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Neural computations underlying arbitration between model-based and model-free learning.

Sang Wan Lee1, Shinsuke Shimojo2, John P O'Doherty1

  • 1Computation & Neural Systems, MC228-77, California Institute of Technology, Pasadena, CA 91125, USA; Behavioral & Social Neuroscience, MC228-77, California Institute of Technology, Pasadena, CA 91125, USA; Division of Humanities and Social Sciences, MC228-77, California Institute of Technology, Pasadena, CA 91125, USA.

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Summary
This summary is machine-generated.

The brain uses a "control arbitrator" to decide between deliberative (model-based) and reflexive (model-free) action systems. This arbitrator adjusts control based on prediction reliability, influencing behavior selection.

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Decision Neuroscience

Background:

  • Two systems guide action selection: deliberative (model-based) and reflexive (model-free).
  • The mechanism for switching control between these systems remains unclear.

Purpose of the Study:

  • To investigate the neural basis of arbitration between model-based and model-free systems.
  • To identify brain regions involved in dynamically allocating control.

Main Methods:

  • Investigated neural evidence for arbitration mechanisms.
  • Analyzed reliability signals and their comparison within prefrontal cortex regions.
  • Examined functional connectivity between arbitration and valuation areas.

Main Results:

  • Evidence for an arbitration mechanism allocating control based on prediction reliability.
  • Inferior lateral prefrontal and frontopolar cortex encode reliability signals and comparison outputs.
  • Connectivity to model-free valuation areas is modulated by the arbitrator's control allocation.

Conclusions:

  • The inferior lateral prefrontal and frontopolar cortex play a key role in arbitrating between model-based and model-free control.
  • Arbitration likely involves modulating the model-free valuation system based on prediction reliability.