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W. Einhaeuser, T. N. Mundhenk, P. F. Baldi, C. Koch, L. Itti, A bottom-up model of spatial attention predicts human error patterns in rapid scene recognition, Journal of Vision, Vol. 7, No. 10, pp. 1-13, Jul 2007. [2005 impact factor: 3.469] (Cited by 52)
Abstract: Humans demonstrate a peculiar ability to detect complex targets in rapidly presented natural scenes. Recent studies suggest that (nearly) no focal attention is required for overall performance in such tasks. Little is known, however, of how detection performance varies from trial to trial and which stages in the processing hierarchy limit performance: bottom-up visual processing (attentional selection and/or recognition) or top-down factors (e.g., decision-making, memory, or alertness fluctuations)? To investigate the relative contribution of these factors, eight human observers performed an animal detection task in natural scenes presented at 20 Hz. Trial-by-trial performance was highly consistent across observers, far exceeding the prediction of independent errors. This consistency demonstrates that performance is not primarily limited by idiosyncratic factors but by visual processing. Two statistical stimulus properties, contrast variation in the target image and the information-theoretical measure of ``surprise'' in adjacent images, predict performance on a trial-by-trial basis. These measures are tightly related to spatial attention, demonstrating that spatial attention and rapid target detection share common mechanisms. To isolate the causal contribution of the surprise measure, eight additional observers performed the animal detection task in sequences that were reordered versions of those all subjects had correctly recognized in the first experiment. Reordering increased surprise before and/or after the target while keeping the target and distractors themselves unchanged. Surprise enhancement impaired target detection in all observers. Consequently, and contrary to several previously published findings, our results demonstrate that attentional limitations, rather than target recognition alone, affect the detection of targets in rapidly presented visual sequences.
Themes: Model of Top-Down Attentional Modulation, Model of Bottom-Up Saliency-Based Visual Attention, Bayesian Theory of Surprise, Computational Modeling, Human Psychophysics
Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
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