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Summer Sheremata, Sabrina Loftus; Fidelity of VSTM representations across the visual field reflects right-hemisphere specialization. Journal of Vision 2017;17(10):860. doi: 10.1167/17.10.860.
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Previous studies have demonstrated that hemispheric asymmetries in visual short-term memory (VSTM) processing give rise to differences in memory capacity across the visual field. However, correct performance on change detection tasks requires detecting large changes in stimuli and therefore cannot distinguish between two competing theories of the significance of these asymmetries. The first, the right-hemisphere specialization theory, states that the right-hemisphere allocates greater resources to remembering objects. This theory predicts greater precision for each object across set sizes in the left visual field. In contrast, the global processing theory suggests both hemispheres spread equal resources but the right hemisphere spreads them across a greater space or more objects, predicting equal or poorer precision for each object and decreasing precision as set size increases. By investigating the pattern of errors in VSTM, precision paradigms can measure the fidelity of each item held in memory and tease apart these two competing theories. We therefore performed a precision working memory task in which colored-squares were presented to either the left- or right- visual hemifield. The subjects were asked to report the color of a single cued square, by clicking a location on the color-wheel that represented the remembered color. Memory precision was measured for set sizes 3 and 4. There was greater precision in left visual field and no interaction with set size. Left-handed individuals did not show a consistent effect. Our findings demonstrate that the higher capacity for objects presented in the left visual hemifield occurs without a cost to the fidelity of the representation for each object. Together, these results link visual field asymmetries to a right-hemisphere specialization for VSTM processes.
Meeting abstract presented at VSS 2017
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