Proceedings of the Annual Meeting of the Cognitive Science Society

Learning how words refer to aspects of the environment is acomplex task, but one that is supported by numerous cueswithin the environment which constrain the possibilities formatching words to their intended referents. In this paper wetested the predictions of a computational model of multiplecue integration for word learning, that predicted variation inthe presence of cues provides an optimal learning situation. Ina cross-situational learning task with adult participants, wevaried the reliability of presence of distributional, prosodic,and gestural cues. We found that the best learning occurredwhen cues were often present, but not always. The effect ofvariability increased the salience of individual cues for thelearner, but resulted in robust learning that was not vulnerableto individual cues’ presence or absence. Thus, variability ofmultiple cues in the language-learning environment providedthe optimal circumstances for word learning.