David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Minds and Machines 21 (3):389-410 (2011)
Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that probability match the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely. A new account of rationality—either for inference or for decision-making—is required to successfully confirm Bayesian models in cognitive science.
|Keywords||Bayesian modeling Rationality Levels of explanation Methodology in cognitive science|
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Citations of this work BETA
Stephan Hartmann & Matteo Colombo (forthcoming). Bayesian Cognitive Science, Unification and Explanation. British Journal for the Philosophy of Science:axv036.
David Peebles & Richard P. Cooper (2015). Thirty Years After Marr's Vision: Levels of Analysis in Cognitive Science. Topics in Cognitive Science 7 (2):187-190.
David Danks & Frederick Eberhardt (2011). Keeping Bayesian Models Rational: The Need for an Account of Algorithmic Rationality. Behavioral and Brain Sciences 34 (4):197-197.
Clark Glymour (2011). Osiander's psychology. Behavioral and Brain Sciences 34 (4):199-200.
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