Mentalizing impairments across 11 psychiatric conditions: A transdiagnostic systematic review and network meta-analysis of tasks with static illustrations.

BACKGROUND

Impairments in mentalizing, or theory of mind, occur across psychiatric disorders. Static illustrations are widely used to assess mentalizing due to their simplicity, and they allow assessment of specific cognitive processes. However, systematic comparisons of impairments between psychiatric disorders, neurodevelopmental disorders, and at-risk groups in mentalizing tasks with static illustrations are currently lacking.

METHODS

A systematic review with pairwise and network meta-analyses (NMA) was conducted to evaluate mentalizing impairments using tasks with static illustrations across psychiatric disorders compared to healthy controls (HCs) and between groups. Subgroup analyses examined specific mentalizing domains (false belief, humor, and intentionality), and meta-regression analyses explored potential moderators. The ceiling effects of specific tasks were also examined.

RESULTS

Eighty-nine studies were included, involving 9,038 participants and 11 psychiatric conditions. Significant mentalizing deficits were observed across all conditions versus HCs, except for the familial risk for bipolar disorder group. NMA demonstrated that schizophrenia (g = -0.960) and early schizophrenia (g = -0.785) exhibited the most pronounced impairments, followed by borderline personality disorder (g = -0.612) and obsessive-compulsive disorder (g = -0.613). Particularly, schizophrenia showed significantly greater deficits than autism, bipolar disorder, clinical and familial high risk for schizophrenia, and depression. Domain-specific analyses highlighted differential impairment patterns. The presence of prominent ceiling effects suggests major limitations of tasks with static illustrations.

CONCLUSIONS

This review provides detailed insights into transdiagnostic and disorder-specific patterns of mentalizing impairments with tasks using static illustrations. Findings highlight the importance of domain-specific approaches, examining interindividual variability, refining assessment tools, and implementing targeted interventions.