ReviewNeuroscience and biobehavioral reviews2023
A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders.
Review in Neuroscience and biobehavioral reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Modeling how autistic and non-autistic groups learn about their own and each other's preferences.Nature. Mental health · 2026Article
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- Anchored and Propagated Updating Within Pseudoscientific Belief Systems.Annals of the New York Academy of Sciences · 2026Article
- Estimating and learning personality traits of and from women with borderline personality disorder.Borderline personality disorder and emotion dysregulation · 2026Article
- Psychosocial functioning in personality disorders.Borderline personality disorder and emotion dysregulation · 2025Review
- How the self-concept structures social role learning: insights from computational models.Royal Society open science · 2025Article
- Feature identification learning both shapes and is shaped by spatial object-similarity representations.Communications psychology · 2025Article
- Dissociating social reward learning and behavior in alcohol use disorder.Translational psychiatry · 2025Article
- Using the cognitive rigidity-flexibility dimension to deepen our understanding of the autism spectrum.Personality neuroscience · 2025Review
- Feeling close to others? Social cognitive mechanisms of intimacy in personality disorders.Borderline personality disorder and emotion dysregulation · 2024Article
- Individual differences in autism-like traits are associated with reduced goal emulation in a computational model of observational learning.Nature. Mental health · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.