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

@article{Hunt2017ADH,
  title={A distributed, hierarchical and recurrent framework for reward-based choice},
  author={Laurence T. Hunt and Benjamin Y. Hayden},
  journal={Nature Reviews Neuroscience},
  year={2017},
  volume={18},
  pages={172-182}
}
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… CONTINUE READING
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