Center for Mind and Brain, University of California, Davis, USA.
Department of Psychology, University of California, Davis, USA.
Sci Rep. 2018 Sep 10;8(1):13504. doi: 10.1038/s41598-018-31894-5.
Intelligent analysis of a visual scene requires that important regions be prioritized and attentionally selected for preferential processing. What is the basis for this selection? Here we compared the influence of meaning and image salience on attentional guidance in real-world scenes during two free-viewing scene description tasks. Meaning was represented by meaning maps capturing the spatial distribution of semantic features. Image salience was represented by saliency maps capturing the spatial distribution of image features. Both types of maps were coded in a format that could be directly compared to maps of the spatial distribution of attention derived from viewers' eye fixations in the scene description tasks. The results showed that both meaning and salience predicted the spatial distribution of attention in these tasks, but that when the correlation between meaning and salience was statistically controlled, only meaning accounted for unique variance in attention. The results support theories in which cognitive relevance plays the dominant functional role in controlling human attentional guidance in scenes. The results also have practical implications for current artificial intelligence approaches to labeling real-world images.
视觉场景的智能分析要求对重要区域进行优先级排序，并进行注意力选择以进行优先处理。这种选择的基础是什么？在这里，我们在两个自由观看场景描述任务中比较了真实场景中意义和图像显著度对注意力引导的影响。意义由捕捉语义特征空间分布的意义图表示。图像显著度由捕捉图像特征空间分布的显著度图表示。这两种类型的图都以一种格式进行编码，可以与从场景描述任务中观众的眼动注视中得出的注意力空间分布图直接进行比较。结果表明，在这些任务中，意义和显著度都可以预测注意力的空间分布，但当对意义和显著度之间的相关性进行统计控制时，只有意义可以解释注意力的独特变化。研究结果支持了这样一种理论，即认知相关性在控制人类在场景中的注意力引导方面起着主导作用。研究结果还对当前人工智能对真实世界图像进行标记的方法具有实际意义。
