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Parsimonious estimation of signal detection models from confidence ratings
Signal detection theory (SDT) is used to quantify people’s ability and bias in discriminating stimuli. The ability to detect a stimulus is often measured through confidence ratings. In SDT models, the use of confidence ratings necessitates the estimation of confidence category thresholds, a requirem...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797662/ https://www.ncbi.nlm.nih.gov/pubmed/31069712 http://dx.doi.org/10.3758/s13428-019-01231-3 |
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author | Selker, Ravi van den Bergh, Don Criss, Amy H. Wagenmakers, Eric-Jan |
author_facet | Selker, Ravi van den Bergh, Don Criss, Amy H. Wagenmakers, Eric-Jan |
author_sort | Selker, Ravi |
collection | PubMed |
description | Signal detection theory (SDT) is used to quantify people’s ability and bias in discriminating stimuli. The ability to detect a stimulus is often measured through confidence ratings. In SDT models, the use of confidence ratings necessitates the estimation of confidence category thresholds, a requirement that can easily result in models that are overly complex. As a parsimonious alternative, we propose a threshold SDT model that estimates these category thresholds using only two parameters. We fit the model to data from Pratte et al. (Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 224–232 2010) and illustrate its benefits over previous threshold SDT models. |
format | Online Article Text |
id | pubmed-6797662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-67976622019-11-01 Parsimonious estimation of signal detection models from confidence ratings Selker, Ravi van den Bergh, Don Criss, Amy H. Wagenmakers, Eric-Jan Behav Res Methods Article Signal detection theory (SDT) is used to quantify people’s ability and bias in discriminating stimuli. The ability to detect a stimulus is often measured through confidence ratings. In SDT models, the use of confidence ratings necessitates the estimation of confidence category thresholds, a requirement that can easily result in models that are overly complex. As a parsimonious alternative, we propose a threshold SDT model that estimates these category thresholds using only two parameters. We fit the model to data from Pratte et al. (Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 224–232 2010) and illustrate its benefits over previous threshold SDT models. Springer US 2019-05-08 2019 /pmc/articles/PMC6797662/ /pubmed/31069712 http://dx.doi.org/10.3758/s13428-019-01231-3 Text en © The Author(s) 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 Selker, Ravi van den Bergh, Don Criss, Amy H. Wagenmakers, Eric-Jan Parsimonious estimation of signal detection models from confidence ratings |
title | Parsimonious estimation of signal detection models from confidence ratings |
title_full | Parsimonious estimation of signal detection models from confidence ratings |
title_fullStr | Parsimonious estimation of signal detection models from confidence ratings |
title_full_unstemmed | Parsimonious estimation of signal detection models from confidence ratings |
title_short | Parsimonious estimation of signal detection models from confidence ratings |
title_sort | parsimonious estimation of signal detection models from confidence ratings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797662/ https://www.ncbi.nlm.nih.gov/pubmed/31069712 http://dx.doi.org/10.3758/s13428-019-01231-3 |
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