Cited by 141Memory Processes and InfluencesMemory, History, Trauma, IdentityComplex Network Analysis Techniques

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

Open access
Coman, Alin, Momennejad, Ida, Drach, Rae D., Geana, Andra·2016·Proceedings of the National Academy of Sciences

Abstract

Significance: Human memory is highly malleable. Because of this malleability, jointly remembering the past along with another individual often results in increased similarity between the conversational partners’ memories. We propose an approach that examines the conversation between a pair of participants as part of a larger network of social interactions that has the potential to reveal how human communities form collective memories. Empirical evidence indicating that dyadic-level conversational alignment processes give rise to community-wide shared memories is presented. We find that individual-level memory updating phenomena and social network structure are two fundamental factors that contribute to the emergence of collective memories.

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Crossref · 10.1073/pnas.1525569113

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  • Synchronization among individuals at a local level leads to the emergence of collective memories and beliefs at a community level.

    “And third, synchronization among individuals at a local level leads to the emergence of collective memories and beliefs at a community level (Coman et al., 2016).”

    From Stories in Action. (2022) · cites this paper as Coman et al., 2016

    The Abstract and Discussion (page numbers unavailable) report that local conversational processes and network structure contribute to collective memories in laboratory-created communities. The Abstract also describes shared beliefs as an emergent outcome thought to arise through communication, but the study does not establish that local synchronization leads to collective beliefs. (LLM, conf=0.86)

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Research works printed but not linked: 2

  1. [28]Coman A, Kolling A, Lewis M, Hirst W. Mnemonic convergence: From empirical data to simulations. In: Agarwal N, Xu K, Osgood N, editors. Social Computing, Behavioral-Cultural Modeling and Prediction. Springer; New York: 2012. pp. 256–265. [Google Scholar]
  2. [34]Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27(2):209–220. [PubMed] [Google Scholar]

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Authors
Coman, A., Momennejad, I., Drach, R. D., & Geana, A.
Affiliations
  • Princeton University (US)
  • State University of New York (US)
Venue
Proceedings of the National Academy of Sciences
Published
2016-06-29
Volume
113
Issue
29
Pages
8171-8176
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2471181430
Cited by
141
References
38
Citations by year
2026: 7 · 2025: 19 · 2024: 9 · 2023: 21 · 2022: 14 · 2021: 11 · 2020: 26 · 2019: 20 · 2018: 10 · 2017: 3 · 2016: 1
FWCI
7.21
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
  • Social Sciences › Arts and Humanities › Philosophy › Memory, History, Trauma, Identity
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
collective memory, social networks, information sharing, memory updating, emergent properties, computer-mediated communication, information dissemination
MeSH terms
Adolescent, Adult, Cognition, Communication, Female, Humans, Male, Mental Recall, Young Adult, Social Networking

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