Fast ensemble representations for abstract visual impressions
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Updated: Mar 12, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Allison Yamanashi Leib1, Anna Kosovicheva2, David Whitney1
1University of California Berkeley, Whitney Lab, 3210 Tolman Hall, Berkeley, California 94720, USA.
Abstract:
Much of the richness of perception is conveyed by implicit, rather than image or feature-level, information. The perception of animacy or lifelikeness of objects, for example, cannot be predicted from image level properties alone. Instead, perceiving lifelikeness seems to be an inferential process and one might expect it to be cognitively demanding and serial rather than fast and automatic. If perceptual mechanisms exist to represent lifelikeness, then observers should be able to perceive this information quickly and reliably, and should be able to perceive the lifelikeness of crowds of objects. Here, we report that observers are highly sensitive to the lifelikeness of random objects and even groups of objects. Observers' percepts of crowd lifelikeness are well predicted by independent observers' lifelikeness judgements of the individual objects comprising that crowd. We demonstrate that visual impressions of abstract dimensions can be achieved with summary statistical representations, which underlie our rich perceptual experience.
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