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Related Experiment Video

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The Impact of Motor Task Conditions on Goal-Directed Arm Reaching Kinematics and Trunk Compensation in Chronic Stroke Survivors
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A model of the go/no-go task.

Pablo Gomez1, Roger Ratcliff, Manuel Perea

  • 1Department of Psychology, DePaul University, Chicago, IL 60614, US. pgomez1@depaul.edu

Journal of Experimental Psychology. General
|August 19, 2007
PubMed
Summary

This study compared two-choice and go/no-go tasks, finding core information processing remains consistent. Differences lie mainly in response demands, not decision criteria.

Area of Science:

  • Cognitive Psychology
  • Decision Making
  • Computational Neuroscience

Background:

  • Investigates the cognitive processes underlying decision-making tasks.
  • Addresses the debate on whether task variants (e.g., two-choice vs. go/no-go) engage different core information processing or response demands.

Purpose of the Study:

  • To explicitly compare two-choice and go/no-go task variants using a theory-based approach.
  • To determine if core information processing differs between these task variants.

Main Methods:

  • Employed diffusion models to analyze data from lexical decision, numerosity discrimination, and recognition memory experiments.
  • Compared standard diffusion models for two-choice tasks with four different diffusion models for go/no-go tasks.
  • Fit models to empirical data from tasks with both two-choice and go/no-go variants.

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Main Results:

  • Models incorporating an implicit decision criterion for no-go responses yielded superior fits to the data.
  • The optimal model indicated that only response criteria and non-decisional processing components varied between task variants.
  • No significant differences were found in the core information used for decision-making between the two task variants.

Conclusions:

  • Supports the view that the fundamental information processing is invariant across two-choice and go/no-go task variants.
  • Highlights the importance of response criteria and non-decisional factors in differentiating task performance.
  • Provides a computational framework for understanding decision-making processes in response inhibition tasks.