While many studies have investigated incorrect evaluations of cognitive bias type and how age-related decisions are influenced by it, few have compared this phenomenon across different age groups (i.e., adults and children). In this study, we examined developmental changes by comparing the number of correct answers given by children and that given by adults when performing different types of cognitive bias tasks. The participant sample comprised 36 children (21 boys and 15 girls with an average age of 12.3 years) and 26 adults (15 men and 11 women with an average age of 27.3 years). The cognitive bias task created by previous study was modified and then implemented to better understand the performance of the children. This study identified four types of developmental changes in cognitive bias. Consequently, it was suggested that in terms of cognitive reflection, conjunction bias and confirmation bias improved performance, and framing did not change for either children or adults. It was also suggested that the confirmation bias tasks’ lack of consideration for the subcategory of ADHD was associated with performance among both the children and the adults. Results show that some cognitive biases may reveal developmental changes, depending on the category. As such, we were able to consider the factors involved in human decision-making and obtain a perspective on developmental changes to improve future decision-making.
References
[1]
Blair, C., & Razza, R. (2007). Relating Effortful Control of Executive Function and Understanding of False Belief in Emerging Math and Literacy Ability in Kindergarten. Child Development, 78, 647-663. https://doi.org/10.1111/j.1467-8624.2007.01019.x
[2]
Bruine de Bruin, W. (2012). Judgment and Decision Making in Adolescents. In M. K. Dhami, A. Schlottemann, & M. Waldmann (Eds.), Judgment & Decision Making as a Skill: Learning, Development, & Evolution (pp. 85-112). New York: Cambridge University Press. https://doi.org/10.1017/CBO9781139015684.008
[3]
Carstensen, L. L., Mikels, J. A., & Mather, M. (2006). Aging and the Intersection of Cognition, Motivation, and Emotion. In J. Birren, & K. W. Schaie (Eds.), Handbook of the Psychology of Aging (pp. 343-362). New York: Academic Press. https://doi.org/10.1016/B9-78-012101-2/64950-0185
[4]
Fischhoff, B. (2008). Assessing Adolescent Decision-Making Competence. Development Review, 28, 12-28. https://doi.org/10.1016/j.dr.2007.08.001
[5]
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., & Toga, A. W. (2004). Dynamic Mapping of Human Cortical Development during Childhood through Early Adulthood. Proceedings of the National Academy of Science of the United States of America, 101, 8174-8179. https://doi.org/10.1073/pnas.0402680101
[6]
Hertwig, R. N., & Gigerenzer, G. (1999). The “Conjunction Fallacy” Revisited: How Intelligent Inferences Look Like Reasoning Errors. Journal of Behavioral Decision Making, 12, 275-305. https://doi.org/10.1002/(SICI)1099-0771(199912)12:4<275::AID-BDM323>3.0.CO;2-M
[7]
Hertwig, R. N., Zangerl, M. A., Biedert, E., & Margraf, J. (2008). The Public’s Probabilistic Numeracy: How Tasks, Education and Exposure to Games of Chance Shape It. Journal of Behavioral Decision Making, 21, 457-470. https://doi.org/10.1002/bdm.611
[8]
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511809477
[9]
Levin, I. P., & Hart, S. S. (2003). Risk Preferences in Young Children: Early Evidence of Individual Differences in Reaction to Potential Gains and Losses. Journal of Behavioral Decision Making, 16, 397-414. https://doi.org/10.1002/bdm.453
[10]
Matsuura, T. (2006). Longitudinal and Cross-Sectional Study on the Actual State of Probability Judgment of Children. Japan Academic Society of Mathematics Education, 12, 141-151.
[11]
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter, S. (2000). The Unity and Diversity of Executive Functions and their Contributions to Complex “Frontal Lobe’’ Tasks: A Latent Variable Analysis. Cognitive Psychology, 41, 49-100. https://doi.org/10.1006/cogp.1999.0734
[12]
Nagamine, M., Hara, S., & Nobuhara, Y. (2009). Bank Transfer Fraud: A Neuroscientific Approach. Sociotechnica, 6, 177-186. (In Japanese) https://doi.org/10.3392/sociotechnica.6.177
[13]
Ninomiya, Y., & Fujiki, D. (2018). Relationship between Comprehension Process of Conjunction Fallacy Task and Judgement Depending on Representation Heuristic. Cognitive Studies, 25, 421-434.
[14]
Peters, E. M., & Bruine de Bruin, W. (2012). Aging and Decision Skills. In M. K. Dhami, A. Schlottmann, & M. Waldmann (Eds.), Judgment and Decision Making as a Skill: Learning, Development, and Evolution (pp. 113-140). New York: Cambridge University Press. https://doi.org/10.1017/CBO9781139015684.009
[15]
Reyna, V. F. (2004). How People Make Decisions That Involve Risk: A Dual Process Approach. Current Directions in Psychological Science, 13, 60-66. https://doi.org/10.1111/j.0963-7214.2004.00275.x
[16]
Reyna, V. F., & Brainerd, C. J. (2011). Dual Process in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model. Developmental Review, 31, 180-206. https://doi.org/10.1016/j.dr.2011.07.004
[17]
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. https://doi.org/10.1111/j.1529-1006.2006.00026.x
[18]
Reyna, V. F., & Mills, B. A. (2007). Interference Processes in Fuzzy-Trace Theory: Aging, Alzheimer’s Disease, and Development. In C. MacLeod, & D. Gorfein (Eds.), Inhibition in Cognition (pp. 185-210). Washington DC: APA Press. https://doi.org/10.1037/11587-010
[19]
Sasaki, H. (2010). Three Types of Decision-Making Framing Effects from the Perspective of Infant Development and Childcare. Niigata Chuo University Hyosung Journal, 60, 55-72.
[20]
Schlottmann, A., & Tring, J. (2005). How Children Reason about Gains and Losses: Framing Effects in Judgment and Choice. Swiss Journal of Psychology, 64, 153-171. https://doi.org/10.1024/1421-0185.64.3.153
[21]
Strough, J., McKarns, T. E., & Schlossnagle, L. (2011). Decision-Making Heuristics and Biases across the Life Span. Annals of the New York Academy of Science, 1235, 57-74. https://doi.org/10.1111/j.1749-6632.2011.06208.x
[22]
Suzuki, A., Megumi, A. & Yasumura, A. (2020). Relationships between Cognitive Bias and Autistic Traits in Children. Kumamoto University Studies in Social and Cultural Sciences, 18, 65-74.
[23]
Takahashi, H., & Omori, T. (2011). The Role of Social Belief Effect in Social Interaction and Its Neural Mechanism. Cognitive Studies, 18, 138-157.
[24]
Toplak, M. E., West, R. F., & Stanovich, K. E. (2013). Rational Thinking and Cognitive Sophistication: Development, Cognitive Abilities, and Thinking Dispositions. Developmental Psychology, 50, 1037-1048. https://doi.org/10.1037/a0034910
[25]
Tversky, A., & Kahneman, D. (1983). Extensional versus Intuitive Reasoning: The Conjunction Fallacy in Probability Judgment. Psychological Review, 90, 293-315. https://doi.org/10.1037/0033-295X.90.4.293
[26]
Wood, S., Busemeyer, J., Koling, A., Cox, C. R., & Davis, H. (2005). Older Adults as Adaptive Decision Makers: Evidence from the Iowa Gaming Task. Psychology and Aging, 20, 220-225. https://doi.org/10.1037/0882-7974.20.2.220
