Proceedings of the Annual Meeting of the Cognitive Science Society

Languages partition semantic space into linguistic cate-gories in systematic ways. In this study, we investigatea semantic space which has received sustained attentionin theoretical linguistics: person. Person systems con-vey the roles entities play in the conversational context(i.e., speaker(s), addressee(s), other(s)). Like other lin-guistic category systems (e.g. color and kinship terms),not all ways of partitioning the person space are equallylikely. We use an artificial language learning paradigm totest whether typological frequency correlates with learn-ability of person paradigms. We focus on first personsystems (e.g., ‘I’ and ‘we’ in English), and test the predic-tions of a set of theories which posit a universal set of fea-tures (±exclusive, and ±minimal) to capture this space.Our results provide the first experimental evidence forfeature-based theories of person systems.