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Technical reasoning is important for cumulative technological culture

Abstract

Human technology has evolved in an unparalleled way, allowing us to expand across the globe. One fascinating question is, how do we understand the cognitive origins of this phenomenon, which is known as cumulative technological culture (CTC)? The dominant view posits that CTC results from our unique ability to learn from each other. The cultural niche hypothesis even minimizes the involvement of non-social cognitive skills in the emergence of CTC, claiming that technologies can be optimized without us understanding how they work, but simply through the retention of small improvements over generations. Here we conduct a partial replication of the experimental study of Derex et al. (Nature Human Behaviour, 2019) and show that the improvement of a physical system over generations is accompanied by an increased understanding of it. These findings indicate that technical-reasoning skills (non-social cognitive skills) are important in the acquisition, understanding and improvement of technical content—that is, specific to the technological form of cumulative culture—thereby making social learning a salient source of technical inspiration.

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Fig. 1: Experimental task and design.
Fig. 2: Parallel improvement of the wheel system and of its understanding (experiment 1).
Fig. 3: Parallel improvement of the wheel system and of its understanding (experiment 2).
Fig. 4: Links between the centre of mass and inertia scores in the three understanding tests for the participants in the experimental group (experiment 2).

Data availability

The data that support the findings of this study are available at https://osf.io/athg5/.

Code availability

Codes used in this study are available at https://osf.io/athg5/.

References

  1. 1.

    Boyd, R. & Richerson, P. J. Why culture is common but cultural evolution is rare. Proc. Brit. Acad. 88, 77–93 (1996).

    Google Scholar 

  2. 2.

    Dean, L. G., Vale, G. L., Laland, K. N., Flynn, E. & Kendal, R. L. Human cumulative culture: a comparative perspective. Biol. Rev. 89, 234–301 (2014).

    Article  Google Scholar 

  3. 3.

    Henrich, J. The Secret of Our Success: How Culture is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter (Princeton Univ. Press, 2015).

  4. 4.

    Csibra, G. & Gergely, G. Natural pedagogy. Trends Cogn. Sci. 13, 148–153 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Kendal, R. L. et al. Social learning strategies: bridge-building between fields. Trends Cogn. Sci. 22, 651–665 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Tomasello, M., Carpenter, M. & Call, J. Understanding sharing intentions: the origins of cultural cognition. Behav. Brain Sci. 28, 675–735 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Caldwell, C. A. & Millen, A. E. Social learning mechanisms and cumulative cultural evolution: is imitation necessary? Psychol. Sci. 20, 1478–1483 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Dean, L. G., Kendal, R. L., Schapiro, S. J., Thierry, B. & Laland, K. N. Identification of the social and cognitive processes underlying human cumulative culture. Science 335, 1114–1118 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Caldwell, C. A., Renner, E. & Atkinson, M. Human teaching and cumulative cultural evolution. Rev. Philos. Psychol. 9, 751–770 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Herrmann, E., Call, J., Hernandez-Lloreda, M., Hare, B. & Tomasello, M. Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317, 1360–1366 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Heyes, C. Who knows? Metacognitive social learning strategies. Trends Cogn. Sci. 20, 204–213 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Whiten, A. Social learning and culture in child and chimpanzee. Annu. Rev. Psychol. 68, 129–154 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  13. 13.

    Tomasello, M. Becoming Human. A Theory of Ontogeny (Belknap Press, 2019).

  14. 14.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Derex, M. & Boyd, R. Technical reasoning alone does not take humans this far. Behav. Brain Sci. 43, e156 (2020).

    Article  Google Scholar 

  16. 16.

    Derex, M., Bonnefon, J. F., Boyd, R. & Mesoudi, A. Causal understanding is not necessary for the improvement of culturally evolving technology. Nat. Hum. Behav. 3, 446–452 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

    Osiurak, F. & Reynaud, E. The elephant in the room: what matters cognitively in cumulative technological culture. Behav. Brain Sci. 43, e156 (2020).

    Article  Google Scholar 

  18. 18.

