Show simple item record

dc.contributor.authorHsu, Aen_US
dc.contributor.authorGriffiths, TLen_US
dc.date.accessioned2016-06-23T12:48:23Z
dc.date.available2016-06-13en_US
dc.date.issued2016en_US
dc.date.submitted2016-06-14T00:03:43.329Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/13050
dc.description.abstractA classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.en_US
dc.format.extente0156597 - ?en_US
dc.languageengen_US
dc.relation.ispartofPLoS Oneen_US
dc.rightsTo be published by PLoS One
dc.subjectAdulten_US
dc.subjectComprehensionen_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectLanguageen_US
dc.subjectLanguage Developmenten_US
dc.subjectLearningen_US
dc.subjectLinguisticsen_US
dc.subjectMaleen_US
dc.subjectModels, Statisticalen_US
dc.titleSampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.en_US
dc.typeArticle
dc.identifier.doi10.1371/journal.pone.0156597en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/27310576en_US
pubs.issue6en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume11en_US
dcterms.dateAccepted2016-05-17en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record