). Cooperative computation of stereo disparity. Science, 194 (4262), 283–287.Google Scholar
Minsky, M. L., & Papert, S. A. (1969). Perceptrons. Cambridge, MA: MIT Press.Google Scholar
Ng, H. W., & Winkler, S. (2014). A data-driven approach to cleaning large face datasets. In IEEE International Conference on Image Processing (ICIP) (pp. 343–347).Google Scholar
Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., & Kriegeskorte, N. (2014). A toolbox for representational similarity analysis. PLoS Computational Biology, 10 (4), e1003553.Google Scholar
Reymond, A. (1927). History of the Sciences in Greco-Roman Antiquity (p. 182). London: Methuen.Google Scholar
Rosenblatt, F. (1958). The Perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65 (6), 386–408. https://doi.org/10.1037/h0042519Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323 (6088), 533–536.Google Scholar
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: a unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 815–823).Google Scholar
Szegedy, C., Zaremba, W., Sutskever, I., et al. (2014). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199.Google Scholar
Tang, H., Schrimpf, M., Lotter, W., et al. (2018). Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences, 115 (35), 8835–8840.Google Scholar
Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520–522.Google Scholar
Titchener, E. B. (1929). Systematic Psychology: Prolegomena. New York: Macmillan.Google Scholar
Vanderah, T. W., & Gould, D. J. (2016). Nolte’s: The Human Brain (7th ed.). Philadelphia, PA: Elsevier.Google Scholar
Vogelsang, L., Gilad-Gutnick, S., Ehrenberg, E., et al. (2018). Potential downside of high initial visual acuity. Proceedings of the National Academy of Sciences, 115 (44), 11333–11338.Google Scholar
von Helmholtz, H. (1925). Handbuch der Physiologischen Optik, English translation, Southall, J. P. D. (Ed.) (p. 455). Rochester, NY: Optical Society of America.Google Scholar
Wertheimer, M. (1938). [Original work published 1924]. Gestalt theory. In Ellis, W. D. (Ed.), A Source Book of Gestalt Psychology. London: Routledge & Kegan Paul.Google Scholar
Wilson, H. R. (1993). Theories of infant visual development. In Simons, K. (Ed.), Early Visual Development: Normal and Abnormal (pp. 560–569). New York, NY: Oxford University Press.Google Scholar
Winer, G. A., Cottrell, J. E., Gregg, V., Fournier, J. S., & Bica, L. A. (2002). Fundamentally misunderstanding visual perception: adults’ beliefs in visual emissions. American Psychologist, 57, 417–424.Google Scholar
Wundt, W. M. (1897). Outlines of Psychology. Leipzig: Wilhelm Engelmann.Google Scholar
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9 (4), 611–629.Google Scholar
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111 (23), 8619–8662.Google Scholar

References

Abeles, M., Diesmann, M., Flash, T., Geisel, T., Herrmann, M., & Teicher, M. (2013). Compositionality in neural control: an interdisciplinary study of scribbling movements in primates. Frontiers in Computational Neuroscience, 7, 103. https://doi.org/10.3389/fncom.2013.00103Google Scholar
Abend, W., Bizzi, E., & Morasso, P. (1982). Human arm trajectory formation. Brain, 105 (Pt 2), 331–348. https://doi.org/10.1093/brain/105.2.331Google Scholar
Alessandro, C., Carbajal, J. P., & d’Avella, A. (2013). A computational analysis of motor synergies by dynamic response decomposition. Frontiers in Computational Neuroscience, 7, 191. https://doi.org/10.3389/fncom.2013.00191Google Scholar
Aoi, S., & Funato, T. (2016). Neuromusculoskeletal models based on the muscle synergy hypothesis for the investigation of adaptive motor control in locomotion via sensory-motor coordination. Neuroscience Research, 104, 88–95. https://doi.org/10.1016/j.neures.2015.11.005Google Scholar
