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Bayesian analyses of cognitive architecture


Houpt, JW and Heathcote, A and Eidels, A, Bayesian analyses of cognitive architecture, Psychological Methods, 22, (2) pp. 288-303. ISSN 1082-989X (2017) [Refereed Article]

Copyright Statement

2017 American Psychological Association

DOI: doi:10.1037/met0000117


The question of cognitive architecture-how cognitive processes are temporally organized-has arisen in many areas of psychology. This question has proved difficult to answer, with many proposed solutions turning out to be spurious. Systems factorial technology (Townsend & Nozawa, 1995) provided the first rigorous empirical and analytical method of identifying cognitive architecture, using the survivor interaction contrast (SIC) to determine when people are using multiple sources of information in parallel or in series. Although the SIC is based on rigorous nonparametric mathematical modeling of response time distributions, for many years inference about cognitive architecture has relied solely on visual assessment. Houpt and Townsend (2012) recently introduced null hypothesis significance tests, and here we develop both parametric and nonparametric (encompassing prior) Bayesian inference. We show that the Bayesian approaches can have considerable advantages.

Item Details

Item Type:Refereed Article
Keywords:mental architecture, human information processing, survivor interaction contrast, nonparametric, Bayesian
Research Division:Psychology
Research Group:Cognitive and computational psychology
Research Field:Memory and attention
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:113334
Year Published:2017
Web of Science® Times Cited:9
Deposited By:Psychology
Deposited On:2016-12-21
Last Modified:2018-07-25

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