Abstract
A widespread and popular belief posits that humans possess a cognitive capacity that is limited to keeping track of and maintaining stable relationships with approximately 150 people. This influential number, ‘Dunbar's number’, originates from an extrapolation of a regression line describing the relationship between relative neocortex size and group size in primates. Here, we test if there is statistical support for this idea. Our analyses on complementary datasets using different methods yield wildly different numbers. Bayesian and generalized least-squares phylogenetic methods generate approximations of average group sizes between 69–109 and 16–42, respectively. However, enormous 95% confidence intervals (4–520 and 2–336, respectively) imply that specifying any one number is futile. A cognitive limit on human group size cannot be derived in this manner.
Keywords: phylogenetic comparative studies, social evolution, brain evolution, primates, mammals
1. Introduction
‘Dunbar's number’ is the notion that there exists a cognitive limit on human groups of about 150 individuals. [1,2] This because ‘[t]o maintain group cohesion, individuals must be able to meet their own requirements, as well as coordinate their behaviour with other individuals in the group. They must also be able to defuse the direct and indirect conflicts that are generated by foraging in the same space’. [3] According to the hypothesis, since the neocortex is commonly believed to play a crucial role in handling social relationships [4], its size should set an upper limit on the number of stable social relationships that primate brains can keep track of and maintain.
The number 150 was established by extrapolating a regression line describing the relationship between group size and relative neocortex size in primates, to humans. [1,2,5,6] That there exists a correlation between group size and relative neocortex size has been replicated in several studies (e. g. [7–14]), though in some cases only for female primates [15,16], but often not finding a significant relationship (making an estimate of Dunbar's number unachievable) [11,14,17,18]. However, the replication studies are of somewhat limited value as most studies have used the same brain data. [14] Additional studies have been made using similar reasoning, but analysing relative brain size instead of relative neocortex size (e. g. [9,19,20]).
The expected human group size of 150 has been substantiated by observations of human communities with group sizes ranging between 100 and 200, including hunter–gatherer communities, military units, businesses, 18th-century and Neolithic villages, information from the Domesday Book [2] and Christmas card networks [6].
‘Dunbar's number’ is often cited1, has had great impact in popular culture (e.g. it featured prominently in Malcolm Gladwell's book Tipping point [21]) and has had consequences such as the Swedish Tax Authority restructuring their offices to stay within the 150-person limit [22], with the implicit but hopefully unintended assumption that their employees have neither family nor friends outside work.
Attempting to make a decisive deconstruction of the empirical basis of Dunbar's number, we here perform Bayesian and generalized least-squares (GLS) phylogenetic comparative analyses on larger datasets of the relationship between group size and both relative brain and relative neocortex sizes, and then extrapolate from these relationships to arrive at an updated estimate of the cognitive limit on human group size, including confidence intervals.
2. Methods
Volumes on brain components were averaged from three sources [18,23,24]. Only measurements of individuals where both brain volume and neocortex volume were available were included in analyses of neocortex size. However, one of the datasets contains brain region measurements that differ significantly from the others [25]. For this reason, we replicated our analyses using data from only the most carefully collected dataset [18]. These results are presented in the electronic supplementary material, appendix S1 (electronic supplementary material, tables S2 and S3). The results from these analyses do not differ in any meaningful way from those provided in the main paper, however. We also present results from measurements of endocranial volumes with corresponding estimates of body mass [26] and replicate all analyses also on anthropoid primates only (electronic supplementary material, tables S1 and S3). Average brain weights, body weights and group size data were taken from one recent study [20], complemented with additional group size data where available [26]. Measures on the human brain [27] and body mass [28] were collected separately. Consensus phylogenies for each dataset were obtained from the 10kTrees website [29]. All brain measures were log-transformed prior to analysis, except when using ratios, that instead were arcsine-square root transformed.
All analyses were executed in R using the packages NLME [30], APE [31], MASS [32] and BRMS [33]. We used Bayesian multilevel models with varying intercepts over species specified by a covariance matrix and phylogenetic generalized least-squares (PGLS) regressions throughout. In all Bayesian analyses, group size was modelled as a continuous gamma distribution and not discrete Poisson because available data are averages calculated from different sources. Results for the Bayesian analyses will differ some for each time running the analyses and are only fully reproducible using the same machine. For the Bayesian analyses, uncertainty associated with phylogeny was added by taking random samples from the posterior distribution of the existing random effects, centred around zero and used for calculating confidence intervals. Ninety-five per cent of confidence intervals for GLS were calculated without phylogenetic effects. As electronic supplementary material, appendix S2, we include the R-code and resulting output.
