Book contents
- The Cambridge Handbook of Cognitive Development
- The Cambridge Handbook of Cognitive Development
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Part I Neurobiological Constraints and Laws of Cognitive Development
- Part II Fundamentals of Cognitive Development from Infancy to Adolescence and Young Adulthood
- Introduction
- Subpart II.1 Infancy: The Roots of Human Thinking
- Subpart II.2 Childhood and Adolescence: The Development of Human Thinking
- 15 Development of Qualitative Thinking
- 16 Development of Numerical Knowledge
- 17 Numerical Cognition and Executive Functions
- 18 Developing Theory of Mind and Counterfactual Reasoning in Children
- 19 Development of Executive Function Skills in Childhood
- 20 Developing Cognitive Control and Flexible Adaptation during Childhood
- 21 Reasoning Bias and Dual Process Theory
- 22 Social Cognitive Development
- 23 Behavioral and Neural Development of Cognitive Control and Risky Decision-Making across Adolescence
- 24 The Triadic Neural Systems Model through a Machine-Learning Mill
- Part III Education and School-Learning Domains
- Index
- Plate Section (PDF Only)
- References
21 - Reasoning Bias and Dual Process Theory
Developmental Considerations and Current Directions
from Subpart II.2 - Childhood and Adolescence: The Development of Human Thinking
Published online by Cambridge University Press: 24 February 2022
Book contents
- The Cambridge Handbook of Cognitive Development
- The Cambridge Handbook of Cognitive Development
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Part I Neurobiological Constraints and Laws of Cognitive Development
- Part II Fundamentals of Cognitive Development from Infancy to Adolescence and Young Adulthood
- Introduction
- Subpart II.1 Infancy: The Roots of Human Thinking
- Subpart II.2 Childhood and Adolescence: The Development of Human Thinking
- 15 Development of Qualitative Thinking
- 16 Development of Numerical Knowledge
- 17 Numerical Cognition and Executive Functions
- 18 Developing Theory of Mind and Counterfactual Reasoning in Children
- 19 Development of Executive Function Skills in Childhood
- 20 Developing Cognitive Control and Flexible Adaptation during Childhood
- 21 Reasoning Bias and Dual Process Theory
- 22 Social Cognitive Development
- 23 Behavioral and Neural Development of Cognitive Control and Risky Decision-Making across Adolescence
- 24 The Triadic Neural Systems Model through a Machine-Learning Mill
- Part III Education and School-Learning Domains
- Index
- Plate Section (PDF Only)
- References
Summary
If you are racing toward a cliff or a steep mountain drop-off, the sensible thing to do is to slow yourself down and stop – and as a matter of some urgency. Braking in this case is natural and intuitive. Yet, when you are hang gliding, not only should you not slow down as you approach a cliff, it is important that you accelerate, gaining velocity with every single stride as you approach the drop-off in order to have the necessary wind speed to fly. In the last moments before flight, you should be nearly sprinting toward the edge. For many aspiring hang gliders this can be quite challenging. A successful hang glide start requires you to override your intuitive tendency to stop when seeing a cliff. Although this natural “stopping” intuition is usually very useful, failing to control it when needed can have dramatic consequences.
