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Related Experiment Video

Updated: Jul 17, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Towards Dependent Race Models for the Stop-Signal Paradigm: The Copula Approach.

Hans Colonius¹, Paria Jahansa¹, Harry Joe²

¹Department of Psychology, Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstraße 114-118, Oldenburg, 26129 Germany.

Computational Brain & Behavior

|July 16, 2026

Summary

The race model for stop signal processing assumes context independence. This study shows that stochastic dependencies, not context failures, explain violations in stop signal reaction time data.

Keywords:

Copula Dependent censoring Race model Stop signal paradigm

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Area of Science:

Cognitive psychology
Computational neuroscience
Statistics

Background:

The race model is a standard framework for understanding stop signal processing.
Empirical data has challenged the assumption of context independence in this model.
Previous interpretations suggested a failure of context independence.

Purpose of the Study:

To propose an alternative explanation for violations of the independent race model.
To demonstrate that stochastic dependencies can account for observed data.
To introduce copula-based methods for modeling stop signal processing.

Main Methods:

Developed stochastically dependent race models.
Utilized copulas from statistics to derive these models.
Analyzed stop signal reaction time data.

Main Results:

Stochastic dependency between go and stop processes can explain empirical violations.
Copula-based models provide a viable alternative to context-independent models.
The issue of non-observable stop signal processing time is linked to random dependent censoring.

Conclusions:

Context independence in race models can be maintained alongside stochastic dependencies.
Stochastic dependent race models offer a more flexible framework for stop signal processing.
This work bridges cognitive modeling with advanced statistical techniques.