A Change Localization Benefit for Mixed Arrays Over Uniform Arrays

Given relatively sharp capacity limits in visual working memory (WM), there has been sustained interest in whether these limits are based on the number of individuated items stored, or based on interference that varies as a function of inter-item similarity. Object-based models predict that the costs of concurrent storage will be determined only by the total number of items stored, while feature-based models of capacity often argue that competition for feature-specific resources is the key limiting factor. To examine this question we manipulated both the number of stimuli within each memory array, as well as the similarity between those items. Thus, we measured visual WM performance with uniform arrays with only one type of feature (e.g. color), or mixed arrays with two feature-types (e.g. color and orientation). Consistent with object-based models, we observed a significant cost of increasing load in both the mixed and uniform conditions. However, we also saw evidence for a modest advantage in the mixed array conditions, a difference that is not predicted by pure object-based models. Our follow-up work examined whether the advantage in the mixed condition reflects differences in whether items are stored, or differences in memory fidelity. We found that this advantage indeed reflected a change in memory fidelity, specifically a better precision in memory of items in the mixed arrays than in uniform arrays. Additionally, there was no evidence that participants were storing a larger number of items in the mixed condition. These results suggest that working memory performance is subject to item-based limits independent of item similarity, but the fidelity of those memories can be shaped by inter-item similarity.