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Experimental Programme Investigating Cumulative Culture

Periodic Reporting for period 1 - EPICC (Experimental Programme Investigating Cumulative Culture)

Reporting period: 2017-09-01 to 2019-08-31

Humans exhibit a rich and complex material culture with no equivalent in other animals. The unique human capability to accumulate cultural innovations across time allows us to develop powerful technology and drives technological progress. Despite a growing literature investigating cumulative culture, the underlying processes at play in the accumulation of cultural knowledge remain puzzling. This project aimed at identifying the requirements of human cumulative culture both at the individual and population levels and improving our understanding of factors that affect the pace of cultural accumulation. Three lines of research have been investigated: 1) Identify the cognitive requirements of innovation production and transmission, 2) Evaluate the interplay between population structure and cultural accumulation, 3) Evaluate the effects of ecological factors on the rate of innovation.
The project was structured in four work packages.

WP1 Evaluation of the interplay between population structure and cultural accumulation.
WP1 involved developing a computer game in which participants can discover and share innovations. A pilot experiment has been run before preregistering hypotheses, predictions and data collection and analyses plans on the Open Science Framework. 480 individuals have been recruited to take part in the final experiment. Data have been analysed and a manuscript has been written and submitted to Organization Science. This work involved supervising a master student and has been conducted in collaboration with economists.
Mathematical models and simulations have been used to further explored the relationship between population structure and cultural accumulation. This work involved supervising a master student and has been conducted in collaboration with biologists and mathematicians. A manuscript has been written and will be submitted in the next few weeks.

WP2. Identification of cognitive requirements to innovation production and transmission.
WP2 involved designing and building a physical task in order to test the role of causal understanding in the accumulation of innovations. A pilot experiment has been run before preregistering hypotheses, predictions and data collection and analyses plans on the Open Science Framework (https://osf.io/afwmr/). 140 individuals have been recruited to take part in the final experiment. Data have been analysed and a manuscript has been written, submitted, accepted and published by the journal Nature Human Behaviour.
A second experiment has been run in order to test the extent to which social information canalises individuals’ exploration. A pilot experiment has been run before preregistering hypotheses, predictions and data collection and analyses plans on the Open Science Framework. 200 individuals have been recruited to take part in the final experiment. Data are currently being analysed.

WP3. Evaluation of the effects of ecological factors on the rate of innovation.
WP3 involved developing a computer game in which participants can progress along different cultural evolutionary pathways in variable environments. A collaboration with a digital and creative school that trains students to become video game programmers has been established. Different prototypes have been developed and the collaboration is ongoing to finalize an innovative computer game that will allow the fellow to rigorously evaluate the effects of ecological factors on the rate of innovation.

WP4. Identifying the building blocks of cumulative culture.
WP4 involved writing review papers. Three review papers are in preparation. One covers the relationship between population size and structure and cumulative culture and reviews recent theoretical models, experiments and archaeological/anthropological studies that have investigated the topic. A second paper covers the mechanisms of cultural evolution beyond social learning. A third paper highlights the difficulty for researchers to agree on a consistent set of defining criteria for cumulative culture and proposes a new set of defining criteria.
In a first set of experiments we asked successive ‘cultural generations’ of participants to optimize a physical system, and measured participants’ understanding of how the device worked at each generation. Our results show that optimized technologies emerge through the retention of small improvements across generations without requiring understanding of how these technologies work. Moreover, we found 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. Additionally, our results show that the transmission of these inaccurate theories constrains learners’ exploration and has downstream effects on their understanding. Overall, this experiment indicates that one should be cautious when interpreting complex archaeological materials as evidence for sophisticated cognitive abilities (such as reasoning, problem solving or planning), since these abilities are not the sole driver of technological sophistication.

In a second set of experiments we investigated how different aspects of population structure (network connectivity and fluidity) affect individuals’ probability of sharing innovations with their group members. Results show that individuals are more likely to share innovations when they are part of static networks with low-connectivity. Low-connectivity static networks promote repeated interactions among a small number of individuals, which favours the emergence of small and highly cooperative clusters. These results suggest that this type of population structure should promote cumulative culture by favouring the sharing of innovations.

A mathematical model has also been developed to study the dynamics of knowledge creation and propagation within human groups. The model features multiple social influences in structured populations and confirms that demography has a strong impact on cumulative culture. Results show that larger populations exhibit higher cultural complexity but reveal that the pace of cultural accumulation can be negatively affected by population size: the rate of accumulation is faster in large populations when individuals develop few competences, but slower when individuals develop many competences. There results reveal that the relationship between population size and cultural accumulation is probably more complex than previously assumed.
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