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Rational decision-making in inhibitory control.

Pradeep Shenoy1, Angela J Yu

  • 1Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA.

Frontiers in Human Neuroscience
|June 8, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a rational decision-making framework for inhibitory control, explaining how individuals optimize stopping actions. The model links optimal decision-making to simpler race models, predicting behavioral changes.

Keywords:
inhibitory controloptimal decision-makingspeed-accuracy tradeoffstop signal task

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Area of Science:

  • Cognitive Neuroscience
  • Decision Science
  • Computational Psychiatry

Background:

  • Inhibitory control is crucial for cognitive flexibility, enabling dynamic action modification.
  • The stop-signal paradigm is a key task for studying inhibitory control.
  • Existing models often simplify the complex decision-making processes involved.

Purpose of the Study:

  • To formulate a probabilistic, rational decision-making framework for inhibitory control.
  • To explain behavioral characteristics observed in the stop-signal paradigm using this framework.
  • To link optimal decision-making to computationally simpler models like the race model.

Main Methods:

  • Developed a Bayes-optimal decision-making model for inhibitory control.
  • The model incorporates continually updated sensory representations and value assessments.
  • Analyzed the model's ability to predict behavior under varying reward and motivational conditions.

Main Results:

  • The rational decision-making model accurately predicts fundamental and subtle behavioral patterns in the stop-signal task.
  • Contextual factors like reward contingencies and motivation significantly influence decision-making.
  • The classical race model is identified as a computationally simpler approximation of optimal decision-making.

Conclusions:

  • A rational, probabilistic framework provides a comprehensive account of inhibitory control.
  • This framework elucidates the relationship between optimal strategies and simpler neural implementations.
  • The model allows predictions regarding how race model parameters adapt to task demands and individual differences.