Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory.
Proc Natl Acad Sci U S A
; 115(10): 2502-2507, 2018 03 06.
Article
en En
| MEDLINE
| ID: mdl-29463751
ABSTRACT
Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Refuerzo en Psicología
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Electroencefalografía
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Aprendizaje
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Memoria a Corto Plazo
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Modelos Neurológicos
Tipo de estudio:
Prognostic_studies
Límite:
Adolescent
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Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Año:
2018
Tipo del documento:
Article