Word learning is a complex phenomenon because it is tied to many different behaviors that are linked to multiple perceptual and cognitive systems. Further, recent research suggests that the course of word learning builds from effects at the level of individual referent selection or noun generalization decisions that accumulate on a moment-to-moment timescale and structure subsequent word learning behaviors. Thus, what is needed for any unified theory of word learning is 1) an account of how individual decisions are made across different contexts, including the details of how objects are encoded, represented, and selected in the course of a word learning behavior; and 2) a mechanism that builds on these individual, contextually specific decisions. Here, the authors present a Dynamic Neural Field (DNF) Model that captures processes at both the second-to-second and developmental timescales and provides a process-based account of how individual behaviors accumulate to create development. Simulations illustrate how the model captures multiple word learning behaviors such as comprehension, production, novel noun generalization (in yes/no or forced choice tasks), referent selection, and learning of hierarchical nominal categories. They also discuss how the model ties developments in these tasks to developments in object perception, working memory, and the representation and tracking of objects in space. Finally, the authors review empirical work testing novel predictions of the model regarding the roles of competition and selection in forced-choice and yes/no generalization tasks and the role of space in early name-object binding.
|Title of host publication||Theoretical and Computational Models of Word Learning|
|Subtitle of host publication||Trends in Psychology and Artificial Intelligence|
|Number of pages||27|
|Publication status||Published - 2013|