Inferring Learning Strategies from Cultural Frequency Data

  • Anne KandlerEmail author
  • Adam Powell
Part of the Replacement of Neanderthals by Modern Humans Series book series (RNMH)


Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population. We review the previous methodology that has attempted to infer the underlying social learning processes from such data, showing that they may apply statistical methods with insufficient power to draw reliable inferences. We then introduce a generative inference framework that allows robust inferences on the social learning processes that underlie cultural frequency data. Using developments in population genetics—in the form of generative simulation modelling and approximate Bayesian computation—as our model, we demonstrate the strength of this method with an example based on simulated data.


Social learning Cultural evolution Generative inference Approximate Bayesian computation 



We thank Kenichi Aoki and Jeremy Kendal for their constructive comments which helped improving this chapter.


  1. Acerbi A, Bentley RA (2014) Biases in cultural transmission shape the turnover of popular traits. Evol Hum Behav 35:228–236CrossRefGoogle Scholar
  2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723CrossRefGoogle Scholar
  3. Aoki K, Wakano JY, Feldman MW (2005) The emergence of social learning in a temporally changing environment: a theoretical model. Curr Anthropol 46:334–340CrossRefGoogle Scholar
  4. Bass FM (1969) A new product growth model for consumer durables. Manag Sci 15:215–227CrossRefGoogle Scholar
  5. Baum WM, Richerson PJ, Efferson CM, Paciotti BM (2004) Cultural evolution in laboratory microsocieties including traditions of rule giving and rule following. Evol Hum Behav 25:305–326CrossRefGoogle Scholar
  6. Beaumont MA (2010) Approximate Bayesian computation in evolution and ecology. Annu Rev Ecol Evol Syst 41:379–406CrossRefGoogle Scholar
  7. Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162(4):2025–2035Google Scholar
  8. Beaumont MA, Nielsen R, Robert C, Hey J, Gaggiotti O, Knowles L, Estoup A, Panchal M, Corander J, Hickerson M, Sisson SA, Fagundes N, Chikhi L, Beerli P, Vitalis R, Cornuet J-M, Huelsenbeck J, Foll M, Yang Z, Rousset F, Balding D, Excoffier L (2010) In defence of model-based inference in phylogeography. Mol Ecol 19:436–446CrossRefGoogle Scholar
  9. Beheim BA, Thigpen C, McElreath R (2014) Strategic social learning and the population dynamics of human behavior: the game of Go. Evol Hum Behav 35:351–357. doi: 10.1016/j.evolhumbehav.2014.04.001 CrossRefGoogle Scholar
  10. Bentley RA, Shennan SJ (2003) Cultural transmission and stochastic network growth. Am Antiq 68:459–485CrossRefGoogle Scholar
  11. Bentley RA, Hahn MW, Shennan SJ (2004) Random drift and culture change. Proc R Soc B 271:1443–1450CrossRefGoogle Scholar
  12. Bentley RA, Lipo CP, Herzog HA, Hahn MW (2007) Regular rates of popular culture change reflect random copying. Evol Hum Behav 28:151–158CrossRefGoogle Scholar
  13. Boyd R, Richerson PJ (1985) Culture and the evolutionary process. The University of Chicago Press, ChicagoGoogle Scholar
  14. Boyd R, Richerson PJ (1988) An evolutionary model of social learning: the effects of spatial and temporal variation. In: Zentall T, Galef BG Jr (eds) Social learning. Erlbaum, Hillsdale, pp 29–48Google Scholar
  15. Burnham K, Anderson D (2002) Model selection and multi-model inference: a practical information-theoretic approach. Springer, BerlinGoogle Scholar
  16. Caldwell CA, Millen AE (2008) Experimental models for testing hypotheses about cumulative cultural evolution. Evol Hum Behav 29:165–171CrossRefGoogle Scholar
  17. Cavalli-Sforza L, Feldman MW (1981) Cultural transmission and evolution: a quantitative approach. Princeton University Press, PrincetonGoogle Scholar
  18. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. Soc Ind Appl Math Rev 51(4):661–703Google Scholar
  19. Coultas JC (2004) When in Rome … an evolutionary perspective on conformity. Group Process Intergroup Relations 7(4):317–331CrossRefGoogle Scholar
