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Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797658/ https://www.ncbi.nlm.nih.gov/pubmed/30187434 http://dx.doi.org/10.3758/s13428-018-1110-z |
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author | van Maanen, Leendert Katsimpokis, Dimitris van Campen, A. Dilene |
author_facet | van Maanen, Leendert Katsimpokis, Dimitris van Campen, A. Dilene |
author_sort | van Maanen, Leendert |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6797658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-67976582019-11-01 Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships van Maanen, Leendert Katsimpokis, Dimitris van Campen, A. Dilene Behav Res Methods Article 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. Springer US 2018-09-05 2019 /pmc/articles/PMC6797658/ /pubmed/30187434 http://dx.doi.org/10.3758/s13428-018-1110-z Text en © The Author(s) 2018, corrected publication 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article van Maanen, Leendert Katsimpokis, Dimitris van Campen, A. Dilene Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships |
title | Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships |
title_full | Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships |
title_fullStr | Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships |
title_full_unstemmed | Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships |
title_short | Fast and slow errors: Logistic regression to identify patterns in accuracy–response time relationships |
title_sort | fast and slow errors: logistic regression to identify patterns in accuracy–response time relationships |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797658/ https://www.ncbi.nlm.nih.gov/pubmed/30187434 http://dx.doi.org/10.3758/s13428-018-1110-z |
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