Exploring the Robustness of Cross-Situational Learning Under Zipfian Distributions
Article first published online: 23 JAN 2012
Copyright © 2012 Cognitive Science Society, Inc.
Volume 36, Issue 4, pages 726–739, May/June 2012
How to Cite
Vogt, P. (2012), Exploring the Robustness of Cross-Situational Learning Under Zipfian Distributions. Cognitive Science, 36: 726–739. doi: 10.1111/j.1551-6709.2011.1226.x
- Issue published online: 4 MAY 2012
- Article first published online: 23 JAN 2012
- Received 26 January 2011; received in revised form 30 June 2011; accepted 4 July 2011
- Word learning;
- Cross-situational learning;
- Lexicon learning time;
- Referential uncertainty;
- Zipf's law
Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according to Zipf's law, they applied a uniform distribution of meanings. In this article, Zipf's law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty.