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Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task

View ORCID ProfileDavid B. Kastner, Eric A. Miller, Zhounan Yang, Demetris K. Roumis, Daniel F. Liu, Loren M. Frank, Peter Dayan
doi: https://doi.org/10.1101/808006
David B. Kastner
Department of Psychiatry, University of California, San Francisco, CA 94143, USAKavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, CA 94158, USA
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  • ORCID record for David B. Kastner
  • For correspondence: david.kastner2@ucsf.edu
Eric A. Miller
Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, CA 94158, USA
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Zhounan Yang
Department of Psychiatry, University of California, San Francisco, CA 94143, USA
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Demetris K. Roumis
Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, CA 94158, USA
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Daniel F. Liu
Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, CA 94158, USA
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Loren M. Frank
Kavli Institute for Fundamental Neuroscience and Department of Physiology, University of California, San Francisco, CA 94158, USAHoward Hughes Medical Institute
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Peter Dayan
Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany
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Summary

In novel situations, behavior necessarily reduces to latent biases. How these biases interact with new experiences to enable subsequent behavior remains poorly understood. We exposed rats to a family of spatial alternation contingencies and developed a series of reinforcement learning agents to describe the behavior. The performance of these agents shows that accurately describing the learning of individual animals requires accounting for their individual dynamic preferences as well as general, shared, cognitive processes. Agents that include only memory of past choice do not account for the behavior. Adding an explicit representation of biases allows agents to perform the task as rapidly as the rats, to accurately predict critical facets of their behavior on which it was not fitted, and to capture individual differences quantitatively. Our results illustrate the value of making explicit models of learning and highlight the importance of considering the initial state of each animal in understanding behavior.

Footnotes

  • Lead contact: David B. Kastner: david.kastner2{at}ucsf.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 17, 2019.
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Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task
David B. Kastner, Eric A. Miller, Zhounan Yang, Demetris K. Roumis, Daniel F. Liu, Loren M. Frank, Peter Dayan
bioRxiv 808006; doi: https://doi.org/10.1101/808006
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Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task
David B. Kastner, Eric A. Miller, Zhounan Yang, Demetris K. Roumis, Daniel F. Liu, Loren M. Frank, Peter Dayan
bioRxiv 808006; doi: https://doi.org/10.1101/808006

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