Psychological medicine · 2026
Transdiagnostic factors differentially shape choices and decision times in social evaluative learning.
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Sohail Aamir, Magda Aleksandra, Said Muna, Phillips Laura, Lu Yifei, Reynolds Georgina, Comer-Fenn Alice, Russell Jessie, Huafeng Lu, Sims Tally, Saulin Anne, Zhang Lei
Аннотация
BACKGROUND: Learning from feedback provided by others is important for navigating complex social environments. Aberrant processing of such social feedback is implicated in the psychopathology of several mental health disorders including social anxiety and depression. Recent psychiatric research increasingly adopts transdiagnostic approaches that examine mechanisms and constructs cutting across traditional diagnostic categories.
METHODS: Here, we applied reinforcement learning models together with transdiagnostic measures of psychopathology to formally assess social evaluation learning. Human participants (n = 193) completed the Social Evaluation Learning Task, learning whether computer personas 'liked', 'disliked', or were 'neutral' toward them. Participants also completed six questionnaires assessing socially relevant psychiatric traits.
RESULTS: Computational modeling revealed that a model with separate learning parameters for positive and negative feedback best accounted for the learning behavior. Exploratory factor analysis identified three transdiagnostic factors. A factor reflecting social avoidance predicted enhanced learning from negative feedback and a reduced positive learning bias. Socially avoidant individuals with heightened negative learning rates additionally showed slower decision times specifically in positive social contexts. A second factor linking with emotional insensitivity for others predicted a lower choice accuracy in both positively and negatively valenced conditions. No significant associations emerged for a third factor reflecting depressive and mood-related symptoms.
CONCLUSIONS: These results demonstrate how transdiagnostic trait dimensions shape social learning mechanisms through specific behavioral and computational processes.
METHODS: Here, we applied reinforcement learning models together with transdiagnostic measures of psychopathology to formally assess social evaluation learning. Human participants (n = 193) completed the Social Evaluation Learning Task, learning whether computer personas 'liked', 'disliked', or were 'neutral' toward them. Participants also completed six questionnaires assessing socially relevant psychiatric traits.
RESULTS: Computational modeling revealed that a model with separate learning parameters for positive and negative feedback best accounted for the learning behavior. Exploratory factor analysis identified three transdiagnostic factors. A factor reflecting social avoidance predicted enhanced learning from negative feedback and a reduced positive learning bias. Socially avoidant individuals with heightened negative learning rates additionally showed slower decision times specifically in positive social contexts. A second factor linking with emotional insensitivity for others predicted a lower choice accuracy in both positively and negatively valenced conditions. No significant associations emerged for a third factor reflecting depressive and mood-related symptoms.
CONCLUSIONS: These results demonstrate how transdiagnostic trait dimensions shape social learning mechanisms through specific behavioral and computational processes.