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Id | Title * | Authors * | Abstract * | Picture * | Thematic fields * | Recommender▼ | Reviewers | Submission date | |
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16 Nov 2024
Discrepancies in the perception of social support relationships (Stage 1 Registered Report)Heike Krüger, Thomas Grund, Srebrenka Letina, Emily Long, Julie Riddell, Claudia Zucca, Mark McCann https://doi.org/10.31219/osf.io/uc2qySocial Support Discrepancies in Adolescence: Dual Perspectives on Perception, Gender Dynamics, and Mental HealthRecommended by Cédric SueurSocial support encompasses various functions within social networks, facilitating emotional, instrumental, and informational exchanges that promote well-being (House et al. 1988; Thoits 2011; Sueur et al. 2021). Emotional support, such as empathy and reassurance, directly contributes to psychological health and can buffer against stress. However, perceived social support often correlates more strongly with well-being than enacted support, which may sometimes yield contrary effects, as studies have shown (Haber et al. 2007; Chu et al. 2010). This discrepancy between perceived and provided support underscores the role of individual perception in social dynamics (Sueur et al. 2024). The cognitive triad theory by Beck (1979) suggests that depressive thought patterns—negative views of self, environment, and future—distort perceptions, which may affect social support recognition. Individuals with depression often struggle to perceive or remember supportive behaviors accurately, filtering out positive feedback (Gotlib and Joormann 2010). These biases highlight the importance of subjective interpretation in social relationships, with social cognition research suggesting that social support exhibits trait-like stability and that pre-existing cognitive schemas shape support perception (Mankowski and Wyer 1997). Gender differences in support perception have been widely documented, with young women generally perceiving and offering more social support than men (Rueger et al. 2016). Socialization influences may explain these discrepancies; for instance, girls often learn to express warmth and empathy more readily, enhancing both their recognition of and access to support (Brashears et al. 2016). Consequently, support dynamics are not only shaped by individual mental health and social network structure but also by sociocultural factors that influence emotional processing and relationship assessment. Krüger et al. (2024) brings innovative elements to understanding social support discrepancies among adolescents by employing a dual-perspective network analysis. Unlike traditional studies that focus on either the support provider’s or receiver’s perspective, this research uses both perspectives within adolescent social networks to reveal the degree of mismatch in support perception. For example, “provided but not perceived” and “perceived but not provided” support discrepancies were identified, illuminating how gender influences support dynamics. Findings reveal that young men are more likely to experience unnoticed support provision, suggesting that gender norms around emotional expression could hinder recognition of support in male-provided interactions. Additionally, the study finds that discrepancies are more common in opposite-sex dyads than same-sex ones, highlighting how gender-based socialization impacts support perceptions. Adolescents, especially in cross-gender interactions, may face interpretative challenges in recognizing support, possibly due to gendered expectations around emotional engagement. This gender-focused insight into social support perception is unique, providing a new layer of understanding for support network dynamics in adolescence. Another innovative aspect is the study’s integration of mental health and loneliness as variables. Contrary to previous assumptions, these factors do not significantly impact support perception discrepancies, challenging the view that mental health primarily skews support perception. This finding suggests that social support recognition issues may be less about individual mental health status and more about relational dynamics and social norms. In methodological terms, the use of multi-level modeling to account for school-level variations and individual differences further advances social support research by offering a more granular view of how environmental and personal factors intersect to shape support perceptions among adolescents. It would be particularly interesting to explore how this methodology could be applied to animal social network analyses (Sueur et al. 2012; Battesti et al. 2015; Borgeaud et al. 2017; Romano et al. 2018), especially given evidence that loneliness exists in monkeys (Capitanio et al. 2014, 2019). For example, studies could investigate whether similar