NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1137762
Record Type: Journal
Publication Date: 2017
Pages: 22
Abstractor: As Provided
ISSN: ISSN-1048-9223
Evaluation, Use, and Refinement of Knowledge Representations through Acquisition Modeling
Pearl, Lisa
Language Acquisition: A Journal of Developmental Linguistics, v24 n2 p126-147 2017
Generative approaches to language have long recognized the natural link between theories of knowledge representation and theories of knowledge acquisition. The basic idea is that the knowledge representations provided by Universal Grammar enable children to acquire language as reliably as they do because these representations highlight the relevant aspects of the available linguistic data. So, one reasonable evaluation of any theory of representation is how useful it is for acquisition. This means that when we have multiple theories for how knowledge is represented, we can try to evaluate these theoretical options by seeing how children might use them during acquisition. Computational models of the acquisition process are an effective tool for determining this, since they allow us to incorporate the assumptions of a representation into a cognitively plausible learning scenario and see what happens. We can then identify which representations work for acquisition and what those representations need to work. This in turn allows us to refine both our theories of how knowledge is represented and how those representations are used by children during acquisition. I discuss two case studies of this approach for representations in metrical stress and syntax and consider what we learn from this computational acquisition evaluation in each domain.
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: BCS0843896; BCS1347028