Prefrontal solution to the bias-variance tradeoff during reinforcement learning

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The goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.
Publisher
CELL PRESS
Issue Date
2021-12
Language
English
Article Type
Article
Citation

CELL REPORTS, v.37, no.13

ISSN
2211-1247
DOI
10.2139/ssrn.3811830
URI
http://hdl.handle.net/10203/291866
Appears in Collection
BiS-Journal Papers(저널논문)
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