Social Networking

Cognitive representations of social networks in isolated villages

Abstract

People not only form social networks, they construct mental maps of them. We develop a sampling strategy to evaluate network cognition in 10,072 adults across 82 Honduras villages and systematically map the underlying village networks. In 17 villages, we also discern the genetic relatedness of all 1,333 residents. Observers overestimate the social interactions among kin and are 33.38 percentage points (J) more accurate in judgements of ties between non-kin (95% confidence interval: 31.27–35.49). Counterintuitively, observers had more accurate beliefs about non-kin pairs, especially when the observers were popular, middle-aged, or educated. Observers were less able to accurately judge ties across different religions or wealth. Individuals in villages that cultivate coffee, requiring coordinated effort, demonstrated greater bias to view networks as connected. Finally, more accurate respondents had better access to information that we experimentally introduced to their peers. Overall, people inflate the number of connections in their networks and exhibit varying accuracy and bias, with implications for how people affect and are affected by the social world.



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Fig. 1: Outline of the survey procedure.
Fig. 2: Receiver operator characteristic space.
Fig. 3: Accuracy of social network beliefs.
Fig. 4: Tie determinants of respondent accuracy.
Fig. 5: Village-level perspectives.
Fig. 6: Accuracy within individual respondents and association with exogenous information.
Fig. 7: Bias in error commission.

Data availability

Compliant with our privacy and confidentiality assurances to our research participants and with other legal obligations, data will be made available on our secure server, subject to data release provisions in force at Yale and the Yale Institute for Network Science (or successor entities) at the time of release. Access to data requires proof of IRB approval and human participants certification. Contact nicholas.christakis@yale.edu for inquiries regarding the data.

Code availability

All analysis was conducted in the Julia programming language104 (v.1.10.2). The sampling procedure was executed with the ‘SamplingPerceivedNetworks.jl’ Julia package105 (which we are pleased to release). See the Supplementary Methods for details on software packages used. Additional paper replication materials are available on GitHub at https://github.com/emfeltham/honduras-css-paper-release.git (ref. 106). See Supplementary Results for further details.

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Acknowledgements

We thank all the study participants and the local organizations, doctors and community leaders in Honduras with whom we have interacted. We have benefited from many local connections and support, including the following local partner organizations that played a role in the development or surveying of our cohort: Soluciones para Estudios de la Salud (SES), MURE Consultores, and World Vision. We thank the Ministry of Health in Honduras and Inter-American Developmental Bank for their extensive support and cooperation; J. E. Gámez and E. J. Urrea Carbajal for coordinating the field work in Honduras; R. Negron, L. Nicoll, A.L. Rodriguez de la Rosa, and T. Keegan for support with respect to field operations, data collection and data management; W. Israel for work on modifying the TRELLIS interface to collect the data; B. Valentin and E. Liu for research assistance; D. Gilbert, S. Lou and M. Gerstein for helpful comments. This research was supported by NIH grants R01AG081814 (N.A.C.) and R01AG062668 (N.A.C.) from the National Institute on Aging. Development of the underlying cohort was supported by the Bill and Melinda Gates Foundation (N.A.C.) and the Pershing Square Foundation (N.A.C.), and the genotype information was collected with partial support from the Rothberg Catalyzer (N.A.C.) and the NOMIS Foundation (N.A.C.). Additional support was also obtained from The Paul Graham Foundation (N.A.C.). The funders played no role in the design, data collection or analysis of this paper.

Author information

Authors and Affiliations

  1. Human Nature Lab, New Haven, CT, USA

    Eric Feltham, Laura Forastiere & Nicholas A. Christakis

  2. Department of Sociology, Yale University, New Haven, CT, USA

    Eric Feltham & Nicholas A. Christakis

  3. Department of Biostatistics, Yale University, New Haven, CT, USA

    Laura Forastiere

  4. Department of Statistics and Data Science, Yale University, New Haven, CT, USA

    Laura Forastiere & Nicholas A. Christakis

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  1. Eric Feltham
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Contributions

E.F. conceptualized the project with L.F. and N.A.C. N.A.C. acquired funding. E.F. developed the methodology with L.F. and N.A.C. E.F. conducted formal analysis and wrote the original draft. All authors reviewed and edited the paper.

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Correspondence to
Nicholas A. Christakis.

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Nature Human Behaviour thanks Michael Macy, Javier Mejia, Doug Speed and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Alternative definitions of kinship.

