05.11. 2018 16:15 Uhr - 18:00 Uhr


Max-Planck-Institut für ethnologische Forschung

Advokatenweg 36 | 06114 | Halle/Saale


Bettina Mann



Speaker’s Abstract
Humans show extraordinary demographic success to occupy virtually every terrestrial habitat and a huge diversity of behavioural adaptations. While a variety of local genetic adaptations exist within our species, it seems certain that the same basic genetic make-up produces arctic foraging, tropical horticulture, and desert pastoralism. The behavioural adaptations that explain the success of our species are widely thought to be cultural: they are transmitted among individuals by social learning and have accumulated over generations. However, social learning, i.e. learning influenced by observations or interactions with other individuals, or its products, can occur in a large variety of forms. Therefore, one of the goals of cultural evolutionary research is to understand when and how humans learn socially.

Fine-grained individual-level data, detailing who learns from whom, would be most suited to answer this question empirically but this kind of data is difficult to obtain. Pre-modern or anthropological datasets typically describe the temporal frequencies of different cultural variants in sparse samples from the whole population confronting us with an inverse problem: we can only observe aggregated, population-level frequency data but aim to identify the underlying individual-level learning processes that gave rise to them. To approach this problem, we develop a generative inference framework, consisting of a generative model that establishes a causal link between learning processes and observable frequency data that then are evaluated for statistical consistency. Besides identifying the most likely learning process given the data, this framework determines the breadth of processes that could have produced these data equally well, which in turn allows us to evaluate the limits of inferring social learning processes from population-level data of a specific temporal and spatial resolution. We apply our framework to a dataset describing pottery from settlements of some of the first farmers in Europe.