RT Journal Article
A1 Walker, Drew
A1 Lew, Timothy
T1 Decreases in Variance are Detected Better than Inceases in Variance
JF Journal of Vision
JO Journal of Vision
YR 2015
DO 10.1167/15.12.844
VO 15
IS 12
SP 844
OP 844
SN 1534-7362
AB 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
RD 6/12/2021
UL https://doi.org/10.1167/15.12.844