Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A theory of learning to infer

Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum, Samuel J. Gershman
doi: https://doi.org/10.1101/644534
Ishita Dasgupta
1Harvard University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: idasgupta@physics.harvard.edu
Eric Schulz
1Harvard University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joshua B. Tenenbaum
2Massachusetts Institute of Technology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samuel J. Gershman
1Harvard University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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 under-react to prior probabilities (base rate neglect), other studies find that people under-react 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 under-react to the data or the prior, and a new experiment demonstrates that these two forms of under-reaction 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted May 20, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A theory of learning to infer
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A theory of learning to infer
Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum, Samuel J. Gershman
bioRxiv 644534; doi: https://doi.org/10.1101/644534
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A theory of learning to infer
Ishita Dasgupta, Eric Schulz, Joshua B. Tenenbaum, Samuel J. Gershman
bioRxiv 644534; doi: https://doi.org/10.1101/644534

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Animal Behavior and Cognition
Subject Areas
All Articles
  • Animal Behavior and Cognition (4395)
  • Biochemistry (9613)
  • Bioengineering (7110)
  • Bioinformatics (24914)
  • Biophysics (12642)
  • Cancer Biology (9978)
  • Cell Biology (14377)
  • Clinical Trials (138)
  • Developmental Biology (7967)
  • Ecology (12132)
  • Epidemiology (2067)
  • Evolutionary Biology (16008)
  • Genetics (10937)
  • Genomics (14764)
  • Immunology (9889)
  • Microbiology (23712)
  • Molecular Biology (9492)
  • Neuroscience (50963)
  • Paleontology (370)
  • Pathology (1544)
  • Pharmacology and Toxicology (2688)
  • Physiology (4031)
  • Plant Biology (8677)
  • Scientific Communication and Education (1512)
  • Synthetic Biology (2403)
  • Systems Biology (6446)
  • Zoology (1346)