Plausibility validation of a decision making model using subjects’ explanations of decisions

Abstract The purpose of this work is to present a procedure to validate the cognitive plausibility of decision making models generated from a knowledge-based computational modeling method. In order to probe the plausibility of the models, this study compared the explanations given by participants and models when they both make the same decision throughout the Iowa Gambling Task. The procedure used in the comparison is based on the average of the positions of the concepts identified in the participant’s explanation in an importance-ordered list of concepts obtained from the model. The results demonstrate a close relation between the knowledge contained in both kinds of explanations.

[1]  A. Damasio,et al.  Insensitivity to future consequences following damage to human prefrontal cortex , 1994, Cognition.

[2]  P. Glimcher,et al.  The Neurobiology of Decision: Consensus and Controversy , 2009, Neuron.

[3]  M. Dolores del Castillo,et al.  A Comparison of Hybrid Decision Making Methods for Emergency Support , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[4]  H. Damasio,et al.  The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers , 2005, Trends in Cognitive Sciences.

[5]  H. Robbins.  Some aspects of the sequential design of experiments , 1952 .

[6]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[7]  Ulrich Hoffrage,et al.  How causal knowledge simplifies decision-making , 2006, Minds and Machines.

[8]  James L. McClelland,et al.  A reexamination of the evidence for the somatic marker hypothesis: what participants really know in the Iowa gambling task. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[9]  M. Dolores del Castillo,et al.  A computational knowledge-based model for emulating human performance in the Iowa Gambling Task , 2012, Neural Networks.

[10]  B. Sahakian,et al.  The innovative brain , 2008, Nature.

[11]  C. Starmer.  Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk , 2000 .

[12]  Colin Camerer,et al.  Neuroeconomics: decision making and the brain , 2008 .

[13]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[14]  Keith E Stanovich,et al.  Decision-making and cognitive abilities: A review of associations between Iowa Gambling Task performance, executive functions, and intelligence. , 2010, Clinical psychology review.

[15]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[16]  G. Loewenstein,et al.  Neural Predictors of Purchases , 2007, Neuron.

[17]  Andrea Stocco,et al.  The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words , 2007, Cognitive Systems Research.

[18]  Joseph E LeDoux,et al.  Equipotentiality of thalamo-amygdala and thalamo-cortico-amygdala circuits in auditory fear conditioning , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[19]  A. Damasio.  Descartes’ Error. Emotion, Reason and the Human Brain. New York (Grosset/Putnam) 1994. , 1994 .

[20]  M. Dolores del Castillo,et al.  Knowledge-based Modeling in Dynamic Decision Making , 2012, CogSci.

[21]  A. Cowey,et al.  Post-decision wagering objectively measures awareness , 2007, Nature Neuroscience.

[22]  R. Duncan Luce,et al.  Individual Choice Behavior: A Theoretical Analysis , 1979 .

[23]  Edward T. Cokely,et al.  Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation , 2009, Judgment and Decision Making.

[24]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[25]  A. Damasio.  Descartes' error: emotion, reason, and the human brain. avon books , 1994 .

[26]  S. Quartz,et al.  Reason, emotion and decision-making: risk and reward computation with feeling , 2009, Trends in Cognitive Sciences.

[27]  I. Grant,et al.  Working memory deficits affect risky decision-making in methamphetamine users with attention-deficit/hyperactivity disorder. , 2012, Journal of psychiatric research.

[28]  A. Damasio,et al.  Deciding Advantageously Before Knowing the Advantageous Strategy , 1997, Science.

[29]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.