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Model-based hierarchical reinforcement learning and human action control.

Matthew Botvinick1, Ari Weinstein2

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Summary
This summary is machine-generated.

Hierarchical model-based control, integrating goal-directed choice and action hierarchy, significantly expands human decision-making capabilities. This framework explains complex behaviors by evaluating potential outcomes prospectively.

Keywords:
goal-directed behaviourhierarchyreinforcement learning

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Growing interest in goal-directed (model-based) choice, involving prospective outcome evaluation.
  • Increasing attention to the role of hierarchy in decision-making and action control.

Purpose of the Study:

  • Investigate the intersection of model-based choice and hierarchical control.
  • Characterize hierarchical model-based control mechanisms in human decision-making.

Main Methods:

  • Utilizing the computational framework of hierarchical reinforcement learning.
  • Interpreting recent empirical findings within this computational framework.

Main Results:

  • Hierarchical model-based mechanisms play a pivotal role in human decision-making.
  • These mechanisms extend the scope and complexity of human behavior.

Conclusions:

  • Hierarchical model-based control is crucial for complex human actions.
  • This framework offers insights into the neural basis of sophisticated decision-making.