Online Processing is Essential for Learning: Understanding Fast Mapping and Word Learning in a Dynamic Connectionist Architecture
Year: 2006
Type: article
Abstract: The acquisition of word meaning is often partially attributed to fast mapping.However, recent research suggests that fast mapping and word learning may represent distinct components of language acquisition.Here we examine their interaction with a Hebbian Normalized Recurrence Network, a connectionist architecture that captures both online processing and long-term statistical learning.After training on a small lexicon, the model performed well above chance on a fast mapping task.Careful analyses of the weight changes, however, suggest that the fast mapping task can be solved with minimal learning.Thus, this model not only captures both long-term learning and online processes, but also provides unique insights regarding the relationship between fast mapping and word learning and that the two should be carefully distinguished.
Institution: University of Iowa
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W587117725