    Reynaud, E., Lesourd, M., Navarro, J. & Osiurak, F. On the neurocognitive origins of human tool use. A critical review of neuroimaging data. Neurosci. BioBehav. Rev. 64, 421–437 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Reynaud, E., Navarro, J., Lesourd, M. & Osiurak, F. To watch is to work: a critical review of neuroimaging data on Tool-use Observation Network (ToON). Neuropsychol. Rev. 29, 484–497 (2019).

    PubMed  Article  Google Scholar 

  20. 20.

    Osiurak, F. et al. Physical intelligence does matter to cumulative technological culture. J. Exp. Psychol. Gen. 145, 941–948 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    De Oliveira, E., Reynaud, E. & Osiurak, F. Roles of technical reasoning, theory of mind, creativity, and fluid cognition in cumulative technological culture. Hum. Nat. 30, 326–340 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  22. 22.

    Osiurak, F., De Oliveira, E., Navarro, J. & Reynaud, E. The castaway island: distinct roles of theory of mind and technical reasoning in cumulative technological culture. J. Exp. Psychol. Gen. 149, 58–66 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Derex, M. & Boyd, R. The foundations of the human cultural niche. Nat. Comm. 6, 8398 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Derex, M. & Boyd, R. Social information can potentiate understanding despite inhibiting cognitive effort. Sci. Rep. 8, 9980 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Whiten, A., Horner, V. & de Waal, F. B. M. Conformity to cultural norms of tool use in chimpanzees. Nature 437, 737–740 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Claidière, N. & Whiten, A. Integrating the study of conformity and culture in humans and nonhuman animals. Psychol. Bull. 138, 126–145 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Whiten, A. et al. Cultures in chimpanzees. Nature 399, 682–685 (1999).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Horner, V., Whiten, A., Flynn, E. & de Waal, F. B. M. Faithful replication of foraging techniques along cultural transmission chains by chimpanzees and children. Proc. Natl Acad. Sci. USA 103, 13878–13883 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Gruber, T., Muller, M. N., Strimling, P., Wrangham, R. W. & Zuberbühler, K. Wild chimpanzees rely on cultural knowledge to solve an experimental honey acquisition task. Curr. Biol. 19, 1806–1810 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Gruber, T. Great apes do not learn novel tool use easily: conservatism, functional fixedness, or cultural influence? Int. J. Primatol. 37, 296–316 (2016).

    Article  Google Scholar 

  31. 31.

    Tennie, C., Call, J. & Tomasello, M. Push or pull: imitation vs. emulation in great apes and human children. Ethology 112, 1159–1169 (2006).

    Article  Google Scholar 

  32. 32.

    Bandini, E. & Tennie, C. Spontaneous reoccurrence of “scooping’, a wild tool-use behaviour, in naïve chimpanzees. PeerJ 5, e3814 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Clay, Z. & Tennie, C. Is overimitation a uniquely human phenomenon? Insights from human children as compared to bonobos. Child Dev. 89, 1535–1544 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Whiten, A., Horner, V. & Marshall-Pescini, S. R. J. Cultural panthropology. Evol. Anthropol. 12, 92–105 (2003).

    Article  Google Scholar 

  35. 35.

    Martin-Ordas, G., Call, J. & Colmenares, F. Tubes, tables and traps: great apes solve two functionally equivalent trap tasks but show no evidence of transfer across tasks. Anim. Cogn. 11, 423–430 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Logan, C. J., Breen, A. J., Taylor, A. H., Gray, R. D. & Hoppitt, J. E. How New Caledonian crows solve novel foraging problems and what it means for cumulative culture. Learn. Behav. 44, 18–28 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Gruber, R. et al. New Caledonian crows use mental representations to solve metatool problems. Curr. Biol. 29, 686–692 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Jelbert, S. A., Hosking, R. J., Taylor, A. H. & Gray, R. D. Mental template matching is a potential cultural transmission mechanism for New Caledonian crow tool manufacturing traditions. Sci. Rep. 8, 8956 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Moll, H. & Tomasello, M. Cooperation and human cognition: the Vygotskian intelligence hypothesis. Philos. Trans. R. Soc. B 362, 639–648 (2007).

