Proceedings of the Annual Meeting of the Cognitive Science Society

In the field of cultural evolution, cognitive agents are either seen as perfect imitators who reproduce cultural variantsveridically (e.g. Boyd & Richerson 1985) or as imperfect imitators who transform the variants as they replicate them (e.g.Sperber 1996). In this poster, I explain how the transformative view of cognition applies to not only to the generation ofvariants, but also to the way we learn frequency distributions. Probability matching is a widely-observed human behaviorwhere learners reproduce a frequency distribution over variants with a small amount of error and is equivalent to Wright-Fisher drift when the variance in error is binomial/multinomial. However, humans and learning algorithms can produce errordistributions that are non-binomial/non-multinomial, which constitute a broader class of drift processes than those that exist ingenetic evolution or in perfect-imitator models of cultural evolution.