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量化与任务相关的注视。

Quantifying task-related gaze.

机构信息

Department of Psychology, Northeastern University, Boston, MA, USA.

出版信息

Atten Percept Psychophys. 2024 May;86(4):1318-1329. doi: 10.3758/s13414-024-02883-w. Epub 2024 Apr 9.

DOI:10.3758/s13414-024-02883-w
PMID:38594445
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11093728/
Abstract

Competing theories attempt to explain what guides eye movements when exploring natural scenes: bottom-up image salience and top-down semantic salience. In one study, we apply language-based analyses to quantify the well-known observation that task influences gaze in natural scenes. Subjects viewed ten scenes as if they were performing one of two tasks. We found that the semantic similarity between the task and the labels of objects in the scenes captured the task-dependence of gaze (t(39) = 13.083; p < 0.001). In another study, we examined whether image salience or semantic salience better predicts gaze during a search task, and if viewing strategies are affected by searching for targets of high or low semantic relevance to the scene. Subjects searched 100 scenes for a high- or low-relevance object. We found that image salience becomes a worse predictor of gaze across successive fixations, while semantic salience remains a consistent predictor (X(1, N=40) = 75.148, p < .001). Furthermore, we found that semantic salience decreased as object relevance decreased (t(39) = 2.304; p = .027). These results suggest that semantic salience is a useful predictor of gaze during task-related scene viewing, and that even in target-absent trials, gaze is modulated by the relevance of a search target to the scene in which it might be located.

摘要

有理论试图解释在探索自然场景时是什么引导眼球运动

自下而上的图像显著性和自上而下的语义显著性。在一项研究中,我们应用基于语言的分析来量化一个众所周知的观察结果,即任务会影响自然场景中的注视。受试者观看了十个场景,就好像他们正在执行两个任务之一。我们发现,任务和场景中物体标签之间的语义相似性捕捉到了注视的任务依赖性(t(39)=13.083;p<0.001)。在另一项研究中,我们研究了在搜索任务中是图像显著性还是语义显著性更好地预测注视,以及观看策略是否受到搜索与场景高度或低度相关的目标的影响。受试者在 100 个场景中搜索一个高或低相关的目标。我们发现,图像显著性在连续注视中成为较差的注视预测指标,而语义显著性仍然是一致的预测指标(X(1,N=40)=75.148,p<0.001)。此外,我们发现语义显著性随着目标相关性的降低而降低(t(39)=2.304;p=0.027)。这些结果表明,语义显著性是与任务相关的场景观看中注视的有用预测指标,即使在没有目标的试验中,注视也会被搜索目标与可能位于其中的场景的相关性所调节。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/e4f1dcb9eae3/13414_2024_2883_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/7650228485b8/13414_2024_2883_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/85cf72452c03/13414_2024_2883_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/efe309b6315f/13414_2024_2883_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/5fd9402bc75b/13414_2024_2883_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/e4f1dcb9eae3/13414_2024_2883_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/7650228485b8/13414_2024_2883_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/671addf5795b/13414_2024_2883_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/b7d30a6a8737/13414_2024_2883_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/85cf72452c03/13414_2024_2883_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/efe309b6315f/13414_2024_2883_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/5fd9402bc75b/13414_2024_2883_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53e6/11093873/e4f1dcb9eae3/13414_2024_2883_Fig7_HTML.jpg

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Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes.寻找语义相似性:语义向量空间模型可以告诉我们关于现实场景中注意力的什么信息。
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The meaning and structure of scenes.
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Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.基于深度卷积神经网络的意义图和显著度模型在预测人类注视时对图像意义不敏感。
Cognition. 2021 Jan;206:104465. doi: 10.1016/j.cognition.2020.104465. Epub 2020 Oct 20.
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The Linguistic Analysis of Scene Semantics: LASS.场景语义的语言分析:LASS。
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