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Shaul Hochstein, Marina Pavlovskaya, Yoram S. Bonneh, Nachum Soroker; Comparing set summary statistics and outlier pop out in vision. Journal of Vision 2018;18(13):12. doi: 10.1167/18.13.12.
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© ARVO (1962-2015); The Authors (2016-present)
Visual scenes are too complex to perceive immediately in all their details. Two strategies (among others) have been suggested as providing shortcuts for evaluating scene gist before its details: (a) Scene summary statistics provide average values that often suffice for judging sets of objects and acting in their environment. Set summary perception spans simple/complex dimensions (circle size, face emotion), various statistics (mean, variance, range), and separate statistics for discernible sets. (b) Related to set summary perception is detecting outliers from sets, called “pop out,” which allows rapid perception of presence and properties of unusual, and thus, possibly salient items in the scene. To understand better visual system mechanisms underlying these two set-related perceptual phenomena, we now study their properties and the relationship between them. We present observers with two clouds of bars with distributed orientations and ask them to discriminate their mean orientations, reporting which cloud is oriented more clockwise, on average. In the second experiment, the two clouds had the same mean orientation, but one had a bar with an outlier orientation, which observers detected. We find that cloud mean orientation discrimination depends mainly on the difference in means, whereas outlier detection depends mainly on the distance of the outlier orientation from the edge of the cloud orientation range. Neither percept depends largely on the range of the set itself. A unified model of a population-code mechanism underlying these two phenomena is discussed.
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