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A Bayesian hierarchical model for the measurement of working memory capacity

Morey, Richard D. ORCID: 2011. A Bayesian hierarchical model for the measurement of working memory capacity. Journal of Mathematical Psychology 55 (1) , pp. 8-24. 10.1016/

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Working memory is the memory system that allows for conscious storage and manipulation of information. The capacity of working memory is extremely limited. Measurements of this limit, and what affects it, are critical to understanding working memory. Cowan (2001) and Pashler (1988) suggested applying multinomial tree models to data from change detection paradigms in order to estimate working memory capacity. Both Pashler and Cowan suggested simple formulas for estimating capacity with these models. However, in many cases, these simple formulas are inadequate, and may lead to inefficient or biased estimation of working memory capacity. I propose a Bayesian hierarchical alternative to the Pashler and Cowan formulas, and show that the hierarchical model outperforms the traditional formulas. The models are easy to use and appropriate for a wide range of experimental designs. An easy-to-use graphical user interface for fitting the hierarchical model to data is available.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Uncontrolled Keywords: Hierarchical modeling; Bayesian methods; Hybrid Monte Carlo; Working memory; Working memory capacity; Multinomial models.
Publisher: Elsevier
ISSN: 0022-2496
Last Modified: 27 Oct 2022 10:07

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