Work in learning word meanings has argued that associative learning mechanisms are insufficient because word learning is too fast, confronts too much ambiguity, or is based on social principles. This critiques an outdated view of association, focusing on the information being learned, not the mechanism of learning. The authors present a model that embeds association learning in a richer system, which includes both internal representations to and real-time competition that enable it to select the referent of novel and familiar words. A series of simulations validate these theoretical assumptions showing better learning and novel word inference when both factors are present. The authors then use this model to understand the apparent rapidity of word learning and value of high and low informative learning situations. Finally, the authors scale the model up to examine interactions between auditory and visual categorization and account for conflicting results as to whether words help or hinder categorization.
|Title of host publication||Theoretical and Computational Models of Word Learning|
|Subtitle of host publication||Trends in Psychology and Artificial Intelligence|
|Editors||Lakshmi Gogate, George Hollich|
|Number of pages||32|
|Publication status||Published - 2013|