Emotional intelligence and facial emotion recognition from static freeze-frame images in a simulated video-call display

Digital displays increasingly shape how people perceive others in remote and computer-mediated settings, potentially altering access to facial emotional cues. When nonverbal information is constrained, individuals may rely on limited facial signals to infer affective meaning. This study examines whether emotional intelligence (EI) is associated with static facial emotion recognition accuracy in simulated video-call displays. Empirically, we used a single-shot forced-choice recognition task based on static freeze-frame screenshots presented in a video-call interface, without live interaction, dynamic video, audio cues, or turn-taking, thereby isolating a constrained perceptual component of emotion decoding. Using data from 154 participants who completed the Wong and Law Emotional Intelligence Scale alongside the task, results indicate that others’ emotion appraisal (OEA) is positively associated with decoding accuracy, whereas regulation of emotion (ROE), self-emotional appraisal (SEA), and use of emotion (UOE) are not reliably related to performance. Overall, EI accounts for only a small share of variance in static decoding accuracy. Taken together, the findings suggest that interpersonal appraisal abilities may be modestly relevant for static facial emotion decoding in reduced-cue digital displays and that EI plays a limited perceptual role in this paradigm, offering a starting point for future research using more dynamic and context-embedded designs.