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Joshua Eayrs, Nilli Lavie; Individual differences in subitizing range predict visual detection ability.. Journal of Vision 2016;16(12):422. https://doi.org/10.1167/16.12.422.
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© ARVO (1962-2015); The Authors (2016-present)
We used the load theory framework to test the hypothesis that an individual's 'subitizing' range (the number of items whose estimate can be made rapidly without serial counting) can be used to measure visual perception capacity. Previous findings that loading perceptual capacity results in reduced visual detection ability (Carmel et al. 2011, Macdonald & Lavie, 2008, Raveh & Lavie, 2014) led us to predict that a lower subitizing capacity would be associated with lower visual detection abilities. We tested this prediction in two experiments assessing individual differences in subitizing capacity and in visual detection tasks using the Inattentional Blindness and Change Blindness paradigms. In both experiments, participants performed a visual enumeration task consisting of estimating the number of randomly positioned black squares (from 1 to 9) presented briefly on-screen. In Experiment 1 participants also performed a modified Inattentional Blindness task (judging cross arm length while also detecting the occasional appearance of a small square shape). In Experiment 2 participants performed a change detection task requiring them to detect changes between flickering natural scene images (e.g. Rensink et al. 1997) in addition to the enumeration task. An individual's breakpoint from subitizing to counting (their subitizing range) was positively correlated with detection sensitivity in both the Inattentional Blindness (r= .31) and Change Blindness (r= .42) tasks. Multilevel regression demonstrated that subitizing range significantly predicts detection when controlling for counting accuracy in set sizes beyond the subitizing range. We conclude that an individual's subitizing range (as measured by the breakpoint of switching to counting) can provide an effective measure of their visual perception capacity and can be used to predict their visual detection ability.
Meeting abstract presented at VSS 2016
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