3. Results
Our results (table 1) reveal that estimates of expected human group sizes vary depending on method and variable choice (Bayesian approximations between 69.2 and 108.6 and GLE approximations between 16.4 and 42.0). Note that these estimates (as was true for Dunbar's original estimated group size) are averages, not estimates of upper bounds. If an upper constraint from this type of statistical reasoning was to be determined, a better approach would be to specify the upper boundary of the 95% confidence interval. As is shown in table 1, however, 95% confidence intervals yield enormous variation in their estimates, 3.8–520.0 and 2.1–336.3, respectively, and thus indicate upper limits far exceeding 150 in almost all cases.
Table 1.
Estimates of human group sizes from phylogenetic comparative analyses of relative primate brain and neocortex sizes. Due to the probabilistic nature of Bayesian statistical tests, replications of these analyses will yield similar but not necessarily identical results.
| model | estimated human group size | lower 95% bound | upper 95% bound |
|---|---|---|---|
| Bayesian analyses | |||
| group sizea ∼ neocortexb + rest of brainb (n = 71) | 69.2 | 3.8 | 292.0 |
| group sizea ∼ neocortexb/rest of brainb (n = 71) | 108.6 | 4.6 | 520.0 |
| group sizea ∼ brain weightc + body weightc (n = 142) | 79.8 | 4.1 | 329.9 |
| generalized least-squares | |||
| group sizea ∼ neocortexb + rest of brainb (n = 71) | 16.4 | 2.1 | 127.7 |
| group sizea ∼ neocortexb/rest of brainb (n = 71) | 42.0 | 5.2 | 336.3 |
| group sizea ∼ brain weightc + body weightc (n = 142) | 23.6 | 3.2 | 175.6 |
| Bayesian analyses | |||
| group sizea ∼ brain volumed + body weightd (n = 126) | 79.1 | 4.0 | 335.4 |
| generalized least-squares | |||
| group sizea ∼ brain volumed + body weightd (n = 126) | 23.8 | 3.2 | 177.8 |
The best model to test the hypothesis, given Dunbar's original formulation (a cognitive limit on group size deduced from relative neocortex size) and the current state-of-the-art of comparative phylogenetic studies, is the Bayesian analyses of group size using the volume of the neocortex, with the volume of the rest of the brain included as a covariate. The estimate from this analysis indicates a human group size average of 69.2 individuals, with a 95% confidence interval ranging from 3.8 to 292.0 individuals. This is not very informative. We also provide results from other methods and variable choices, for comparison, both in table 1 and in electronic supplementary material, appendix S1. These do not provide estimates with more enlightening estimated confidence intervals, though one of our estimates actually came close to 150 (electronic supplementary material, appendix S1; table 1, top row: 134.0), but again, the confidence intervals were enormous (7.0 to 583.2).
4. Discussion
Most research on primate social evolution has not concerned cognitive limitations but instead generally focused on the so-called ‘socio-ecological model of primate social evolution’ where primate group size mainly is determined by socio-ecological factors having to do with foraging and predation, infanticide and sexual selection—not on brain or neocortex size. According to this model of social evolution, females go where it is safe and where there are resources, while males go where the females are [35–38].
Further, it is easily observed that human brains function differently from those of other primates [38–42], as is evidenced by the existence of cumulative cultural evolution resulting in marvels such as Stockholm, symphonies and science [43–45]. This was concisely summarized by de Ruiter et al. [22] in their examination of Dunbar's number: ‘Dunbar's assumption that the evolution of human brain physiology corresponds with a limit in our capacity to maintain relationships ignores the cultural mechanisms, practices, and social structures that humans develop to counter potential deficiencies’.
Also, researchers have disputed the empirical observation of mean human group sizes approximately averaging around 150 persons, presenting empirical observations of group sizes indicating a wide variety of other numbers [46–53]. Thus, ecological research on primate sociality, the uniqueness of human thinking and empirical observations all indicate that there is no hard cognitive limit on human sociality. Our reanalysis provides the last piece of evidence needed to disregard Dunbar's number.
In summary, extrapolating human cognitive limits from regressions on non-human primate data is of limited value for both theoretical and empirical reasons. It is our hope, though perhaps futile, that this study will put an end to the use of ‘Dunbar's number’ within science and in popular media. ‘Dunbar's number’ is a concept with limited theoretical foundation lacking empirical support.
