Categorical templates are more useful when features are consistent: Evidence from eye movements during search for societally important vehicles.

Unlike in laboratory visual search tasks-wherein participants are typically presented with a pictorial representation of the item they are asked to seek out-in real-world searches, the observer rarely has veridical knowledge of the visual features that define their target. During categorical search, observers look for any instance of a categorically defined target (e.g., helping a family member look for their mobile phone). In these circumstances, people may not have information about noncritical features (e.g., the phone's color), and must instead create a broad mental representation using the features that define (or are typical of) the category of objects they are seeking out (e.g., modern phones are typically rectangular and thin). In the current investigation (Experiment 1), using a categorical visual search task, we add to the body of evidence suggesting that categorical templates are effective enough to conduct efficient visual searches. When color information was available (Experiment 1a), attentional guidance, attention restriction, and object identification were enhanced when participants looked for categories with consistent features (e.g., ambulances) relative to categories with more variable features (e.g., sedans). When color information was removed (Experiment 1b), attention benefits disappeared, but object recognition was still better for feature-consistent target categories. In Experiment 2, we empirically validated the relative homogeneity of our societally important vehicle stimuli. Taken together, our results are in line with a category-consistent view of categorical target templates (Yu, Maxfield, & Zelinsky in, Psychological Science, 2016. doi: 10.1177/0956797616640237 ), and suggest that when features of a category are consistent and predictable, searchers can create mental representations that allow for the efficient guidance and restriction of attention as well as swift object identification.