Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA.
Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, Maryland, USA.
Autism Res. 2021 Aug;14(8):1670-1683. doi: 10.1002/aur.2540. Epub 2021 May 19.
Eye tracking provides insights into social processing deficits in autism spectrum disorder (ASD), especially in conjunction with dynamic, naturalistic free-viewing stimuli. However, the question remains whether gaze characteristics, such as preference for specific facial features, can be considered a stable individual trait, particularly in those with ASD. If so, how much data are needed for consistent estimations? To address these questions, we assessed the stability and robustness of gaze preference for facial features as incremental amounts of movie data were introduced for analysis. We trained an artificial neural network to create an object-based segmentation of naturalistic movie clips (14 s each, 7410 frames total). Thirty-three high-functioning individuals with ASD and 36 age- and IQ-equated typically developing individuals (age range: 12-30 years) viewed 22 Hollywood movie clips, each depicting a social interaction. As we evaluated combinations of one, three, five, eight, and 11 movie clips, gaze dwell times on core facial features became increasingly stable at within-subject, within-group, and between-group levels. Using a number of movie clips deemed sufficient by our analysis, we found that individuals with ASD displayed significantly less face-centered gaze (centralized on the nose; p < 0.001) but did not significantly differ from typically developing participants in eye or mouth looking times. Our findings validate gaze preference for specific facial features as a stable individual trait and highlight the possibility of misinterpretation with insufficient data. Additionally, we propose the use of a machine learning approach to stimuli segmentation to quickly and flexibly prepare dynamic stimuli for analysis. LAY SUMMARY: Using a data-driven approach to segmenting movie stimuli, we examined varying amounts of data to assess the stability of social gaze in individuals with autism spectrum disorder (ASD). We found a reduction in social fixations in participants with ASD, driven by decreased attention to the center of the face. Our findings further support the validity of gaze preference for face features as a stable individual trait when sufficient data are used.
眼动追踪提供了对自闭症谱系障碍(ASD)中社会处理缺陷的深入了解,尤其是在与动态、自然主义的自由观看刺激相结合时。然而,问题仍然是,注视特征,如对面部特征的偏好,是否可以被视为稳定的个体特征,特别是在 ASD 患者中。如果是这样,需要多少数据才能进行一致的估计?为了解决这些问题,我们评估了随着分析中引入越来越多的电影数据,对面部特征的注视偏好的稳定性和稳健性。我们训练了一个人工神经网络,对面部特征进行基于对象的分割。33 名高功能 ASD 个体和 36 名年龄和智商匹配的正常发育个体(年龄范围:12-30 岁)观看了 22 个好莱坞电影片段,每个片段都描绘了一个社会互动。当我们评估一个、三个、五个、八个和十一个电影片段的组合时,核心面部特征的注视停留时间在个体内、组内和组间水平上变得越来越稳定。使用我们的分析认为足够的电影片段数量,我们发现 ASD 个体的面部注视明显减少(集中在鼻子上;p<0.001),但与正常发育的参与者在眼睛或嘴巴注视时间上没有显著差异。我们的研究结果验证了对面部特定特征的注视偏好是一种稳定的个体特征,并强调了数据不足可能导致的误解。此外,我们建议使用机器学习方法对面部特征进行刺激分割,以便快速灵活地准备动态刺激进行分析。