On the relative dominance of global and local shape features in generalization: Moderating variables and general principles

Abstract

There is a long-standing debate concerning the holistic or analytic nature of perceptual processing and whether the representation builds up from global to local or from local to global. Despite many fruitful contributions demonstrating the global and local levels in object recognition, little systematic attention has been directed at the local-global dominance in generalization and categorization. We investigated the global and local dominance of visual cues in a name generalization task. Two obvious features, one on a global scale and one on a more local scale, were implemented in the stimulus set. Stimuli were always 2-D shapes, generated by Boundary Descriptors. Participants were asked to infer new members of a category and could freely choose new instances in the stimulus set. By evaluating the inferred exemplars, the global-local dominance in generalization was studied. The results demonstrate that general conclusions are hard to draw based on the theoretically presumed allocation of the visual cues to the local or the global level of processing. However, by a thorough evaluation of the visual cues in terms of their regularities in the stimulus set, we were able to draw a general conclusion: Participants derive category membership by relying on regularities on the most global scale. Regularities on a more global scale are conceived as less coincidental features in the stimulus set, and therefore, they are preferred as a basis to infer new instances. Additionally, we tested the moderating influence of particular context variables like superordinate category ownership, planar rotations, and complexity on participants' reliance on global or local scale shape properties in generalization. Superordinate category ownership had no influence but more complex shapes and arbitrary planar rotations of the stimuli led to huge differences in participants' reliance on visual cues. We will discuss the results in light of some contemporary models of object recognition.