Acknowledgements
We wish to thank Simone Blomberg for helpful advice on the statistical analyses.
Endnotes
Dunbar's original paper has over 2500 citations on Google Scholar.
Data accessibility
Data used in this study have been deposited in the Dryad Digital Repository: https://doi.org/10.5061/dryad.ghx3ffbnn [54].
Authors' contributions
All authors made substantial contributions to conception and design, acquisition of data, analysis and interpretation of data. All authors drafted the article and revised it critically for important intellectual content. All authors approved the final version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Competing interests
We declare we have no competing interests.
Funding
This work was supported by Knut and Alice Wallenberg Foundation, KAW 2015.005 (J.L.).
References
- 1.Dunbar RIM. 1992. Neocortex size as a constraint on group size in primates. J. Hum. Evol. 22, 469-493. ( 10.1016/0047-2484(92)90081-J) [DOI] [Google Scholar]
- 2.Dunbar RIM. 1993. Coevolution of neocortical size, group size and language in humans. Behav. Brain Sci. 16, 681-694. ( 10.1017/S0140525X00032325) [DOI] [Google Scholar]
- 3.Dunbar RIM, Shultz S. 2007. Evolution of the social brain. Science 317, 1344-1347. ( 10.1126/science.1145463) [DOI] [PubMed] [Google Scholar]
- 4.Rilling JK, Sanfey AG. 2011. The neuroscience of social decision-making. Ann. Rev. Psych. 62, 3-48. ( 10.1146/annurev.psych.121208.131647) [DOI] [PubMed] [Google Scholar]
- 5.Dunbar RIM. 2010. How many friends does one person need? Dunbar's number and other evolutionary quirks. London, UK: Faber & Faber. [Google Scholar]
- 6.Hill RA, Dunbar RIM. 2003. Social network size in humans. Hum. Nat. 14, 53-72. ( 10.1007/s12110-003-1016-y) [DOI] [PubMed] [Google Scholar]
- 7.Sawaguchi T, Kudo H. 1990. Neocortical development and social structure in primates. Primates 31, 283-289. ( 10.1007/BF02380949) [DOI] [Google Scholar]
- 8.Barton RA. 1996. Neocortex size and behavioural ecology in primates. Proc. R. Soc. Lond. B Biol. Sci. 263, 173-177. ( 10.1098/rspb.1996.0028) [DOI] [PubMed] [Google Scholar]
- 9.Barton RA. 2001. The evolutionary ecology of the primate brain. In Comparative primate socioecology, vol. 22 (ed. Lee PC), pp. 167-203. Cambridge, UK: Cambridge University Press. [Google Scholar]
- 10.Joffe TH, Dunbar RIM. 1997. Visual and socio–cognitive information processing in primate brain evolution. Proc. R. Soc. Lond. B Biol. Sci. 264, 1303-1307. ( 10.1098/rspb.1997.0180) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Deaner RO, Nunn CL, van Schaik CP. 2000. Comparative tests of primate cognition: different scaling methods produce different results. Brain Behav. Evol. 55, 44-52. ( 10.1159/000006641) [DOI] [PubMed] [Google Scholar]
- 12.Kudo H, Dunbar RIM. 2001. Neocortex size and social network size in primates. Anim. Behav. 62, 711-722. ( 10.1006/anbe.2001.1808) [DOI] [Google Scholar]
- 13.Walker R, Burger O, Wagner J, Von Rueden CR. 2006. Evolution of brain size and juvenile periods in primates. J. Hum. Evol. 51, 480-489. ( 10.1016/j.jhevol.2006.06.002) [DOI] [PubMed] [Google Scholar]
