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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/.

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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).

Author information

Authors and Affiliations

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 5, 1643–1651 (2021). https://doi.org/10.1038/s41562-021-01159-9

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