Evaluates a new framework for social cognition analysis, suggesting finer assessments in adults.
Objective Current social cognition assessments often yield global summary scores that obscure clinically meaningful variability across subcomponents. This study proposes a multilevel interpretive framework for the Social Cognition Battery (Batterie de Cognition Sociale, BCS-Greco) that enables fine-grained analysis of facial emotion recognition, mental state inference, and theory of mind reasoning. Method Analyses were based on partially overlapping normative samples of healthy French-speaking adults (age range: 20–69 years) who completed facial emotion recognition (FER/IDEMO; n = 155), Reading the Mind in the Eyes Test (RMET; n = 175), and theory of mind (ToM) tasks assessing first-order and second-order belief attribution as well as faux pas recognition ( n = 129–145; adapted from Baron-Cohen, 2000). A data-driven stratification approach identified optimal normative groupings based on empirically observed age and sex effects. Scoring dimensions included emotion-specific accuracy, intensity-dependent performance profiles, systematic misattribution patterns (confusion matrices), response latencies, and ToM subcomponents by inferential order and cognitive dimension. Results Age-related decline emerged for anger and fear recognition ( p = .001) but not for joy or disgust, which showed ceiling effects. Response times revealed nonlinear age trajectories, with adults aged 50–59 demonstrating longer latencies than those over 60 ( p < .001). Older adults (60+) exhibited significantly elevated misattribution rates ( p = .008), whereas a descriptive tendency toward higher anger-disgust confusion in young men did not reach significance after correction for within-participant clustering. Theory of mind performance remained stable across age groups, with significant differentiation across inferential orders ( p < .001) and cognitive dimensions ( p < .001). Conclusions This multidimensional framework provides normative benchmarks that may enhance clinical utility by enabling identification of emotion-specific performance profiles, detection of processing efficiency variations through response latencies, and characterization of systematic error patterns; thereby extending the interpretive precision of well-validated instruments whose clinical sensitivity has been established across neurological and psychiatric populations.
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