Huafeng Lu

BackgroundLearning 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. MethodsHere, 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. ResultsComputational 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 faster reaction 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 valanced conditions and was associated with faster reaction times overall. No significant associations emerged for a third factor reflecting depressive and mood-related symptoms. ConclusionsThese results demonstrate how transdiagnostic trait dimensions shape social learning mechanisms through specific behavioral and computational processes.

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.

Digitalisation has transformed public transport services worldwide, yet disparities in access, skills, and outcomes continue to reinforce a digital divide in how citizens engage with these services. While previous studies have examined the digital divide in internet use and smart technologies, few have explored how these inequalities manifest in everyday interactions with digital public transport platforms, extending to citizen participation in government-provided digital services. To this end, we investigated user experiences of public transport applications in Birmingham, whose digital inclusion is disproportionate to its status as a major urban and economic hub in the UK, by analysing 4,275 user reviews on public transport services collected from the Apple App Store and Google Play Store between 2016 and 2025. Using a mixed natural language processing (NLP) approach combining topic modelling and sentiment analysis, we identified key themes and emotional tones in user feedback. Results revealed three dominant topics: App Performance, Usability, and Service Satisfaction, each conceptually aligned with the access, ability, and motivation dimensions of the digital divide theory. Sentiment analysis showed generally negative evaluations, particularly around technical access barriers and satisfaction issues. By integrating user-generated data with digital divide theory, these results provide novel empirical evidence of how inequalities in digital engagement persist within digital twin planning and smart city transport systems, and highlight the importance of inclusive digital design for equitable and sustainable mobility access.