ArticleBehavior research methods2019
Fast and slow errors: Logistic regression to identify patterns in accuracy-response time relationships.
Article in Behavior research methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Error Cancellation During Early Task Performance.Experimental psychology · 2025Article
- Letter to the editor: Statistical methodology critique and alternative approaches in H5Nx avian influenza seroprevalence study among French cats.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Exploring the spatial interference effects elicited by social and non-social targets: A conditional accuracy function approach.British journal of psychology (London, England : 1953) · 2025Article
- New Insights Into Visual Word Recognition: Analyzing Error Distribution in Typical Readers.Journal of cognition · 2025Article
- A sequential model of two-choice intensity identification.Frontiers in cognition · 2025Article
- Probabilistic causal reasoning under time pressure.PloS one · 2024Article
- The Curve of Learning With and Without Instructions.Journal of cognition · 2024Article
- Inconsistent flanker congruency effects across stimulus types and age groups: A cautionary tale.Behavior research methods · 2023Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding error and response time patterns is essential for making inferences in several domains of cognitive psychology. Crucial insights on cognitive performance and typical behavioral patterns are disclosed by using distributional analyses such as conditional accuracy functions (CAFs) instead of mean statistics. Several common behavioral error patterns revealed by CAFs are frequently described in the literature: response capture (associated with relatively fast errors), time pressure or urgency paradigms (slow errors), or cue-induced speed-accuracy trade-off (evenly distributed errors). Unfortunately, the standard way of computing CAFs is problematic, because accuracy is averaged in RT bins. Here we present a novel way of analyzing accuracy-RT relationships on the basis of nonlinear logistic regression, to handle these problematic aspects of RT binning. First we evaluate the parametric robustness of the logistic regression CAF through parameter recovery. Second, we apply the function to three existing data sets showing that specific parametric changes in the logistic regression CAF can consistently describe common behavioral patterns (such as response capture, time pressure, and speed-accuracy trade-off). Finally, we discuss potential modifications for future research.
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