Fast rule switching and slow rule updating in a perceptual categorization task
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.
Codes and data supporting the findings of this study is available on GitHub (https://github.com/buschman- lab/FastRuleSwitchingSlowRuleUpdating).
Article and author information
U.S. Army Research Office (ARO W911NF-16-1-047)
- Nathaniel D Daw
- Timothy J Buschman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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.
- David Badre, Brown University, United States
- Preprint posted: January 30, 2022 (view preprint)
- Received: August 8, 2022
- Accepted: November 13, 2022
- Accepted Manuscript published: November 14, 2022 (version 1)
- Version of Record published: November 24, 2022 (version 2)
© 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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