Work

Parsimonious estimation of signal detection models from confidence ratings

Year: 2019

Type: article

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.

Source: Behavior Research Methods

DOI: 10.3758/s13428-019-01231-3

Authors: Ravi Selker, Don van den Bergh, Amy H. Criss, Eric-Jan Wagenmakers

Institutions: University of Amsterdam, Syracuse University

Language: English

Topic: Visual perception and processing mechanisms

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $2,690

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek