Abstract

T. N. Mundhenk, W. Einhaeuser, L. Itti, Automatic Computation of an Image's Statistical Surprise Predicts Performance of Human Observers on a Natural Image Detection Task, Vision Research, Vol. 49, No. 13, pp. 1620-1637, Jun 2009. [2007 impact factor: 2.055] (Cited by 17)

Abstract: To understand the neural mechanisms underlying humans' exquisite ability at processing briefly flashed visual scenes, we present a computer model that predicts human performance in a Rapid Serial Visual Presentation (RSVP) task. The model processes streams of natural scene images presented at a rate of 20Hz to human observers, and attempts to predict when subjects will correctly detect if one of the presented images contains an animal (target). We find that metrics of Bayesian surprise, which models both spatial and temporal aspects of human attention, differ significantly between RSVP sequences on which subjects will detect the target (easy) and those on which subjects miss the target (hard). Extending beyond previous studies, we here assess the contribution of individual image features including color opponencies and Gabor edges. We also investigate the effects of the spatial location of surprise in the visual field, rather than only using a single aggregate measure. A physiologically plausible feed-forward system, which optimally combines spatial and temporal surprise metrics for all features, predicts performance in 79.5% of human trials correctly. This is significantly better than a baseline maximum likelihood Bayesian model (71.7%). We can see that attention as measured by surprise, accounts for a large proportion of observer performance in RSVP. The time course of surprise in different feature types (channels) provides additional quantitative insight in rapid bottom-up processes of human visual attention and recognition, and illuminates the phenomenon of attentional blink and lag-1 sparing. Surprise also reveals classical Type-B like masking effects intrinsic in natural image RSVP sequences. We summarize these with the discussion of a multistage model of visual attention.

Themes: Model of Bottom-Up Saliency-Based Visual Attention, Bayesian Theory of Surprise, Computational Modeling