Fast rule switching and slow rule updating in a perceptual categorization task

  1. Flora Bouchacourt
  2. Sina Tafazoli
  3. Marcelo Mattar
  4. Timothy J Buschman
  5. Nathaniel D Daw  Is a corresponding author
  1. Princeton University, United States
  2. University of California, San Diego, United States

Abstract

To adapt to a changing world, we must be able to switch between rules already learned and, at other times, learn rules anew. Often we must do both at the same time, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions.

Data availability

Codes and data supporting the findings of this study is available on GitHub (https://github.com/buschman- lab/FastRuleSwitchingSlowRuleUpdating).

Article and author information

Author details

  1. Flora Bouchacourt

    Department of Psychology, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8893-0143
  2. Sina Tafazoli

    Department of Psychology, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Marcelo Mattar

    Department of Cognitive Science, University of California, San Diego, San Diego, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Timothy J Buschman

    Department of Psychology, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1298-2761
  5. Nathaniel D Daw

    Department of Psychology, Princeton University, Princeton, United States
    For correspondence
    ndaw@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5029-1430

Funding

U.S. Army Research Office (ARO W911NF-16-1-047)

  • Nathaniel D Daw

NIMH (R01MH129492)

  • Timothy J Buschman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All experimental procedures were approved by Princeton University Institutional Animal Care and Use Committee (protocol #3055) and were in accordance with the policies and procedures of the National Institutes of Health.

Reviewing Editor

  1. David Badre, Brown University, United States

Publication history

  1. Preprint posted: January 30, 2022 (view preprint)
  2. Received: August 8, 2022
  3. Accepted: November 13, 2022
  4. Accepted Manuscript published: November 14, 2022 (version 1)
  5. Version of Record published: November 24, 2022 (version 2)

Copyright

© 2022, Bouchacourt et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Flora Bouchacourt
  2. Sina Tafazoli
  3. Marcelo Mattar
  4. Timothy J Buschman
  5. Nathaniel D Daw
(2022)
Fast rule switching and slow rule updating in a perceptual categorization task
eLife 11:e82531.
https://doi.org/10.7554/eLife.82531

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