Encoding-specificity instead of online integration of real-world spatial regularities for objects in working memory

In daily life scenarios, most objects are not independent of each other; rather, they show a high degree of spatial regularity (e.g., beach umbrellas appear above beach chairs, not under them). Previous studies have shown a benefit of spatial regularities in visual working memory (VWM) performance of real-world objects, termed the spatial regularity effect. However, the mechanisms underlying this effect remain unclear. The spatial regularity effect can be explained by an “encoding-specificity” hypothesis or a “perception-alike” hypothesis. The former suggests that spatial regularity will enhance the visual encoding process but will not operate in information integration during VWM maintenance, while the latter suggests that spatial regularity will play a role in both the visual encoding and VWM maintenance processes. We tested these two hypotheses by investigating whether VWM integrates sequentially presented real-world objects by focusing on the existence of the spatial regularity effect. In Experiment 1, we manipulated the presentation (simultaneous vs. sequential) and regularity (with vs. without regularity) of memory arrays among pairs of real-world objects. The spatial regularity of memory objects improved the VWM performance in simultaneous presentation trials, but not in sequential presentation trials. In Experiment 2, we examined whether overburdened memory load hindered the spatial regularity effect in sequential presentation trials. We again found an absence of the spatial regularity effect, regardless of the memory load. These results suggest that participants were unable to integrate real-world objects into pairs based on spatial regularity during the VWM maintenance process. Therefore, the present results support the “encoding-specificity” hypothesis, implying that although the spatial regularity of real-world objects can enhance the efficiency of the encoding process in VWM, VWM cannot exploit spatial regularity to help organize sampled sequential information into meaningful groups.