Back

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

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

2026-07-06 physiology
10.64898/2026.07.01.735852 bioRxiv
Show 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.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Psychophysiology
77 papers in training set
Top 0.1%
15.9%
2
Scientific Reports
3612 papers in training set
Top 1%
15.9%
3
PLOS ONE
5266 papers in training set
Top 11%
15.9%
4
Proceedings of the Royal Society B: Biological Sciences
393 papers in training set
Top 1%
5.8%
50% of probability mass above
5
npj Digital Medicine
118 papers in training set
Top 1.0%
5.8%
6
Annals of the New York Academy of Sciences
17 papers in training set
Top 0.1%
5.1%
7
Frontiers in Psychology
56 papers in training set
Top 0.3%
3.7%
8
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 23%
2.2%
9
Royal Society Open Science
214 papers in training set
Top 3%
2.0%
10
Nature Communications
5641 papers in training set
Top 44%
1.8%
11
eLife
5828 papers in training set
Top 55%
1.2%
12
PeerJ
308 papers in training set
Top 7%
1.2%
13
Social Cognitive and Affective Neuroscience
39 papers in training set
Top 0.3%
1.2%
14
Consciousness and Cognition
19 papers in training set
Top 0.2%
1.2%
15
International Journal of Psychophysiology
15 papers in training set
Top 0.2%
1.0%
16
Behavior Research Methods
30 papers in training set
Top 0.5%
0.9%
17
iScience
1154 papers in training set
Top 33%
0.9%
18
Frontiers in Human Neuroscience
77 papers in training set
Top 2%
0.9%
19
Cognition
47 papers in training set
Top 0.6%
0.9%
20
Biology Open
156 papers in training set
Top 3%
0.9%
21
IEEE Access
35 papers in training set
Top 1%
0.6%
22
Journal of The Royal Society Interface
235 papers in training set
Top 4%
0.6%
23
PLOS Computational Biology
1863 papers in training set
Top 23%
0.5%
24
Cerebral Cortex
396 papers in training set
Top 6%
0.5%
25
Frontiers in Physiology
106 papers in training set
Top 4%
0.5%
26
Human Brain Mapping
329 papers in training set
Top 5%
0.5%
27
Neuroscience of Consciousness
16 papers in training set
Top 0.4%
0.5%
28
International Journal of Environmental Research and Public Health
128 papers in training set
Top 6%
0.5%
29
Communications Biology
993 papers in training set
Top 38%
0.5%