Bayesian Just-So Stories in Psychology and Neuroscience

Autor/inn/en	Bowers, Jeffrey S.; Davis, Colin J.
Titel	Bayesian Just-So Stories in Psychology and Neuroscience
Quelle	In: Psychological Bulletin, 138 (2012) 3, S.389-414 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-2909
DOI	10.1037/a0026450
Schlagwörter	Bayesian Statistics; Psychology; Brain; Theories; Evidence; Prediction; Cognitive Science; Probability; Perception; Word Recognition; Decision Making; Logical Thinking; Memory; Psychomotor Skills; Bias; Statistical Inference; Biology; Evolution; Comparative Analysis + Suchen Sie Ihr Suchwort? Psychologie; Gehirn; Theory; Theorie; Evidenz; Vorhersage; Kognitionswissenschaft; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Wahrnehmung; Worterkennung; Decision-making; Entscheidungsfindung; Gedächtnis; Psychomotorische Aktivität; Inferential statistics; Schließende Statistik; Biologie
Abstract	According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak. This weakness relates to the many arbitrary ways that priors, likelihoods, and utility functions can be altered in order to account for the data that are obtained, making the models unfalsifiable. It further relates to the fact that Bayesian theories are rarely better at predicting data compared with alternative (and simpler) non-Bayesian theories. Second, we show that the empirical evidence for Bayesian theories in neuroscience is weaker still. There are impressive mathematical analyses showing how populations of neurons could compute in a Bayesian manner but little or no evidence that they do. Third, we challenge the general scientific approach that characterizes Bayesian theorizing in cognitive science. A common premise is that theories in psychology should largely be constrained by a rational analysis of what the mind ought to do. We question this claim and argue that many of the important constraints come from biological, evolutionary, and processing (algorithmic) considerations that have no adaptive relevance to the problem per se. In our view, these factors have contributed to the development of many Bayesian "just so" stories in psychology and neuroscience; that is, mathematical analyses of cognition that can be used to explain almost any behavior as optimal. (As Provided).
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Update	2017/4/10