 Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual AttentionSepp Kollmorgen, Nora Nortmann, Sylvia Schröder and Peter König PLOS Computational Biology, 2010, vol. 6, issue 5, 1-20 Abstract: Visual attention is thought to be driven by the interplay between low-level visual features and task dependent information content of local image regions, as well as by spatial viewing biases. Though dependent on experimental paradigms and model assumptions, this idea has given rise to varying claims that either bottom-up or top-down mechanisms dominate visual attention. To contribute toward a resolution of this discussion, here we quantify the influence of these factors and their relative importance in a set of classification tasks. Our stimuli consist of individual image patches (bubbles). For each bubble we derive three measures: a measure of salience based on low-level stimulus features, a measure of salience based on the task dependent information content derived from our subjects' classification responses and a measure of salience based on spatial viewing biases. Furthermore, we measure the empirical salience of each bubble based on our subjects' measured eye gazes thus characterizing the overt visual attention each bubble receives. A multivariate linear model relates the three salience measures to overt visual attention. It reveals that all three salience measures contribute significantly. The effect of spatial viewing biases is highest and rather constant in different tasks. The contribution of task dependent information is a close runner-up. Specifically, in a standardized task of judging facial expressions it scores highly. The contribution of low-level features is, on average, somewhat lower. However, in a prototypical search task, without an available template, it makes a strong contribution on par with the two other measures. Finally, the contributions of the three factors are only slightly redundant, and the semi-partial correlation coefficients are only slightly lower than the coefficients for full correlations. These data provide evidence that all three measures make significant and independent contributions and that none can be neglected in a model of human overt visual attention.Author Summary: In our lifetime we make about 5 billion eye movements. Yet our knowledge about what determines where we look at is surprisingly sketchy. Some traditional approaches assume that gaze is guided by simple image properties like local contrast (low-level features). Recent arguments emphasize the influence of tasks (high-level features) and motor constraints (spatial bias). The relative importance of these factors is still a topic of debate. In this study, subjects view and classify natural scenery and faces while their eye movements are recorded. The stimuli are composed of small image patches. For each of these patches we derive a measure for low-level features and spatial bias. Utilizing the subjects' classification responses, we additionally derive a measure reflecting the information content of a patch with respect to the classification task (high-level features). We show that the effect of spatial bias is highest, that high-level features are a close runner-up, and that low-level features have, on average, a smaller influence. Remarkably, the different contributions are mostly independent. Hence, all three measures contribute to the guidance of eye movements and have to be considered in a model of human visual attention. Date: 2010 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000791 DOI: 10.1371/journal.pcbi.1000791 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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