Bennequin, D., Fuchs, R., Berthoz, A., & Flash, T. (2009). Movement timing and invariance arise from several geometries. PLoS Computational Biology, 5 (7), e1000426. https://doi.org/10.1371/journal.pcbi.1000426Google Scholar
Berger, D. J., Gentner, R., Edmunds, T., Pai, D. K., & d’Avella, A. (2013). Differences in adaptation rates after virtual surgeries provide direct evidence for modularity. Journal of Neuroscience, 33 (30), 12384–12394. https://doi.org/10.1523/JNEUROSCI.0122-13.2013Google Scholar
Berniker, M., & Kording, K. P. (2015). Deep networks for motor control functions. Frontiers in Computational Neuroscience, 9, 32. https://doi.org/10.3389/fncom.2015.00032Google Scholar
Bernstein, N. (1967). The Coordination and Regulation of Movements: Oxford: Pergamon Press.Google Scholar
Binet, A., & Courtier, J. (1893). Sur la vitesse des mouvements graphiques. Revue Philosophique de la France et de l’Étranger, Presses Universitaires de France Stable, pp. 664–671.Google Scholar
Bizzi, E., Giszter, S. F., Loeb, E., Mussa-Ivaldi, F. A., & Saltiel, P. (1995). Modular organization of motor behavior in the frog’s spinal cord. Trends in Neuroscience, 18 (10), 442–446. https://doi.org/10.1016/0166-2236(95)94494-pGoogle Scholar
Buono, P. L., & Golubitsky, M. (2001). Models of central pattern generators for quadruped locomotion I. Primary gaits. Journal of Mathematical Biology, 42 (4), 291–326. https://doi.org/10.1007/s002850000058Google Scholar
Byadarhaly, K. V., Perdoor, M. C., & Minai, A. A. (2012). A modular neural model of motor synergies. Neural Networks, 32, 96–108. https://doi.org/10.1016/j.neunet.2012.02.003Google Scholar
Cabel, D. W., Cisek, P., & Scott, S. H. (2001). Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. Journal of Neurophysiology, 86 (4), 2102–2108. https://doi.org/10.1152/jn.2001.86.4.2102Google Scholar
Caminiti, R., Johnson, P. B., Galli, C., Ferraina, S., & Burnod, Y. (1991). Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets. Journal of Neuroscience, 11 (5), 1182–1197. www.ncbi.nlm.nih.gov/pubmed/2027042Google Scholar
Cartan, E. (1937). La théorie des groupes finis et continus et la géométrie différentielle, traitées par la méthode du repère mobile. Paris: Gauthier-Villars.Google Scholar
Catavitello, G., Ivanenko, Y., & Lacquaniti, F. (2018). A kinematic synergy for terrestrial locomotion shared by mammals and birds. Elife, 7. https://doi.org/10.7554/eLife.38190Google Scholar
Cheney, P. D., & Fetz, E. E. (1980). Functional classes of primate corticomotoneuronal cells and their relation to active force. Journal of Neurophysiology, 44 (4), 773–791. https://doi.org/10.1152/jn.1980.44.4.773Google Scholar
Chiovetto, E., Berret, B., & Pozzo, T. (2010). Tri-dimensional and triphasic muscle organization of whole-body pointing movements. Neuroscience, 170 (4), 1223–1238. https://doi.org/10.1016/j.neuroscience.2010.07.006Google Scholar
Chiovetto, E., d’Avella, A., & Giese, M. A. (2016). A unifying framework for the identification of motor primitives. BioArXiv, 1603.06879.Google Scholar
Chiovetto, E., & Giese, M. A. (2013). Kinematics of the coordination of pointing during locomotion. PLoS One, 8 (11), e79555. https://doi.org/10.1371/journal.pone.0079555Google Scholar
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012). Neural population dynamics during reaching. Nature, 487 (7405), 51–56. https://doi.org/10.1038/nature11129Google Scholar
Churchland, M. M., & Shenoy, K. V. (2007a). Delay of movement caused by disruption of cortical preparatory activity. Journal of Neurophysiology, 97 (1), 348–359. https://doi.org/10.1152/jn.00808.2006Google Scholar
