Judging emotional authenticity from dynamic facial expressions: a preregistered Bayesian analysis of individual differences

Abstract Accurately judging whether another person’s emotional display is genuine or deliberate is a core component of social interaction, yet prior research linking this ability to socio-cognitive traits has produced mixed and imprecise findings. Much of this inconsistency stems from small samples, static stimuli, and analytic approaches that overstate weak effects. In a high-precision preregistered study ( N = 605), we examined how Theory of Mind, empathy, and alexithymia relate to emotional authenticity judgments using dynamic facial expressions that better approximate naturalistic emotion perception. Participants evaluated the genuineness, perceived intensity, and confidence of dynamic surprise expressions that were either genuinely elicited or deliberately produced through simulation, imitation, or rehearsal. Bayesian Model Averaging revealed that Theory of Mind—but not empathy or alexithymia—was reliably associated with greater accuracy in authenticity judgments, accounting for approximately 9% of variance and primarily reflecting reduced extremity in genuineness and intensity ratings. In contrast, individual differences in imaginative engagement were associated with greater subjective confidence, without corresponding gains in accuracy. These findings clarify which socio-cognitive traits meaningfully contribute to authenticity detection under realistic conditions and highlight the importance of large samples, dynamic stimuli, and principled (Bayesian) modeling for resolving longstanding inconsistencies in emotion perception research.