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Related Experiment Video

Updated: Jan 8, 2026

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

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Errors, fast and slow.

Carlos Alós-Ferrer1, Michele Garagnani2

  • 1Lancaster University Management School, Department of Economics, United Kingdom; Universidad Jaume I de Castellón, Department of Economics, Spain.

Cognitive Psychology
|December 13, 2025
PubMed
Summary
This summary is machine-generated.

Human errors in cognitive tasks can be faster or slower than correct responses. A new model predicts error speed ex ante, offering insights into cognitive conflict and decision-making processes.

Keywords:
ErrorsMultiple processesResponse timesSpeed of errors

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Area of Science:

  • Cognitive Psychology
  • Human Factors
  • Computational Neuroscience

Background:

  • Human errors in cognitive tasks exhibit variable response times (RTs), being faster or slower than correct responses.
  • Existing models can fit observed error RT distributions but struggle with ex ante prediction of error speed.
  • Predicting error RT characteristics is crucial for understanding underlying cognitive processes in decision-making.

Purpose of the Study:

  • To empirically validate a simple nonparametric model for predicting when human errors are faster or slower than correct responses.
  • To assess the model's applicability to generalized conflict tasks and its ability to predict error rate differences.
  • To test the hypothesis of process multiplicity in cognitive tasks based on predictive performance.

Main Methods:

  • Utilized 20 diverse datasets comprising 31 experiments across various domains.
  • Employed a simple nonparametric model to predict error RTs relative to correct response times.
  • Analyzed predictions for generalized Stroop effects and error rate differences in conflict tasks.

Main Results:

  • The nonparametric model successfully predicted whether errors would be faster or slower than correct responses across datasets.
  • Model predictions accurately encompassed generalized Stroop effects and error rate variations.
  • The model's predictions were overwhelmingly supported by empirical data, validating its hypotheses.

Conclusions:

  • A simple nonparametric model can reliably predict error RT characteristics ex ante in cognitive tasks.
  • The model's success in generalized conflict tasks supports the assumption of process multiplicity.
  • This approach provides a novel framework for generating and testing hypotheses about cognitive control and decision-making.