Proceedings of the Annual Meeting of the Cognitive Science Society

Animacy is prevalent in as a semantic basis for noun classification systems (i.e., grammatical gender, noun classes and classifiers), but colour is completely absent, despite its visual salience. The absence of colour in such systems is sometimes argued to suggest domain-specific constraints on what is grammatically encodable. Here, we investigate whether this tendency could instead be explained by the superior predictive power of animacy (i.e., the degree to which it predicts other features) compared to colour. In a series of experiments, we find that animacy-based noun classes are learned better than colour-based ones. However, when participants are encouraged, by manipulating predictive power, to sort images based on colour, they are worse at learning animacy-based noun classes. The results suggest the animacy bias in grammar may have its roots in domain-general categorisation principles. They further serve as evidence for the role of cognitive biases in constraining cross-linguistic variation.