A remarkable aspect of human vision is our ability to recognize familiar objects across variations in size, viewpoint, and lighting. Moreover, people can recognize objects at different levels of classification: An object might be identified as a vehicle, acar, or a Honda Civic. However, little is known about the contribution of basic image-based features (e.g., curvature) as opposed to semantic features (e.g., wheels) in these visual recognition processes.Davitt et al. (2013,JEP:HPP)used eye tracking with novel objects to probe the role of shape-based features in object recognition and categorization. Participants were trained to recognize novel objects at either the basic (e.g., “car”) or the subordinate (e.g., “Honda Civic”) level. After training, they were tested on a sequential matching task, in which they were asked to determine whether two objects belonged to the same category (basic-level classification group) or were the same individual (subordinate-level classification group). Fixation patterns during the sequential matching task were compared against three algorithmically generated models based on either external-convex regions, external-concave regions, or internal part boundaries. Results showed fixation preferences for internal part boundaries and for concave over convex contours in both tasks. However, saccade amplitudes were shorter in basic-versus subordinate-level matching. These results suggest that although the same shape representations mediate basic-and subordinate-level recognition, the type of categorization task constrains the speed with which information in those representations is sampled