Proceedings of the Annual Meeting of the Cognitive Science Society

Seasonal patterns vary dramatically around the world, andwe explore the extent to which systems of season categoriessupport efficient communication about the local environment.Our analyses build on a domain-general information-theoreticmodel of categorization across languages, and we identify sev-eral qualitative predictions that emerge when this model is ap-plied to season naming, including the prediction that systemswith even numbers of categories should be more common thansystems with odd sizes. We test the model quantitatively usinga collection of season systems drawn from the linguistic andanthropological literature and data specifying temperature andprecipitation in locations associated with these systems. Ourresults support the predicted even-odd asymmetry, and we alsofind that the model makes a number of successful predictionsabout the locations of boundaries between seasons.