The Rational Inattention (RI) model has attracted attention in recent years as a promising candidate for modeling bounded rationality in the fields of decision making and game theory research. The model assumes that there is a cognitive cost (cost of information processing) that is proportional to the amount of mutual information obtained from signals, thereby making it possible to explain various phenomena observed in the market at a certain level. However, the RI model still lacks a sufficient cognitive foundation. In this study, we conducted an experiment to examine whether the cognitive costs and constraints on information processing, which are the assumptions of the Rational Inattention Model, are reasonable from the perspective of neuroeconomics using biometric data such as gaze information and brain responses. We adopted the sequential investment task with a view to applying it to finance. Our results showed that the stochastic choice rational inattention model fit the behavioral data of the present experiment, the larger the cognitive cost the more activated the brain regions involved in costly cognition, And the consistency between gaze information and the capacity constraint of the Kalman filter type model, as expected, when there is a lot of information, not all information can be processed, so more accurate decisions cannot be made.
Published in | Journal of Finance and Accounting (Volume 10, Issue 2) |
DOI | 10.11648/j.jfa.20221002.17 |
Page(s) | 141-150 |
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2022. Published by Science Publishing Group |
Rational Inattention, Mutual Information, Experiment
[1] | Christopher A. Sims: Implications of rational inattention. Journal of Monetary Economics 50 (3), 665–690 (2003). |
[2] | Filip Matejka and Alisdair McKay: Rational inattention to discrete choices: A new foundation for the multinomial logit model. American Economic Review 105 (1), 272-298 (2015). |
[3] | Christopher A. Sims: Rational Inattention: Beyond the Linear-Quadratic Case. American Economic Review 96 (2), 158-163 (2006). doi: 10.1257/000282806777212431. |
[4] | Filip Matejka: Rigid pricing and rationally inattentive consumer. Journal of Economic Theory 158, 656-678 (2015). doi: 10.1016/j.jet.2015.01.021. |
[5] | D’Esposito, M., Detre, J. A., Alsop, D. C., et al.: The neural basis of the central executive system of working memory. Nature 378 (6554), 279-281 (1995). doi: 10.1038/378279a0. |
[6] | Smith, Edward E., and John Jonides: Neuroimaging analyses of human working memory. Proceedings of the National Academy of Sciences 95 (20), 12061-12068 (1998). doi: 10.1073/pnas.95.20.12061. |
[7] | Smith, Edward E and Jonides, John: Working memory: A view from neuroimaging. Cognitive psychology 33 (1), 5-42 (1997). doi: 10.1006/cogp.1997.0658. |
[8] | Gupta, Rashmi, and Tranel, Daniel: Memory, neural substrates. Encyclopedia of human behavior, 593-600 (2012) DOI: 10.1016/B978-0-12-375000-6.00230-5. |
[9] | Germann, J. and Petrides, M: Area 8A within the posterior middle frontal gyrus underlies cognitive selection between competing visual targets. Neuro 7 (5), (2020) doi: 10.1523/ENEURO.0102-20.2020. |
[10] | Hamada Hamid: Networks in Mood and Anxiety Disorders. Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics 327-334 (2014) doi: 10.1016/B978-0-12-415804-7.00024-1. |
[11] | Yang Ming: Coordination with flexible information acquisition. Journal of Economic Theory 158, 721-738 (2015). doi: 10.1016/j.jet.2014.11.017. |
[12] | Ravid, Doron: Bargaining with rational inattention. Available at SSRN 2957890 (2015). doi: 10.2139/ssrn.2957890. |
[13] | Daniel Martin: Strategic pricing with rational inattention to quality. Games and Economic Behavior 104, 131-145 (2017). doi: 10.1016/j.geb.2017.03.007. |
[14] | Mark Deanyand Nathaniel Nelighz: Experimental tests of rational inattention (2017). doi: 10.7916/d8-4w4k-3q85. |
[15] | Ambuj Dewan and Nathaniel Neligh: Estimating information cost functions in models of rational inattention. Journal of Economic Theory 187, 105011 (2020). doi: 10.1016/j.jet.2020.105011. |
[16] | Jakub Steiner, Colin Stewart, Filip Matějka: Rational inattention dynamics: Inertia and delay in decision-making. Econometrica 85 (2), 521-553 (2017). doi: 10.3982/ECTA13636. |
[17] | Bartosz Mackowiak, Filip Matejka, Mirko Wiederholt: Rational Inattention: A Review. ECB Working Paper, No. 2570 (2021). DOI: 10.2866/417246. |
[18] | Bertoli S, Moraga J F H, Guichard L: Rational inattention and migration decisions. Journal of International Economics 126 103364 (2020). DOI: 10.1016/j.jinteco.2020.103364. |
