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.
journal_name
Trends Cogn Scijournal_title
Trends in cognitive sciencesauthors
Sanborn AN,Chater Ndoi
10.1016/j.tics.2016.10.003subject
Has Abstractpub_date
2016-12-01 00:00:00pages
883-893issue
12eissn
1364-6613issn
1879-307Xpii
S1364-6613(16)30156-5journal_volume
20pub_type
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