- Type
- Chapter
- Information
- The Cambridge Handbook of Cognitive Development , pp. 472 - 480Publisher: Cambridge University PressPrint publication year: 2022
References
Bago, B., & De Neys, W. (2017). Fast logic?: Examining the time course assumption of dual process theory. Cognition, 158, 90–109.CrossRefGoogle ScholarPubMed
Bago, B., & De Neys, W. (2020a). Advancing the specification of dual process models of higher cognition: A critical test of the hybrid model view. Thinking & Reasoning, 26, 1–30.CrossRefGoogle Scholar
Bago, B., & De Neys, W. (2020b). The smart system 1: Evidence for the intuitive nature of correct responding on the bat-and-ball problem. Thinking & Reasoning, 26, 1–30.Google Scholar
Ball, L., Thompson, V., & Stupple, E. (2017). Conflict and dual process theory: The case of belief bias. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 100–120). Oxford: Routledge.CrossRefGoogle Scholar
Banks, A. (2017). Comparing dual process theories: Evidence from event-related potentials. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 66–81). Oxford: Routledge.CrossRefGoogle Scholar
Barrouillet, P. (2011). Dual process theories of reasoning: The test of development. Developmental Review, 31, 151–179.Google Scholar
Brainerd, C. J., & Reyna, V. F. (2001). Fuzzy-trace theory: Dual processes in memory, reasoning, and cognitive neuroscience. In Reese, H. W., & Kail, R. (eds.), Advances in Child Development and Behavior (Vol. 28, pp. 41–100). San Diego, CA: Academic Press.Google Scholar
Brainerd, C. J., Reyna, V. F., & Ceci, S. J. (2008). Developmental reversals in false memory: A review of data and theory. Psychological Bulletin, 134, 343– 382.CrossRefGoogle ScholarPubMed
Davidson, D. (1995). The representativeness heuristic and the conjunction fallacy effect in children’s decision making. Merrill-Palmer Quarterly, 41, 328–346.Google Scholar
De Neys, W. (2006). Dual processing in reasoning: Two systems but one reasoner. Psychological Science, 17, 428–433.CrossRefGoogle ScholarPubMed
De Neys, W. (2012). Bias and conflict a case for logical intuitions. Perspectives on Psychological Science, 7, 28–38.CrossRefGoogle ScholarPubMed
De Neys, W. (2013). Heuristics, biases, and the development of conflict detection during reasoning. In Markovits, H. (ed.), The Developmental Psychology of Reasoning and Decision Making (pp. 130–147). Hove: Psychology Press.Google Scholar
De Neys, W. (2017). Bias, conflict, and fast logic: Towards a hybrid dual process future? In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 47–65). Oxford: Routledge.CrossRefGoogle Scholar
De Neys, W., & Feremans, V. (2013). Development of heuristic bias detection in elementary school. Developmental Psychology, 49, 258–269.CrossRefGoogle ScholarPubMed
De Neys, W., & Glumicic, T. (2008). Conflict monitoring in dual process theories of thinking. Cognition, 106, 1248–1299.Google Scholar
De Neys, W., Rossi, S., & Houdé, O. (2013). Bats, balls, and substitution sensitivity: Cognitive misers are no happy fools. Psychonomic Bulletin & Review, 20, 269–273.CrossRefGoogle ScholarPubMed
De Neys, W., & Vanderputte, K. (2011). When less is not always more: Stereotype knowledge and reasoning development. Developmental Psychology, 47, 432–441.Google Scholar
De Neys, W., Vartanian, O., & Goel, V. (2008). Smarter than we think when our brains detect that we are biased. Psychological Science, 19, 483–489.CrossRefGoogle ScholarPubMed
Evans, J. St. B. T. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7, 454–459.CrossRefGoogle ScholarPubMed
Evans, J. St. B. T. (2008). Dual-processing accounts of reasoning, judgement and social cognition. Annual Review of Psychology, 59, 255–278.CrossRefGoogle ScholarPubMed
Evans, J. St. B. T. (2010). Intuition and reasoning: A dual process perspective. Psychological Inquiry, 21, 313–326.CrossRefGoogle Scholar
Evans, J. St. B. T. (2017). Dual process theories: Perspectives and problems. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 137–155). Oxford: Routledge.CrossRefGoogle Scholar
