A distributed, hierarchical and recurrent framework for reward-based choice

Laurence T. Hunt, Benjamin Y. Hayden

Research output: Contribution to journalReview articlepeer-review

132 Scopus citations


Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this Opinion article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organization of timescales for information processing across the cortex. This account also suggests that certain correlates of value are emergent rather than represented explicitly in the brain.

Original languageEnglish (US)
Pages (from-to)172-182
Number of pages11
JournalNature Reviews Neuroscience
Issue number3
StatePublished - Mar 1 2017

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