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Jiehui Qian, Shengxi Lei, Ke Zhang, Quan Lei; Storage unit in visual working memory depends on the visual information load of a memory display. Journal of Vision 2018;18(10):1296. doi: 10.1167/18.10.1296.
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Visual working memory (VWM) is a cognitive memory buffer for temporarily processing and storing visual information. Previous studies suggest that its capacity is severely limited, and there is an ongoing debate on whether the capacity is fixed or flexible depending on the complexity of the items retained in the VWM. In the present study, a change detection task was employed to investigate whether and how the visual information load can affect VWM, specifically, its capacity and the unit of storage. Information load was manipulated through the set size and the complexity of memory items. We tested two types of stimuli: the single-feature type where each item in the memory array was composed of a single feature (color/shape), and the conjunctive-feature type where each item was composed of a conjunction of two features (color & shape). We varied perceptual complexity by using different types of feature and the demanded resolution of representation. Experiment 1 replicated the previous findings that the memory capacity for color is larger than shape, and capacity in VWM decreased as the resolution demand increased (i.e., changes were more subtle to detect) regardless of the type of feature tested. In Experiment 2, we analyzed and compared the results from single-feature objects and conjunctive-feature objects in the low- and high- resolution conditions while controlling for the number of to-be-remembered features. By directly matching the estimated capacity based on object-unit and feature-unit with the theoretical prediction, the results showed that the unit of storage in VWM tended to be feature-based if visual information load was low, and to be object-based if information load was high. This suggests that visual working memory is dynamic and flexible, dependent on the load of the current task.
Meeting abstract presented at VSS 2018
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