Deep Neural Network Reveals the World of Autism From a First‐Person Perspective
Deep Neural Network Reveals the World of Autism From a First‐Person Perspective
复制标题
深度神经网络从第一人称视角揭示自闭症世界
DOI：
10.1002/aur.2376
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发表时间：
2020
期刊：
影响因子：
4.7
通讯作者：
Wang, Shuo
中科院分区：
文献类型：
--
作者：
Ruan, Mindi;Webster, Paula J.;Li, Xin;Wang, Shuo
自闭症谱系障碍(ASD)患者在观看物理世界的图像时，会表现出对社会刺激的非典型关注和异常凝视。然而，他们是如何从第一人称的角度来看待世界的，目前还不得而知。在这项研究中，我们使用机器学习将三种不同类别(人、室内和室外)拍摄的照片分类为患有ASD的个人拍摄的照片和没有ASD的同龄人拍摄的照片。我们的分类器有效地区分了所有三个类别的照片，但在对人的照片进行分类时尤其成功，准确率达到80%。重要的是，我们模型的可视化揭示了导致成功区分的关键特征，并表明我们的模型采用了类似于ASD专家的策略。此外，我们首次表明，ASD患者拍摄的照片中包含的突出对象较少，特别是在中央视野中。值得注意的是，我们的模型比ASD专家对这些照片的分类效果更好。我们共同展示了一种有效和新颖的方法，能够识别ASD患者拍摄的照片，并从独特的第一人称视角揭示ASD患者的异常视觉注意力。我们的方法反过来可能为自闭症患者的个体评估提供一个客观的衡量标准。然而，很大程度上仍不清楚他们是如何从第一人称的角度来看待世界的。在这项研究中，我们采用深度学习的方法来分析患有和不患有ASD的人拍摄的独特的照片集。我们的计算机建模不仅能够识别哪些照片是由ASD患者拍摄的，表现优于ASD专家，而且重要的是，它揭示了导致成功区分的关键特征，从他们的第一人称视角揭示了ASD患者非典型视觉注意的一些方面。
People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first‐person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first‐person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD.Lay SummaryPeople with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first‐person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first‐person perspective.
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影响因子：
--
作者：
Klin, A;Jones, W;Cohen, D
通讯作者：
Cohen, D
DOI：
10.1111/j.1365-2788.2008.01142.x
发表时间：
2009-02-01
影响因子：
3.6
作者：
Riby, D.;Hancock, P. J. B.
通讯作者：
Hancock, P. J. B.
影响因子：
2.9
作者：
Shic F;Bradshaw J;Klin A;Scassellati B;Chawarska K
通讯作者：
Chawarska K
影响因子：
4.9
作者：
Riby DM;Hancock PJ;Jones N;Hanley M
通讯作者：
Hanley M