- 14.Wartel A, Lindenfors P, Lind J. 2019. Whatever you want: inconsistent results are the rule, not the exception, in the study of primate brain evolution. PLoS ONE 14, e0218655. ( 10.1371/journal.pone.0218655) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lindenfors P 2005. Neocortex evolution in primates: the ‘social brain’ is for females. Biol. Lett. 1, 407-410. ( 10.1098/rsbl.2005.0362) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lindenfors P, Nunn CL, Barton RA. 2007. Primate brain architecture and selection in relation to sex. BMC Biol. 5: 20. ( 10.1186/1741-7007-5-20) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Street SE, Navarrete AF, Reader SM, Laland KN. 2017. Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates. Proc. Natl Acad. Sci. USA 114, 7908-7914. ( 10.1073/pnas.1620734114) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.DeCasien AR, Higham JP. 2019. Primate mosaic brain evolution reflects selection on sensory and cognitive specialization. Nat. Ecol. Evol. 3, 1483-1493. ( 10.1038/s41559-019-0969-0) [DOI] [PubMed] [Google Scholar]
- 19.Shultz S, Dunbar RIM. 2007. The evolution of the social brain: anthropoid primates contrast with other vertebrates. Proc. R. Soc. B Biol. Sci. 274, 2429-2436. ( 10.1098/rspb.2007.0693) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.DeCasien AR, Williams SA, Higham JP. 2017. Primate brain size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112. ( 10.1038/s41559-017-0112) [DOI] [PubMed] [Google Scholar]
- 21.Gladwell M 2000. The tipping point: how little things can make a big difference. Boston, MA: Little, Brown. [Google Scholar]
- 22.De Ruiter J, Weston G, Lyon SM. 2011. Dunbar's number: group size and brain physiology in humans reexamined. Am. Anth. 113, 557-568. ( 10.1111/j.1548-1433.2011.01369.x) [DOI] [PubMed] [Google Scholar]
- 23.Stephan H, Frahm H, Baron G. 1981. New and revised data on volumes of brain structures in insectivores and primates. Folia Primatol. 35, 1-29. ( 10.1159/000155963) [DOI] [PubMed] [Google Scholar]
- 24.Navarrete AF, Blezer EL, Pagnotta M, De Viet ES, Todorov OS, Lindenfors P, Laland KN, Reader SM. 2018. Primate brain anatomy: new volumetric MRI measurements for neuroanatomical studies. Brain Behav. Evol. 91, 109-117. ( 10.1159/000488136) [DOI] [PubMed] [Google Scholar]
- 25.Navarrete AF, Blezer EL, Pagnotta M, De Viet ES, Todorov OS, Lindenfors P, Laland KN, Reader SM. 2019. Erratum. Brain Behav. Evol. 92, 182-184. ( 10.1159/000496658) [DOI] [PubMed] [Google Scholar]
- 26.Isler K, Kirk EC, Miller JM, Albrecht GA, Gelvin BR, Martin RD. 2008. Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set. J. Hum. Evol. 55, 967-978. ( 10.1016/j.jhevol.2008.08.004) [DOI] [PubMed] [Google Scholar]
- 27.Allen, JS, Damasio H, Grabowski TJ. 2002. Normal neuroanatomical variation in the human brain: an MRI-volumetric study. Am. J. Phys. Anth. 118, 341-358. ( 10.1002/ajpa.10092) [DOI] [PubMed] [Google Scholar]
- 28.Walpole SC, Prieto-Merino D, Edwards P, Cleland J, Stevens G, Roberts I. 2012. The weight of nations: an estimation of adult human biomass. BMC Public Health 12, 1-6. ( 10.1186/1471-2458-12-439) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Arnold C, Matthews LJ, Nunn CL. 2010. The 10kTrees website: a new online resource for primate phylogeny. Evol. Anth. Issues News Rev. 19, 114-118. ( 10.1002/evan.20251) [DOI] [Google Scholar]
- 30.Pinheiro J. 2009. nlme: linear and nonlinear mixed effects models. R package version 3.1-96. http://cranrproject.org/web/packages/nlme/.
