A theory of variability discrimination: Finding differences

Visual variability discrimination requires an observer to categorize collections of items on the basis of the variability in the collection; such discriminations may be vital to the adaptive actions of both humans and other animals. We present a theory of visual variability discrimination that aggregates localized differences between nearby items, and we compare this finding differences model with a previously proposed positional entropy model across several data sets involving both people and pigeons. We supplement those previously published data sets with four new experiments, three of which involve arrays comprising items entailing systematic, quantitative differences. Although both theories provide strong and similar fits of the published data sets, only the finding differences model is applicable to investigations involving quantitative item differences, providing excellent fits in these new experiments.