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Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual Attention

机译:低水平刺激特征,任务相关因素和空间偏见对明显视觉注意的影响

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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.
机译:视觉注意力被认为是由低级视觉特征与局部图像区域的任务相关信息内容之间的相互作用以及空间观看偏差引起的。尽管依赖于实验范式和模型假设,但这种想法引起了人们不断变化的说法,即自下而上或自上而下的机制主导着视觉注意力。为了有助于解决此讨论,在此我们量化了这些因素的影响及其在一组分类任务中的相对重要性。我们的刺激包括单个图像补丁(气泡)。对于每个气泡,我们得出三种度量:基于低级刺激特征的显着性度量,基于从我们的分类响应得出的任务相关信息内容的显着性度量以及基于空间观看偏差的显着性度量。此外,我们根据被测者观察到的视线来测量每个气泡的经验显着性,从而表征每个气泡获得的明显视觉关注。多元线性模型将三种显着性度量与明显的视觉注意力联系起来。它表明所有这三个显着性措施都做出了重大贡献。在不同任务中,空间观看偏差的影响最大,并且是恒定的。任务相关信息的贡献是紧随其后的。具体而言,在判断面部表情的标准化任务中,它的得分很高。平均而言,低级功能的贡献要低一些。但是,在没有可用模板的原型搜索任务中,它与其他两种措施相比具有很大的贡献。最后,这三个因素的贡献只是略微多余,半部分相关系数仅略低于完全相关系数。这些数据提供了证据,表明所有这三种措施都做出了重要且独立的贡献,并且在人类公开的视觉注意力模型中,任何一项都不能忽略。

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