    Article  Google Scholar 

  40. 40.

    Baillargeon, R., Needham, A. & DeVos, J. The development of young infants’ intuitions about support. Early Dev. Parent. 1, 69–78 (1992).

    Article  Google Scholar 

  41. 41.

    Pinker, S. The cognitive niche: coevolution of intelligence, sociality, and language. Proc. Natl Acad. Sci. USA 107, 8993–8999 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Vaesen, K. The cognitive bases of human tool use. Behav. Brain Sci. 35, 203–218 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    Heyes, C. Enquire within: cultural evolution and cognitive science. Philos. Trans. R. Soc. B. 373, 20170051 (2018).

    Article  Google Scholar 

  44. 44.

    R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2011); https://www.R-project.org/

  45. 45.

    Gabry, J., & Goodrich, B. rstanarm: Bayesian Applied Regression Modeling via Stan R package v.2.15.3 (R Foundation for Statistical Computing, 2017); http://mc-stan.org/rstanarm/

  46. 46.

    Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).

Download references

Acknowledgements

We thank T. Pownall for the English proofreading of this manuscript, and V. Gaujoux, G. Jarjat and N. Baltenneck for assistance in data collection. This work was performed within the framework of the LABEX CORTEX (grant no. ANR-11-LABX-0042) of Université de Lyon, within the program ‘Investissements d’Avenir’ (grant no. ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).

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Authors

Contributions

F.O. developed the research question. F.O., S.L., J.A., J.B., A.B., J.N. and E.R. helped design the experimental task and protocol. J.B. built the wheel system. S.L., J.A. and F.O. performed the experiments. F.O. and E.R. analysed the data. F.O. wrote the paper with input from S.L., J.A., J.B., A.B., J.N. and E.R.

Corresponding author

Correspondence to François Osiurak.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Enrico Crema and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Derex et al.’s16 data (from both the configurations and the configurations + theory conditions, n = 140).

a. As reported by Derex et al.16 (see also their signed Comments), the centre of mass and inertia scores were negatively linked (centre of mass score 95% credible interval (CI): −0.59 to −0.16; median = −0.36; Bayes Factor (BF) = 5.33). b. and c. A positive relationship was also found between the number of ‘no difference’ answers on centre of mass items and the inertia score (inertia score 95% CI: 0.12 to 0.42; median = 0.28; BF = 14.05) and between the number of ‘no difference’ answers on inertia items and the centre of mass score (centre of mass score 95% CI: 0.05 to 0.32; median = 0.19; BF = 1.27). d. The understanding score (centre of mass + inertia items) was positively linked to the wheel speed (wheel speed 95% CI: 0.03 to 0.09; median = 0.06; BF = 39.09). Four participants obtained a speed of 0 m h−1. Therefore, we removed them from the analysis, meaning that the total number of participants included here was 136. Statistical analyses were carried out based on Derex et al.’s16 data obtained from both the configurations and the configurations + theory conditions (pooled together). We used Bayesian applied regression modelling in R44 (rstanarm package45). Inferences were made using 95% CIs. We fitted a generalized linear model with ‘variable of interest 1 (for example, inertia score)’ as outcome variable, ‘variable of interest 2 (for example, centre of mass score)’ as fixed effect, and ‘generation’, ‘chain’s identity’ and ‘treatment’ (configurations versus configurations + theory) as random effects.

Extended Data Fig. 2 Evolution of wheel configurations over generations (experiment 1).

Only the last two trials of each generation are shown. The position of each weight corresponds to the median position of the weight for each trial.

