Neurostimulation Reveals Context-Dependent Arbitration Between Model-Based and Model-Free Reinforcement Learning.
Cereb Cortex
; 29(11): 4850-4862, 2019 12 17.
Article
en En
| MEDLINE
| ID: mdl-30888032
ABSTRACT
While it is established that humans use model-based (MB) and model-free (MF) reinforcement learning in a complementary fashion, much less is known about how the brain determines which of these systems should control behavior at any given moment. Here we provide causal evidence for a neural mechanism that acts as a context-dependent arbitrator between both systems. We applied excitatory and inhibitory transcranial direct current stimulation over a region of the left ventrolateral prefrontal cortex previously found to encode the reliability of both learning systems. The opposing neural interventions resulted in a bidirectional shift of control between MB and MF learning. Stimulation also affected the sensitivity of the arbitration mechanism itself, as it changed how often subjects switched between the dominant system over time. Both of these effects depended on varying task contexts that either favored MB or MF control, indicating that this arbitration mechanism is not context-invariant but flexibly incorporates information about current environmental demands.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Refuerzo en Psicología
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Corteza Prefrontal
/
Toma de Decisiones
Tipo de estudio:
Prognostic_studies
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Cereb Cortex
Asunto de la revista:
CEREBRO
Año:
2019
Tipo del documento:
Article
País de afiliación:
Suiza