Hierarchical Bayesian measurement models for continuous reproduction of visual features from working memory

Abstract

The article presents Bayesian hierarchical modeling frameworks for two measurement models for visual working memory. The models can be applied to the distributions of responses on a circular feature dimension, as obtained with the continuous reproduction (a.k.a. delayed estimation) task. The first measurement model is a mixture model that describes the response distributions as a mixture of one (Zhang & Luck, 2008) or several (Bays, Catalao, & Husain, 2009) von-Mises distribution(s) and a uniform distribution. The second model is a novel, interference-based measurement model. We present parameter recovery simulations for both models, demonstrating that the hierarchical framework enables precise parameter estimates when a small number of trials are compensated by a large number of subjects. Simulations with the mixture model show that the Bayesian hierarchical framework minimizes the previously observed estimation bias for memory precision in conditions of low performance. Unbiased and reasonably precise parameter estimates can also be obtained from the interference measurement model, though some parameters of this model demand a relatively large amount of data for precise measurement. Both models are applied to two experimental data sets. Experiment 1 measures the effect of memory set size on the model parameters. Experiment 2 provides evidence for the assumption in the interference model that the target feature tends to be confused with features of those nontargets that are close to the target on the dimension used as retrieval cue.

Recall cues interfere with retrieval from visuospatial working memory

“…Plots for target response in the first line, for non‐target responses in the second line, for guessing responses in the third line, and for precision in the bottom line. Error bars represent highest density interval from a sample of representative values, estimated as shortest credible interval (Oberauer et al ., ). [Colour figure can be viewed at wileyonlinelibrary.com]…”

Is refreshing in working memory impaired in older age? Evidence from the retro‐cue paradigm

“…Simulation work has shown that nonhierarchical modeling requires at least double this amount of trials to recover unbiased model estimates . Recent modeling work, however, indicated that hierarchical models do accurately recover parameters with a low number of trials as used here …”

“…The hierarchical implementation reported here only included the two‐parameter model version. We also fitted our data using the hierarchical three‐parameter model implemented by Oberauer and colleagues . Evaluation of the posterior of the estimated parameters indicated that aging mainly affected the guessing parameter (the results are available on the OSF).…”

Where to attend next: guiding refreshing of visual, spatial, and verbal representations in working memory

“…We note, however, that there is an ongoing debate regarding how to best model responses in continuous reproduction tasks . The mixture model used here, although one of the most popular, may not be the best descriptor of all of the cognitive processes involved in performance in this task . Hence, we caution against overinterpreting the psychological meaning of the estimated parameters.…”