Evans, J. St. B. T., & Over, D. E. (1996). Rationality and Reasoning. Hove: Psychology Press.Google Scholar
Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition advancing the debate. Perspectives on Psychological Science, 8, 223–241.CrossRefGoogle ScholarPubMed
Franssens, S., & De Neys, W. (2009). The effortless nature of conflict detection during thinking. Thinking & Reasoning, 15, 105–128.CrossRefGoogle Scholar
Gangemi, A., Bourgeois-Gironde, S., & Mancini, F. (2015). Feelings of error in reasoning – in search of a phenomenon. Thinking & Reasoning, 21, 383–396.CrossRefGoogle Scholar
Houdé, O. (1997). Rationality in reasoning: The problem of deductive competence and the inhibitory control of cognition. Current Psychology of Cognition, 16, 108–113.Google Scholar
Houdé, O. (2000). Inhibition and cognitive development: Object, number, categorization, and reasoning. Cognitive Development, 15, 63–73.CrossRefGoogle Scholar
Houdé, O. (2007). First insights on neuropedagogy of reasoning. Thinking & Reasoning, 13, 81–89.CrossRefGoogle Scholar
Houdé, O., & Borst, G. (2014). Measuring inhibitory control in children and adults: Brain imaging and mental chronometry. Frontiers in Psychology, 5, 616.Google ScholarPubMed
Jacobs, J. E., & Klaczynski, P. A. (2002). The development of decision making during childhood and adolescence. Current Directions in Psychological Science, 4, 145–149.CrossRefGoogle Scholar
Jacobs, J. E., & Potenza, M. (1991). The use of judgment heuristics to make social and object decisions: A developmental perspective. Child Development, 62, 166–178.Google Scholar
Johnson, E. D., Tubau, E., & De Neys, W. (2016). The doubting system 1: Evidence for automatic substitution sensitivity. Acta Psychologica, 164, 56–64.CrossRefGoogle ScholarPubMed
Kahneman, D. (2002, December). Maps of bounded rationality: A perspective on intuitive judgement and choice. Nobel Prize Lecture. Available from http://nobelprize.org/nobel_prizes/economics/laureates/2002/kahnemann-lecture.pdf, Last accessed January 11, 2006.Google Scholar
Kahneman, D. & Frederick, S. (2005). A model of heuristic judgement. In Holyoak, K. J., & Morrison, R. G. (eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 267–293). Cambridge, MA: Cambridge University Press.Google Scholar
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.CrossRefGoogle Scholar
Klaczynski, P. A., Byrnes, J. B., & Jacobs, J. E. (2001). Introduction: Special issue on decision making. Journal of Applied Developmental Psychology, 22, 225–236.CrossRefGoogle Scholar
Klaczynski, P. A., & Narashimham, G. (1998). Representations as mediators of adolescent deductive reasoning. Developmental Psychology, 5, 865–881.CrossRefGoogle Scholar
Kokis, J. V., Macpherson, R., Toplak, M. E., West, R. F., & Stanovich, K. E. (2002). Heuristic and analytic processing: Age trends and associations with cognitive ability and cognitive styles. Journal of Experimental Child Psychology, 83, 26–52.CrossRefGoogle ScholarPubMed
Markovits, H., & Barrouillet, P. (2004). Why is understanding the development of reasoning important? Thinking and Reasoning, 10, 113–121.CrossRefGoogle Scholar
Mevel, K., Poirel, N., Rossi, S., Cassotti, M., Simon, G., Houdé, O., & Neys, W. D. (2015). Bias detection: Response confidence evidence for conflict sensitivity in the ratio bias task. Journal of Cognitive Psychology, 27, 227–237.CrossRefGoogle Scholar
Newman, I., Gibb, M., & Thompson, V. A. (2017). Rule-based reasoning is fast and belief-based reasoning can be slow: Challenging current explanations of belief -bias and base-rate neglect. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43, 1154–1170.Google ScholarPubMed
Pennycook, G. (2017). A perspective on the theoretical foundation of dual process models. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 5–27). Oxford: Routledge.CrossRefGoogle Scholar
Pennycook, G., Cheyne, J. A., Barr, N., Koehler, D. J., & Fugelsang, J. A. (2014a). Cognitive style and religiosity: The role of conflict detection. Memory & Cognition, 42, 1–10.CrossRefGoogle ScholarPubMed
Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2012). Are we good at detecting conflict during reasoning. Cognition, 124, 101–106.CrossRefGoogle ScholarPubMed
Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2015). What makes us think? A three-stage dual-process model of analytic engagement. Cognitive Psychology, 80, 34–72.Google Scholar
Pennycook, G., Trippas, D., Handley, S. J., & Thompson, V. A. (2014b). Base rates: Both neglected and intuitive. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 544–554.Google ScholarPubMed
Piaget, J. (1952/1941). The Child’s Conception of Number. New York: Routledge & Kegan Paul.Google Scholar
Raoelison, M., Boissin, E., Borst, G., & De Neys, W. (2021). From slow to fast logic: The development of logical intuitions. Thinking & Reasoning, 1–25, online doi.org/10.1080/13546783.2021.1885488.Google Scholar
Raoelison, M., Thompson, V., & De Neys, W. (2020). The smart intuitor: Cognitive capacity predicts intuitive rather than deliberate thinking. Cognition, 204, 104381.CrossRefGoogle Scholar
Reyna, V. F. (2004). How people make decisions that involve risk: A dual-processes approach. Current Directions in Psychological Science, 13, 60–66.CrossRefGoogle Scholar
Reyna, V. F., & Brainerd, C. J. (1994). The origins of probability judgment: A review of data and theories. In Wright, G., & Ayton, P. (eds.), Subjective Probability (pp. 239–272). New York: Wiley.Google Scholar
Reyna, V. F., & Farley, F. (2006). Risk and rationality in adolescent decision making: Implications for theory, practice, and public policy. Psychological Science in the Public Interest, 7, 1–44.CrossRefGoogle ScholarPubMed
Reyna, V. F., Lloyd, F. J., & Brainerd, C. J. (2003). Memory, development, and rationality: An integrative theory of judgement and decision-making. In Schneider, S., & Shanteau, J. (eds.), Emerging Perspectives on Judgment and Decision Research (pp. 201–245). New York: Cambridge University Press.CrossRefGoogle Scholar
Simon, G., Lubin, A., Houdé, O., & De Neys, W. (2015). Anterior cingulate cortex and intuitive bias detection during number conservation. Cognitive Neuroscience, 6, 158–168.CrossRefGoogle ScholarPubMed
Stanovich, K. E. (2011). Rationality and the Reflective Mind. Oxford: Oxford University Press.Google Scholar
Stanovich, K. E. (2018). Miserliness in human cognition: The interaction of detection, override and mindware. Thinking & Reasoning, 24, 423–444.CrossRefGoogle Scholar
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral and Brain Sciences, 23, 645–665.Google Scholar
Stanovich, K. E., West, R. F., & Toplak, M. E. (2011). The complexity of developmental predictions from dual process models. Developmental Review, 31, 103–118.CrossRefGoogle Scholar
Stupple, E. J., Ball, L. J., Evans, J. S. B., & Kamal-Smith, E. (2011). When logic and belief collide: Individual differences in reasoning times support a selective processing model. Journal of Cognitive Psychology, 23, 931–941.CrossRefGoogle Scholar
Thompson, V. A., & Johnson, S. C. (2014). Conflict, metacognition, and analytic thinking. Thinking & Reasoning, 20, 215–244.Google Scholar
Thompson, V. A., & Newman, I. (2017). Logical intuitions and other conundra for dual process theories. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 121–136). Oxford: Routledge.CrossRefGoogle Scholar
Thompson, V. A., Pennycook, G., Trippas, D., & Evans, J. S. B. (2018). Do smart people have better intuitions? Journal of Experimental Psychology: General, 147, 945.Google Scholar
Thompson, V. A., Turner, J. A. P., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63, 107–140.CrossRefGoogle ScholarPubMed
Trippas, D., & Handley, S. (2017). The parallel processing model of belief bias: Review and extensions. In De Neys, W. (ed.), Dual Process Theory 2.0 (pp. 28–46). Oxford: Routledge.Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.CrossRefGoogle ScholarPubMed
Vartanian, O., Beatty, E. L., Smith, I., Blackler, K., Lam, Q., Forbes, S., & De Neys, W. (2018). The reflective mind: Examining individual differences in susceptibility to base rate neglect with FMRI. Journal of Cognitive Neuroscience, 30, 1011–1022.CrossRefGoogle ScholarPubMed