The role of sequence order in determining view canonicality for novel wire-frame objects.

Objects are best recognized from so-called "canonical" views. The characteristics of canonical views of arbitrary objects have been qualitatively described using a variety of different criteria, but little is known regarding how these views might be acquired during object learning. We address this issue, in part, by examining the role of object motion in the selection of preferred views of novel objects. Specifically, we adopt a modeling approach to investigate whether or not the sequence of views seen during initial exposure to an object contributes to observers' preferences for particular images in the sequence. In two experiments, we exposed observers to short sequences depicting rigidly rotating novel objects and subsequently collected subjective ratings of view canonicality (Experiment 1) and recall rates for individual views (Experiment 2). Given these two operational definitions of view canonicality, we attempted to fit both sets of behavioral data with a computational model incorporating 3-D shape information (object foreshortening), as well as information relevant to the temporal order of views presented during training (the rate of change for object foreshortening). Both sets of ratings were reasonably well predicted using only 3-D shape; the inclusion of terms that capture sequence order improved model performance significantly.