Bayes factors for state-trace analysis

Davis-Stober, Clintin P., Morey, Richard D., Gretton, Matthew and Heathcote, Andrew 2016. Bayes factors for state-trace analysis. Journal of Mathematical Psychology 72 , pp. 116-129. 10.1016/j.jmp.2015.08.004

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Abstract

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 Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	Elsevier
ISSN:	0022-2496
Last Modified:	09 Jul 2019 09:55
URI:	http://orca.cf.ac.uk/id/eprint/103448

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