Something about "us": Learning first person pronoun systems

Languages partition semantic space into linguistic categories in systematic ways. In this study, we investigate a semantic space which has received sustained attention in theoretical linguistics: person. Person systems convey the roles entities play in the conversational context(i.e., speaker(s), addressee(s), other(s)). Like other linguistic category systems (e.g. color and kinship terms),not all ways of partitioning the person space are equally likely. We use an artificial language learning paradigm to test whether typological frequency correlates with learnability of person paradigms. We focus on first persons ystems (e.g., ‘I’ and ‘we’ in English), and test the predictions of a set of theories which posit a universal set of features (±exclusive, and ±minimal) to capture this space.Our results provide the first experimental evidence for feature-based theories of person systems