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Designing state-trace experiments to assess the number of latent psychological variables underlying binary choices

Citation

Hawkins, G and Prince, M and Brown, S and Heathcote, A, Designing state-trace experiments to assess the number of latent psychological variables underlying binary choices, Cognition in Flux: Proceedings of the 32nd Annual Meeting of the Cognitive Science Society, 11-14 August, 2010, Portland, Oregon ISBN 978-0-9768318-6-0 (2010) [Refereed Conference Paper]


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Official URL: http://mindmodeling.org/cogsci2010/cogsci10_procee...

Abstract

State-trace analysis is a non-parametric method that can identify the number of latent variables (dimensionality) required to explain the effect of two or more experimental factors on performance. Heathcote, Brown & Prince (submitted) recently proposed a Bayes Factor method for estimating the evidence favoring one or more than one latent variable in a state-trace experiment, known as Bayesian Ordinal Analysis of State-Traces (BOAST). We report results from a series of simulations indicating that for larger sample sizes BOAST performs well in identifying dimensionality for single and multiple latent variable models. A method of group analysis convenient for smaller sample sizes is presented with mixed results across experimental designs. We use the simulation results to provide guidance on designing state-trace experiments to maximize the probability of correct classification of dimensionality.

Item Details

Item Type:Refereed Conference Paper
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Decision Making
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Psychology and Cognitive Sciences
Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:98955
Year Published:2010
Deposited By:Medicine (Discipline)
Deposited On:2015-03-10
Last Modified:2016-02-29
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