The Meso-Expression Test (MET): A Novel Assessment of Emotion Perception

Abstract

Emotion perception is a primary facet of Emotional Intelligence (EI) and the underpinning of interpersonal communication. In this study, we examined meso-expressions—the everyday, moderate-intensity emotions communicated through the face, voice, and body. We theoretically distinguished meso-expressions from other well-known emotion research paradigms (i.e., macro-expression and micro-expressions). In Study 1, we demonstrated that people can reliably discriminate between meso-expressions, and we created a corpus of 914 unique video displays of meso-expressions across a race- and gender-diverse set of expressors. In Study 2, we developed a novel video-based assessment of emotion perception ability: The Meso-Expression Test (MET). In this study, we found that the MET is psychometrically valid and demonstrated measurement equivalence across Asian, Black, Hispanic, and White perceiver groups and across men and women. In Study 3, we examined the construct validity of the MET and showed that it converged with other well-known measures of emotion perception and diverged from cognitive ability. Finally, in Study 4, we showed that the MET is positively related to important psychosocial outcomes, including social well-being, social connectedness, and empathic concern and is negatively related to alexithymia, stress, depression, anxiety, and adverse social interactions. We conclude with a discussion focused on the implications of our findings for EI ability research and the practical applications of the MET.

Development and Validation of an Ability Measure of Emotion Understanding: The Core Relational Themes of Emotion (CORE) Test

“…We then generated a confusion matrix (see Tables S3-S6). A confusion matrix indicates the proportion of participants who selected the target response on the diagonal of the matrix (i.e., how often they picked the target response), and the proportion of participants that selected any "distractor" responses on the off-diagonal (e.g., LaPalme et al 2023;Laukka et al 2016). To aid interpretability, we converted raw hit rates into a proportion index based on the total number of response options (i.e., 24 possible responses/emotions).…”