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Bayes factors for state-trace analysis
journal contribution
posted on 2023-05-18, 18:15 authored by Davis-Stober, CP, Morey, RD, Gretton, M, Heathcote, AState-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.
History
Publication title
Journal of Mathematical PsychologyVolume
72Pagination
116-129ISSN
0022-2496Department/School
School of Psychological SciencesPublisher
Academic Press Inc Elsevier SciencePlace of publication
United StatesRights statement
© 2015 Elsevier Inc. All rights reserved.Repository Status
- Restricted