Abstract

P. F. Baldi, L. Itti, Attention: Bits versus Wows, In: Proc. IEEE International Conference on Neural Networks and Brain, Beijing, China, (M. Zhao, Z. Shi Ed.), Vol. 1, pp. PL56-PL61, Oct 2005. (Cited by 24)

Abstract: The concept of surprise is central to sensory processing, adaptation and learning, attention, and decision making. 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. Humans are strongly attracted to locations of high Bayesian surprise, 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: Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Computational Modeling, Bayesian Theory of Surprise, Human Eye-Tracking Research