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Self-other generalisation shapes social interaction and is disrupted in borderline personality disorder.

Joseph M Barnby | Jen Nguyen | Julia Griem | Magdalena Wloszek | Henry Burgess | Linda J Richards | Jessica Kingston | Gavin Cooper | P Read Montague | Peter Dayan | Tobias Nolte | Peter Fonagy | London Personality and Mood Disorders Consortium
eLife | 2025

Generalising information from ourselves to others, and others to ourselves allows for both a dependable source of navigation and adaptability in interpersonal exchange. Disturbances to social development in sensitive periods can cause enduring and distressing damage to lasting healthy relationships. However, identifying the mechanisms of healthy exchange has been difficult. We introduce a theory of self-other generalisation tested with data from a three-phase social value orientation task - the Intentions Game. We involved humans with (n=50) and without (n=53) a diagnosis of borderline personality disorder and assessed whether infractions to self-other generalisation may explain prior findings of disrupted social learning and instability. Healthy controls initially used their preferences to predict others and were influenced by their partners, leading to self-other convergence. In contrast, individuals with borderline personality disorder maintained distinct self-other representations when learning about others. This allowed for equal predictive performance compared to controls despite reduced updating sensitivity. Furthermore, we explored theory-driven individual differences underpinning contagion. Overall, the findings provide a clear explanation of how self-other generalisation constrains and assists learning, and how childhood adversity is associated with separation of internalised beliefs. Our model makes clear predictions about the mechanisms of social information generalisation concerning both joint and individual reward.

Pubmed ID: 40657727

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Associated grants

  • Agency: Wellcome Trust, United Kingdom
  • Agency: Wellcome Trust, United Kingdom
    Id: 228268/Z/23/Z
  • Agency: Machine Learning Cluster of Excellence,
    Id: 2064/1

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MATLAB (tool)

RRID:SCR_001622

Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

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