ArticleNature neuroscience2023
Neurocomputational mechanism of real-time distributed learning on social networks.
Article in Nature neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Distinct patterns of social contagion under risk and ambiguity.Communications psychology · 2026Article
- Review
- Layers of Social Meaning: Innate, Acquired, and Deliberative Levels of Referential Signal Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Review
- Whom do we prefer to learn from in observational reinforcement learning?PLoS computational biology · 2025Article
- Humans learn generalizable representations through efficient coding.Nature communications · 2025Article
- The Relative Contributions of Traits and Contexts on Social Network Learning.Open mind : discoveries in cognitive science · 2025Article
- Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions.Nature communications · 2024Article
- Pandora's Box.BJPsych international · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social networks shape our decisions by constraining what information we learn and from whom. Yet, the mechanisms by which network structures affect individual learning and decision-making remain unclear. Here, by combining a real-time distributed learning task with functional magnetic resonance imaging, computational modeling and social network analysis, we studied how humans learn from observing others' decisions on seven-node networks with varying topological structures. We show that learning on social networks can be approximated by a well-established error-driven process for observational learning, supported by an action prediction error encoded in the lateral prefrontal cortex. Importantly, learning is flexibly weighted toward well-connected neighbors, according to activity in the dorsal anterior cingulate cortex, but only insofar as social observations contain secondhand, potentially intertwining, information. These data suggest a neurocomputational mechanism of network-based filtering on the sources of information, which may give rise to biased learning and the spread of misinformation in an interconnected society.
Indexed as
Identifiers
36797365What OpenQuestion holds
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.