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Rebecca Goldstein, W. Joseph Delaune, Melissa Beck; Can visual working memory capture result in long-term memory representations of irrelevant features?. Journal of Vision 2016;16(12):1015. doi: 10.1167/16.12.1015.
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© 2017 Association for Research in Vision and Ophthalmology.
The contents of visual working memory (VWM) can capture attention during visual search (Dowd & Mitroff, 2013) and a consistent target can lead to a long-term memory (LTM) representation that can capture attention (Carlisle et al, 2011). Can information attended during VWM capture lead to a LTM representation that can capture attention? Participants completed 70 training trials in which a colored shape was held in VWM while searching for a rotated N among upright Ns. All search items were placed on top of larger colored shapes, and one of the distractors was on a colored shape that matched the color, but not the shape, of the VWM item. Although the color of the VWM item changed on each trial, the shape of the VWM color matched distractor was the same on every training trial. To test if the consistent shape of the color matched distractor was learned and could affect search, 15 test trials were completed in which the consistent shape was present as a distractor, but was not the same color as the item in VWM (no color match). The remaining 15 test trials were the same as the training trials (color match). The consistent distractor was fixated faster in the color match compared to the no color match test trials. Furthermore, the consistent shape distractor was fixated more often and longer than a random distractor in the color match, but not the no color match, test trials. These results demonstrate that the contents of VWM capture attention, but the shape associated with the attended color does not lead to a LTM representation that captures attention. This may be due to the lack of bound shape-color representations in VWM, and different results may be found with features that are more readily bound (e.g., color and location).
Meeting abstract presented at VSS 2016
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