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Click to download PDF version Click to download BibTeX data Clik to view abstract L. Itti, P. F. Baldi, Bayesian Surprise Attracts Human Attention, In: Advances in Neural Information Processing Systems, Vol. 19 (NIPS*2005), pp. 547-554, Cambridge, MA:MIT Press, 2006. [2005 acceptance rate: 24%] (Cited by 791)

Abstract: The concept of surprise is central to sensory processing, adaptation, learning, and attention. Yet, no widely-accepted mathematical theory currently exists to quantitatively characterize surprise elicited by a stimulus or event, for observers that range from single neurons to complex natural or engineered systems. We describe a formal Bayesian definition of surprise that is the only consistent formulation under minimal axiomatic assumptions. Surprise quantifies how data affects a natural or artificial observer, by measuring the difference between posterior and prior beliefs of the observer. Using this framework we measure the extent to which humans direct their gaze towards surprising items while watching television and video games. We find that subjects are strongly attracted towards surprising locations, with 72 percent of all human gaze shifts directed towards locations more surprising than the average, a figure which rises to 84 percent when considering only gaze targets simultaneously selected by all subjects. The resulting theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.

Themes: Bayesian Theory of Surprise, Computational Modeling, Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Human Eye-Tracking Research


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