Bows and arrows, houses and kayaks are just a few examples of the highly optimized tools that humans have produced and used to colonize new environments1,2. Because there is much evidence that humans’ cognitive abilities are unparalleled3,4, many believe that such technologies resulted from our superior causal reasoning abilities5,6,7. However, others have stressed that the high dimensionality of human technologies makes them very difficult to understand causally8. Instead, they argue that optimized technologies emerge through the retention of small improvements across generations without requiring understanding of how these technologies work1,9. Here we show that a physical artefact becomes progressively optimized across generations of social learners in the absence of explicit causal understanding. Moreover, we find that the transmission of causal models across generations has no noticeable effect on the pace of cultural evolution. The reason is that participants do not spontaneously create multidimensional causal theories but, instead, mainly produce simplistic models related to a salient dimension. Finally, we show that the transmission of these inaccurate theories constrains learners’ exploration and has downstream effects on their understanding. These results indicate that complex technologies need not result from enhanced causal reasoning but, instead, can emerge from the accumulation of improvements made across generations.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $8.67 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data that support the findings of this study are available at https://osf.io/afwmr/.
Codes used in this paper are available at https://osf.io/afwmr/.
Henrich, J. The Secret of our Success: How Culture is Driving Human Evolution, Domesticating our Species, and Making us Smarter (Princeton Univ. Press, 2015).
Richerson, P. J. & Boyd, R. Not by Genes Alone (Univ. of Chicago Press, 2005).
Povinelli, D. J. World Without Weight: Perspectives on an Alien Mind (Oxford Univ. Press, 2011).
Reader, S. M. & Laland, K. N. Social intelligence, innovation, and enhanced brain size in primates. Proc. Natl Acad. Sci. USA 99, 4436–4441 (2002).
Pinker, S. The cognitive niche: coevolution of intelligence, sociality, and language. Proc. Natl Acad. Sci. USA 107, 8993–8999 (2010).
Barrett, H. C., Cosmides, L. & Tooby, J. in The Evolution of Mind: Fundamental Questions and Controversies 241–248 (Guilford Press, 2007).
Bingham, P. M. Human uniqueness: a general theory. Q. Rev. Biol. 74, 133–169 (1999).
Boyd, R., Richerson, P. J. & Henrich, J. in Cultural Evolution: Society, Technology, Language, and Religion 119–142 (The MIT Press, 2013).
Boyd, R., Richerson, P. J. & Henrich, J. The cultural niche: why social learning is essential for human adaptation. Proc. Natl Acad. Sci. USA 108, 10918–10925 (2011).
Kyriacou, A. & Bruner, E. Innovation and the evolution of human behavior brain evolution, innovation, and endocranial variations in fossil hominids. PaleoAnthropology 2011, 130–143 (2011).
Fuentes, A. The Creative Spark: How Imagination Made Humans Exceptional (Penguin, 2017).
Derex, M. & Boyd, R. The foundations of the human cultural niche. Nat. Commun. 6, 8398 (2016).
Baker, T. in The Traditional Bower’s Bible 43–116 (The Lyons Press, 1992).
Muthukrishna, M. & Henrich, J. Innovation in the collective brain. Phil. Trans. R. Soc. B 371, 20150192 (2016).
Proffitt, D. R., Kaiser, M. K. & Whelan, S. M. Understanding wheel dynamics. Cogn. Psychol. 22, 342–373 (1990).
Bonawitz, E. et al. The double-edged sword of pedagogy: instruction limits spontaneous exploration and discovery. Cognition 120, 322–330 (2011).
Wood, L. A., Kendal, R. L. & Flynn, E. G. Does a peer model’s task proficiency influence children’s solution choice and innovation? J. Exp. Child Psychol. 139, 190–202 (2015).
Proffitt, D. R. & Gilden, D. L. Understanding natural dynamics. J. Exp. Psychol. Hum. Percept. Perform. 15, 384–393 (1989).
Kubricht, J. R., Holyoak, K. J. & Lu, H. Intuitive physics: current research and controversies. Trends Cogn. Sci. 21, 749–759 (2017).
Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010).
Henrich, J. Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses: the Tasmanian case. Am. Antiq. 69, 197–214 (2004).
Powell, A., Shennan, S. & Thomas, M. G. Late Pleistocene demography and the appearance of modern human behavior. Science 324, 1298–1301 (2009).
Kline, M. A. & Boyd, R. Population size predicts technological complexity in Oceania. Proc. R. Soc. B 277, 2559–2564 (2010).
Derex, M., Beugin, M.-P., Godelle, B. & Raymond, M. Experimental evidence for the influence of group size on cultural complexity. Nature 503, 389–391 (2013).
Muthukrishna, M., Shulman, B. W., Vasilescu, V. & Henrich, J. Sociality influences cultural complexity. Proc. R. Soc. B 281, 20132511 (2014).
Hill, K. R., Wood, B. M., Baggio, J., Hurtado, A. M. & Boyd, R. T. Hunter-gatherer inter-band interaction rates: implications for cumulative culture. PLoS One 9, e102806 (2014).
Derex, M. & Boyd, R. Partial connectivity increases cultural accumulation within groups. Proc. Natl Acad. Sci. USA 113, 2982–2987 (2016).
Creanza, N., Kolodny, O. & Feldman, M. W. Greater than the sum of its parts? Modelling population contact and interaction of cultural repertoires. J. R. Soc. Interface 14, 20170171 (2017).
Derex, M., Perreault, C. & Boyd, R. Divide and conquer: intermediate levels of population fragmentation maximize cultural accumulation. Phil. Trans. R. Soc. B 373, 20170062 (2018).
Caldwell, C. A. & Millen, A. E. Studying cumulative cultural evolution in the laboratory. Phil. Trans. R. Soc. B 363, 3529–3539 (2008).
Zwirner, E. & Thornton, A. Cognitive requirements of cumulative culture: teaching is useful but not essential. Sci. Rep. 5, 16781 (2015).
R Development Core Team R : A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2011).
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (CRC Press, 2016).
We thank J. Clewett for valuable advice about the building of the wheel, F. Gosselin and A. Deymier for organizing the experimental sessions, and members of the Laboratory for Experimental Anthropology for helpful discussions during the development of the experimental protocol. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement number 748310. Support from the ANR-Labex Institute for Advanced Study in Toulouse is acknowledged. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Journal of Archaeological Science: Reports (2019)
Nature Human Behaviour (2019)