Actors' Facial Movement Magnitude and Cardiac Dynamics Predict Observers' Emotion Believability Ratings

Authors

Galvez-Pol, A., Rambaud, V., Christensen, J. F., Kilner, J. M.

Abstract

In non-verbal communication, observers infer emotions from visible facial movements, yet emotional experiences are described in internal bodily terms (e.g., "my heart skipped a beat"). This contrast highlights a tension between external sensory cues and internal signals. In this context, we examined an overlooked gap in affective science: what makes an emotional portrayal believable, and do believability judgments reflect only what observers can see or also the portraying person's internal cardiac dynamics? To test this, we created 311 scenario-driven acting clips designed to avoid prototypical posed displays. For each clip, we quantified facial movement magnitude from the video, recorded ECG during preparation and enactment, and collected actors' self-reports. Online participants (N = 371) viewed these clips and provided emotion recognition responses and continuous ratings of believability, valence, or arousal. The results show that believability decreased as movement magnitude increased, with a non-linear relationship indicating a stronger penalty as motion increased. Valence further shaped this pattern, with increasing movement reducing believability more strongly for portrayals with negative valence. This effect persisted after accounting for intended emotion, perceived arousal, and emotion recognizability. Cardiac dynamics varied during performance, and actors' higher heart rate variability was associated with higher believability for positively valenced portrayals. Together, these findings show that believability is driven by visible movement cues interpreted in relation to valence, with actors' cardiac dynamics showing selective alignment with believability. These results identify core components of believable emotional expressions and provide a basis for studying such judgments in everyday social interaction.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 07 Jul 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.