A quantitative confidence signal detection model: 1. Fitting psychometric functions

Journal of Neurophysiology
Yongwoo Yi, Daniel M Merfeld

Abstract

Perceptual thresholds are commonly assayed in the laboratory and clinic. When precision and accuracy are required, thresholds are quantified by fitting a psychometric function to forced-choice data. The primary shortcoming of this approach is that it typically requires 100 trials or more to yield accurate (i.e., small bias) and precise (i.e., small variance) psychometric parameter estimates. We show that confidence probability judgments combined with a model of confidence can yield psychometric parameter estimates that are markedly more precise and/or markedly more efficient than conventional methods. Specifically, both human data and simulations show that including confidence probability judgments for just 20 trials can yield psychometric parameter estimates that match the precision of those obtained from 100 trials using conventional analyses. Such an efficiency advantage would be especially beneficial for tasks (e.g., taste, smell, and vestibular assays) that require more than a few seconds for each trial, but this potential benefit could accrue for many other tasks.

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Citations

Jun 24, 2016·Journal of Neurophysiology·Benjamin T Crane
Dec 24, 2019·The Spanish Journal of Psychology·Miguel Ángel García-Pérez, Rocío Alcalá-Quintana
Mar 27, 2020·Journal of Neurophysiology·Koeun LimDaniel M Merfeld
Sep 7, 2018·Psychonomic Bulletin & Review·Elizabeth J Saccone, Philippe A Chouinard
May 3, 2019·Psychonomic Bulletin & Review·Elizabeth J SacconePhilippe A Chouinard
Jun 20, 2019·Journal of Neurophysiology·Yongwoo YiDaniel M Merfeld
Jan 24, 2018·Journal of Neurophysiology·Torin K ClarkDaniel M Merfeld
Jun 5, 2021·Neuroscience·Ana Diaz-Artiles, Faisal Karmali

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