Centre des Sciences Du Goût et de L'Alimentation, AgroSup Dijon, CNRS, INRA, Université Bourgogne Franche-Comté, F-21000, Dijon, France.
Psychological Sciences Research Institute, Institute of Neuroscience, University of Louvain, 1348, Louvain-la-Neuve, Belgium.
Neuroimage. 2018 Oct 1;179:235-251. doi: 10.1016/j.neuroimage.2018.06.048. Epub 2018 Jun 18.
Efficient decoding of even brief and slight intensity facial expression changes is important for social interactions. However, robust evidence for the human brain ability to automatically detect brief and subtle changes of facial expression remains limited. Here we built on a recently developed paradigm in human electrophysiology with full-blown expressions (Dzhelyova et al., 2017), to isolate and quantify a neural marker for the detection of brief and subtle changes of facial expression. Scalp electroencephalogram (EEG) was recorded from 18 participants during stimulation of a neutral face changing randomly in size at a rapid rate of 6 Hz. Brief changes of expression appeared every five stimulation cycle (i.e., at 1.2 Hz) and expression intensity increased parametrically every 20 s in 20% steps during sweep sequences of 100 s. A significant 1.2 Hz response emerged in the EEG spectrum already at 40% of facial expression-change intensity for most of the 5 emotions tested (anger, disgust, fear, happiness, or sadness in different sequences), and increased with intensity steps, predominantly over right occipito-temporal regions. Given the high signal-to-noise ratio of the approach, thresholds for automatic detection of brief changes of facial expression could be determined for every single individual brain. A time-domain analysis revealed three components, the two first increasing linearly with increasing intensity as early as 100 ms after a change of expression, suggesting gradual low-level image-change detection prior to visual coding of facial movements. In contrast, the third component showed abrupt sensitivity to increasing expression intensity beyond 300 ms post expression-change, suggesting categorical emotion perception. Overall, this characterization of the detection of subtle changes of facial expression and its temporal dynamics open promising tracks for precise assessment of social perception ability during development and in clinical populations.
即使是短暂的、轻微的面部表情变化的有效解码对于社交互动也很重要。然而，人类大脑自动检测短暂而微妙的面部表情变化的能力的有力证据仍然有限。在这里，我们在人类电生理学中一个最近发展的、具有完整表情的范式（Dzhelyova 等人，2017）的基础上，分离并量化了一个用于检测短暂而微妙的面部表情变化的神经标记物。我们在 18 名参与者的头皮脑电图（EEG）记录中，在一个快速率为 6Hz 的中性面部变化的刺激下。表情的短暂变化每五个刺激周期（即 1.2Hz）出现一次，而在 100s 的扫视序列中，表情强度以 20%的步长参数化地每 20s 增加一次。在大多数情况下，在 5 种测试情绪（愤怒、厌恶、恐惧、快乐或悲伤）中，5 种测试情绪中，在表情变化强度的 40%时，脑电图频谱中就出现了显著的 1.2Hz 反应，并且随着强度的增加而增加，主要集中在右枕颞区。考虑到该方法的高信噪比，可以为每个个体大脑确定自动检测短暂面部表情变化的阈值。在时域分析中，发现了三个成分，前两个成分在表情变化后 100ms 左右就随着强度的增加呈线性增加，表明在对面部运动进行视觉编码之前，就有对图像变化的逐渐低级检测。相比之下，第三个成分在表情变化后 300ms 后对增加的表情强度表现出突然的敏感性，表明情绪的类别感知。总的来说，对面部表情细微变化的检测及其时间动态的这种特征化，为在发展过程中和临床人群中精确评估社会感知能力提供了有希望的途径。