discrepancies exist in animal groups, such as unrecognized affiliative behaviors or mismatches in perceived versus actual social bonds. By adapting this approach, researchers could examine how social perception and interaction influence group cohesion, stress buffering, and overall well-being in animal societies, potentially offering a deeper understanding of the evolutionary and ecological drivers of social support in non-human species. References Battesti M, Pasquaretta C, Moreno C, et al (2015) Ecology of information: social transmission dynamics within groups of non-social insects. Proc R Soc Lond B Biol Sci 282:20142480. https://doi.org/10.1098/rspb.2014.2480 Beck AT (1979) Cognitive Therapy and the Emotional Disorders. Penguin Borgeaud C, Sosa S, Sueur C, Bshary R (2017) The influence of demographic variation on social network stability in wild vervet monkeys. Anim Behav 134:155–165. https://doi.org/10.1016/j.anbehav.2017.09.028 Brashears ME, Hoagland E, Quintane E (2016) Sex and network recall accuracy. Soc Netw 44:74–84. https://doi.org/10.1016/j.socnet.2015.06.002 Capitanio JP, Cacioppo S, Cole SW (2019) Loneliness in monkeys: neuroimmune mechanisms. Curr Opin Behav Sci 28:51–57. https://doi.org/10.1016/j.cobeha.2019.01.013 Capitanio JP, Hawkley LC, Cole SW, Cacioppo JT (2014) A Behavioral Taxonomy of Loneliness in Humans and Rhesus Monkeys (Macaca mulatta). PLOS ONE 9:e110307. https://doi.org/10.1371/journal.pone.0110307 Chu PS, Saucier DA, Hafner E (2010) Meta-Analysis of the Relationships Between Social Support and Well-Being in Children and Adolescents. J Soc Clin Psychol 29:624–645. https://doi.org/10.1521/jscp.2010.29.6.624 Gotlib IH, Joormann J (2010) Cognition and Depression: Current Status and Future Directions. Annu Rev Clin Psychol 6:285–312. https://doi.org/10.1146/annurev.clinpsy.121208.131305 Haber MG, Cohen JL, Lucas T, Baltes BB (2007) The relationship between self-reported received and perceived social support: A meta-analytic review. Am J Community Psychol 39:133–144. https://doi.org/10.1007/s10464-007-9100-9 House JS, Umberson D, Landis KR (1988) Structures and processes of social support. Annu Rev Sociol 14:293–318. https://doi.org/10.1146/annurev.so.14.080188.001453 Heike Krüger, Thomas Grund, Srebrenka Letina, Emily Long, Julie Riddell, Claudia Zucca, Mark McCann (2024) Discrepancies in the perception of social support relationships (Stage 1 Registered Report). OSF preprints, ver.5 peer-reviewed and recommended by PCI Network Science https://doi.org/10.31219/osf.io/uc2qy Mankowski ES, Wyer RS (1997) Cognitive Causes and Consequences of Perceived Social Support. In: Pierce GR, Lakey B, Sarason IG, Sarason BR (eds) Sourcebook of Social Support and Personality. Springer US, Boston, MA, pp 141–165 Romano V, Shen M, Pansanel J, et al (2018) Social transmission in networks: global efficiency peaks with intermediate levels of modularity. Behav Ecol Sociobiol 72:154. https://doi.org/10.1007/s00265-018-2564-9 Rueger SY, Malecki CK, Pyun Y, et al (2016) A meta-analytic review of the association between perceived social support and depression in childhood and adolescence. Psychol Bull 142:1017–1067. https://doi.org/10.1037/bul0000058 Sueur C, Fancello G, Naud A, et al (2024) The Complexity of Social Networks in Healthy Aging: Novel Metrics and Their Associations with Psychological Well-Being. Peer Community J 4:. https://doi.org/10.24072/pcjournal.388 Sueur C, King AJ, Pelé M, Petit O (2012) Fast and accurate decisions as a result of scale-free network properties in two primate species. In: Lecture Notes in Computer Science Sueur C, Quque M, Naud A, et al (2021) Social capital: an independent dimension of healthy ageing. Peer Community J 1:. https://doi.org/10.24072/pcjournal.33 Thoits PA (2011) Mechanisms Linking Social Ties and Support to Physical and Mental Health. J Health Soc Behav 52:145–161. https://doi.org/10.1177/0022146510395592
| Discrepancies in the perception of social support relationships (Stage 1 Registered Report) | Heike Krüger, Thomas Grund, Srebrenka Letina, Emily Long, Julie Riddell, Claudia Zucca, Mark McCann | <p>Objective: Prior research in the area of social support suggests that it is an important influential factor of mental health. Yet, it often remains unclear how much overlap there is between provided support, the perceived availability of suppor... | Social networks | Cédric Sueur | 2024-06-18 13:48:02 | View | ||
14 Jun 2021
