Hippocampal pattern separation supports reinforcement learning

Ian C. Ballard (), Anthony D. Wagner and Samuel M. McClure
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Ian C. Ballard: Stanford University
Anthony D. Wagner: Stanford University
Samuel M. McClure: Arizona State University

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract Animals rely on learned associations to make decisions. Associations can be based on relationships between object features (e.g., the three leaflets of poison ivy leaves) and outcomes (e.g., rash). More often, outcomes are linked to multidimensional states (e.g., poison ivy is green in summer but red in spring). Feature-based reinforcement learning fails when the values of individual features depend on the other features present. One solution is to assign value to multi-featural conjunctive representations. Here, we test if the hippocampus forms separable conjunctive representations that enables the learning of response contingencies for stimuli of the form: AB+, B−, AC−, C+. Pattern analyses on functional MRI data show the hippocampus forms conjunctive representations that are dissociable from feature components and that these representations, along with those of cortex, influence striatal prediction errors. Our results establish a novel role for hippocampal pattern separation and conjunctive representation in reinforcement learning.

Date: 2019
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DOI: 10.1038/s41467-019-08998-1

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