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Dawn Sarno, Mark Neider; The Capacity for Depth: Working Memory in Multiple Depth Planes. Journal of Vision 2018;18(10):701. doi: 10.1167/18.10.701.
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The extent to which depth information engenders benefits to attentional processing is inconsistent across a variety of tasks (e.g., visual search, multiple-object tracking, working memory). One factor that may underlie disparate findings regarding depth is working memory load. In the present studies, we systematically manipulated working memory load to determine how distributing visual information across multiple depth planes affects overall memory capacity. In two experiments, participants performed a change detection task where they determined whether a change occurred in an array of colored cubes. In Experiment 1 items appeared in one or two depth planes. Targets either appeared in their own depth plane, or in the same depth plane as distractors across seven set sizes (2-8). The results indicated a 9% decrement in accuracy for multiple depth planes at set size 3, but a 6% benefit for multiple depth planes at set size 5. In Experiment 2, we examined how the distribution of items in multiple depth planes affects performance. Targets either appeared in their own depth plane or items were evenly distributed between two depth planes. In order to evenly distribute items only set sizes 4, 6, and 8 were used. Improved accuracy (7%) was found for displays with items evenly distributed compared to arrays where the target was in its own depth at set size 8. Interestingly, in both experiments participants exhibited working memory benefits associated with depth when the number of items in the display exceeded average maximum capacity (~4 items in both experiments as measured by Cohen's K). Overall, these results suggest that when working memory load is below max capacity depth information is not useful. However, when memory load exceeds max capacity participants can utilize depth information to bolster memory processes, and effectively increase capacity.
Meeting abstract presented at VSS 2018
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