Extended Data Fig. 3 Derex et al.’s16 wheel speed, wheel speed in experiment 1 and wheel speed in experiment 2 as a function of weight positions.

a, b, and c show the sensitivity to the position of centre of mass for each wheel (the right, bottom and left weights are at position 1, while the top weight varies from position 1 to position 12; see also the bottom table). As rightly stressed by Derex et al. (signed comments), the wheel used in experiment 1 was not sufficiently sensitive to the centre of mass (b) as compared to Derex et al.’s16 wheel (a). This methodological limitation was overcome with the wheel used in experiment 2 (c). d. Derex et al.’s16 wheel speed as a function of experiment 2’s wheel speed. As shown, even if the wheel in experiment 2 was faster than Derex et al.’s16 wheel, a linear relationship is found between the two wheels, indicating that the wheel in experiment 2 behaved similarly to Derex et al.’s16 wheel (rhomedian = 0.99; 95% credible interval: 0.98 to 0.99; BF > 100). To investigate this relationship, we used 12 weight configurations that are sensitive to the position of the centre of mass (the right, bottom and left weights are at position 1, while the top weight varies from position 1 to position 12) and 12 weight configurations that are sensitive to the moment of inertia (all the weights are at the same position: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12). The bottom table gives an overview of these data. Note that Derex et al.’s16 wheel speed were collected from the data obtained with their participants and those in experiment 1 from the data obtained with our participants. This explains why the number of trials (N) associated with each configuration is variable. For the wheel used in experiment 2, we conducted a series of pre-tests before starting the experiment to ensure that the wheel behaved similarly to Derex et al.’s16 wheel. The data reported in the bottom table corresponds to the last pre-test with 10 trials for each configuration.

Extended Data Fig. 4 Centre of mass and inertia scores as a function of the position in the chain from Derex et al.’s16 data.

The results reported here concern only the configurations + theory condition, namely the condition for which these authors observed a significant increase in understanding—when the centre of mass and inertia items were pooled together—over generations. As shown, the global increase in understanding was mainly due to an increase in understanding of the inertia dimension (green) compared to the relative stability over generations for the centre of mass score (yellow).

Extended Data Fig. 5 Evolution of wheel configurations over generations (experiment 2).

Only the last two trials of each generation are shown. The position of each weight corresponds to the median position of the weight for each trial.

Extended Data Fig. 6 Links between the three-option score and the analogous and transfer scores in both the experimental group and the control group (experiment 2).

a. The three-option score was positively linked to the analogous score (analogous score 95% credible interval (CI): 0.07 to 0.13; median = 0.10; Bayes Factor (BF) > 100) and, b, to the transfer score (transfer score 95% CI: 0.07 to 0.14; median = 0.10; BF > 100) in the experimental group. c. The three-option score was positively linked to the analogous score (rhomedian = 0.31; 95% CI: 0.13 to 0.46; BF = 10.75) in the control group. d. No link was reported between the three-option score and the transfer score (rhomedian = 0.13; 95% CI: −0.04 to 0.31; BF = 0.51) in the control group.

Extended Data Fig. 7 Centre of mass and inertia scores over generations for the three understanding tests (experiment 2).

As shown, a similar pattern is found for the three tests, with an increase in understanding mainly for the inertia score (green) and relative stability over generations for the centre of mass score (yellow).

Extended Data Fig. 8 Links between the centre of mass and inertia scores in the three understanding tests for the participants in the control group (experiment 2).

a. No link was reported between the centre of mass and inertia scores for the three-option test (rhomedian = 0.02; 95% credible interval (CI): −0.16 to 0.21; Bayes Factor (BF) = 0.28). b. and c. There was no correlation between the number of ‘no difference’ answers on centre of mass items and the inertia score (rhomedian = −0.05; 95% CI: −0.25 to 0.11; BF = 0.30) or between the number of ‘no difference’ answers on inertia items and the centre of mass score (rhomedian = −0.18; 95% CI: −0.35 to 0.01; BF = 0.87). d. and e. No statistical relationship was reported between the centre of mass and inertia scores for the analogous test (rhomedian = −0.17; 95% CI: −0.35 to 0.01; BF = 0.94) and for the transfer test (rhomedian = −0.04; 95% CI: −0.23 to 0.14; BF = 0.28).

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Osiurak, F., Lasserre, S., Arbanti, J. et al. Technical reasoning is important for cumulative technological culture. Nat Hum Behav (2021). https://doi.org/10.1038/s41562-021-01159-9

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