A distributional code for value in dopamine-based reinforcement learning.
Nature
; 577(7792): 671-675, 2020 01.
Article
en En
| MEDLINE
| ID: mdl-31942076
ABSTRACT
Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain1-3. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning4-6. We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Refuerzo en Psicología
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Recompensa
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Dopamina
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Aprendizaje
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Modelos Neurológicos
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Año:
2020
Tipo del documento:
Article