Hayes Taylor R, Henderson John M
Center for Mind and Brain, University of California, Davis, CA, USA.
Department of Psychology, University of California, Davis, CA, USA.
Atten Percept Psychophys. 2020 Jun;82(3):985-994. doi: 10.3758/s13414-019-01849-7.
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) 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.
我们如何在现实场景中确定注意力的集中点呢？图像显著性理论认为，我们的注意力会被吸引到在低层次图像特征上存在差异的场景区域。然而，将图像显著性理论形式化的模型往往包含显著的与场景无关的空间偏差。在本研究中，使用了三种不同的观察任务来评估图像显著性模型是否主要基于与场景相关的低层次特征对比度或其与场景无关的空间偏差来解释场景注视密度的变化。为了进行比较，还将注视密度与使用对孤立场景块的人类评级生成的语义特征图（意义图；亨德森和海斯，《自然·人类行为》，1，743 - 747，2017）进行了比较。计算了场景注视密度与每个图像显著性模型的中心偏差、每个完整图像显著性模型以及意义图之间的平方相关性（R）。结果表明，在产生观察者中心偏差的任务中，图像显著性模型平均解释的场景注视密度变化比仅其中心偏差少23%。相比之下，意义图平均解释的变化比仅中心偏差多10%。我们得出结论，图像显著性理论对现实场景的概括性较差。
