Computational, Cognitive, and Neural Models of Decision-making Biases

• Jonathan Malmaud, Brain and Cognitive Sciences, Massachusetts Institute of Technology
• Joshua Tenenbaum, Brain and Cognitive Sciences, Massachusetts Institute of Technology
• Peter Dayan, Gatsby Computational Neuroscience Unit, UCL
• Laurence Maloney, Dept. of Psychology, New York University
• Edward Vul, Dept. of Psychology, University of California, San Diego
• Nick Chater, Division of Psychology and Language Sciences, UCL

Abstract

The question for the symposium is how best to understand biases in decision-making, going beyond traditional judgment and decision-making (JDM) accounts such as prospect theory to take a more modern reverse-engineering perspective bridging rational computational, algorithmic, and neural levels of explanation, and viewing decision-making under risk and uncertainty not just as a simple matter of evaluating lotteries but in the context of cognition more broadly, taking seriously learning, perception, motor control, memory, and action planning.

• The Paper: Computational, Cognitive, and Neural Models of Decision-making Biases

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