[19] | Caplin A, Dean M, Leahy J: Rational inattention, optimal consideration sets, and stochastic choice. The Review of Economic Studies, 86 (3): 1061-1094 (2019). DOI: 10.1093/restud/rdy037. |
[20] | Lin Y H: Stochastic choice and rational inattention. Journal of Economic Theory, 202, 105450 (2022). DOI: 10.1016/j.jet.2022.105450. |
APA Style
Qi Wu, Tetsuya Shimokawa. (2022). Cognitive Capacity Constraint and Attention Allocation in Human Decision Making. Journal of Finance and Accounting, 10(2), 141-150. https://doi.org/10.11648/j.jfa.20221002.17
ACS Style
Qi Wu; Tetsuya Shimokawa. Cognitive Capacity Constraint and Attention Allocation in Human Decision Making. J. Finance Account. 2022, 10(2), 141-150. doi: 10.11648/j.jfa.20221002.17
AMA Style
Qi Wu, Tetsuya Shimokawa. Cognitive Capacity Constraint and Attention Allocation in Human Decision Making. J Finance Account. 2022;10(2):141-150. doi: 10.11648/j.jfa.20221002.17
@article{10.11648/j.jfa.20221002.17, author = {Qi Wu and Tetsuya Shimokawa}, title = {Cognitive Capacity Constraint and Attention Allocation in Human Decision Making}, journal = {Journal of Finance and Accounting}, volume = {10}, number = {2}, pages = {141-150}, doi = {10.11648/j.jfa.20221002.17}, url = {https://doi.org/10.11648/j.jfa.20221002.17}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20221002.17}, abstract = {The Rational Inattention (RI) model has attracted attention in recent years as a promising candidate for modeling bounded rationality in the fields of decision making and game theory research. The model assumes that there is a cognitive cost (cost of information processing) that is proportional to the amount of mutual information obtained from signals, thereby making it possible to explain various phenomena observed in the market at a certain level. However, the RI model still lacks a sufficient cognitive foundation. In this study, we conducted an experiment to examine whether the cognitive costs and constraints on information processing, which are the assumptions of the Rational Inattention Model, are reasonable from the perspective of neuroeconomics using biometric data such as gaze information and brain responses. We adopted the sequential investment task with a view to applying it to finance. Our results showed that the stochastic choice rational inattention model fit the behavioral data of the present experiment, the larger the cognitive cost the more activated the brain regions involved in costly cognition, And the consistency between gaze information and the capacity constraint of the Kalman filter type model, as expected, when there is a lot of information, not all information can be processed, so more accurate decisions cannot be made.}, year = {2022} }
TY - JOUR T1 - Cognitive Capacity Constraint and Attention Allocation in Human Decision Making AU - Qi Wu AU - Tetsuya Shimokawa Y1 - 2022/04/20 PY - 2022 N1 - https://doi.org/10.11648/j.jfa.20221002.17 DO - 10.11648/j.jfa.20221002.17 T2 - Journal of Finance and Accounting JF - Journal of Finance and Accounting JO - Journal of Finance and Accounting SP - 141 EP - 150 PB - Science Publishing Group SN - 2330-7323 UR - https://doi.org/10.11648/j.jfa.20221002.17 AB - The Rational Inattention (RI) model has attracted attention in recent years as a promising candidate for modeling bounded rationality in the fields of decision making and game theory research. The model assumes that there is a cognitive cost (cost of information processing) that is proportional to the amount of mutual information obtained from signals, thereby making it possible to explain various phenomena observed in the market at a certain level. However, the RI model still lacks a sufficient cognitive foundation. In this study, we conducted an experiment to examine whether the cognitive costs and constraints on information processing, which are the assumptions of the Rational Inattention Model, are reasonable from the perspective of neuroeconomics using biometric data such as gaze information and brain responses. We adopted the sequential investment task with a view to applying it to finance. Our results showed that the stochastic choice rational inattention model fit the behavioral data of the present experiment, the larger the cognitive cost the more activated the brain regions involved in costly cognition, And the consistency between gaze information and the capacity constraint of the Kalman filter type model, as expected, when there is a lot of information, not all information can be processed, so more accurate decisions cannot be made. VL - 10 IS - 2 ER -