Deliberate Facial Mimicry As a Skill That Predicts Emotion Recognition Performance

How do we recognize emotions in others' faces? Embodied-simulation theory suggests we mimic observed expressions to enhance our understanding. However, empirical evidence for this online mimicry account remains mixed. Prior research typically measured facial mimicry and emotion recognition concurrently. Here, we test a complementary embodied-simulation account by measuring deliberate facial mimicry quality as an independent skill. We test whether individual differences in this skill - potentially reflecting richer sensorimotor representations - predict emotion recognition performance. In Study 1 (N = 34), participants completed three facial-emotion-recognition tasks, then performed an instructed-mimicry task. We developed and validated a deep-learning-based algorithm to quantify mimicry accuracy and timing, applied diffusion modeling to capture recognition efficiency, and linked mimicry skill to emotion recognition performance. Study 2 (N = 50) was a preregistered replication. Results showed that accurate mimickers recognized emotions more precisely and efficiently. Additionally, longer mimicry latencies predicted higher recognition accuracy. Our findings link facial mimicry quality and emotion recognition performance, highlighting mimicry as a measurable skill that predicts social-cognitive ability.

The online version contains supplementary material available at 10.1007/s42761-026-00366-9.