Churchland, M. M., & Shenoy, K. V. (2007b). Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. Journal of Neurophysiology, 97 (6), 4235–4257. https://doi.org/10.1152/jn.00095.2007Google Scholar
Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G., & Shenoy, K. V. (2006). Neural variability in premotor cortex provides a signature of motor preparation. Journal of Neuroscience, 26 (14), 3697–3712. https://doi.org/10.1523/JNEUROSCI.3762-05.2006Google Scholar
Chvatal, S. A., Torres-Oviedo, G., Safavynia, S. A., & Ting, L. H. (2011). Common muscle synergies for control of center of mass and force in nonstepping and stepping postural behaviors. Journal of Neurophysiology, 106 (2), 999–1015. https://doi.org/10.1152/jn.00549.2010Google Scholar
D’Andola, M., Cesqui, B., Portone, A., Fernandez, L., Lacquaniti, F., & d’Avella, A. (2013). Spatiotemporal characteristics of muscle patterns for ball catching. Frontiers in Computational Neuroscience, 7, 107. https://doi.org/10.3389/fncom.2013.00107Google Scholar
d’Avella, A., & Bizzi, E. (2005). Shared and specific muscle synergies in natural motor behaviors. Proceedings of the National Academy of Sciences of the United States of America, 102 (8), 3076–3081. https://doi.org/10.1073/pnas.0500199102Google Scholar
D’Avella, A., Fernandez, L., Portone, A., & Lacquaniti, F. (2008). Modulation of phasic and tonic muscle synergies with reaching direction and speed. Journal of Neurophysiology, 100 (3), 1433–1454. https://doi.org/10.1152/jn.01377.2007Google Scholar
d’Avella, A., Giese, M., Ivanenko, Y. P., Schack, T., & Flash, T. (2015). Editorial: Modularity in motor control: from muscle synergies to cognitive action representation. Frontiers in Computational Neuroscience, 9, 126. https://doi.org/10.3389/fncom.2015.00126Google Scholar
d’Avella, A., Portone, A., Fernandez, L., & Lacquaniti, F. (2006). Control of fast-reaching movements by muscle synergy combinations. Journal of Neuroscience, 26 (30), 7791–7810. https://doi.org/10.1523/JNEUROSCI.0830-06.2006Google Scholar
d’Avella, A., Saltiel, P., & Bizzi, E. (2003). Combinations of muscle synergies in the construction of a natural motor behavior. Nature Neuroscience, 6 (3), 300–308. https://doi.org/10.1038/nn1010Google Scholar
d’Avella, A., & Tresch, M. C. (2002). Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies. Advances in Neural Information Processing Systems, 1, 141–148.Google Scholar
Dayan, E., Casile, A., Levit-Binnun, N., Giese, M. A., Hendler, T., & Flash, T. (2007). Neural representations of kinematic laws of motion: evidence for action-perception coupling. Proceedings of the National Academy of Sciences of the United States of America, 104 (51), 20582–20587. https://doi.org/10.1073/pnas.0710033104Google Scholar
Delis, I., Panzeri, S., Pozzo, T., & Berret, B. (2014). A unifying model of concurrent spatial and temporal modularity in muscle activity. Journal of Neurophysiology, 111 (3), 675–693. https://doi.org/10.1152/jn.00245.2013Google Scholar
DeWolf, T., Stewart, T. C., Slotine, J. J., & Eliasmith, C. (2016). A spiking neural model of adaptive arm control. Biological Sciences, 283 (1843). https://doi.org/10.1098/rspb.2016.2134Google Scholar
Dominici, N., Ivanenko, Y. P., Cappellini, G., et al. (2011). Locomotor primitives in newborn babies and their development. Science, 334 (6058), 997–999. https://doi.org/10.1126/science.1210617Google Scholar
Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M., & Cunningham, J. P. (2016). Reorganization between preparatory and movement population responses in motor cortex. Nature Communications, 7, 13239. https://doi.org/10.1038/ncomms13239Google Scholar
Fetz, E. E., Perlmutter, S. I., Prut, Y., Seki, K., & Votaw, S. (2002). Roles of primate spinal interneurons in preparation and execution of voluntary hand movement. Brain Research Reviews, 40 (1–3), 53–65. https://doi.org/10.1016/s0165-0173(02)00188-1Google Scholar
