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Bayesian Brains without Probabilities
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DOI：10.1016/j.tics.2016.10.003.png)
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Abstract
En 中文
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.
Keywords:
CONJUNCTION FALLACY
LEARNING ALGORITHM
MODELS
JUDGMENT
INFERENCE
REPRESENTATIVENESS
PERCEPTUOMOTOR
UNCERTAINTY
PERSPECTIVE
DECISIONS
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