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Doug Barrett, Oliver Zobay; Stochastic noise decreases the accuracy of distractor rejection in dual- compared to single-target search. Journal of Vision 2016;16(12):751. doi: 10.1167/16.12.751.
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© 2017 Association for Research in Vision and Ophthalmology.
Simultaneous search for two targets is slower and less accurate than independent searches for the same two targets. Within the SDT framework, this can be attributed to the division of attentional resources across multiple stimulus sets during search (Palmer, Ames & Lindsey, 1993). The current study used one or two cues to elicit single- and dual-target searches in single-fixation and free view displays. Landholt's Cs were used to group display objects into leftward and rightward facing sets. The angle of separation between the target and distractors in each set was adjusted to observers' 80% correct thresholds during a single-target pre-test at a set size of 1. In Experiment 1, the accuracy of "yes-no" judgements was compared on single- and dual-target searches at set-sizes of 1, 2 and 4. The data revealed a reduction in accuracy consistent with an increase in decision-noise in dual- compared to single-target searches. In Experiment 2, the accuracy and latency of observers' initial fixations in single- and dual-target searches were compared. Fixations on single-target searches were highly selective towards the target. The probability of fixating distractors facing the same way as the target was also significantly higher than for those facing the opposite direction. On dual-target searches, fixations were significantly less accurate then on single-target searches. The probability of fixating leftward and rightward facing distractors was also comparable, indicating competition for selection from both sets of objects during dual-target search. These results suggest the dual-target cost reflects a decrease in the accuracy of distractor rejection when objects in the display are compared against multiple representations in visual working memory.
Meeting abstract presented at VSS 2016
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