  20. Crema ER, Edinborough K, Kerig T, Shennan SJ (2014) An approximate Bayesian computation approach for inferring patterns of cultural evolutionary change. J Archaeol Sci 50:160–170CrossRefGoogle Scholar
  21. Currat M, Ray N, Excoffier L (2004) Splatche: a program to simulate genetic diversity taking into account environmental heterogeneity. Mol Ecol Notes 4(1):139–142CrossRefGoogle Scholar
  22. Del Moral P, Doucet A, Jasra A (2012) An adaptive sequential Monte Carlo method for approximate Bayesian computation. Stat Comput 22(5):1009–1020CrossRefGoogle Scholar
  23. Efferson C, Lalive R, Richerson PJ, McElreath R, Lubell M (2008) Conformists and mavericks: the empirics of frequency-dependent cultural transmission. Evol Hum Behav 29:56–64CrossRefGoogle Scholar
  24. Epstein JM (2007) Generative social science: studies in agent-based computational modeling. Princeton University Press, PrincetonGoogle Scholar
  25. Epstein JM, Axtell RL (1996) Growing artificial societies: social science from the bottom up. MIT and Brookings Institution, Washington, DCGoogle Scholar
  26. Eriksson K, Enquist M, Ghirlanda S (2007) Critical points in current theory of conformist social learning. J Evol Psychol 5(1–4):67–87CrossRefGoogle Scholar
  27. Eriksson A, Betti L, Friend AD, Lycett SJ, Singarayer JS, von Cramon-Taubadel N, Valdes PJ, Balloux F, Manica A (2012) Late Pleistocene climate change and the global expansion of anatomically modern humans. Proc Natl Acad Sci U S A 109:16089–16094CrossRefGoogle Scholar
  28. Ewens WJ (2004) Mathematical population genetics, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  29. Fearnhead P, Prangle D (2012) Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. J R Stat Soc Ser B 74:419–474CrossRefGoogle Scholar
  30. Feldman MW, Aoki K, Kumm J (1996) Individual versus social learning: evolutionary analysis in a fluctuating environment. Anthropol Sci 104:209–232CrossRefGoogle Scholar
  31. Fisher RA (1930) The genetical theory of natural selection. Clarendon, OxfordCrossRefGoogle Scholar
  32. Frank SA (2009) The common patterns of nature. J Evol Biol 22:1563–1585CrossRefGoogle Scholar
  33. Franz M, Nunn CL (2009) Network-based diffusion analysis: a new method for detecting social learning. Proc R Soc B 276(1663):1829–1836CrossRefGoogle Scholar
  34. Giraldeau L-A, Valone TJ, Templeton JJ (2002) Potential disadvantages of using socially acquired information. Philos Trans R Soc Lond B 357:1559–1566. doi: 10.1098/rstb.2002.1065 CrossRefGoogle Scholar
  35. Green RE, Krause J, Briggs AW et al (2010) A draft sequence of the Neandertal genome. Science 328:710–722CrossRefGoogle Scholar
  36. Gutenberg B, Richter CF (1944) Frequency of earthquakes in California. Bull Seismol Soc Am 34:185–188Google Scholar
  37. Hahn MW, Bentley RA (2003) Drift as a mechanism for cultural change: an example from baby names. Proc R Soc B 270:120–123CrossRefGoogle Scholar
  38. Hellenthal G, Busby GBJ, Band G, Wilson JF, Capelli C, Falush D, Myers S (2014) A genetic atlas of human admixture history. Science 343(6172):747–751CrossRefGoogle Scholar
  39. Henrich J (2001) Cultural transmission and the diffusion of innovations: adoption dynamic indicate that biased cultural transmission is the predominant force in behavioural change. Am Anthropol 103(4):992–1013CrossRefGoogle Scholar
  40. Henrich J, Boyd R (1998) The evolution of conformist transmission and the emergence of between-group differences. Evol Hum Behav 19:215–241CrossRefGoogle Scholar
  41. Henrich J, Broesch J (2011) On the nature of cultural transmission networks: evidence from Fijian villages for adaptive learning biases. Philos Trans R Soc B 366:1139–1148CrossRefGoogle Scholar
  42. Herzog HA, Bentley RA, Hahn MW (2004) Random drift and large shifts in popularity of dog breeds. Proc R Soc B 271:1443–1450CrossRefGoogle Scholar
  43. Heyes CM (1994) Social learning in animals: categories and mechanisms. Biol Rev 69:207–231CrossRefGoogle Scholar
  44. Hoppitt WJE, Laland KN (2013) Social learning: an introduction to mechanisms, methods, and models. Princeton University Press, PrincetonCrossRefGoogle Scholar
  45. Hoppitt WJE, Boogert NJ, Laland KN (2010a) Detecting social transmission in networks. J Theor Biol 263(4):544–555CrossRefGoogle Scholar
