Proceedings of the Annual Meeting of the Cognitive Science Society

Language exhibits striking systematicity in its form-meaningmappings: Similar meanings are assigned similar forms. Herewe study how systematicity relates to another well-studiedphenomenon, linguistic regularization, the removal of unpre-dictable variation in linguistic variants. Systematicity is ulti-mately a property of classes of form-meaning mappings, eachmember of which can be acted upon independently by linguis-tic regularization. Both are supported by a cognitive bias forsimplicity, but this leaves open the question of how they inter-act to structure the lexicon. Using data from a recent artificialgesture learning experiment by Verhoef, Padden, and Kirby(2016), we formalize cognitive biases at the item level and thelanguage level as inductive biases in a hierarchical Bayesianmodel. Simulated data from models that lack either one ofthose biases show how both are necessary to capture subjects’systematicity preferences. Our results bring conceptual clar-ity about the relationship between regularization and system-aticity and promote a multi-level approach to cognitive biasesin artificial language learning and language evolution.