Gavagai is as Gavagai does:learning nouns and verbs from cross-situational statistics

Monaghan, Padraic and Mattock, Karen and Davies, Robert A. I. and Smith, Alastair C. (2015) Gavagai is as Gavagai does:learning nouns and verbs from cross-situational statistics. Cognitive Science, 39 (5). pp. 1099-1112. ISSN 0364-0213

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Abstract

Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross-situational learning studies have shown that word-object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust when objects are moving, and that verbs can also be learned from similar scenes without additional syntactic information. Furthermore, we show that both nouns and verbs can be acquired simultaneously, thus resolving category-level as well as individual word-level ambiguity. However, nouns were learned more quickly than verbs, and we discuss this in light of previous studies investigating the noun advantage in word learning.

Item Type:
Journal Article
Journal or Publication Title:
Cognitive Science
Additional Information:
This is the peer reviewed version of the following article: Monaghan, P., Mattock, K., Davies, R. A. I. and Smith, A. C. (2015), Gavagai Is as Gavagai Does: Learning Nouns and Verbs From Cross-Situational Statistics. Cognitive Science, 39: 1099–1112. doi: 10.1111/cogs.12186 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/cogs.12186/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3200/3205
Subjects:
ID Code:
76801
Deposited By:
Deposited On:
23 Nov 2015 10:36
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Jun 2020 03:26