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

Date Published: July 19, 2017

Publisher: Public Library of Science

Author(s): Dionysios Perdikis, Jakob Volhard, Viktor Müller, Kathrin Kaulard, Timothy R. Brick, Christian Wallraven, Ulman Lindenberger, Marina A. Pavlova.

http://doi.org/10.1371/journal.pone.0181225

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.

Partial Text

Human social behavior can be viewed as an ongoing interaction among individuals through a flow of coordinated processes of action and perception such as speech, gestures and facial expressions, which serve to communicate our intentions, emotions, and experiences [1]. Brain processes have evolved to facilitate social coordination [1–4]. Here, we focus on one class of such processes, namely, the perception of facial emotional expressions (FEEs), and study to what degree the associated brain mechanisms are specific to the natural dynamics of FEEs, with implications for related experimental designs in cognitive, affective and social neuroscience.

The natural movement course of a FEE starts from an initial, emotionally “neutral” face and gradually develops to an emotional expression, following a highly nonlinear trajectory [29]. We aimed at learning more about how specific brain responses to FEEs are to this dynamic trajectory. To that end, we studied how these brain activations change when we stimulate the brain with static and unnatural dynamic FEEs, which each impair the dynamics in distinct ways. Specifically, we analyzed EEG measures of local brain synchrony and phase locking with high time and frequency resolution, by employing “mean-centering task PLS”, a data-driven multivariate method that finds contrasts between conditions in a way that maximizes differences in neural response. The manipulation was successful in that three of the four PLS analyses found the maximal contrast to be between motion types. However, the fourth PLS analysis revealed the maximal contrast not between motion levels, but between levels with high and low task difficulties, as indicated by behavioral error rates and participant reports. Our results showed that the way the brain processes a FEE depends on the stimulus’ dynamics in a complex manner that shows qualitative differences above and beyond the additive influences of total variability, movement, or quantity of task-specific information in the stimuli. In the remaining part of the Discussion, we first discuss briefly the implications of the behavioral results for the interpretation of the EEG analysis. We then discuss some aspects of our most prominent results from the perspective of how the impaired dynamics of FEEs (either static or unnatural dynamic) differentiates the brain processing of FEEs of natural dynamics.

 

Source:

http://doi.org/10.1371/journal.pone.0181225

 

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