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The comparable strategic flexibility of model-free and model-based learning

View ORCID ProfileAlexandre L. S. Filipowicz, Jonathan Levine, View ORCID ProfileEugenio Piasini, View ORCID ProfileGaia Tavoni, Joseph W. Kable, View ORCID ProfileJoshua I. Gold
doi: https://doi.org/10.1101/2019.12.28.879965
Alexandre L. S. Filipowicz
aDepartments of Neuroscience, University of Pennsylvania
bPsychology, University of Pennsylvania
cComputational Neuroscience Initiative, University of Pennsylvania
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  • ORCID record for Alexandre L. S. Filipowicz
  • For correspondence: alsfilip@pennmedicine.upenn.edu
Jonathan Levine
aDepartments of Neuroscience, University of Pennsylvania
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Eugenio Piasini
cComputational Neuroscience Initiative, University of Pennsylvania
dPhysics, University of Pennsylvania
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  • ORCID record for Eugenio Piasini
Gaia Tavoni
cComputational Neuroscience Initiative, University of Pennsylvania
dPhysics, University of Pennsylvania
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  • ORCID record for Gaia Tavoni
Joseph W. Kable
bPsychology, University of Pennsylvania
cComputational Neuroscience Initiative, University of Pennsylvania
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Joshua I. Gold
aDepartments of Neuroscience, University of Pennsylvania
cComputational Neuroscience Initiative, University of Pennsylvania
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  • ORCID record for Joshua I. Gold
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Abstract

Different learning strategies are thought to fall along a continuum that ranges from simple, inflexible, and fast “model-free” strategies, to more complex, flexible, and deliberative “model-based strategies”. Here we show that, contrary to this proposal, strategies at both ends of this continuum can be equally flexible, effective, and time-intensive. We analyzed behavior of adult human subjects performing a canonical learning task used to distinguish between model-free and model-based strategies. Subjects using either strategy showed similarly high information complexity, a measure of strategic flexibility, and comparable accuracy and response times. This similarity was apparent despite the generally higher computational complexity of model-based algorithms and fundamental differences in how each strategy learned: model-free learning was driven primarily by observed past responses, whereas model-based learning was driven primarily by inferences about latent task features. Thus, model-free and model-based learning differ in the information they use to learn but can support comparably flexible behavior.

Statement of Relevance The distinction between model-free and model-based learning is an influential framework that has been used extensively to understand individual- and task-dependent differences in learning by both healthy and clinical populations. A common interpretation of this distinction that model-based strategies are more complex and therefore more flexible than model-free strategies. However, this interpretation conflates computational complexity, which relates to processing resources and generally higher for model-based algorithms, with information complexity, which reflects flexibility but has rarely been measured. Here we use a metric of information complexity to demonstrate that, contrary to this interpretation, model-free and model-based strategies can be equally flexible, effective, and time-intensive and are better distinguished by the nature of the information from which they learn. Our results counter common interpretations of model-free versus model-based learning and demonstrate the general usefulness of information complexity for assessing different forms of strategic flexibility.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* co-senior authors

  • We have removed the optimality analysis using the information bottleneck as we no longer feel like this analysis is appropriate for this particular task.

  • https://osf.io/z3bpk/

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 July 20, 2020.
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The comparable strategic flexibility of model-free and model-based learning
Alexandre L. S. Filipowicz, Jonathan Levine, Eugenio Piasini, Gaia Tavoni, Joseph W. Kable, Joshua I. Gold
bioRxiv 2019.12.28.879965; doi: https://doi.org/10.1101/2019.12.28.879965
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The comparable strategic flexibility of model-free and model-based learning
Alexandre L. S. Filipowicz, Jonathan Levine, Eugenio Piasini, Gaia Tavoni, Joseph W. Kable, Joshua I. Gold
bioRxiv 2019.12.28.879965; doi: https://doi.org/10.1101/2019.12.28.879965

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