Brain synchronization during perception of facial emotional expressions with natural and unnatural dynamics

Abstract

Research on the perception of facial emotional expressions (FEEs) often uses static images that do not capture the dynamic character of social coordination in natural settings. Recent behavioral and neuroimaging studies suggest that dynamic FEEs (videos or morphs) enhance emotion perception. To identify mechanisms associated with the perception of FEEs with natural dynamics, the present EEG (Electroencephalography)study compared (i) ecologically valid stimuli of angry and happy FEEs with natural dynamics to (ii) FEEs with unnatural dynamics, and to (iii) static FEEs. FEEs with unnatural dynamics showed faces moving in a biologically possible but unpredictable and atypical manner, generally resulting in ambivalent emotional content. Participants were asked to explicitly recognize FEEs. Using whole power (WP) and phase synchrony (Phase Locking Index, PLI), we found that brain responses discriminated between natural and unnatural FEEs (both static and dynamic). Differences were primarily observed in the timing and brain topographies of delta and theta PLI and WP, and in alpha and beta WP. Our results support the view that biologically plausible, albeit atypical, FEEs are processed by the brain by different mechanisms than natural FEEs. We conclude that natural movement dynamics are essential for the perception of FEEs and the associated brain processes.

Facial Expression Processing Is Not Affected by Parkinson’s Disease, but by Age-Related Factors

“…In addition to the open question whether or not PD patients have impaired face expression processing, these previous studies have two important limitations: first, they solely used static stimulus presentation and, second, they relied only on the six basic facial expressions. Concerning the first point, a series of recent studies has shown, however, that perception of facial expressions when presented in their real-life dynamic form (Bülthoff et al, 2011), results in significantly different performance patterns (Ambadar et al, 2005; Cunningham and Wallraven, 2009) as well as involvement of different brain areas (Kilts et al, 2003; Sato et al, 2004; Yoshikawa and Sato, 2006; Trautmann et al, 2009; Perdikis et al, 2017) compared to processing of static expressions. The second, important aspect that has been neglected in previous studies is that in daily life, expressions do not only consist of the six basic expressions, but they also include a much wider range of communicational, conversational, and emotional expressions and facial gestures such as tiredness, boredom, flirting, etc.…”

Effects of Transcranial Random Noise Stimulation on Perception of Dynamic Audio-Visual Emotional Facial Stimuli

“…However, participants differ in their response to static stimuli compared to dynamic stimuli. Participants report higher levels of arousal to a dynamic image (a neutral face morphing into an emotional face) than to a static image of an emotional face (Sato & Yoshikawa, 2007 as cited in Perdikis et al, 2017). When completing an emotion perception task with dynamic stimuli, participants were more accurate than those with static stimuli (Ambadar et al, 2005 as cited in Perdikis et al, 2017).…”

“…Participants report higher levels of arousal to a dynamic image (a neutral face morphing into an emotional face) than to a static image of an emotional face (Sato & Yoshikawa, 2007 as cited in Perdikis et al, 2017). When completing an emotion perception task with dynamic stimuli, participants were more accurate than those with static stimuli (Ambadar et al, 2005 as cited in Perdikis et al, 2017). Participants also elicit greater facial mimicry to dynamic than static stimuli (Rymarczyk et al, 2011;Sato et al, 2007).…”

Perception of “Live” Facial Expressions

“…Например, компьютерная анимация, при которой изменения лица происходят по кратчайшей прямой, приводит к замедленному и менее точному распознаванию эмоций, а также к более низким оценкам интенсивности, искренности и типичности по сравнению с действительным развертыванием экспрессии [38; 130]. В силу нелинейности и асинхронии активности мимических мышц при переживании эмоции [88], естественные динамические экспрессии и переходы между ними воспринимаются иначе, чем искусственные динамические последовательности, созданные путем линейного морфинга [81], и приводят к различиям в волновой активности головного мозга [103]. Точность категоризации снижается при инверсии естественных видеофрагментов во времени [81; 107].…”