People / Faculty
Aude Oliva, Ph.D.
Associate Professor of Cognitive Science
Computational Visual Cognition
My research program is in the field of Computational Visual Cognition, a framework that strives to identify the substrates of complex visual recognition tasks and to develop models inspired by human perception and cognition. The natural visual environment is composed of three-dimensional objects, with textures, colors, and materials, embedded in an explicit spatial layout. Yet, the human brain understands scenes, places and events quickly and effortlessly, outperforming the most advanced artificial vision system. In the lab, we use multi-disciplinary techniques from behavioral sciences, cognitive neuroscience and computational vision, to identify key principles of human object, scene and space understanding and evaluate the capacity and fidelity of human memory systems for guiding the development of computational and theoretical frameworks in computational cognition. Ultimately, the results of characterizing human perceptual and cognitive abilities and limitations in a natural setting holds promise for inspiring the next generation of artificial vision systems but also gives insights for the understanding of visual and cognitive disorders. Our research programs bring together disciplines such as perceptual science, cognitive science and neuroscience, neuropsychology, photography, architecture, image processing, computer vision and computer graphics.
Park, S., Brady, T.F., Greene, M.R., & Oliva, A. (2011). Disentangling scene content from its spatial boundary: Complementary roles for the PPA and LOC in representing real-world scenes Journal of Neuroscience, 31(4), 1333-1340.
Konkle, T., Brady, T.F., Alvarez, G.A., & Oliva, A. (2010). Scene memory is more detailed than you think: the role of categories in visual long-term memory. Psychological Science, 21(11), 1551-1556.
Greene, M.R., & Oliva, A. (2009). Recognition of Natural Scenes from Global
Properties: Seeing the Forest Without Representing the Trees. Cognitive Psychology, 58(2), 137-179.
Alvarez, G.A., and Oliva, A. (2009). Spatial Ensemble Statistics:
Efficient Codes that Can be Represented with Reduced Attention.
Proceedings of the National Academy of Sciences, 106, 7345-7350.