Dunbar's Number: Group Size and Brain Physiology in Humans Reexamined

Ruiter, Jan de; Weston, Gavin; Lyon, Stephen M.

doi:10.1111/j.1548-1433.2011.01369.x

Cited by 34 publications

(23 citation statements)

References 50 publications

(57 reference statements)

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“…While it is true that many human groups exceed the Dunbar’s threshold [82], it is worth noting that his model does leave room for that possibility, provided that groups find a way to counteract the divisive forces that increase with increasing group size [83], as Johnson has pointed out. In this respect, Carneiro [84] (see also [5]) contrasts the case of the Yanomama villages (fissioning when size rises above 200) to that of the Kayapò (whose villages reach 600/800 inhabitants), arguing that what accounts for the difference is the organization of the second into nested social segments.…”

Section: Introductionmentioning

confidence: 97%

Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective

Alberti¹

2014

PLoS ONE

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Johnson’s scalar stress theory, describing the mechanics of (and the remedies to) the increase in in-group conflictuality that parallels the increase in groups’ size, provides scholars with a useful theoretical framework for the understanding of different aspects of the material culture of past communities (i.e., social organization, communal food consumption, ceramic style, architecture and settlement layout). Due to its relevance in archaeology and anthropology, the article aims at proposing a predictive model of critical level of scalar stress on the basis of community size. Drawing upon Johnson’s theory and on Dunbar’s findings on the cognitive constrains to human group size, a model is built by means of Logistic Regression on the basis of the data on colony fissioning among the Hutterites of North America. On the grounds of the theoretical framework sketched in the first part of the article, the absence or presence of colony fissioning is considered expression of not critical vs. critical level of scalar stress for the sake of the model building. The model, which is also tested against a sample of archaeological and ethnographic cases: a) confirms the existence of a significant relationship between critical scalar stress and group size, setting the issue on firmer statistical grounds; b) allows calculating the intercept and slope of the logistic regression model, which can be used in any time to estimate the probability that a community experienced a critical level of scalar stress; c) allows locating a critical scalar stress threshold at community size 127 (95% CI: 122–132), while the maximum probability of critical scale stress is predicted at size 158 (95% CI: 147–170). The model ultimately provides grounds to assess, for the sake of any further archaeological/anthropological interpretation, the probability that a group reached a hot spot of size development critical for its internal cohesion.

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Section: Introductionmentioning

confidence: 97%

Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective

Alberti¹

2014

PLoS ONE

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“…For the caller-callee interactions in mobile communication there are four fundamental possibilities, namely male to male, male to female, female to male, and female to female, which together with age, kinship, and different levels of friendships affect the strengths of social interactions, giving rise to a wide scope of human sociality. The studies of primate brain size and its relation to their average social group size suggest that humans are able to maintain of the order of 150 stable relationships (Dunbar number) [1][2][3]. In addition the Social Brain hypothesis suggests that on the basis of emotional closeness human social networks can be divided into four cumulative layers of 5, 15, 50 and 150 individuals, respectively [4].…”

Section: Introductionmentioning

confidence: 99%

Quantifying gender preferences in human social interactions using a large cellphone dataset

et al. 2019

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In human relations individuals' gender and age play a key role in the structures and dynamics of their social arrangements. In order to analyze the gender preferences of individuals in interaction with others at different stages of their lives we study a large mobile phone dataset. To do this we consider four fundamental gender-related caller and callee combinations of human interactions, namely male to male, male to female, female to male, and female to female, which together with age, kinship, and different levels of friendship give rise to a wide scope of human sociality. Here we analyse the relative strength of these four types of interaction using call detail records. Our analysis suggests strong age dependence for an individual of one gender choosing to call an individual of either gender. We observe a strong bonding with the opposite gender across most of their reproductive age. However, older women show a strong tendency to connect to another female that is one generation younger in a way that is suggestive of the grandmothering effect. We also find that the relative strength among the four possible interactions depends on phone call duration. For calls of medium and long duration, opposite gender interactions are significantly more probable than same gender interactions during the reproductive years, suggesting potential emotional exchange between spouses. By measuring the fraction of calls to other generations we find that mothers tend to make calls more to their daughters than to their sons, whereas fathers make calls more to their sons than to their daughters. For younger callers, most of their calls go to the same generation contacts, while older people call the younger people more frequently, which supports the suggestion that affection flows downward. Our study primarily rests on resolving the nature of interactions by examining the durations of calls. In addition, we analyse the intensity of the observed effects using a score based on a null model.

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“…As a result, the approach presented here shows the best results for people with more than 150 contacts on Facebook. There is, however, a cognitive limit to the number of friends with whom someone can maintain a steady relationship [33]; this number is held to be about 150 people, but this is not exact, and proposed values vary between 100 and 230 contacts. The students in this study fell within this range.…”

Section: A Challenges Of Using Self-social Media Datamentioning

confidence: 97%

Personalizing Sample Databases With Facebook Information to Increase Intrinsic Motivation

et al. 2017

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Motivation is fundamental for students to achieve successful and complete learning. Motivation can be extrinsic, i.e., driven by external rewards, or intrinsic, i.e., driven by internal factors. Intrinsic motivation is the most effective and must be inspired by the task at hand. Here, a novel strategy is presented to increase intrinsic motivation toward activities requiring the use of sample data sets, by automatically extracting the student's social data from a social media network, to create a personalized sample data set. A tool was developed to access Facebook and create a database to be used in practical assignments on database queries. A user study (N = 74) measured intrinsic motivation before and after the assignment in three different groups of first-year university students. Each group had to create a set of queries of similar difficulty, with each group using a different database. Intrinsic motivation was found to be significantly higher for the group using their own social media data than for the groups using social media data from an unknown person or a business. The approach presented here can be applied to other subjects, such as data mining, image processing, or statistics.

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Dunbar's Number: Group Size and Brain Physiology in Humans Reexamined

Cited by 34 publications

References 50 publications

Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective

Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective

Quantifying gender preferences in human social interactions using a large cellphone dataset

Personalizing Sample Databases With Facebook Information to Increase Intrinsic Motivation

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