Flash, T., & Handzel, A. A. (2007). Affine differential geometry analysis of human arm movements. Biological Cybernetics, 96 (6), 577–601. https://doi.org/10.1007/s00422-007-0145-5Google Scholar
Flash, T., & Hochner, B. (2005). Motor primitives in vertebrates and invertebrates. Current Opinion in Neurobiology, 15 (6), 660–666. https://doi.org/10.1016/j.conb.2005.10.011Google Scholar
Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience, 5 (7), 1688–1703.Google Scholar
Flash, T., Karklinsky, M., Fuchs, R., Berthoz, A., Bennequin, D., & Meirovitch, Y. (2019). Motor compositionality and timing: combined geometrical and optimization approaches. In Venture, G., Laumond, J. P., & Watier, B. (Eds.), Biomechanics of Anthropomorphic Systems. Springer Tracts in Advanced Robotics (Vol. 124, pp. 155–184). Cham: Springer.Google Scholar
Giszter, S. F. (2015). Motor primitives: new data and future questions. Current Opinion in Neurobiology, 33, 156–165. https://doi.org/10.1016/j.conb.2015.04.004Google Scholar
Giszter, S. F., Mussa-Ivaldi, F. A., & Bizzi, E. (1993). Convergent force fields organized in the frog’s spinal cord. Journal of Neuroscience, 13 (2), 467–491. www.ncbi.nlm.nih.gov/pubmed/8426224Google Scholar
Graziano, M. (2006). The organization of behavioral repertoire in motor cortex. Annual Review of Neuroscience, 29, 105–134. https://doi.org/10.1146/annurev.neuro.29.051605.112924Google Scholar
Gribble, P. L., & Ostry, D. J. (1996). Origins of the power law relation between movement velocity and curvature: modeling the effects of muscle mechanics and limb dynamics. Journal of Neurophysiology, 76 (5), 2853–2860. https://doi.org/10.1152/jn.1996.76.5.2853Google Scholar
Guigon, E., Baraduc, P., & Desmurget, M. (2007). Computational motor control: redundancy and invariance. Journal of Neurophysiology, 97 (1), 331–347. https://doi.org/10.1152/jn.00290.2006Google Scholar
Hagio, S., & Kouzaki, M. (2018). Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry. Journal of the Royal Society Interface, 15 (147), 20180249. https://doi.org/10.1098/rsif.2018.0249Google Scholar
Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394 (6695), 780–784. https://doi.org/10.1038/29528Google Scholar
Hart, C. B., & Giszter, S. F. (2010). A neural basis for motor primitives in the spinal cord. Journal of Neuroscience, 30 (4), 1322–1336. https://doi.org/10.1523/JNEUROSCI.5894-08.2010Google Scholar
Hogan, N. (1984). An organizing principle for a class of voluntary movements. Journal of Neuroscience, 4 (11), 2745–2754. www.ncbi.nlm.nih.gov/pubmed/6502203Google Scholar
Hogan, N., & Sternad, D. (2012). Dynamic primitives of motor behavior. Biological Cybernetics, 106 (11–12), 727–739. https://doi.org/10.1007/s00422-012-0527-1Google Scholar
Holden, D., Saito, J., & Komura, T. (2016). A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics, 138 (4).Google Scholar
Huh, D., & Sejnowski, T. J. (2015). Spectrum of power laws for curved hand movements. Proceedings of the National Academy of Sciences, 112 (29), E3950–E3958. https://doi.org/10.1073/pnas.1510208112Google Scholar
Huh, D., & Todorov, E. (2009). Real-time motor control using recurrent neural networks. In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (pp. 42–49). https://doi.org/10.1109/ADPRL.2009.4927524Google Scholar
Ijspeert, A. J. (2008). Central pattern generators for locomotion control in animals and robots: a review. Neural Networks, 21 (4), 642–653. https://doi.org/10.1016/j.neunet.2008.03.014Google Scholar
Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical movement primitives: learning attractor models for motor behaviors. Neural Computation, 25 (2), 328–373. https://doi.org/10.1162/NECO_a_00393Google Scholar
Ivanenko, Y. P., Poppele, R. E., & Lacquaniti, F. (2004). Five basic muscle activation patterns account for muscle activity during human locomotion. Journal of Physiology, 556 (Pt 1), 267–282. https://doi.org/10.1113/jphysiol.2003.057174Google Scholar
Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 304 (5667), 78–80. https://doi.org/10.1126/science.1091277Google Scholar
Kadmon Harpaz, N., Flash, T., & Dinstein, I. (2014). Scale-invariant movement encoding in the human motor system. Neuron, 81 (2), 452–462. https://doi.org/10.1016/j.neuron.2013.10.058Google Scholar
Kalaska, J. F., Cohen, D. A., Hyde, M. L., & Prud’homme, M. (1989). A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task. Journal of Neuroscience, 9 (6), 2080–2102. www.ncbi.nlm.nih.gov/pubmed/2723767Google Scholar
Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2014). Cortical activity in the null space: permitting preparation without movement. Nature Neuroscience, 17 (3), 440–448. https://doi.org/10.1038/nn.3643Google Scholar
Kaufman, M. T., Churchland, M. M., & Shenoy, K. V. (2013). The roles of monkey M1 neuron classes in movement preparation and execution. Journal of Neurophysiology, 110 (4), 817–825. https://doi.org/10.1152/jn.00892.2011Google Scholar
Kelso, J. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge, MA: MIT Press.Google Scholar
Kim, T., Hamade, K. C., Todorov, D., et al. (2017). Reward-based motor adaptation mediated by basal ganglia. Frontiers in Computational Neuroscience, 11. https://doi.org/10.3389/fncom.2017.00019Google Scholar
Kober, J., & Peters, J. (2011). Policy search for motor primitives in robotics. Machine Learning, 84 (1–2), 171–203.Google Scholar
Kuo, L. C., Chen, S. W., Lin, C. J., Lin, W. J., Lin, S. C., & Su, F. C. (2013). The force synergy of human digits in static and dynamic cylindrical grasps. PLoS One, 8 (3), e60509. https://doi.org/10.1371/journal.pone.0060509Google Scholar
Lacquaniti, F., Terzuolo, C., & Viviani, P. (1983). The law relating the kinematic and figural aspects of drawing movements. Acta Psychologica (Amst), 54 (1–3), 115–130. https://doi.org/10.1016/0001-6918(83)90027-6Google Scholar
Maass, W., Natschlager, T., & Markram, H. (2002). Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation, 14 (11), 2531–2560. https://doi.org/10.1162/089976602760407955Google Scholar
Maoz, U., Portugaly, E., Flash, T., & Weiss, Y. (2006). Noise and the two-thirds power law. In Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada.Google Scholar
McCrea, D. A., & Rybak, I. A. (2008). Organization of mammalian locomotor rhythm and pattern generation. Brain Research Reviews, 57 (1), 134–146. https://doi.org/10.1016/j.brainresrev.2007.08.006Google Scholar
Meirovitch, Y. (2014). Movement decomposition and compositionality based on geometric and kinematic principles. Ph.D. dissertation, Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.Google Scholar
Meirovitch, Y., Harris, H., Dayan, E., Arieli, A., & Flash, T. (2015). Alpha and beta band event-related desynchronization reflects kinematic regularities. Journal of Neuroscience, 35 (4), 1627–1637.Google Scholar
Merel, J., Botvinick, M., & Wayne, G. (2019). Hierarchical motor control in mammals and machines. Nature Communication, 10 (1), 5489. https://doi.org/10.1038/s41467-019-13239-6Google Scholar
Merkle, L. A., Layne, C. S., Bloomberg, J. J., & Zhang, J. J. (1998). Using factor analysis to identify neuromuscular synergies during treadmill walking. Journal of Neuroscience Methods, 82 (2), 207–214. https://doi.org/10.1016/s0165-0270(98)00054-5Google Scholar
