WoMMBAT: A user interface for hierarchical Bayesian estimation of working memory capacity

The change detection paradigm has become an important tool for researchers studying working memory. Change detection is especially useful for studying visual working memory, because recall paradigms are difficult to employ in the visual modality. Pashler (Perception & Psychophysics, 44, 369–378, 1988) and Cowan (Behavioral and Brain Sciences, 24, 87–114, 2001) suggested formulas for estimating working memory capacity from change detection data. Although these formulas have become widely used, Morey (Journal of Mathematical Psychology, 55, 8–24, 2011) showed that the formulas suffer from a number of issues, including inefficient use of information, bias, volatility, uninterpretable parameter estimates, and violation of ANOVA assumptions. Morey presented a hierarchical Bayesian extension of Pashler’s and Cowan’s basic models that mitigates these issues. Here, we present WoMMBAT (Working Memory Modeling using Bayesian Analysis Techniques) software for fitting Morey’s model to data. WoMMBAT has a graphical user interface, is freely available, and is cross-platform, running on Windows, Linux, and Mac operating systems.