Graduate Program in Neuroscience, Vanderbilt University, Nashville, USA.
Frist Center for Autism and Innovation, Vanderbilt University, Nashville, USA.
J Neurodev Disord. 2022 Aug 1;14(1):43. doi: 10.1186/s11689-022-09451-z.
It is unclear whether atypical patterns of facial expression production metrics in autism reflect the dynamic and nuanced nature of facial expressions across people or a true diagnostic difference. Furthermore, the heterogeneity observed across autism symptomatology suggests a need for more adaptive and personalized social skills programs. Towards this goal, it would be useful to have a more concrete and empirical understanding of the different expressiveness profiles within the autistic population and how they differ from neurotypicals.
We used automated facial coding and an unsupervised clustering approach to limit inter-individual variability in facial expression production that may have otherwise obscured group differences in previous studies, allowing an "apples-to-apples" comparison between autistic and neurotypical adults. Specifically, we applied k-means clustering to identify subtypes of facial expressiveness in an autism group (N = 27) and a neurotypical control group (N = 57) separately. The two most stable clusters from these analyses were then further characterized and compared based on their expressiveness and emotive congruence to emotionally charged stimuli.
Our main finding was that a subset of autistic adults in our sample show heightened spontaneous facial expressions irrespective of image valence. We did not find evidence for greater incongruous (i.e., inappropriate) facial expressions in autism. Finally, we found a negative trend between expressiveness and emotion recognition within the autism group.
The results from our previous study on self-reported empathy and current expressivity findings point to a higher degree of facial expressions recruited for emotional resonance in autism that may not always be adaptive (e.g., experiencing similar emotional resonance regardless of valence). These findings also build on previous work indicating that facial expression intensity is not diminished in autism and suggest the need for intervention programs to focus on emotion recognition and social skills in the context of both negative and positive emotions.
自闭症患者面部表情产生指标的非典型模式是否反映了人们对面部表情的动态和细微差别，或者是否反映了真正的诊断差异，目前还不清楚。此外，自闭症症状表现的异质性表明，需要更具适应性和个性化的社交技能方案。为此，更具体和实证地了解自闭症人群中的不同表达特征，以及它们与神经典型人群的不同之处，将是很有用的。
我们使用自动化面部编码和无监督聚类方法来限制面部表情产生中的个体间可变性，否则这可能会掩盖以前研究中的组间差异，从而可以在自闭症和神经典型成年人之间进行“苹果对苹果”的比较。具体来说，我们分别对自闭症组（N=27）和神经典型对照组（N=57）应用 k-均值聚类来识别表情表达的亚型。然后，根据这些分析中最稳定的两个聚类的表情表达和情感一致性来进一步对其进行特征描述和比较，以确定其对情绪化刺激的反应。
我们的主要发现是，我们样本中的一部分自闭症成年人无论图像效价如何，都会表现出更高的自发面部表情。我们没有发现自闭症中存在更多不协调（即不适当）的面部表情的证据。最后，我们在自闭症组中发现了表达能力和情绪识别之间的负相关趋势。
我们之前关于自我报告的同理心的研究结果和当前的表达力发现表明，自闭症患者更倾向于调动面部表情来产生情感共鸣，而这种共鸣可能并不总是适应性的（例如，无论效价如何，都会产生类似的情感共鸣）。这些发现还建立在以前的工作基础上，表明自闭症患者的面部表情强度并没有减弱，并表明干预计划需要在正负情绪的背景下，将重点放在情绪识别和社交技能上。
