Learned feature variance is encoded in the target template and drives visual search.

Real world visual search targets are frequently imperfect perceptual matches to our internal target templates. For example, the same friend on different occasions is likely to wear different clothes, hairstyles, and accessories, but some of these may be more likely to vary than others. The ability to deal with template-to-target variability is important to visual search in natural environments, but we know relatively little about how feature variability is handled by the attentional system. In these studies, we test the hypothesis that top-down attentional biases are sensitive to the variance of target feature dimensions over time and prioritize information from less-variable dimensions. On each trial, subjects were shown a target cue composed of colored dots moving in a specific direction followed by a working memory probe (30%) or visual search display (70%). Critically, the target features in the visual search display differed from the cue, with one feature drawn from a distribution narrowly centered over the cued feature (low-variance dimension), and the other sampled from a broader distribution (high-variance dimension). The results demonstrate that subjects used knowledge of the likely cue-to-target variance to set template precision and bias attentional selection. Moreover, an individual's working memory precision for each feature predicted search performance. Our results suggest that observers are sensitive to the variance of feature dimensions within a target and use this information to weight mechanisms of attentional selection.