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Drew Walker, Timothy Lew; Decreases in Variance are Detected Better than Inceases in Variance. Journal of Vision 2015;15(12):844. doi: https://doi.org/10.1167/15.12.844.
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© ARVO (1962-2015); The Authors (2016-present)
Although many investigations of visual summary representations (“ensemble statistics”) have focused on how people compute the central tendency of stimuli such as average set size (e.g. Ariely, 2001), orientation (e.g. Parks, et al., 2001), or facial emotion (e.g. Haberman & Whitney, 2009), less attention has been given to representations of set heterogeneity. People rapidly extract set variance (Michael, et al., 2013), and the variance of a set affects how ensembles are averged (Corbett et al., 2012; Fouriezos et al., 2008; Im & Halberda, 2013). We investigated the ability to detect changes in the variance of circle sizes across sets, using a staircase algorithm. On each trial subjects (n = 23) were presented first with a pedestal display of circles followed by a test display, and had to judge if the variance of the circle sizes (the logarithm of the circle diameter) of the test display was the same as the pedestal set, or if it had changed (the mean was held constant). In one block of 200 trials the changed test variance increased compared to the pedestal variance, while in the other block of 200 trials the changed test variance decreased compared to the pedestal (block order was counterbalanced). We found that people could detect smaller differences between the pedestal and test variance when the variance had decreased, compared to equivalent changes when the variance increased.
Meeting abstract presented at VSS 2015
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