IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008162.html
   My bibliography  Save this article

Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder

Author

Listed:
  • Lara Henco
  • Andreea O Diaconescu
  • Juha M Lahnakoski
  • Marie-Luise Brandi
  • Sophia Hörmann
  • Johannes Hennings
  • Alkomiet Hasan
  • Irina Papazova
  • Wolfgang Strube
  • Dimitris Bolis
  • Leonhard Schilbach
  • Christoph Mathys

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 nonsocial 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 MDD patients showed the opposite pattern and SCZ 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 summary: People 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 BPD 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 values. Instead, we found exagerrated 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 in both disorders.

Suggested Citation

  • Lara Henco & Andreea O Diaconescu & Juha M Lahnakoski & Marie-Luise Brandi & Sophia Hörmann & Johannes Hennings & Alkomiet Hasan & Irina Papazova & Wolfgang Strube & Dimitris Bolis & Leonhard Schilbac, 2020. "Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-22, September.
  • Handle: RePEc:plo:pcbi00:1008162
    DOI: 10.1371/journal.pcbi.1008162
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008162
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008162&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1008162?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Klaas E Stephan & Karl J Friston, 2010. "Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitrije Marković & Jan Gläscher & Peter Bossaerts & John O’Doherty & Stefan J Kiebel, 2015. "Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-34, October.
    2. Benjamin Patrick Evans & Mikhail Prokopenko, 2021. "A maximum entropy model of bounded rational decision-making with prior beliefs and market feedback," Papers 2102.09180, arXiv.org, revised May 2021.
    3. Andreea O Diaconescu & Christoph Mathys & Lilian A E Weber & Jean Daunizeau & Lars Kasper & Ekaterina I Lomakina & Ernst Fehr & Klaas E Stephan, 2014. "Inferring on the Intentions of Others by Hierarchical Bayesian Learning," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-19, September.
    4. Falk Lieder & Klaas E Stephan & Jean Daunizeau & Marta I Garrido & Karl J Friston, 2013. "A Neurocomputational Model of the Mismatch Negativity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
    5. Giovanni Leone & Charlotte Postel & Alison Mary & Florence Fraisse & Thomas Vallée & Fausto Viader & Vincent Sayette & Denis Peschanski & Jaques Dayan & Francis Eustache & Pierre Gagnepain, 2022. "Altered predictive control during memory suppression in PTSD," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    6. Benjamin Skerritt-Davis & Mounya Elhilali, 2018. "Detecting change in stochastic sound sequences," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-24, May.
    7. Marie Devaine & Jean Daunizeau, 2017. "Learning about and from others' prudence, impatience or laziness: The computational bases of attitude alignment," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-28, March.
    8. Jean Daunizeau & Kerstin Preuschoff & Karl Friston & Klaas Stephan, 2011. "Optimizing Experimental Design for Comparing Models of Brain Function," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-18, November.
    9. Payam Piray & Nathaniel D. Daw, 2024. "Computational processes of simultaneous learning of stochasticity and volatility in humans," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    10. Jean Daunizeau & Hanneke E M den Ouden & Matthias Pessiglione & Stefan J Kiebel & Karl J Friston & Klaas E Stephan, 2010. "Observing the Observer (II): Deciding When to Decide," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-19, December.
    11. Marie Devaine & Guillaume Hollard & Jean Daunizeau, 2014. "The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn?," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-14, December.
    12. Fabien Vinckier & Lionel Rigoux & Irma T Kurniawan & Chen Hu & Sacha Bourgeois-Gironde & Jean Daunizeau & Mathias Pessiglione, 2019. "Sour grapes and sweet victories: How actions shape preferences," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-24, January.
    13. Jean Daunizeau & Vincent Adam & Lionel Rigoux, 2014. "VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-16, January.
    14. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1008162. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.