Facial Affect Recognition in Depression Using Human Avatars

Abstract

This research assesses facial emotion recognition in depressed patients using a novel dynamic virtual face (DVF) collection. The participant sample comprised 54 stable depressed patients against 54 healthy controls. The experiment entailed a non-immersive virtual reality task of recognizing emotions with DVFs representing the six basic emotions. Depressed patients exhibited a deficit in facial affect recognition in comparison to healthy controls. The average recognition score for healthy controls was 88.19%, while the score was 75.17% for the depression group. Gender and educational level showed no influence on the recognition rates in depressed patients. As for age, the worst results were found in older patients as compared to other cohorts. The average recognition rate for the younger group was 84.18%, 78.63% for the middle-aged group, and 61.97% for the older group, with average reaction times of 4.00 s, 4.07 s, and 6.04 s, respectively.