  46. Hoppitt WJE, Kandler A, Kendal JR, Laland KN (2010b) The effect of task structure on diffusion dynamics: implications for diffusion curve and network-based analyses. Learn Behav 38(3):243–251CrossRefGoogle Scholar
  47. Hudson RR (1983) Properties of a neutral allele model with intragenic recombination. Theor Popul Biol 23:183–201CrossRefGoogle Scholar
  48. Itan Y, Powell A, Beaumont MA, Burger J, Thomas MG (2009) The origins of lactase persistence in Europe. PLoS Comput Biol 5:e1000491CrossRefGoogle Scholar
  49. Joyce P, Marjoram P (2008) Approximately sufficient statistics and Bayesian computation. Stat Appl Genet Mol Biol 7:26Google Scholar
  50. Kandler A, Laland KN (2013) Tradeoffs between the strength of conformity and number of conformists in variable environments. J Theor Biol 332:191–202CrossRefGoogle Scholar
  51. Kandler A, Steele J (2010) Social learning, economic inequality and innovation diffusion. In: O'Brian MJ, Shennan S (eds) Innovation in cultural systems. The MIT Press, Cambridge, MA, pp 193–214Google Scholar
  52. Kendal JR, Kendal RL, Laland KN (2007) Quantifying and modelling social learning processes in monkey populations. Int J Psychol Psychol Ther 7(2):123–138Google Scholar
  53. Kendal JR, Giraldeau L-A, Laland KN (2009) The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission. J Theor Biol 260:210–219CrossRefGoogle Scholar
  54. Kimura M, Crow JF (1964) The number of alleles that can be maintained in a finite population. Genetics 49:725–738Google Scholar
  55. Kingman JFC (1982) The coalescent. Stochastic Process Appl 13:235–248CrossRefGoogle Scholar
  56. Kirby S, Cornish H, Smith K (2008) Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proc Natl Acad Sci U S A 105(31):10681–10686CrossRefGoogle Scholar
  57. Kohler TA, Van Buskirk S, Ruscavage-Barz S (2004) Vessels and villages: evidence for conformist transmission in early village aggregations on the Pajarito Plateau, New Mexico. J Anthropol Archaeol 23:100–118CrossRefGoogle Scholar
  58. Laland KN (2004) Social learning strategies. Learn Behav 32(1):4–14CrossRefGoogle Scholar
  59. Laland KN, Richerson PJ, Boyd R (1996) Developing a theory of animal social learning. In: Heyes CM, Galef BG Jr (eds) Social learning in animals: the roots of culture. Academic Press, London, pp 129–154CrossRefGoogle Scholar
  60. Laland KN, Odling-Smee FJ, Myles S (2010) How culture shaped the human genome: bringing genetics and the human sciences together. Nat Rev Genet 11:137–148CrossRefGoogle Scholar
  61. Mahajan V, Muller E, Bass FM (1995) Diffusion of new products: empirical generalizations and managerial uses. Mark Sci 14(3):79–88CrossRefGoogle Scholar
  62. Malécot G (1948) The mathematics of heredity (trans: Yermanos DM 1969). WH Freeman, San FranciscoGoogle Scholar
  63. Marjoram P, Wall JD (2006) Fast “coalescent” simulation. BMC Genet 7:16CrossRefGoogle Scholar
  64. Maynard Smith J (1978) Optimization theory in evolution. Annu Rev Ecol Syst 9:31–56CrossRefGoogle Scholar
  65. McElreath R, Bell AV, Efferson C, Lubell M, Richerson PJ, Waring T (2008) Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies. Philos Trans R Soc B 363:3515–3528CrossRefGoogle Scholar
  66. McElreath R, Fasolo B, Wallin A (2011) The evolutionary rationality of social learning. In: Hertwig R, Hoffrage U (eds) Simple heuristics in a social world. Oxford University Press, New YorkGoogle Scholar
  67. McVean GAT, Cardin NJ (2005) Approximating the coalescent with recombination. Philos Trans R Soc B 360:1387–1393CrossRefGoogle Scholar
  68. Mesoudi A, Lycett SJ (2009) Random copying, frequency-dependent copying and culture change. Evol Hum Behav 30(1):41–48CrossRefGoogle Scholar
  69. Mesoudi A, O’Brien MJ (2008) The cultural transmission of great basin projectile-point technology I: an experimental simulation. Am Antiq 73(1):3–28Google Scholar
  70. Morgan TJH, Rendell L, Ehn M, Hoppitt WJE, Laland KN (2012) The evolutionary basis of human social learning. Proc R Soc B 279:653–662CrossRefGoogle Scholar
  71. Nakahashi W (2007) The evolution of conformist transmission in social learning when the environment changes periodically. Theor Popul Biol 72:52–66CrossRefGoogle Scholar
