ArticleNature communications2022
Incorporating social knowledge structures into computational models.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Modeling how autistic and non-autistic groups learn about their own and each other's preferences.Nature. Mental health · 2026Article
- Computational origins of cortical brain circuits for social cognition.Nature reviews. Neuroscience · 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
- Self-esteem instability in major depressive disorder: a computational analysis of expectation updating in response to performance feedback.Frontiers in psychiatry · 2026Article
- Selective Integration of Social Feedback Promotes a Stable and Positively Biased Self-Concept.Scandinavian journal of psychology · 2025Article
- Learning from financial rewards and punishments reduces the in-group bias in social approach without changing the in-group bias in impressions.Royal Society open science · 2025Article
- 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
- Article
- Self-utility distance as a computational approach to understanding self-concept clarity.Communications psychology · 2025Article
- Distinct neural computations scale the violation of expected reward and emotion in social transgressions.Communications biology · 2025Article
- A computational cognitive neuroscience approach for characterizing individual differences in autism: Introduction to Special Issue.Personality neuroscience · 2025Article
- Using the cognitive rigidity-flexibility dimension to deepen our understanding of the autism spectrum.Personality neuroscience · 2025Review
- A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.Communications biology · 2024Article
- Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders.Neuroscience and biobehavioral reviews · 2023Review
- A Fluid Self-Concept: How the Brain Maintains Coherence and Positivity across an Interconnected Self-Concept While Incorporating Social Feedback.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Computational models of behavioral addictions: State of the art and future directions.Addictive behaviors · 2023Review
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Authors and funding
3 authors.
Funding
Abstract
To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points-with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.
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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.