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Model based planners reflect on their model-free propensities.

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Model-based (MB) planning systems anticipate and incorporate influences from model-free (MF) systems. This self-reflective planning was demonstrated in a novel bandit task where participants favored MF tendencies when assigning rewards.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Dual-reinforcement learning theory posits two systems: model-free (MF) for value caching and model-based (MB) for prospective planning.
  • A key question is how MB systems integrate influences from MF systems during planning.

Purpose of the Study:

  • To investigate whether MB planners exhibit self-reflection by anticipating MF influences.
  • To explore the integration of MF tendencies within MB planning processes.

Main Methods:

  • Development of a novel bandit task allowing participants to design their environment.
  • Analysis of reward assignments in relation to MF tendencies within the task.

Main Results:

  • Participants' reward assignments were consistent with an MB system anticipating its own MF propensities.
  • Higher rewards were assigned to bandits associated with stronger MF tendencies.

Conclusions:

  • MB planning systems can be sophisticatedly self-reflective, incorporating anticipated MF influences.
  • Findings have implications for understanding decision-making in areas like addiction, pre-commitment, and economic choices.