Calculating Probabilities Simplifies Word Learning

Children can use the statistical regularities of their environ-ment to learn word meanings, a mechanism known as cross-situational learning. We take a computational approach to in-vestigate how the information present during each observationin a cross-situational framework can affect the overall acqui-sition of word meanings. We do so by formulating variousin-the-moment learning mechanisms that are sensitive to dif-ferent statistics of the environment, such as counts and con-ditional probabilities. Each mechanism introduces a uniquesource of competition or mutual exclusivity bias to the model;the mechanism that maximally uses the model’s knowledge ofword meanings performs the best. Moreover, the gap betweenthis mechanism and others is amplified in more challenginglearning scenarios, such as learning from few examples. Key-words: cross-situational word learning; computational model-ing; word learning biase