Michael Muthukrishna1, Ben W. Shulman, Vlad Vasilescu1 and Joseph Henrich1
Proc. R. Soc. B 7 January 2014 vol. 281 no. 1774 20132511. doi: 10.1098/rspb.2013.2511
Abstract: Archaeological and ethnohistorical evidence suggests a link between a population's size and structure, and the diversity or sophistication of its toolkits or technologies. Addressing these patterns, several evolutionary models predict that both the size and social interconnectedness of populations can contribute to the complexity of its cultural repertoire. Some models also predict that a sudden loss of sociality or of population will result in subsequent losses of useful skills/technologies. Here, we test these predictions with two experiments that permit learners to access either one or five models (teachers). Experiment 1 demonstrates that naive participants who could observe five models, integrate this information and generate increasingly effective skills (using an image editing tool) over 10 laboratory generations, whereas those with access to only one model show no improvement. Experiment 2, which began with a generation of trained experts, shows how learners with access to only one model lose skills (in knot-tying) more rapidly than those with access to five models. In the final generation of both experiments, all participants with access to five models demonstrate superior skills to those with access to only one model. These results support theoretical predictions linking sociality to cumulative cultural evolution.
Using the Google N-Gram corpus to measure cultural complexity
Lit Linguist Computing (2013) 28 (4): 668-675. doi: 10.1093/llc/fqt017
Abstract: Empirical studies of broad-ranging aspects of culture, such as ‘cultural complexities’ are often extremely difficult. Following the model of Michel et al. (Michel, J.-B., Shen, Y. K., Aiden, A. P. et al. (2011). Quantitative analysis of culture using millions of digitized books. Science, 331(6014): 176–82), and using a set of techniques originally developed to measure the complexity of language, we propose a text-based analysis of a large corpus of topic-uncontrolled text to determine how cultural complexity varies over time within a single culture. Using the Google Books American 2Gram corpus, we are able to show that (as predicted from the cumulative nature of culture), US culture has been steadily increasing in complexity, even when (for economic reasons) the amount of actual discourse as measured by publication volume decreases. We discuss several implication of this novel analysis technique as well as its implications for discussion of the meaning of ‘culture.’
Cultural selection drives the evolution of human communication systems
Proc. R. Soc. B 281: 20140488. http://dx.doi.org/10.1098/rspb.2014.0488
Abstract: Human communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that communication variants spread in a population following neutral evolutionary dynamics (also known as drift models), we tested the role of two cultural selection models: coordination- and content-biased. We constructed a parametrized mixed probabilistic model of the spread of communicative variants in four 8-person laboratory micro-societies engaged in a simple communication game. We found that selectionist models, working in combination, explain the majority of the empirical data. The best-fitting parameter setting includes an egocentric bias and a content bias, suggesting that participants retained their own previously used communicative variants unless they encountered a superior (content-biased) variant, in which case it was adopted. This novel pattern of results suggests that (i) a theory of the cultural evolution of human communication systems must integrate selectionist models and (ii) human communication systems are functionally adaptive complex systems.