Neurostimulation Reveals Context-Dependent Arbitration Between Model-Based and Model-Free Reinforcement Learning

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 79
  • Download : 0
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.
Publisher
OXFORD UNIV PRESS INC
Issue Date
ACCEPT
Language
English
Citation

CEREBRAL CORTEX

ISSN
1047-3211
DOI
10.1093/cercor/bhz019
URI
http://hdl.handle.net/10203/262853
Appears in Collection
BiS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0