  Children learn word meanings by tapping into the commonalities across
different situations in which words are used and overcome the high level of
uncertainty involved in early word learning experiences. We propose a modeling
framework to investigate the role of mutual exclusivity bias - asserting
one-to-one mappings between words and their meanings - in reducing uncertainty
in word learning. In a set of computational studies, we show that to
successfully learn word meanings in the face of uncertainty, a learner needs to
use two types of competition: words competing for association to a referent
when learning from an observation and referents competing for a word when the
word is used. Our work highlights the importance of an algorithmic-level
analysis to shed light on the utility of different mechanisms that can
implement the same computational-level theory.
