A theory of learning to infer.
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644534v2.full.pdf
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Submitted version
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3.46 MB
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Author(s)
Dasgupta, Ishita
Schulz, Eric
Tenenbaum, Joshua B
Gershman, Samuel J
Date Issued
2020
Journal
Psychological Review
Publisher
American Psychological Association (APA)
Citation
Dasgupta, Ishita, Schulz, Eric, Tenenbaum, Joshua B and Gershman, Samuel J. 2020. "A theory of learning to infer.." Psychological Review, 127 (3).
Version
Original manuscript
Abstract
Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some studies suggest that people underreact to prior probabilities (base rate neglect), other studies find that people underreact to the likelihood of the data (conservatism). We argue that these deviations arise because the human brain does not rely solely on a general-purpose mechanism for approximating Bayesian inference that is invariant across queries. Instead, the brain is equipped with a recognition model that maps queries to probability distributions. The parameters of this recognition model are optimized to get the output as close as possible, on average, to the true posterior. Because of our limited computational resources, the recognition model will allocate its resources so as to be more accurate for high probability queries than for low probability queries. By adapting to the query distribution, the recognition model learns to infer. We show that this theory can explain why and when people underreact to the data or the prior, and a new experiment demonstrates that these two forms of underreaction can be systematically controlled by manipulating the query distribution. The theory also explains a range of related phenomena: memory effects, belief bias, and the structure of response variability in probabilistic reasoning. We also discuss how the theory can be integrated with prior sampling-based accounts of approximate inference. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
MIT Department
Center for Brains, Minds, and Machines
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1037/REV0000178
