Citation

Sanborn AN, Chater N. Trends Cogn. Sci. 2016; 20(12): 883-893.

Affiliation

Warwick Business School, Coventry, UK.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.tics.2016.10.003

PMID

28327290

Abstract

Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.

Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Language: en

Keywords

Bayesian models of cognition; reasoning biases; sampling