Center bias outperforms image salience but not semantics in accounting for attention during scene viewing
Center bias outperforms image salience but not semantics in accounting for attention during scene viewing
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DOI：
10.3758/s13414-019-01849-7
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发表时间：
2020-06-01
影响因子：
1.7
通讯作者：
Henderson, John M.
中科院分区：
文献类型：
--
作者：
Hayes, Taylor R.;Henderson, John M.
我们如何确定在现实世界的场景中将注意力集中在哪里？图像显著性理论认为，我们的注意力被“拉”到低层次图像特征不同的场景区域。然而，形式化的图像显着性理论的模型往往包含显着的场景无关的空间偏差。在本研究中，三种不同的观看任务被用来评估图像显着性模型是否占主要基于场景依赖的，低级别的功能对比度，或在其场景独立的空间偏差的场景固定密度的方差。为了进行比较，还将注视密度与语义特征图（Meaning Maps;亨德森& Hayes，Nature Human Behaviour，1，743- 747，2017）进行了比较，语义特征图是使用孤立场景块的人类评级生成的。计算场景注视密度与每个图像显著性模型的中心偏差、每个全图像显著性模型和意义图之间的平方相关性（R-2）。结果表明，在产生观察者中心偏差的任务中，图像显着性模型平均解释了23%的场景固定密度的方差比单独的中心偏差。相比之下，意义图平均比中心偏差多解释10%的方差。我们的结论是，图像显着性理论推广到现实世界的场景。
How do we determine where to focus our attention in real-world scenes? Image saliency theory proposes that our attention is 'pulled' to scene regions that differ in low-level image features. However, models that formalize image saliency theory often contain significant scene-independent spatial biases. In the present studies, three different viewing tasks were used to evaluate whether image saliency models account for variance in scene fixation density based primarily on scene-dependent, low-level feature contrast, or on their scene-independent spatial biases. For comparison, fixation density was also compared to semantic feature maps (Meaning Maps; Henderson & Hayes,Nature Human Behaviour, 1, 743-747,2017) that were generated using human ratings of isolated scene patches. The squared correlations (R-2) between scene fixation density and each image saliency model's center bias, each full image saliency model, and meaning maps were computed. The results showed that in tasks that produced observer center bias, the image saliency models on average explained 23% less variance in scene fixation density than their center biases alone. In comparison, meaning maps explained on average 10% more variance than center bias alone. We conclude that image saliency theory generalizes poorly to real-world scenes.