Infinite Things to Learn

Edith Invernizzi provides an overview of Fogarty, Wakano, Feldman & Aoki, 2016, “The Driving Forces of Cultural Complexity”.

Whilst all humans share the ability to accumulate cultural innovations, there remains substantial variation in the rates of cultural acquisition and the size of cultural toolkit between contemporary modern humans (MH) and our distant relatives. Cultural complexity in early modern humans and our late Neanderthal contemporaries has been hypothesised to differ from ours due to differences in genetics and cognitive abilities, limiting their ability to innovate or accumulate culture. Alternatively, demographic features (eg. population size or migration rates) may pose different pressures on individuals, and have been deemed particularly relevant in explaining variable toolkit size among contemporary hunter-gatherers. Tasmainian populations, for example, have been suggested to have differentially lost island-to-island parts of a pre-existing common toolkit as a consequence of isolation (Henrich, 2004). Studies conducted on contemporary or recent historical populations, however, often do not show a correlation between population size and the size of toolkit. A recent theoretical model by Aoki et al. (2011), on the other hand, suggests that an increase in cultural complexity should be expected as population size increases under several types of social learning.

Image by carloyuen from Pixabay

A possible factor that determines whether larger populations have greater cultural complexity could be the number of possible cultural traits a species can acquire (i.e. the limited character of cognitive capacity). Fogarty, Wakano, Feldman and Aoki (2016) explore the effect of cognitive limitations with an analytical and agent-based combination model that considers a range of limits to cognitive capacity (called M) across populations of different sizes. Here, the correlation between population size and the number of cultural traits present in the population is evident only until cognitive capacity is reached. At this point, the population reaches cultural saturation and little to no innovation is possible, with smaller cognitive capacity leading to faster leveling of the accumulation curve.

This new formulation of learning space allows agreement between cognitive constraint and demographic models in explaining the Neanderthal – MH cultural complexity divide when population size is equal.


Aoki, K., Lehmann, L., & Feldman, M. W. (2011). Rates of cultural change and patterns of cultural accumulation in stochastic models of social transmission. Theoretical Population Biology, 79 (4), 192–202.

Henrich, J. (2004). Demography and cultural evolution: how adaptive cultural processes can produce maladaptive losses: the Tasmanian case. American Antiquity, 69 (2), 197–214.

Fogarty, L., Wakano, J. Y., Feldman, M. W., & Aoki, K. (2017). The driving forces of cultural complexity. Human Nature, 28(1), 39-52.

About the author

Edith Invernizzi is finishing her PhD at the University of St Andrews (Scotland), where she researches the evolution of collective behaviour. She is using eusocial insects as a model to study how complex systems (e.g. self-organised collective behaviour, information networks) evolve and adapt. At the moment, her research focuses on the expected network structure when the communication network is optimised for certain tasks, and how the number and characteristics of the individuals involved changes depending on the characteristics of the group. Her methods combine simulation modelling with the statistical analysis of observational data. She has a Masters of Philosophy in human behavioural ecology and has a multidisciplinary undergraduate background in biology and languages. She aims at applying complex systems to human behaviour in her postdoctoral research and is currently looking for opportunities. She is the ESLR vice-chair and works on developing ECR resources and networking platforms through the society’s website.