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Elena Gorbunova, Kirill Kozlov; Perceptual similarity and working memory load in visual search for multiple targets. Journal of Vision 2018;18(10):1110. doi: https://doi.org/10.1167/18.10.1110.
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© ARVO (1962-2015); The Authors (2016-present)
Subsequent search misses (SSM) refer to the decrease in accuracy at detecting a second target after a first target has been found (Adamo et al., 2013). Two major explanations of this phenomenon assume perceptual bias and working memory overload created by the first target processing. Two experiments were conducted to reveal the interaction between the factors of perceptual similarity and working memory load. The participant's task was to search for the targets - rectangles with gaps on one predetermined side among distractors - rectangles with gaps on the other sides. On each trial, in could be two, one or no targets. Perceptual similarity for dual-target trials was manipulated as the number of shared features (color and size) in two targets. Working memory load was manipulated with the additional memorization task. The first experiment assumed memorizing the irrelevant shapes, whereas the second assumed memorizing the rectangles similar to visual search task stimuli. Three conditions were used: visual search alone (VS), working memory alone (WM) and combined visual search and working memory condition. In the VS task, the participants had to search for targets among distracters. In WM task, the participants performed a modified change-detection task with shape as the relevant feature: the shapes of the objects were displayed, then it was the retention interval, after which the shapes were displayed again and the subjects had to report if the shapes were equal to the initial ones or not. In the combined task, the participants had to memorize the shapes, conduct the VS task and then give the response to WM task. The visual search task and working memory task accuracy for each condition were compared. The results revealed the interaction between working memory load and perceptual similarity factors. Based on this data, we propose the integrative explanation of SSM errors.
Meeting abstract presented at VSS 2018
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