Inferring learning strategies from cultural frequency data

Kandler, A. & Powell, A. (2015). Inferring learning strategies from cultural frequency data. In: A. Mesoudi & K. Aoki (Eds.), Inferring Learning Strategies from Cultural Frequency Data. (pp. 85-101). Japan: Springer. ISBN 9784431553625

[img] Text - Accepted Version
Restricted to Repository staff only

Download (322kB)

Abstract

Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population. We review the previous methodology that has attempted to infer the underlying social learning processes from such data, showing that they may apply statistical methods with insufficient power to draw reliable inferences. We then introduce a generative inference framework that allows robust inferences on the social learning processes that underlie cultural frequency data. Using developments in population genetics—in the form of generative simulation modelling and approximate Bayesian computation—as our model, we demonstrate the strength of this method with an example based on simulated data.

Item Type: Book Section
Subjects: Q Science > QA Mathematics
Divisions: School of Engineering & Mathematical Sciences > Department of Mathematical Science
URI: http://openaccess.city.ac.uk/id/eprint/12894

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics