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Jing Xu, Kirk Ballew, Alejandro Lleras, Simona Buetti; Do people’s visual ability skill predict search efficiency under difficult search conditions?. Journal of Vision 2019;19(10):233b. doi: https://doi.org/10.1167/19.10.233b.
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Richler, Wilmer and Gauthier (2017) proposed a Novel Object Recognition Test (NOMT) to measure people’s ability to identify a novel visual object among other visual objects that closely resemble it. This task indexes a global visual ability involving making fine comparisons between a stored representation in memory and visual objects that closely resemble it. Across studies, it shows good reliability and is a measure distinct from other psychometric measures like IQ and working memory capacity. We hypothesized that the comparison made between the mental representation and the visual objects in the NOMT test might rely on the same resource that is used in inefficient visual search. In inefficient visual search, people compare each distractor in the display with the target template in mind and reject the disitractors until they find the target. Previously work found no meaningful correlation between people’s NOMT scores and their efficiency in a fixed-target parallel visual search task (Xu, Lleras, and Buetti, 2018). There is reason to believe that different results might be observed in inefficient search. Indeed, Alvarez and Cavanagh (2004) showed that there is a correlation between the efficiency at which participants can search through stimuli of a given category and the number of objects of that category that they can hold in short term memory. This suggests that the efficiency in inefficient visual search might be related to a participant’s ability to do fine visual discriminations. The current study investigated this possibility using an individual differences approach. We measured people’s visual search ability under two very difficult search conditions as well as participants’ NOMT score and their feature-VSTM capacity to control for gross (non-fine) visual working memory ability. The findings help clarify the different roles feature-VSTM and visual skill play in predicting performance in visual search.
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