Event Location: NSH 1305
Bio: Aude Oliva is Associate Professor of Cognitive Science, in the
Department of Brain and Cognitive Sciences, at the Massachusetts
Institute of Technology. After a French baccalaureate in Physics and
Mathematics and a B.Sc in Psychology, she received two M. Sc. degrees
–in Experimental Psychology, and in Cognitive Science and Image
Processing, and was awarded a Ph.D in Cognitive Science in 1995, from
the Institut National Polytechnique of Grenoble, France. After
postdoctoral research positions in the UK, Japan, France and US, she
joined the MIT faculty in 2004. In 2006, she received a National Science
Foundation CAREER award in Computational Neuroscience to pursue research
in human and machine scene understanding.

Her research program is in the field of Computational Visual Cognition,
a framework that strives to identify the substrates of complex visual
and recognition tasks (using behavioral, eye tracking and imaging
methods) and to develop models inspired by human cognition. Her current
research focus lies in studying human abilities at natural image
recognition and memory, including scene, object and space perception as
well as the role of attentional mechanisms and learning in visual search
tasks.

Abstract: The human visual system has been extensively trained to deal with
objects and natural images, giving it the opportunity to develop robust
strategies to quickly encode and recognize categories and exemplars.
Although it is known that human memory capacity for images is massive,
the fidelity with which human memory can represent such a large number
of images is an outstanding question. We conducted three large-scale
memory experiments to determine the details remembered per image
representing object and natural scenes, by varying the amount of detail
required to succeed in subsequent memory tests. Our results show that
contrary to the commonly accepted view that long-term memory
representations contain only the gist of what was seen, long-term memory
can store thousands of items with a large amount of detail per item.
Further analyzes reveal that memory for an item depends on the extent to
which it is conceptually distinct from other items in the memory set,
and not necessarily on the featural distinctiveness along shape or color
dimensions. These findings suggest a “conceptual hook” is necessary for
maintaining a large number of high-fidelity representations in visual
long-term memory. Altogether, the results present a great challenge to
models of object and natural scene recognition, which must be able to
account for such a large and detailed storage capacity. Work in
collaboration with: Timothy Brady, Talia Konkle and George Alvarez.