This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). A three year old infant can recognize visual objects better than any machine. Despite decades of work on the problem of complex object recognition, the principles used by the brain to solve this problem are still unknown. One major experimental difficulty has been the diversity of feature preferences exhibited by visual neurons: For any given cell, it is unknown which set of visual features will result in a robust neural response. Over the past three decades, evidence has gradually accumulated for a system in the temporal lobe that may allow researchers to overcome this difficulty, consisting of a specialized set of areas dedicated to detecting and recognizing faces. The face patch system creates unprecedented possibilities for dissecting the principles of information flow in inferotemporal cortex, by giving direct access to anatomically distinct components of a unified object processing network. The stunning selectivity of this system for faces suggests that face detection is one of its major functions. Understanding in detail the strategies for face detection used by this system should illuminate the most difficult problem in object recognition: how to recognize a visual form despite substantial changes in appearance. With the support of the National Science Foundation, Dr. Doris Tsao and colleagues at the California Institute of Technology will tackle the problem of how cells in face-selective areas of the brain are able to detect faces. The work will use functional magnetic resonance imaging (fMRI) to first identify these areas. Then, the feature selectivity of cells in these regions will be characterized. This will be followed by experiments in which subjects perform tasks in which they actively detect faces, while neural activity is recorded. Neural activity in specific face areas will be artifically increased or decreased, and the effect on face detection behavior will be observed. Results from the present project will likely spawn fertile exchange between neuroscience, computer science, and psychology. The results may lead to new insights into the brain circuits that are altered in prosopagnosia, a selective inability to recognize faces that afflicts a surprisingly large percentage of the population. The results may also provide new insights into social disorders such as autism and social anxiety disorder, since faces are by far the most socially significant class of visual stimuli that we perceive. Understanding the design principles used by the world's best face detection system may motivate design of better artificial face recognition algorithms (which have broad applications in security and entertainment). Finally, the research will offer training opportunities for graduate students and post-doctoral fellows. Pedagogical activities included in Dr. Tsao's CAREER grant include teaching an interdisciplinary course combining computational modeling of object recognition with biological experiments. Face perception is intriguing and accessible to almost everyone--likely because almost everyone has a set of specialized face areas. Thus the results of these investigations will likely be disseminated broadly, to enhance scientific understanding in society.