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@ARTICLE{Henco:887802,
      author       = {Henco, Lara and Diaconescu, Andreea O. and Lahnakoski, Juha
                      M. and Brandi, Marie-Luise and Hörmann, Sophia and
                      Hennings, Johannes and Hasan, Alkomiet and Papazova, Irina
                      and Strube, Wolfgang and Bolis, Dimitris and Schilbach,
                      Leonhard and Mathys, Christoph},
      title        = {{A}berrant computational mechanisms of social learning and
                      decision-making in schizophrenia and borderline personality
                      disorder},
      journal      = {PLoS Computational Biology},
      volume       = {16},
      number       = {9},
      issn         = {1553-7358},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2020-04432},
      pages        = {e1008162 -},
      year         = {2020},
      abstract     = {Psychiatric disorders are ubiquitously characterized by
                      debilitating social impairments. These difficulties are
                      thought to emerge from aberrant social inference. In order
                      to elucidate the underlying computational mechanisms,
                      patients diagnosed with major depressive disorder (N = 29),
                      schizophrenia (N = 31), and borderline personality disorder
                      (N = 31) as well as healthy controls (N = 34) performed a
                      probabilistic reward learning task in which participants
                      could learn from social and non-social information. Patients
                      with schizophrenia and borderline personality disorder
                      performed more poorly on the task than healthy controls and
                      patients with major depressive disorder. Broken down by
                      domain, borderline personality disorder patients performed
                      better in the social compared to the non-social domain. In
                      contrast, controls and major depressive disorder patients
                      showed the opposite pattern and schizophrenia patients
                      showed no difference between domains. In effect, borderline
                      personality disorder patients gave up a possible overall
                      performance advantage by concentrating their learning in the
                      social at the expense of the non-social domain. We used
                      computational modeling to assess learning and
                      decision-making parameters estimated for each participant
                      from their behavior. This enabled additional insights into
                      the underlying learning and decision-making mechanisms.
                      Patients with borderline personality disorder showed slower
                      learning from social and non-social information and an
                      exaggerated sensitivity to changes in environmental
                      volatility, both in the non-social and the social domain,
                      but more so in the latter. Regarding decision-making the
                      modeling revealed that compared to controls and major
                      depression patients, patients with borderline personality
                      disorder and schizophrenia showed a stronger reliance on
                      social relative to non-social information when making
                      choices. Depressed patients did not differ significantly
                      from controls in this respect. Overall, our results are
                      consistent with the notion of a general interpersonal
                      hypersensitivity in borderline personality disorder and
                      schizophrenia based on a shared computational mechanism
                      characterized by an over-reliance on beliefs about others in
                      making decisions and by an exaggerated need to make sense of
                      others during learning specifically in borderline
                      personality disorder.Author summaryPeople suffering from
                      psychiatric disorders frequently experience difficulties in
                      social interaction, such as an impaired ability to use
                      social signals to build representations of others and use
                      these to guide behavior. Compuational models of learning and
                      decision-making enable the characterization of individual
                      patterns in learning and decision-making mechanisms that may
                      be disorder-specific or disorder-general. We employed this
                      approach to investigate the behavior of healthy participants
                      and patients diagnosed with depression, schizophrenia, and
                      borderline personality disorder while they performed a
                      probabilistic reward learning task which included a social
                      component. Patients with schizophrenia and borderline
                      personality disorder performed more poorly on the task than
                      controls and depressed patients. In addition, patients with
                      borderline personality disorder concentrated their learning
                      efforts more on the social compared to the non-social
                      information. Computational modeling additionally revealed
                      that borderline personality disorder patients showed a
                      reduced flexibility in the weighting of newly obtained
                      social and non-social information when learning about their
                      predictive value. Instead, we found exaggerated learning of
                      the volatility of social and non-social information.
                      Additionally, we found a pattern shared between patients
                      with borderline personality disorder and schizophrenia who
                      both showed an over-reliance on predictions about social
                      information during decision-making. Our modeling therefore
                      provides a computational account of the exaggerated need to
                      make sense of and rely on one’s interpretation of
                      others’ behavior, which is prominent},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / 574 -
                      Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-572 / G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32997653},
      UT           = {WOS:000577087300001},
      doi          = {10.1371/journal.pcbi.1008162},
      url          = {https://juser.fz-juelich.de/record/887802},
}