Facial Expression Related vMMN: Disentangling Emotional from Neutral Change Detection

Abstract

Detection of changes in facial emotional expressions is crucial to communicate and to rapidly and automatically process possible threats in the environment. Recent studies suggest that expression-related visual mismatch negativity (vMMN) reflects automatic processing of emotional changes. In the present study we used a controlled paradigm to investigate the specificity of emotional change-detection. In order to disentangle specific responses to emotional deviants from that of neutral deviants, we presented neutral expression as standard stimulus (p = 0.80) and both angry and neutral expressions as deviants (p = 0.10, each). In addition to an oddball sequence, an equiprobable sequence was presented, to control for refractoriness and low-level differences. Our results showed that in an early time window (100–200 ms), the controlled vMMN was greater than the oddball vMMN only for the angry deviant, suggesting the importance of controlling for refractoriness and stimulus physical features in emotion related studies. Within the controlled vMMN, angry and neutral deviants both elicited early and late peaks occurring at 140 and 310 ms, respectively, but only the emotional vMMN presented sustained amplitude after each peak. By directly comparing responses to emotional and neutral deviants, our study provides evidence of specific activity reflecting the automatic detection of emotional change. This differs from broader “visual” change processing, and suggests the involvement of two partially-distinct pre-attentional systems in the detection of changes in facial expressions.

Prediction errors in surface segmentation are reflected in the visual mismatch negativity, independently of task and surface features

“…The mismatch response observed in our early window peaked after 200 ms. Earlier vMMN signals have been reported in the 100-200 ms range (Kovarski et al, 2017;Pazo-Alvarez et al, 2003), although these are not universal. In the current context, several causes could account for the relatively late emergence of the first signal of pattern deviance.…”

“…Visual mismatch signals have also been studied in the context of face perception. An increasing number of studies now describe a mismatch signal for face emotional expressions (Susac, Ilmoniemi, Pihko, & Supek, 2004;Zhao & Li, 2006;Stefanics, Csukly, Komlósi, Czobor, & Czigler, 2012;Astikainen, Cong, Ristaniemi, & Hietanen, 2013;Vogel, Shen, & Neuhaus, 2015;Kovarski et al, 2017). Similarly, face gender regularities have also be shown to be tracked by the visual system Wang et al, 2016).…”

Spatially intermixed objects of different categories are parsed automatically

“…Minimum a priori sample size was set at fifteen participants, based on sample sizes typical for many recent vMMN studies using similar designs (Durant, Sulykos, & Czigler, 2017;Kovarski et al, 2017). ERP data was recorded from 20 (16 female, mean age -22.7 years) neurologically typical students who participated as volunteers (5 additional participants were recorded in the case of potential technical problems).…”

Happy you, happy me: expressive changes on a stranger’s voice recruit faster implicit processes than self-produced expressions

“…There are known effects of social relevance on mismatch responses in the visual and auditory modalities, notably when manipulating the communicative nature of the signals: in sequences of emotional face stimuli, Campanella and colleagues (2002) found earlier and larger mismatch responses to changes of expressions that led to a different emotional appraisal (e.g. a happy face in a sequence of sad faces) than to a different depiction of the same emotion (see also Bayer et al, 2017;Kovarski et al, 2017). In the auditory domain, affiliative signals such as laughter evoke larger MMN than a non-affiliative growl (e.g.…”