Moran, D. W., & Schwartz, A. B. (1999). Motor cortical representation of speed and direction during reaching. Journal of Neurophysiology, 82 (5), 2676–2692. https://doi.org/10.1152/jn.1999.82.5.2676Google Scholar
Mukovskiy, A., Slotine, J. J. E., & Giese, M. A. (2013). Dynamically stable control of articulated crowds. Journal of Computer Science, 4, 304–310.Google Scholar
Mukovskiy, A., Vassallo, C., Naveau, M., Stasse, O., Souères, P. E., & Giese, M. A. (2017). Adaptive synthesis of dynamically feasible full-body movements for the humanoid robot HRP-2 by flexible combination of learned dynamic movement primitives. Robotics and Autonomous Systems, 91(C), 270–283. https://doi.org/10.1016/j.robot.2017.01.010Google Scholar
Mussa-Ivaldi, F. A., Giszter, S. F., & Bizzi, E. (1994). Linear combinations of primitives in vertebrate motor control. Proceedings of the National Academy of Sciences , 91 (16), 7534–7538. https://doi.org/10.1073/pnas.91.16.7534Google Scholar
Omlor, L., & Giese, M. A. (2011). Anechoic blind source separation using Wigner marginals. Journal of Machine Learning Research, 12, 1111–1148.Google Scholar
Overduin, S. A., d’Avella, A., Roh, J., Carmena, J. M., & Bizzi, E. (2015). Representation of muscle synergies in the primate brain. Journal of Neuroscience, 35 (37), 12615–12624. https://doi.org/10.1523/JNEUROSCI.4302-14.2015Google Scholar
Pandarinath, C., O’Shea, D. J., Collins, J., et al. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods, 15 (10), 805–815. https://doi.org/10.1038/s41592-018-0109-9Google Scholar
Paraschos, A., Daniel, C., Peters, J., & Neumann, G. (2018). Using probabilistic movement primitives in robotics. Autonomous Robots, 42, 529–551.Google Scholar
Poggio, T., & Reichardt, W. (1976). Visual control of orientation behaviour in the fly. Part II. Towards the underlying neural interactions. Quarterly Reviews of Biophysics, 9 (3), 377–438. https://doi.org/10.1017/s0033583500002535Google Scholar
Pollick, F. E., Maoz, U., Handzel, A. A., Giblin, P. J., Sapiro, G., & Flash, T. (2009). Three-dimensional arm movements at constant equi-affine speed. Cortex, 45 (3), 325–339. https://doi.org/10.1016/j.cortex.2008.03.010Google Scholar
Pollick, F. E., & Sapiro, G. (1997). Constant affine velocity predicts the 1/3 power law of planar motion perception and generation. Vision Research, 37 (3), 347–353. https://doi.org/10.1016/s0042-6989(96)00116-2Google Scholar
Richardson, M. J., & Flash, T. (2002). Comparing smooth arm movements with the two-thirds power law and the related segmented-control hypothesis. Journal of Neuroscience, 22 (18), 8201–8211. www.ncbi.nlm.nih.gov/pubmed/12223574Google Scholar
Rückert, E., & d’Avella, A. (2013). Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Frontiers in Computational Neuroscience, 7, 138. https://doi.org/10.3389/fncom.2013.00138Google Scholar
Russo, M., D’Andola, M., Portone, A., Lacquaniti, F., & d’Avella, A. (2014). Dimensionality of joint torques and muscle patterns for reaching. Frontiers in Computational Neuroscience, 8, 24. https://doi.org/10.3389/fncom.2014.00024Google Scholar
Santello, M., Flanders, M., & Soechting, J. F. (1998). Postural hand synergies for tool use. Journal of Neuroscience, 18 (23), 10105–10115. www.ncbi.nlm.nih.gov/pubmed/9822764Google Scholar
Saxena, S., & Cunningham, J. P. (2019). Towards the neural population doctrine. Current Opinion in Neurobiology, 55, 103–111. https://doi.org/10.1016/j.conb.2019.02.002Google Scholar
Schaal, S. (2006). Dynamic movement primitives: a framework for motor control in humans and humanoid robotics. In Kimura, H., Tsuchiya, K., Ishiguro, A., & Witte, H. (Eds.), Adaptive Motion of Animals and Machines (pp. 261–280). London: Springer.Google Scholar
