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Katherine Wood, Daniel Simons; Change detection and recognition memory for objects. Journal of Vision 2016;16(12):45. doi: 10.1167/16.12.45.
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© ARVO (1962-2015); The Authors (2016-present)
How can we reconcile exceptional memory for images with findings of change blindness? Although people can remember thousands of scenes or objects with remarkable precision, they often fail to detect changes to small sets of the same objects. We explored whether people can use their detailed long-term memory representations to improve change detection performance. Participants first studied a set of objects and then performed both recognition and change detection tasks for those images. As expected, participants performed better in a two-alternative recognition task than a six-alternative one, although performance was not as accurate as expected based on other recent studies of object memory. In a one-shot change detection task with arrays of six objects, participants performed no better when the presence of a single familiar object in the post-change display indicated the location of the change than when all objects were unfamiliar and provided no additional information about the change; they did not spontaneously use long-term memory to enhance change detection, and in both cases, change detection performance was worse than recognition memory performance. Even when told that any familiar object appearing in the post-change array would be the changed object, meaning that participants could rely exclusively on long-term recognition memory to complete the task, they performed no better than in a change detection task when all items were unfamiliar. When given an explicit strategy to search for a familiar object as a way to improve performance on the change detection task, they performed no better than in the six-alternative recognition memory task. Participants appear unable to combine information from short-term change detection and long-term recognition memory to increase response accuracy beyond that of either task alone.
Meeting abstract presented at VSS 2016
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