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Related Experiment Video

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Vertical T-maze Choice Assay for Arthropod Response to Odorants

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Published on: February 14, 2013

Inhibiting responses to difficult choices.

Dora Matzke¹, Samuel Curley², Charlene Q Gong³

¹Department of Psychology, University of Amsterdam.

Journal of Experimental Psychology. General

|January 1, 2019

Summary

This summary is machine-generated.

This study introduces a new model for the stop-signal paradigm to accurately measure response inhibition, even with errors. The enhanced framework improves the study of difficult choices and provides precise stopping estimates.

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Last Updated: Jan 31, 2026

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A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance

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Published on: March 19, 2020

Area of Science:

Cognitive psychology
Neuroscience
Behavioral science

Background:

The stop-signal paradigm is crucial for studying response inhibition.
The standard race model has limitations in accounting for errors in go tasks.
Existing models struggle with contaminants like failed triggers and difficult choices.

Purpose of the Study:

To propose a parametric framework extending the race model to include go errors.
To enhance the stop-signal paradigm for studying response inhibition in difficult choices.
To address common data contaminants in stop-signal studies.

Main Methods:

Developed a parametric framework extending the 2-runner race model.
Incorporated a method to account for go errors.
Simulated data to test the model's performance under various conditions, including high error rates and task difficulty.

Main Results:

Standard race models can severely bias conclusions about response inhibition with difficult choices.
Even infrequent errors can lead to underestimation of stopping latencies.
The proposed framework accurately characterizes behavior and provides precise stop estimates, even in small samples.

Conclusions:

The enhanced framework expands the utility of the stop-signal paradigm for studying response inhibition in complex scenarios.
Accurate estimation of stopping latencies requires accounting for go errors and potential data contaminants.
This approach facilitates research on inhibition, particularly in tasks involving difficult decisions.