Schaal, S., Kotosaka, S., & Sternad, D. (2000). Nonlinear dynamical systems as movement primitives. Paper presented at the Humanoids2000, First IEEE-RAS International Conference on Humanoid Robots, Cambridge, MA.Google Scholar
Schaal, S., Peters, J., Nakanishi, J., & Ijspeert, A. (2005). Learning movement primitives. Paper presented at the Robotics Research, The Eleventh International Symposium.Google Scholar
Schaal, S., & Sternad, D. (2001). Origins and violations of the 2/3 power law in rhythmic three-dimensional arm movements. Experimental Brain Research, 136 (1), 60–72. https://doi.org/10.1007/s002210000505Google Scholar
Schaal, S., Sternad, D., Osu, R., & Kawato, M. (2004). Rhythmic arm movement is not discrete. Nature Neuroscience, 7 (10), 1136–1143. https://doi.org/10.1038/nn1322Google Scholar
Scholz, J. P., & Schöner, G. (1999). The uncontrolled manifold concept: identifying control variables for a functional task. Experimental Brain Research, 126 (3), 289–306. https://doi.org/10.1007/s002210050738Google Scholar
Schöner, G. (1990). A dynamic theory of coordination of discrete movement. Biological Cybernetics, 63 (4), 257–270. https://doi.org/10.1007/BF00203449Google Scholar
Sergio, L. E., & Kalaska, J. F. (1998). Changes in the temporal pattern of primary motor cortex activity in a directional isometric force versus limb movement task. Journal of Neurophysiology, 80 (3), 1577–1583. https://doi.org/10.1152/jn.1998.80.3.1577Google Scholar
Singh, R. E., Iqbal, K., White, G., & Hutchinson, T. E. (2018). A systematic review on muscle synergies: from building blocks of motor behavior to a neurorehabilitation tool. Applied Bionics and Biomechanics, 2018, 3615368. https://doi.org/10.1155/2018/3615368Google Scholar
Sreenivasa, M., Ayusawa, K., & Nakamura, Y. (2016). Modeling and identification of a realistic spiking neural network and musculoskeletal model of the human arm, and an application to the stretch reflex. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (5), 591–602. https://doi.org/10.1109/TNSRE.2015.2478858Google Scholar
Sussillo, D., Jozefowicz, R., Abbott, L. F., & Pandarinath, C. (2016). LFADS: latent factor analysis via dynamical systems. arXiv, 1608.06315.Google Scholar
Taborri, J., Agostini, V., Artemiadis, P. K., et al. (2018). Feasibility of muscle synergy outcomes in clinics, robotics, and sports: a systematic review. Applied Bionics and Biomechanics, 2018, 3934698. https://doi.org/10.1155/2018/3934698Google Scholar
Takei, T., Confais, J., Tomatsu, S., Oya, T., & Seki, K. (2017). Neural basis for hand muscle synergies in the primate spinal cord. Proceedings of the National Academy of Sciences, 114 (32), 8643–8648. https://doi.org/10.1073/pnas.1704328114Google Scholar
Tanaka, H. (2016). Modeling the motor cortex: optimality, recurrent neural networks, and spatial dynamics. Neuroscience Research, 104, 64–71. https://doi.org/10.1016/j.neures.2015.10.012Google Scholar
Tanneberg, D., Paraschos, A., Peters, J., & Rueckert, E. (2016). Deep spiking networks for model-based planning in humanoids. Paper presented at the International Conference on Humanoid Robots (HUMANOIDS).Google Scholar
Taubert, N., Christensen, A., Endres, D., & Giese, M. A. (2012). Online simulation of emotional interactive behaviors with hierarchical Gaussian process dynamical models. In Proceedings of the ACM Symposium on Applied Perception, Los Angeles, California.Google Scholar
Teka, W. W., Hamade, K. C., Barnett, W. H., et al. (2017). From the motor cortex to the movement and back again. PLoS One, 12 (6), e0179288.Google Scholar
Tesio, L., Rota, V., & Perucca, L. (2011). The 3D trajectory of the body centre of mass during adult human walking: evidence for a speed-curvature power law. Journal of Biomechanics, 44 (4), 732–740. https://doi.org/10.1016/j.jbiomech.2010.10.035Google Scholar
