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• W2952154437 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." @default.
• W2952154437 created "2019-06-27" @default.
• W2952154437 creator A5008250951 @default.
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• W2952154437 date "2019-03-06" @default.
• W2952154437 modified "2023-10-17" @default.
• W2952154437 title "Hippocampal pattern separation supports reinforcement learning" @default.
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• W2952154437 doi "https://doi.org/10.1038/s41467-019-08998-1" @default.
• W2952154437 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6403348" @default.
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• W2952154437 hasPublicationYear "2019" @default.
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