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L. Itti, P. F. Baldi, Bayesian Surprise Attracts Human Attention, Vision Research, Vol. 49, No. 10, pp. 1295-1306, May 2009. [2007 impact factor: 2.055] (Cited by 578)
Abstract: We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Surprise measures how data affects an observer, in terms of differences between posterior and prior beliefs about the world. Only data observations which substantially affect the observer's beliefs yield surprise, irrespectively of how rare or informative in Shannon's sense these observations are. We test the framework by quantifying the extent to which humans may orient attention and gaze towards surprising events or items while watching television. To this end, we implement a simple computational model where a low-level, sensory form of surprise is computed by simple simulated early visual neurons. Bayesian surprise is a strong attractor of human attention, with 72% of all gaze shifts directed towards locations more surprising than the average, a figure rising to 84% when focusing the analysis onto regions simultaneously selected by all observers. The proposed theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
Note: Top cited article 2008-2010 award from Vision Research
Themes: Bayesian Theory of Surprise, Human Eye-Tracking Research
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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