CMU talk: The Capacity and Fidelity of Visual Long Term Memory

VASC Seminar
Monday, November 24, 2008

The Capacity and Fidelity of Visual Long Term Memory

Aude Oliva
Associate Professor of Cognitive Science
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology


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.

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.