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Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference

View ORCID ProfileWolfgang M. Pauli, Matt Jones
doi: https://doi.org/10.1101/591818
Wolfgang M. Pauli
1Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
2Artificial Intelligence Platform, Microsoft, One Microsoft Way, Redmond, WA, 98052, USA
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  • ORCID record for Wolfgang M. Pauli
  • For correspondence: wolfgang.pauli@microsoft.com mcj@colorado.edu
Matt Jones
3Department of Psychology and Neuroscience, University of Colorado Boulder, 345 UCB Boulder, CO 80309
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  • For correspondence: wolfgang.pauli@microsoft.com mcj@colorado.edu
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Abstract

Adaptive behavior in even the simplest decision-making tasks requires predicting future events in an environment that is generally nonstationary. As an inductive problem, this prediction requires a commitment to the statistical process underlying environmental change. This challenge can be formalized in a Bayesian framework as a question of choosing a generative model for the task dynamics. Previous learning models assume, implicitly or explicitly, that nonstationarity follows either a continuous diffusion process or a discrete changepoint process. Each approach is slow to adapt when its assumptions are violated. A new mixture of Bayesian experts framework proposes separable brain systems approximating inference under different assumptions regarding the statistical structure of the environment. This model explains data from a laboratory foraging task, in which rats experienced a change in reward contingencies after pharmacological disruption of dorsolateral (DLS) or dorsomedial striatum (DMS). The data and model suggest DLS learns under a diffusion prior whereas DMS learns under a changepoint prior. The combination of these two systems offers a new explanation for how the brain handles inference in an uncertain environment.

One Sentence Summary Adaptive foraging behavior can be explained by separable brain systems approximating Bayesian inference under different assumptions about dynamics of the environment.

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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-NC 4.0 International license.
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Posted March 28, 2019.
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Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference
Wolfgang M. Pauli, Matt Jones
bioRxiv 591818; doi: https://doi.org/10.1101/591818
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Changepoint detection versus reinforcement learning: Separable neural substrates approximate different forms of Bayesian inference
Wolfgang M. Pauli, Matt Jones
bioRxiv 591818; doi: https://doi.org/10.1101/591818

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