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Models of inhibitory control.

Jeffrey D Schall1, Thomas J Palmeri2, Gordon D Logan2

  • 1Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Department of Psychology, Vanderbilt University, PMB 407817, Nashville, TN 37240-7817, USA jeffrey.d.schall@vanderbilt.edu.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|March 1, 2017
PubMed
Summary
This summary is machine-generated.

This study surveys computational models of response inhibition, including race models, to explain how the brain stops actions. These models link abstract processes to neural activity, aiding understanding of movement suppression.

Keywords:
accumulatorcountermandingresponse inhibitionstochasticstop signal

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Response inhibition is crucial for voluntary action control.
  • Existing models vary in mathematical, computational, and neurobiological specificity.
  • Understanding the neural mechanisms of stopping is an active research area.

Purpose of the Study:

  • To survey and compare different computational models of response inhibition.
  • To explore the neurobiological underpinnings of these models.
  • To identify future research directions in movement suppression.

Main Methods:

  • Review of mathematical and computational models, including the independent race model and interactive race models.
  • Analysis of how these models relate to neurophysiological mechanisms.
  • Consideration of models involving frontal cortex, basal ganglia, and superior colliculus.

Main Results:

  • The independent race model explains stop-signal task performance via competing GO and STOP processes.
  • Interactive race models link abstract processes to neural units (accumulators).
  • Models can quantitatively account for performance and neural activity dynamics.

Conclusions:

  • Computational models provide valuable insights into response inhibition mechanisms.
  • Further research is needed to account for variations across effectors, species, individuals, and clinical conditions.
  • Integrating different modeling approaches is key to advancing the field of movement suppression.