Improving the measurement of emotion recognition ability

The ability to recognize other people's emotions from their face, voice, and body (emotion recognition ability, ERA) is crucial to successful functioning in private and professional life. However, currently available tests to measure this ability are of limited ecological validity and their psychometric quality is unclear. In this thesis, I contributed to the field of ERA by developing and validating a new test based on short videos with sound representing a large number of emotions (Geneva Emotion Recognition Test, GERT). Results supported the satisfactory psychometric quality, construct and predictive validity of the GERT. Specifically, the GERT was a more consistent predictor of participants' performance in dyadic negotiation than standard measures of emotional and cognitive intelligence. Moreover, I showed that ERA is an essentially unidimensional ability across emotions and modalities, and that it is meaningfully related to a wide range of other constucts related to social and emotional effectiveness.