Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Bayes factors for state-trace analysis

Davis-Stober, Clintin P., Morey, Richard D. ORCID: https://orcid.org/0000-0001-9220-3179, 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

Full text not available from this repository.

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: 02 Nov 2022 11:53
URI: https://orca.cardiff.ac.uk/id/eprint/103448

Citation Data

Cited 14 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item