Facelikeness matters: A parametric multipart object set to understand the role of spatial configuration in visual recognition

Abstract

There is a view that faces and objects are processed by different brain mechanisms. Different factors may modulate the extent to which face mechanisms are used for objects. To distinguish these factors, we present a new parametric multipart three-dimensional object set that provides researchers with a rich degree of control of important features for visual recognition such as individual parts and the spatial configuration of those parts. All other properties being equal, we demonstrate that perceived facelikeness in terms of spatial configuration facilitated performance at matching individual exemplars of the new object set across viewpoint changes (Experiment 1). Importantly, facelikeness did not affect perceptual discriminability (Experiment 2) or similarity (Experiment 3). Our findings suggest that perceptual resemblance to faces based on spatial configuration of parts is important for visual recognition even after equating physical and perceptual similarity. Furthermore, the large parametrically controlled object set and the standardized procedures to generate additional exemplars will provide the research community with invaluable tools to further understand visual recognition and visual learning.