One-shot viewpoint invariance in matching novel objects

Humans often evidence little difficulty at recognizing objects from arbitrary orientations in depth. According to one class of theories, this competence is based on generalization from templates specified by metric properties (MPs), that were learned for the various orientations. An alternative class of theories assumes that non-accidental properties (NAPs) might be exploited so that even novel objects can be recognized under depth rotation. After scaling MP and NAP differences so that they were equally detectable when the objects were at the same orientation in depth, the present investigation assessed the effects of rotation on same-different judgments for matching novel objects. Judgments of a sequential pair of images of novel objects, when rendered from different viewpoints, revealed relatively low costs when the objects differed in a NAP of a single part, i.e. a geon. However, rotation dramatically reduced the detectability of MP differences to a level well below that expected by chance. NAPs offer a striking advantage over MPs for object classification and are therefore more likely to play a central role in the representation of objects.