The Relative Contributions of Traits and Contexts on Social Network Learning

Bibliographic Data

ID22157777
AuthorsAmeer Ghouse (0000-0002-2970-8227, Universitat Jaume I), Raphael Kaplan (0000-0002-5023-1566, Universitat Jaume I)
Year2025
Volume9
Pages1506-1527
Publication date2025-09-26
Open AccessYes
TypeARTICLE
VenueOpen MIND (JOURNAL)
Journal identifiersISSN: 2470-2986 • E-ISSN: 2470-2986
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/opmi.a.31
PMID41036496
OpenAlexW4414780716
LanguageEN
References cited92

Navigating the social world is guided by remembering which people know each other. Yet, different factors might influence how social relationships are remembered, where people’s shared attributes could distort a social network’s mnemonic representation. Here, we study whether dyadically shared contexts and personality traits impact how people remember relationships in social networks. Through varying levels of network topological complexity, we find the contexts where people know each other are most memorable and that better contextual retrieval predicts relationship recall. In contrast, shared personality traits affect relationship recall differently depending on social network complexity, where shared negatively valenced traits relate to worse relationship recall in the simple network. Subsequent modeling revealed that as networks become more complex, relationships between more centrally positioned individuals that share negatively valenced traits are better recalled compared to less well-connected individuals. These results suggest contextual memory can serve as a scaffold for remembering relationships in a social network, while affective traits’ impact on social network retrievability depends on emotional valence and the individuals involved. More generally, our findings give insight into how the same social network can be represented differently based on one’s past experience

Big Five personality traits · Mnemonic · Personality · Recall · Social connectedness · Knowledge Management and Sharing

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Open DOIOpen Access