Proceedings of the Annual Meeting of the Cognitive Science Society

Human infants have the remarkable ability to learn any hu-man language. One proposed mechanism for this ability isdistributional learning, where learners infer the underlyingcluster structure from unlabeled input. Computational mod-els of distributional learning have historically been principledbut psychologically-implausible computational-level models,or ad hoc but psychologically plausible algorithmic-level mod-els. Approximate rational models like particle filters can po-tentially bridge this divide, and allow principled, but psycho-logically plausible models of distributional learning to be spec-ified and evaluated. As a proof of concept, I evaluate one suchparticle filter model, applied to learning English voicing cate-gories from distributions of voice-onset times (VOTs). I findthat this model learns well, but behaves somewhat differentlyfrom the standard, unconstrained Gibbs sampler implementa-tion of the underlying rational model.