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- W2080799559 abstract "A fundamental challenge for computational and cognitive neuroscience is to understand how reward-based learning and decision-making are made and how accrued knowledge and internal models of the environment are incorporated. Remarkable progress has been made in the field, guided by the midbrain dopamine reward prediction error hypothesis and the underlying reinforcement learning framework, which does not involve internal models (‘model-free’). Recent studies, however, have begun not only to address more complex decision-making processes that are integrated with model-free decision-making, but also to include internal models about environmental reward structures and the minds of other agents, including model-based reinforcement learning and using generalized prediction errors. Even dopamine, a classic model-free signal, may work as multiplexed signals using model-based information and contribute to representational learning of reward structure." @default.
- W2080799559 created "2016-06-24" @default.
- W2080799559 creator A5025886486 @default.
- W2080799559 date "2014-04-01" @default.
- W2080799559 modified "2023-10-12" @default.
- W2080799559 title "Multiplexing signals in reinforcement learning with internal models and dopamine" @default.
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- W2080799559 doi "https://doi.org/10.1016/j.conb.2014.01.001" @default.
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