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Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance
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Don'T let me do that! - models of precommitment.

Zeb Kurth-Nelson1, A David Redish

  • 1Wellcome Trust Centre for Neuroimaging, University College London London, UK.

Frontiers in Neuroscience
|October 13, 2012
PubMed
Summary
This summary is machine-generated.

Precommitment strategies, like restricting future choices, help manage impulsivity. Recent research suggests a distributed decision-making model with multiple discounting rates best explains this behavior.

Keywords:
decision-makingdiscounting functionneuroeconomicsprecommitmenttemporal diference reinforcement learning

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Behavioral Economics

Background:

  • Impulsivity is a key factor in decision-making breakdowns.
  • Precommitment is a strategy to overcome impulsivity by removing future choices.
  • A multiple-systems view of decision-making is emerging, proposing distinct neural and computational strategies.

Purpose of the Study:

  • To review recent work on precommitment behavior.
  • To explain precommitment using a distributed decision-making system with multiple discounting rates.
  • To connect precommitment to the multiple-systems view of decision-making and temporal difference reinforcement learning.

Main Methods:

  • Review of recent scientific literature on precommitment and decision-making.
  • Analysis of precommitment behavior through the lens of a distributed decision-making model.
  • Conceptual translation of precommitment into temporal difference reinforcement learning.

Main Results:

  • Precommitment is best explained by a distributed decision-making system incorporating multiple discounting rates.
  • This model offers specific predictions for precommitment behavior.
  • The framework aligns with the multiple-systems view of decision-making.

Conclusions:

  • A distributed decision-making model with multiple discounting rates provides a robust explanation for precommitment.
  • Understanding precommitment through this model can unify behavioral and neural data across psychiatric disorders.
  • Translating precommitment into temporal difference reinforcement learning offers a common computational language.