Evaluating the emotional accuracy of AI-generated facial expressions in neurotypical individuals

This study examines the ability of generative artificial intelligence to produce facial expressions representing basic emotions in a neutral context using black-and-white cartoon imagery. Mentalization, the capacity to recognize and interpret one's own and others' mental states, is critical for social interaction and emotional regulation. We explored the emotional validation of artificial intelligence (AI)-generated images by assessing the agreement between human interpretations of emotions and those generated by an AI model. Thirty-four participants evaluated images depicting six basic emotions: sadness, anger, happiness, surprise, fear, and disgust. Our findings revealed significant variability in human agreement, with higher concordance for sadness, anger, and happiness (87%, 73%, and 69%, respectively) and lower agreement for fear, surprise, and disgust (3%, 9%, and 14%, respectively). No significant gender differences were found in emotion recognition accuracy, although a positive correlation between age and accuracy indicated that older participants may possess greater emotional insight. These results suggest that while AI can effectively replicate certain emotional expressions, its capacity to convey more nuanced emotions remains limited. The study underscores the need for advanced training data that encompass a broader range of emotional expressions and cultural nuances to enhance the applicability of AI in mental health and interpersonal communication. Furthermore, this research is particularly relevant for improving AI's ability to generate clear and accurate emotional cues could advance its utility as a tool for social and emotional learning.

The online version contains supplementary material available at 10.1007/s10791-025-09630-1.