  72. Nakahashi W, Wakano JY, Henrich J (2012) Adaptive social learning strategies in temporally and spatially varying environments. Hum Nat 23:386–418CrossRefGoogle Scholar
  73. Neiman FD (1995) Stylistic variation in evolutionary perspective: inferences from decorative diversity and interassemblage distance in Illinois woodland ceramic assemblages. Am Antiq 60:7–36CrossRefGoogle Scholar
  74. Nielsen R, Beaumont MA (2009) Statistical inferences in phylogeography. Mol Ecol 18(6):1034–1047CrossRefGoogle Scholar
  75. Nunes MA, Balding DJ (2010) On optimal selection of summary statistics for approximate Bayesian computation. Stat Appl Genet Mol Biol 9:34Google Scholar
  76. Powell A, Shennan SJ, Thomas MG (in prep) The power of power-laws in cultural evolutionGoogle Scholar
  77. Pritchard JK, Seielstad MT, Perez-Lezaun A, Feldman MW (1999) Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol Biol Evol 16(12):1791–1798CrossRefGoogle Scholar
  78. Rasteiro R, Bouttier P-A, Sousa V, Chikhi L (2012) Investigating sex-biased migration during the Neolithic transition in Europe. Proc R Soc B 279:2409–2416CrossRefGoogle Scholar
  79. Reader SM (2004) Distinguishing social and asocial learning using diffusion dynamics. Anim Learn Behav 32(1):90–104CrossRefGoogle Scholar
  80. Rendell L, Boyd R, Cowden D, Enquist M, Eriksson K, Feldman MW, Fogarty L, Ghirlanda S, Lillicrap T, Laland KN (2010) Why copy others? Insights from the social learning strategies tournament. Science 328(5975):208–213CrossRefGoogle Scholar
  81. Rendell L, Fogarty L, Hoppitt WJE, Morgan TJH, Webster MM, Laland KN (2011) Cognitive culture: theoretical and empirical insights into social learning strategies. Trends Cogn Sci 15(2):68–76CrossRefGoogle Scholar
  82. Robert CP, Corneut J-M, Marin J-M, Pillai NS (2011) Lack of confidence in approximate Bayesian computation model choice. Proc Natl Acad Sci U S A 108:15112–15117CrossRefGoogle Scholar
  83. Rogers AR (1988) Does biology constrain culture? Am Anthropol 90:819–831CrossRefGoogle Scholar
  84. Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New YorkGoogle Scholar
  85. Shennan SJ, Wilkinson JR (2001) Ceramic style change and neutral evolution: a case study from Neolithic Europe. Am Antiq 66:577–594CrossRefGoogle Scholar
  86. Templeton AR (2009) Statistical hypothesis testing in intraspecific phylogeography: nested clade phylogeographical analysis vs. approximate Bayesian computation. Mol Ecol 18:319–331CrossRefGoogle Scholar
  87. Toni T, Stumpf MPF (2010) Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics 26(1):104–110CrossRefGoogle Scholar
  88. Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH (2009) Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface 6:187–202CrossRefGoogle Scholar
  89. Van der Bulte C, Stremersch S (2004) Social contagion and income heterogeneity in new product diffusion: a meta-analytic test. Mark Sci 23(4):530–544CrossRefGoogle Scholar
  90. Veeramah KR, Wegmann D, Woerner A, Mendez F, Watkins J, Destro-Bisol G, Soodyall H, Louie L, Hammer MF (2012) An early divergence of KhoeSan ancestors from those of other modern humans is supported by an ABC-based analysis of autosomal resequencing data. Mol Biol Evol 29:617–630CrossRefGoogle Scholar
  91. Wakano JY, Aoki K (2007) Do social learning and conformist bias coevolve? Henrich and Boyd revisited. Theor Popul Biol 72:504–512CrossRefGoogle Scholar
  92. Wilde S, Timpson A, Kirsanow K, Kaiser E, Kayser M, Unterlaender M, Hollfelder N, Potekhina ID, Schier W, Thomas MG, Burger J (2014) Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans during the last 5,000y. Proc Natl Acad Sci U S A 111(13):4832–4837CrossRefGoogle Scholar
  93. Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159Google Scholar
  94. Zipf GK (1929) Relative frequency as a determinant of phonetic change. Harv Stud Class Philol 15:1–95CrossRefGoogle Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.Department of MathematicsCity University LondonLondonUK
  2. 2.Palaeogenetics Group, Institute of AnthropologyUniversity of MainzMainzGermany

Personalised recommendations