Evidence map›Paper›PMID 41680952›Full record

ArticleBMC psychology2026

Shared enemies as prosocial tool: how to infer positive relationships from negative relationships in social networks.

Yating Chen, Leping Wang, Qiang Zhou

Abstract read
In one paragraph

Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yating ChenDepartment of Psychology, Wenzhou Medical University, Wenzhou, China.
Leping WangThe Center for Geriatric Medicine, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Qiang ZhouDepartment of Psychology, Wenzhou Medical University, Wenzhou, China. zq@wmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial relationships are shaped by a complex interplay of interests and emotions, making negative interactions inevitable. Research on negative bias and loss aversion demonstrates that people are more sensitive to negative interactions, which often leads to social distancing. However, one study found that individuals with anxious attachment styles may actively seek partners who treat them poorly, suggesting that such relationships can strengthen bonds. But the cognitive mechanisms underlying how negative relationships can signal positive relational outcomes remain unclear. Through five experiments, we examine how shared negative relationships help individuals infer positive relationships and explore the underlying mechanisms.

methodsWe conducted five pre-registered experiments (N = 1,085; M

resultsOur results showed that humans can (a) infer positive relationships from shared negative ones and their quantity; (b) adjust these inferences by group size and contextual boundaries-shifting from resource-based inferring in ambiguous settings to identity-based mechanisms in well-defined groups; and (c) shape network representations from incomplete social knowledge, revealing human differences in cognitive mechanisms.

conclusionsWhile negative relationships can weaken social experiences, shared negative interactions can serve as prosocial tools for inferring positive relationships. Research shows that humans adjust their strategies based on relationship quantity and group structure, enabling them to build networks from incomplete social knowledge.

Indexed as

Interpersonal RelationsSocial BehaviorSocial InteractionSocial NetworkingSocial PerceptionAdolescentAdultFemaleGroup DynamicsHumansMaleSocial GroupYoung AdultIntergroup relationsNegative relationshipRelationship inferenceSocial cognitionSocial network

Identifiers

PMID41680952
PMCPMC13005460

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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.