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A Reinforcement Meta-Learning framework of executive function and information demand.

Massimo Silvetti1, Stefano Lasaponara2, Nabil Daddaoua3

  • 1Computational and Translational Neuroscience Lab (CTNLab), Institute of Cognitive Sciences and Technologies, National Research Council (CNR), Rome, Italy.

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Summary

This study introduces a Reinforcement Meta-Learning (RML) model to balance the value and effort of information gathering. The model, linked to brain mechanisms, optimizes visual information policies by minimizing free energy.

Keywords:
EffortFree energyInformation seekingMPFCMeta Reinforcement LearningVisual attention

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Information gathering is vital for decision-making and fitness.
  • Optimal strategies balance information gain against exploration costs.
  • Neuro-computational mechanisms for this tradeoff remain unclear.

Purpose of the Study:

  • To present a computational model for optimizing the value-cost tradeoff in information gathering.
  • To investigate the neuro-computational implementation of information-seeking behavior.
  • To link brain mechanisms to information-driven decision-making.

Main Methods:

  • Developed a Reinforcement Meta-Learning (RML) computational model.
  • Implemented the RML within a biologically plausible neural architecture.
  • Linked catecholaminergic neuromodulators, medial prefrontal cortex, and visual maps.

Main Results:

  • The RML model successfully accounts for behavioral and neural data on information demand.
  • The model's utility function, encoded by dopamine, approximates variational free energy.
  • Demonstrated a mechanism for coordinating motivational, executive, and sensory systems.

Conclusions:

  • The RML model provides a framework for understanding information gathering strategies.
  • Dopamine's role in encoding utility aligns with free energy principles.
  • This work offers a biologically plausible mechanism for generating visual information-seeking policies.