Comparison of basic assumptions embedded in learning models for experience-based decision making |
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Authors: | Email author" target="_blank">Eldad?YechiamEmail author Jerome?R?Busemeyer |
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Institution: | (1) Institute of Neuroscience, School of Life Science, National Yang-Ming University, Taipei, Taiwan;(2) Laboratory of Integrated Brain Research, Department of Medical Research & Education, Taipei Veterans General Hospital, Taipei, Taiwan;(3) Department of Psychology, Soochow University, Taipei, Taiwan;(4) Department of Electrical Engineering, National Central University, Taoyuan, Taiwan;(5) Research Center for Integrative Neuroimaging and Neuroinformatics, National Health Research Institutes, Taipei, Taiwan |
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Abstract: | The present study examined basic assumptions embedded in learning models for predicting behavior in decisions based on experience.
In such decisions, the probabilities and payoffs are initially unknown and are learned from repeated choice with payoff feedback.
We examined combinations of two rules for updating past experience with new payoff feedback and of two choice rule assumptions
for mapping experience onto choices. The combination of these assumptions produced four classes of models that were systematically
compared. Two methods were employed to evaluate the success of learning models for approximating players’ choices: One was
based on estimating parameters from each person’s data to maximize the prediction of choices one step ahead, conditioned by
the observed past history of feedback. The second was based on making a priori predictions for the entire sequence of choices
using parameters estimated from a separate experiment. The results indicated the advantage of a class of models incorporating
decay of previous experience, whereas the ranking of choice rules depended on the evaluation method used. |
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