Proceedings of the Annual Meeting of the Cognitive Science Society

Children learn word meanings by making use of commonalities across the usages of a word in different situations. However, early word learning experiences have a high level of uncertainty. For a word in an utterance, there are many possible meanings in the environment (referential uncertainty). Similarly, for a meaning, there are multiple possible words in the utterance (linguistic uncertainty). We propose a general framework to investigate the role of mutual exclusivity bias (asserting one-to-one mappings between words and their meanings) in early word learning. Through a set of computational studies, we show that to successfully learn word meanings under uncertainty, a model needs to implement two types of competition: words competing for the association to a meaning reduces linguistic uncertainty, and meanings competing for a word limits referential uncertainty. Our work highlights the importance of an algorithmic-level analysis to shed light on different mechanisms that implement a computational-level theory.