A Computational Approach to Search in Visual Working Memory (original) (raw)

Search in visual working memory (VWM) is an important process in everyday life, but it has so far not been investigated sufficiently using psychophysical and computational approaches. Here, we examine whether computational process models from the study of VWM and of visual search also apply to search in VWM. Participants performed a twoalternative VWM-based color localization task in which set size and color similarity were varied. We tested models with a resource-limited encoding stage and a Bayesian decision stage. We found that a variable-precision model fit the data well and that adding an item limit did not improve the fit. Next, we took a resource-rational approach, in which mean precision at each set size is set by optimizing a combination between behavioral performance and encoding cost. We found that a resource-rational model with an ad-hoc cost function captures behavior well, but not better than the best non-resource-rational model. Our results, albeit unsurprising, contribute to the evidence that search in VWM is very similar to search in perception.