Thoroughman, K. A., & Shadmehr, R. (2000). Learning of action through adaptive combination of motor primitives. Nature, 407 (6805), 742–747. https://doi.org/10.1038/35037588Google Scholar
Ting, L. H., & Macpherson, J. M. (2005). A limited set of muscle synergies for force control during a postural task. Journal of Neurophysiology, 93 (1), 609–613. https://doi.org/10.1152/jn.00681.2004Google Scholar
Todorov, E., & Jordan, M. I. (1998). Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements. Journal of Neurophysiology, 80 (2), 696–714. https://doi.org/10.1152/jn.1998.80.2.696Google Scholar
Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5 (11), 1226–1235. https://doi.org/10.1038/nn963Google Scholar
Tresch, M. C., & Bizzi, E. (1999). Responses to spinal microstimulation in the chronically spinalized rat and their relationship to spinal systems activated by low threshold cutaneous stimulation. Experimental Brain Research, 129 (3), 401–416. https://doi.org/10.1007/s002210050908Google Scholar
Tresch, M. C., Cheung, V. C., & d’Avella, A. (2006). Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. Journal of Neurophysiology, 95 (4), 2199–2212. https://doi.org/10.1152/jn.00222.2005Google Scholar
Tresch, M. C., & Jarc, A. (2009). The case for and against muscle synergies. Current Opinion in Neurobiology, 19 (6), 601–607. https://doi.org/10.1016/j.conb.2009.09.002Google Scholar
Umilta, M. A., Escola, L., Intskirveli, I., et al. (2008). When pliers become fingers in the monkey motor system. Proceedings of the National Academy of Sciences, 105 (6), 2209–2213. https://doi.org/10.1073/pnas.0705985105Google Scholar
Uno, Y., Kawato, M., & Suzuki, R. (1989). Formation and control of optimal trajectory in human multijoint arm movement. Minimum torque-change model. Biological Cybernetics, 61 (2), 89–101. https://doi.org/10.1007/BF00204593Google Scholar
Viviani, P., & Cenzato, M. (1985). Segmentation and coupling in complex movements. Journal of Experimental Psychology: Human Perception and Performance, 11 (6), 828–845. https://doi.org/10.1037//0096-1523.11.6.828Google Scholar
Viviani, P., & Flash, T. (1995). Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. Journal of Experimental Psychology: Human Perception and Performance, 21 (1), 32–53. https://doi.org/10.1037//0096-1523.21.1.32Google Scholar
Viviani, P., & McCollum, G. (1983). The relation between linear extent and velocity in drawing movements. Neuroscience, 10 (1), 211–218. https://doi.org/10.1016/0306-4522(83)90094-5Google Scholar
Viviani, P., & Schneider, R. (1991). A developmental study of the relationship between geometry and kinematics in drawing movements. Journal of Experimental Psychology: Human Perception and Performance, 17 (1), 198–218. https://doi.org/10.1037//0096-1523.17.1.198Google Scholar
Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020). Computation through neural population dynamics. Annual Review of Neuroscience, 43, 249–275. https://doi.org/10.1146/annurev-neuro-092619-094115Google Scholar
Wensing, P., & Slotine, J. J. S. (2016). Sparse control for dynamic movement primitives. arXiv, CoRR, abs/1611.05066.Google Scholar
Wojtara, T., Alnajjar, F., Shimoda, S., & Kimura, H. (2014). Muscle synergy stability and human balance maintenance. Journal of NeuroEngineering and Rehabilitation, 11, 129. https://doi.org/10.1186/1743-0003-11-129Google Scholar
Yanai, Y., Adamit, N., Harel, R., Israel, Z., & Prut, Y. (2007). Connected corticospinal sites show enhanced tuning similarity at the onset of voluntary action. Journal of Neuroscience, 27 (45), 12349–12357. https://doi.org/10.1523/JNEUROSCI.3127-07.2007Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×