The Relative Contributions of Traits and Contexts on Social Network Learning

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.

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Publication Details

Journal
Open Mind
Published
2025-01-01
DOI
https://doi.org/10.1162/opmi.a.31
Primary Topic
Knowledge Management and Sharing
Type
article
Field-Weighted Citation Impact
0.00

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article

The Relative Contributions of Traits and Contexts on Social Network Learning

Ameer Ghouse, Raphael Kaplan
Open Mind
Knowledge Management and Sharing
article

The Relative Contributions of Traits and Contexts on Social Network Learning

Ameer Ghouse, Raphael Kaplan
article en

Abstract

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.

Open MindVol. 9
Universitat Jaume I (ES)
Universitat Jaume I
Openalex Percentile: Top 68%
Knowledge Management and Sharing
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The Relative Contributions of Traits and Contexts on Social Network Learning — Ameer Ghouse, Raphael Kaplan · Open Mind (2025) | TGRS Research Map | TGRS