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<p>Inhibitory control is essential to daily function and is a key factor in numerous psychiatric disorders. One popular measure of inhibitory control is the congruency effect, but recent research has highlighted its low reliability, limiting its use for clinical and basic research questions. Here we asked whether it is possible to obtain precise individual estimates of the congruency effect. We sampled more than 5,000 trials from nine participants across four inhibitory control tasks. This dataset, made public for the community, demonstrates that precise individual estimates are achievable but with higher numbers of trials than typically collected with common tools. Using a combination of datasets and simulations, we show that extensive sampling is necessary to reveal true individual differences and improve observations from alternative modelling approaches. We share our dataset as a resource to further understand sources of variation in inhibitory control, ultimately advancing research in this critical field.</p>

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Precise individual measures of inhibitory control

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • Hyejin J. Lee,
  • Derek M. Smith,
  • Clifford E. Hauenstein,
  • Ally Dworetsky,
  • Brian T. Kraus,
  • Megan Dorn,
  • Derek Evan Nee,
  • Caterina Gratton

摘要

Inhibitory control is essential to daily function and is a key factor in numerous psychiatric disorders. One popular measure of inhibitory control is the congruency effect, but recent research has highlighted its low reliability, limiting its use for clinical and basic research questions. Here we asked whether it is possible to obtain precise individual estimates of the congruency effect. We sampled more than 5,000 trials from nine participants across four inhibitory control tasks. This dataset, made public for the community, demonstrates that precise individual estimates are achievable but with higher numbers of trials than typically collected with common tools. Using a combination of datasets and simulations, we show that extensive sampling is necessary to reveal true individual differences and improve observations from alternative modelling approaches. We share our dataset as a resource to further understand sources of variation in inhibitory control, ultimately advancing research in this critical field.