Bayesian Brains without Probabilities.
Saved in:
| Title: | Bayesian Brains without Probabilities. |
|---|---|
| Authors: | Sanborn, Adam N.1 a.n.sanborn@warwick.ac.uk, Chater, Nick2 |
| Source: | Trends in Cognitive Sciences. Dec2016, Vol. 20 Issue 12, p883-893. 11p. |
| Subjects: | Bayesian analysis, Probability theory, Neurosciences, Nervous system, Neurophysiologic monitoring, Base rate fallacies |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Trends in Cognitive Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
