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Retrospective model-based inference guides model-free credit assignment.

Rani Moran1,2, Mehdi Keramati3,4,5, Peter Dayan3,6,7

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Organisms efficiently assign credit using reinforcement learning, even with uncertainty. This study reveals how model-based systems resolve uncertainty after actions, guiding model-free credit assignment for better decision-making.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Reinforcement Learning

Background:

  • Reinforcement learning literature demonstrates efficient credit assignment under state uncertainty.
  • Limited understanding exists regarding credit assignment when state uncertainty is resolved post-action.

Purpose of the Study:

  • To investigate credit assignment mechanisms when state uncertainty is resolved.
  • To propose and experimentally validate a theory of model-based (MB) retrospective-inference guiding model-free (MF) credit assignment.

Main Methods:

  • Developed a theoretical framework for MB retrospective-inference interacting with MF control.
  • Designed an experimental task with initial uncertainty about chosen lotteries.
  • Analyzed participant behavior when outcome-related uncertainty was subsequently resolved.

Main Results:

  • Participants preferentially assigned credit within an MF system to the lottery retrospectively inferred to be responsible for the outcome.
  • Evidence supports the theory that MB systems resolve uncertainty to guide MF credit assignment.

Conclusions:

  • Model-based retrospective-inference plays a crucial role in resolving uncertainty to guide credit assignment.
  • Findings expand the known functions of MB systems and their interactions with MF systems in decision-making.