Choice of choice models: Theory of signal detectability outperforms Bradley-Terry-Luce choice model
- Published
- Accepted
- Subject Areas
- Mathematical Biology, Neuroscience, Psychiatry and Psychology, Statistics
- Keywords
- choice models, signat detection, generalized linear mixed models, binary probit, binary logistic, analysis of proprotions
- Copyright
- © 2018 Kornbrot et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Choice of choice models: Theory of signal detectability outperforms Bradley-Terry-Luce choice model. PeerJ Preprints 6:e26978v1 https://doi.org/10.7287/peerj.preprints.26978v1
Abstract
Identifying the best framework for two-choice decision-making has been a goal of psychology theory for many decades (Bohil, Szalma, & Hancock, 2015; Macmillan & Creelman, 1991). There are two main candidates: the theory of signal detectability (TSD) (Swets, Tanner Jr, & Birdsall, 1961; Thurstone, 1927) based on a normal distribution/probit function, and the choice-model theory (Link, 1975; Luce, 1959) that uses the logistic distribution/logit function. A probit link function, and hence TSD, was shown to have a better Bayesian Goodness of Fit than the logit function for every one of eighteen diverse psychology data sets (Open-Science-Collaboration, 2015a), conclusions having been obtained using Generalized Linear Mixed Models (Lindstrom & Bates, 1990; Nelder & Wedderburn, 1972) . These findings are important, not only for the psychology of perceptual, cognitive and social decision-making, but for any science that use binary proportions to measure effectiveness, as well as the meta-analysis of such studies.
Author Comment
This is a submission to PeerJ for review.
