- Title
- Bayes factors for state-trace analysis
- Creator
- Davis-Stober, Clinton P.; Morey, Richard D.; Gretton, Matthew; Heathcote, Andrew
- Relation
- ARC.DP110100234 & ARC.DP120102907
- Relation
- Journal of Mathematical Psychology Vol. 72, Issue June, p. 116-129
- Publisher Link
- http://dx.doi.org/10.1016/j.jmp.2015.08.004
- Publisher
- Academic Press
- Resource Type
- journal article
- Date
- 2016
- Description
- 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.
- Subject
- monotonicity; state-trace; convex hulls; order-constrained inference
- Identifier
- http://hdl.handle.net/1959.13/1318787
- Identifier
- uon:23688
- Identifier
- ISSN:0022-2496
- Language
- eng
- Reviewed
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