- 31.Paradis E, Claude J, Strimmer K. 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290. ( 10.1093/bioinformatics/btg412) [DOI] [PubMed] [Google Scholar]
- 32.Venables WN, Ripley BD. 2013. Modern applied statistics with S-PLUS. New York, NY: Springer Science & Business Media. [Google Scholar]
- 33.Bürkner PC. 2017. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1-28. ( 10.18637/jss.v080.i01) [DOI] [Google Scholar]
- 34.Kappeler PM, Heymann EW. 1996. Nonconvergence in the evolution of primate life history and socio-ecology. Biol. J. Linn. Soc. 59, 297-326. ( 10.1111/j.1095-8312.1996.tb01468.x) [DOI] [Google Scholar]
- 35.Kappeler PM, van Schaik CP. 2002. Evolution of primate social systems. Int. J. Primatol. 23, 707-740. ( 10.1023/A:1015520830318) [DOI] [Google Scholar]
- 36.Thierry B. 2008. Primate socioecology, the lost dream of ecological determinism. Evol. Anth. Issues News Rev. 17, 93-96. ( 10.1002/evan.20168) [DOI] [Google Scholar]
- 37.Clutton-Brock T, Janson C. 2012. Primate socioecology at the crossroads: past, present, and future. Evol. Anth. Issues News Rev. 21, 136-150. ( 10.1002/evan.21316) [DOI] [PubMed] [Google Scholar]
- 38.Lindenfors P. 2018. Primate social evolution. In International encyclopedia of anthropology (ed. Callan H). Hoboken, NJ: John Wiley & Sons, Ltd. [Google Scholar]
- 39.Acedo-Carmona C, Gomila A. 2016. A critical review of Dunbar's social brain hypothesis. Revista Internacional de Sociología 74, e038. ( 10.3989/ris.2016.74.3.037) [DOI] [Google Scholar]
- 40.Ghirlanda S, Lind J, Enquist M. 2017. Memory for stimulus sequences: a divide between humans and other animals? R. Soc. Open Sci. 4, 161011. ( 10.1098/rsos.161011) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Heyes C. 2018. Cognitive gadgets: the cultural evolution of thinking. Amsterdam, The Netherlands: Harvard University Press. [Google Scholar]
- 42.Tomasello M. 2018. A natural history of human thinking. Amsterdam, The Netherlands: Harvard University Press. [Google Scholar]
- 43.Lindenfors P 2019. Det kulturella djuret. Stockholm, Sweden: Ordfront. [Google Scholar]
- 44.Richerson PJ, Boyd R. 2008. Not by genes alone: how culture transformed human evolution. Chicago, IL: University of Chicago Press. [Google Scholar]
- 45.Henrich J. 2017. The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter. Princeton, NJ: Princeton University Press. [Google Scholar]
- 46.Killworth PD, Johnsen EC, Bernard HR, Shelley GA, McCarty C. 1990. Estimating the size of personal networks. Soc. Netw. 12, 289-312. ( 10.1016/0378-8733(90)90012-X) [DOI] [Google Scholar]
- 47.Bernard HR, Johnsen EC, Killworth PD, Robinson S. 1989. Estimating the size of an average personal network and of an event population. In The small world (ed. Kochen M), pp. 159-175. Norwood, NJ: Ablex Publishing. [Google Scholar]
- 48.McCarty C, Killworth PD, Bernard HR, Johnsen EC, Shelley GA. 2001. Comparing two methods for estimating network size. Hum. Org. 60, 28-39. ( 10.17730/humo.60.1.efx5t9gjtgmga73y) [DOI] [Google Scholar]
- 49.Zheng T, Salganik MJ, Gelman A. 2006. How many people do you know in prison? Using overdispersion in count data to estimate social structure in networks. J. Am. Stat. Ass. 101, 409-423. ( 10.1198/016214505000001168) [DOI] [Google Scholar]
- 50.McCormick TH, Salganik MJ, Zheng T. 2010. How many people do you know?: efficiently estimating personal network size. J. Am. Stat. Ass. 105, 59-70. ( 10.1198/jasa.2009.ap08518) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Apicella CL, Marlowe FW, Fowler JH, Christakis NA. 2012. Social networks and cooperation in hunter-gatherers. Nature 481, 497-501. ( 10.1038/nature10736) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Layton R, O'Hara S, Bilsborough A. 2012. Antiquity and social functions of multilevel social organization among human hunter-gatherers. Int. J. Primatol. 33, 1215-1245. ( 10.1007/s10764-012-9634-z) [DOI] [Google Scholar]
- 53.Mac Carron P, Kaski K, Dunbar R. 2016. Calling Dunbar's numbers. Soc. Netw. 47, 151-155. ( 10.1016/j.socnet.2016.06.003) [DOI] [Google Scholar]
- 54.Lindenfors P, Wartel A, Lind J. 2021. Data from: ‘Dunbar’s number’ deconstructed. Dryad Digital Repository. ( 10.5061/dryad.ghx3ffbnn) [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Citations
- Lindenfors P, Wartel A, Lind J. 2021. Data from: ‘Dunbar’s number’ deconstructed. Dryad Digital Repository. ( 10.5061/dryad.ghx3ffbnn) [DOI] [PMC free article] [PubMed]
Data Availability Statement
Data used in this study have been deposited in the Dryad Digital Repository: https://doi.org/10.5061/dryad.ghx3ffbnn [54].
