Does the same basic-level advantage commonly observed in the categorization literature also hold for targets in a search task? We answered this question by first conducting a category verification task to define a set of categories showing a standard basic-level advantage, which we then used as stimuli in a search experiment. Participants were cued with a picture preview of the target or its category name at either superordinate, basic, or subordinate levels, then shown a target-present/absent search display. Although search guidance and target verification was best using pictorial cues, the effectiveness of the categorical cues depended on the hierarchical level. Search guidance was best for the specific subordinate level cues, while target verification showed a standard basic-level advantage. These findings demonstrate different hierarchical advantages for guidance and verification in categorical search. We interpret these results as evidence for a common target representation underlying categorical search guidance and verification.

The role of target typicality in a categorical visual search task was investigated by cueing observers with a target name, followed by a five-item target present/absent search array in which the target images were rated in a pretest to be high, medium, or low in typicality with respect to the basic-level target cue. Contrary to previous work, we found that search guidance was better for high-typicality targets compared to low-typicality targets, as measured by both the proportion of immediate target fixations and the time to fixate the target. Consistent with previous work, we also found an effect of typicality on target verification times, the time between target fixation and the search judgment; as target typicality decreased, verification times increased. To model these typicality effects, we trained Support Vector Machine (SVM) classifiers on the target categories, and tested these on the corresponding specific targets used in the search task. This analysis revealed significant differences in classifier confidence between the high-, medium-, and low-typicality groups, paralleling the behavioral results. Collectively, these findings suggest that target typicality broadly affects both search guidance and verification, and that differences in typicality can be predicted by distance from an SVM classification boundary.

A generative model of category representation is introduced that uses computer vision methods to extract category-consistent features (CCFs) directly from images of category exemplars. The model was trained on 4800 images of common objects, and CCFs were obtained for 68 categories spanning subordinate, basic, and superordinate levels in a category hierarchy. Participants searched for these same categories. Targets cued at the subordinate level were preferentially fixated, but fixated targets were verified faster following a basic-level cue. The subordinate-level advantage in guidance is explained by the number of target category CCFs, a measure of category specificity that decreases with movement up the category hierarchy. The basic-level advantage in verification is explained by multiplying CCF number by sibling distance, a measure of category distinctiveness. With this model the visual representations of real-world object categories, each learned from the vast numbers of image exemplars accumulated throughout our everyday experience, can finally be studied.

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