Meaning Guides Attention during Real-World Scene Description.

Meaning Guides Attention during Real-World Scene Description.
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DOI:
10.1038/s41598-018-31894-5
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发表时间:
2018-09-10
期刊:
影响因子:
4.6
通讯作者:
Ferreira F
Ferreira F
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Henderson JM;Hayes TR;Rehrig G;Ferreira F

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视觉场景的智能分析需要对重要区域进行优先级排序,并注意选择优先处理。这一选择的依据是什么?在此,我们比较了在两个自由观看场景描述任务中,意义和图像显著性对真实场景中注意引导的影响。语义由语义图表示,语义图捕捉语义特征的空间分布。图像显著性由图像特征空间分布的显著图表示。这两种类型的地图编码的格式,可以直接比较的空间分布的注意力来自观众的眼睛固定在场景描述任务的地图。结果表明,意义和显著性预测的空间分布在这些任务中的注意,但当意义和显著性之间的相关性是统计控制,只有意义解释了独特的注意方差。研究结果支持了认知关联在控制场景中的注意引导中起主导作用的理论。这些结果也对当前标记真实世界图像的人工智能方法具有实际意义。
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.
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