Four experiments were performed to test whether the perceptual priming of face recognition would show invariance to changes in size, position, reflectional orientation (mirror reversal), and picture-plane rotation. In all experiments, subjects recognized faces in two blocks of trials; in the second block, some of the faces were identical to those in the first, and others had undergone metric transformations. The results show that subjects were equally fast to recognize faces whether or not the faces had changed in size, position, or reflectional orientation between the first and second presentations of the faces. In contrast, subjects were slower to recognize both faces and objects when they were planar-rotated between the first and second presentations. The results suggest that the same metric invariances are shown by both face recognition and basic-level object recognition.

The purpose of the present investigation was to determine whether the orientation between an object's parts is coded categorically for object recognition and physical discrimination. In three experiments, line drawings of novel objects in which the relative orientation of object parts varied by steps of 30º were used. Participants performed either an object recognition task, in which they had to determine whether two objects were composed of the same set of parts, or a physical discrimination task, in which they had to determine whether two objects were physically identical. For object recognition, participants found it more difficult to compare the 0º and 30º versions and the 90º and 60º versions of an object than to compare the 30º and 60º versions, but only at an extended interstimulus interval (ISI). Categorical coding was also found in the physical discrimination task. These results suggest that relative orientation is coded categoricallyfor both object recognition and physical discrimination, although metric information appears to be coded as well, especially at brief ISIs.

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Five experiments were conducted to determine how novice and expert drawers represent relative size for the purposes of drawing. Participants were shown images of two-part or three-part geometric figures composed of two spatially separated shapes. In each picture there was a small but noticeable relative-size difference between the constituent shapes (one part of the picture was always 25% larger than another part). Participants later drew the pictures from memory. The results showed that novice and expert drawers consistently exaggerated the relative size relationship between the shapes in the picture when attempting to draw it from memory and when copying (the 'caricature effect'), although the effect was reduced for the experts. The results are consistent with the idea that people represent size in memory using categorical descriptors (e.g., 'smaller than', 'larger than') rather than as precise metrics. Further, the results suggest that the process of becoming a skilled drawer may involve overcoming this categorical bias.

The purpose of the current study was to investigate people's ability to detect changes to familiar scenes. College students were asked either to identify what was wrong with a picture of a familiar location on their college campus (e.g., the library had been removed from the scene), or to estimate the difficulty of change detection for a hypothetical cohort performing the same task. Performance in the change-detection condition was extremely poor, even when changes were large. Participants who were familiar with the scenes and those who were unfamiliar with the scenes both overestimated the actual levels of change-detection performance. A follow-up analysis indicated that the participants who were unfamiliar with the scenes produced estimations of difficulty that were highly correlated with the mathematical area of the change, whereas participants who were familiar with the scenes produced estimations of difficulty that were highly correlated with the actual difficulty of change detection. The results indicate that people's visual long-term memory for familiar scenes lacks the precision to be able to effectively identify even large-scale changes, although subjectively people believe this should be relatively easy.