![]() Behavioural synchronization in a multilevel society of feral horsesTamao Maeda, Cedric Sueur, Satoshi Hirata, Shinya Yamamoto https://doi.org/10.1101/2021.02.21.432190Feral horses synchronize their collective behavior at multiple levels of organizationRecommended by Brenda McCowan based on reviews by Frédéric Amblard, Krishna Balasubramaniam, Krishna Balasubramaniam and 1 anonymous reviewerIn their article “Behavioural synchronization in a multilevel society of feral horses”, Maeda and colleagues (2021) use stochastic multi-agent based modeling to explore the degree to which feral horses synchronize their behavior across multiple levels of organization. The authors compare a drone-derived empirical data set on a feral population of horses with simulated data from multi-agent-based models to determine whether behavioral synchronization of resting and movement states in a multilevel society can be described by one of three models: A) independent model in which horses do not synchronize, B) anonymous model in which horses synchronize with any individual in any unit, C) unit-level social model in which horses synchronize only within units and D) herd-level social model in which horses synchronize across and within units, but internal synchronization is stronger. In a series of 100 simulations for each of seven different models, the authors conclude that evidence for the herd-level model had the strongest support in relation to the empirical data. This finding suggests that connections among individuals in such multi-level societies are rather complex in that local connections are not the only interactions driving social behavior, and specifically synchronization. This approach could be successfully applied to a number of different species that exhibit multi-level organization and possibly fission-fusion dynamics. This study is especially innovative and interesting for three major reasons. First, the use of drone technology to successfully identify individual animals and generate social networks is highly novel and permits the study of large multi-level social groups of animals that previously have been challenging to study due to limitations in collecting data at an appropriate scale. Second, the comparison of multi-agent-based models with actual empirical data is highly applauded. Most agent-based studies design their parameters from previous empirical studies, (sometimes with questionably simple assumptions) but rarely do they actually compare model outputs to their own empirical data. This is an important next step in the burgeoning field of agent-based modeling. Finally, this study sheds light on the utility of using relatively simple mathematical models to explain highly complex behavior. It also highlights that feral horses can synchronize their behavior beyond clustered local connections which suggests that they possess the cognitive ability to track the behavior of individuals at higher social orders. As the authors state, in a multilevel society, inter-unit distance should be moderate, that is “not too close but not too far” because this strategy simultaneously avoids inter-unit competition while also providing the benefits of social buffering that comes with large group living, such as protection from bachelors or predators. As the authors dutifully note, there were also some limitations to the study: (1) the relatively sparse empirical dataset that made it difficult to resolve the relative fitness of the two herd-level models (absolute versus proportional social models), (2) the lack of a temporal component that would provide a better understanding on how synchronization flows through the social/spatial network, and (3) the limited variation in the parameters tested which constrained identification of their true function in the model. Such limitations, however, provide fruitful avenues for further development of the model in future studies. Overall then, this study provides new insights into the processes underlying the behavioral synchronization process and thus nicely contributes to the understanding of collective behaviors in complex animal societies as well as the evolution and functional significance of multi-level animal societies. This study is a fine addition to both the fields of agent-based modeling and the evolution of collective behavior in complex societies. I thus highly endorse its publication. References Maeda T, Sueur C, Hirata S, Yamamoto S (2021) Behavioural synchronization in a multilevel society of feral horses. bioRxiv, 2021.02.21.432190, ver. 3 peer-reviewed and recommended by Peer community in Network Science. https://doi.org/10.1101/2021.02.21.432190 | Behavioural synchronization in a multilevel society of feral horses | Tamao Maeda, Cedric Sueur, Satoshi Hirata, Shinya Yamamoto | <p style="text-align: justify;">Behavioural synchrony among individuals is essential for group-living organisms. It is still largely unknown how synchronization functions in a multilevel society, which is a nested assemblage of multiple social lev... | ![]() | Animal networks, Biological Networks, Dynamics on networks, Network models, Social networks, Synchronization in networks | Brenda McCowan | Krishna Balasubramaniam, Brianne Beisner, Frédéric Amblard | 2021-02-23 02:46:18 | View |
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