Institute for Globally Distributed Open Research and Education (IGDORE), Ubud, Indonesia.
Erasmus Research Services, Erasmus University Rotterdam, Rotterdam, The Netherlands.
PLoS One. 2020 Apr 24;15(4):e0231982. doi: 10.1371/journal.pone.0231982. eCollection 2020.
Our visual system extracts the emotional meaning of human facial expressions rapidly and automatically. Novel paradigms using fast periodic stimulations have provided insights into the electrophysiological processes underlying emotional content extraction: the regular occurrence of specific identities and/or emotional expressions alone can drive diagnostic brain responses. Consistent with a processing advantage for social cues of threat, we expected angry facial expressions to drive larger responses than neutral expressions. In a series of four EEG experiments, we studied the potential boundary conditions of such an effect: (i) we piloted emotional cue extraction using 9 facial identities and a fast presentation rate of 15 Hz (N = 16); (ii) we reduced the facial identities from 9 to 2, to assess whether (low or high) variability across emotional expressions would modulate brain responses (N = 16); (iii) we slowed the presentation rate from 15 Hz to 6 Hz (N = 31), the optimal presentation rate for facial feature extraction; (iv) we tested whether passive viewing instead of a concurrent task at fixation would play a role (N = 30). We consistently observed neural responses reflecting the rate of regularly presented emotional expressions (5 Hz and 2 Hz at presentation rates of 15 Hz and 6 Hz, respectively). Intriguingly, neutral expressions consistently produced stronger responses than angry expressions, contrary to the predicted processing advantage for threat-related stimuli. Our findings highlight the influence of physical differences across facial identities and emotional expressions.
我们的视觉系统能够快速而自动地提取人类面部表情的情感意义。使用快速周期性刺激的新范式为情感内容提取的电生理过程提供了深入了解:仅特定身份和/或情感表达的有规律出现就可以驱动诊断性大脑反应。与威胁的社会线索处理优势一致,我们预计愤怒的面部表情会比中性表情产生更大的反应。在一系列四个 EEG 实验中,我们研究了这种效应的潜在边界条件:(i)我们使用 9 个面部身份和 15 Hz 的快速呈现率(N = 16)来试行情绪线索提取;(ii)我们将面部身份从 9 个减少到 2 个,以评估情绪表达之间的(低或高)变异性是否会调节大脑反应(N = 16);(iii)我们将呈现率从 15 Hz 降低到 6 Hz(N = 31),这是面部特征提取的最佳呈现率;(iv)我们测试了被动观看而不是固定时的并发任务是否会起作用(N = 30)。我们一致观察到反映定期呈现的情感表达率的神经反应(在呈现率为 15 Hz 和 6 Hz 时分别为 5 Hz 和 2 Hz)。有趣的是,中性表情始终产生比愤怒表情更强的反应,与威胁相关刺激的处理优势相反。我们的研究结果强调了面部身份和情感表达之间的物理差异的影响。