Res on Lang and Comput DOI 10.1007/s11168-011-9074-5 Online Learning Mechanisms for Bayesian Models of Word Segmentation

© The Author(s) 2011. This article is published with open access at Springerlink.com Abstract In recent years, Bayesian models have become increasingly popular as a way of understanding human cognition. Ideal learner Bayesian models assume that cognition can be usefully understood as optimal behavior under uncertainty, a hypoth-esis that has been supported by a number of modeling studies across various domains (e.g., Griffiths and Tenenbaum, Cognitive Psychology, 51, 354–384, 2005; Xu and Tenenbaum, Psychological Review, 114, 245–272, 2007). The models in these studies aim to explain why humans behave as they do given the task and data they encounter, but typically avoid some questions addressed by more traditional psychological mod-els, such as how the observed behavior is produced given constraints on memory and processing. Here, we use the task of word segmentation as a case study for investigat-ing these questions within a Bayesian framework. We consider some limitations of the infant learner, and develop several online learning algorithms that take these limitations into account. Each algorithm can be viewed as a different method of approximating the same ideal learner. When tested on corpora of English child-directed speech, we find that the constrained learner’s behavior depends non-trivially on how the learner’s limitations are implemented. Interestingly, sometimes biases that are helpful to an ideal learner hinder a constrained learner, and in a few cases, constrained learners perform equivalently or better than the ideal learner. This suggests that the transition from a computational-level solution for acquisition to an algorithmic-level one is not straightforward