Mutual interference in working memory updating: A hierarchical Bayesian model
We built a hierarchical Bayesian model for the working memory updating task. This model jointly accounts for both responses and reaction times in the memory updating paradigm, which is a commonly used paradigm to measure working memory capacity. To model responses, we adopted a mutual interference framework from Oberauer & Kliegl (2006) that characterized activation levels of working memory items, and extended this framework into a Markov chain structure to characterize a wider range of responses. To model reaction times, we adopted a Wald diffusion framework where the Wald parameters were associated with activation levels of working memory items. This model allows us to investigate the mechanism underlying participant performance in the memory updating task under a joint theoretical framework. We applied this model to an empirical data set investigating the effects of working memory training. Modeling results revealed that training might not improve overall working memory capacity, but may lead to a general improvement in the speed of processing.
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