In addition to the binary definition of kinship (used in the primary analyses) and genetic relatedness (Fig. 3b), we consider the effects of (a) specific type of kinship tie as a categorical variable and (b) distance in the kinship network. We find that the categorical results are consistent with the binary definition, and distance in the kinship network broadly corresponds to that of genetic relationship. In both panels, gray bands (LHS) displays 95% confidence ellipses around the mean estimates. Error bars (RHS) display 95% confidence interval around the mean estimates. Results are from n = 9,998 survey respondents in both panels, corresponding to 177,928 individual responses for the TPR estimates, and 477,393 responses for the FPR estimates.

Extended Data Fig. 2 Individual determinants of respondent accuracy.

We observe that several key demographic characteristics are associated with an individual’s ability to accurately predict the ties in their village network. In each panel, the left-hand image shows the marginal effect of the cognizer characteristic on accuracy in ROC-space (grey shading represents the 95% bootstrapped confidence ellipse of the predictions from the two models), and the right-hand image shows the marginal effect with respect to each individual accuracy measure: the true positive rate, false positive rate, and the overall summary measure of accuracy (Youden’s J). Intervals represent 95% confidence levels, calculated via normal approximation for the two rates, and bootstrapped for the J statistic. (a) Gender, (b) Age, (c) Education, (d) Wealth and (e) Network degree (here, effectively an average of the count of first-degree neighbors for the two relationships analysed, personal-private or free-time). Supplementary Fig. 7 presents additional characteristics. Results are from n = 9,998 survey respondents in both panels, corresponding to 177,928 individual responses for the TPR estimates, and 477,393 responses for the FPR estimates.

Extended Data Fig. 3 Tie determinants of respondent accuracy.

We find that a range of properties of ties have statistically significant associations with their tendency to be accurately conceived. In each panel, LHS, marginal effect on accuracy in ROC-space. Grey shading represents the 95% bootstrapped confidence ellipse of the predictions from the two models. RHS, marginal effect of each individual accuracy measure: the true positive and false positive rates and the summary measure, Youden’s J. Intervals represent 95% confidence levels, calculated via normal approximation for the two rates, and bootstrapped for J, around the mean estimates. Estimates are stratified by whether they are of a tie among kin or not. (a) Relationship type; we include a covariate for the two relationships considered, free-time or personal-private. (b) Gender combination of tie members, for example, both women or both men. (c) Average age of tie members. (d) Difference in age between tie members. (e) Average degree of tie members. (f) Difference in degree between tie members. (g) Cognizer-to-tie geodesic distance. Individuals may or may not have a defined path between them in the reference network; when there is a path, individuals exist at a geodesic distance defined as the minimum number of steps between them; note that individuals who do not have a path between them necessarily have a path in at least one of other networks considered in this study, by design. (h) Distance between tie members. When a tie does not exist between two individuals, a specific geodesic distance may separate them (or they may have no path between them in the network). The TPR is set to the population average; but it does not have a meaningful interpretation in assessments of ties that do not exist. Parameters are fit from separate models of each rate, conditional on tie verity in the reference network. See Methods for details of model specification. Results are from n = 9,998 survey respondents in both panels, corresponding to 177,928 individual responses for the TPR estimates, and 477,393 responses for the FPR estimates.

Extended Data Fig. 4 Tie social identity determinants of respondent accuracy.

We find that characteristics related to the social identity of a pair of individuals (i and j) affects how well that tie is conceived of by individuals k. (a-d) LHS, marginal effects on accuracy in ROC-space. Grey shading represents the 95% bootstrapped confidence ellipse of the predictions from the two models. RHS, marginal effect of each individual accuracy measure: the true positive and false positive rates and the summary measure, Youden’s J. Intervals represent 95% confidence levels, calculated via normal approximation for the two rates, and bootstrapped for J. (a) Religion combination of tie members. (b) Indigenous status of the pair. Parameters are fit from separate models of each rate, conditional on tie verity in the reference network. (c) Absolute difference in wealth between the tie members. (d) Average wealth of the tie members. (e) Interaction between the average wealth of a pair and the cognizer’s wealth on the (LHS) TPR and (RHS) FPR. (f) Interaction between the average wealth of a pair and the cognizer’s wealth on the summary measure, J. See Methods for details of model specification. Results are from n = 9,998 survey respondents in both panels, corresponding to 177,928 individual responses for the TPR estimates, and 477,393 responses for the FPR estimates.

Extended Data Table 1 Social network belief questionnaire
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Extended Data Table 2 Contrasts for tie characteristics
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Extended Data Table 3 Contrasts for respondent and village characteristics
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Extended Data Table 4 Accuracy on kinship ties
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Supplementary information

Supplementary Information

Supplementary Discussion, Methods, Results, Figs. 1–18 and Tables 1–22.

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Feltham, E., Forastiere, L. & Christakis, N.A. Cognitive representations of social networks in isolated villages.
Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02221-6

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