Towards a unified account of face (and maybe object) processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 191-197).
Faces are an important class of visual stimuli, and are thought to be processed differently from objects by the human visual system. Going beyond the false dichotomy of same versus different processing, it is more important to understand how exactly faces are processed similarly or differently from objects. However, even by itself, face processing is poorly understood. Various aspects of face processing, such as holistic, configural, and face-space processing, are investigated in relative isolation, and the relationships between these are unclear. Furthermore, face processing is characteristically affected by various stimulus transformations such as inversion, contrast reversal and spatial frequency filtering, but how or why is unclear. Most importantly, we do not understand even the basic mechanisms of face processing. We hypothesize that what makes face processing distinctive is the existence of large, coarse face templates. We test our hypothesis by modifying an existing model of object processing to utilize such templates, and find that our model can account for many face-related phenomena. Using small, fine face templates as a control, we find that our model displays object-like processing characteristics instead. Overall, we believe that we may have made the first steps towards achieving a unified account of face processing. In addition, results from our control suggest that face and object processing share fundamental computational mechanisms. Coupled with recent advances in brain recording techniques, our results mean that face recognition could form the "tip of the spear" for attacking and solving the problem of visual recognition.
by Cheston Y.-C. Tan.
Ph.D.