Parsimonious estimation of signal detection models from confidence ratings

Ravi Selker, Don van den Bergh, Amy Criss, Eric Jan Wagenmakers

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalBehavior Research Methods
DOIs
StatePublished - Jan 1 2019

Fingerprint

Aptitude
Experimental Psychology
Cognition
Learning
Psychological Signal Detection
Signal Detection
Rating
Confidence
Signal Detection Theory
Stimulus

Keywords

  • Bayesian hierarchical models
  • Confidence ratings
  • Signal detection theory

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)

Cite this

Parsimonious estimation of signal detection models from confidence ratings. / Selker, Ravi; van den Bergh, Don; Criss, Amy; Wagenmakers, Eric Jan.

In: Behavior Research Methods, 01.01.2019.

Research output: Contribution to journalArticle

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