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Bayes factors for state-trace analysis


Davis-Stober, CP and Morey, RD and Gretton, M and Heathcote, A, Bayes factors for state-trace analysis, Journal of Mathematical Psychology, 72 pp. 116-129. ISSN 0022-2496 (2016) [Refereed Article]

Copyright Statement

2015 Elsevier Inc. All rights reserved.

DOI: doi:10.1016/


State-trace methods have recently been advocated for exploring the latent dimensionality of psychological processes. These methods rely on assessing the monotonicity of a set of responses embedded within a state-space. Prince et al. (2012) proposed Bayes factors for state-trace analysis, allowing the assessment of the evidence for monotonicity within individuals. Under the assumption that the population is homogeneous, these Bayes factors can be combined across participants to produce a "group" Bayes factor comparing the monotone hypothesis to the non-monotone hypothesis. However, combining information across individuals without assuming homogeneity is problematic due to the nonparametric nature of state-trace analysis. We introduce group-level Bayes factors that can be used to assess the evidence that the population is homogeneous vs. heterogeneous, and demonstrate their utility using data from a visual change-detection task. Additionally, we describe new computational methods for rapidly computing individual-level Bayes factors.

Item Details

Item Type:Refereed Article
Keywords:monotonicity, state-trace, convex hulls, order-constrained inference
Research Division:Psychology
Research Group:Other psychology
Research Field:Other psychology not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:107776
Year Published:2016
Web of Science® Times Cited:11
Deposited By:Psychology
Deposited On:2016-03-23
Last Modified:2018-03-22

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