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How Many Mechanisms Are Needed to Analyze Speech? A Connectionist Simulation of Structural Rule Learning in Artificial Language Acquisition

dc.contributor.authorLaakso, Aarreen_US
dc.contributor.authorCalvo, Pacoen_US
dc.date.accessioned2011-11-10T15:33:39Z
dc.date.available2012-11-02T18:56:40Zen_US
dc.date.issued2011-09en_US
dc.identifier.citationLaakso, Aarre; Calvo, Paco (2011). "How Many Mechanisms Are Needed to Analyze Speech? A Connectionist Simulation of Structural Rule Learning in Artificial Language Acquisition." Cognitive Science 35(7). <http://hdl.handle.net/2027.42/86908>en_US
dc.identifier.issn0364-0213en_US
dc.identifier.issn1551-6709en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/86908
dc.description.abstractSome empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule‐following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating that a single statistical mechanism can mimic the apparent discovery of structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis.en_US
dc.publisherBlackwell Publishing Ltden_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherArtificial Grammar Learningen_US
dc.subject.otherSpeech Processingen_US
dc.subject.otherLanguage Acquisitionen_US
dc.subject.otherMore Than One Mechanism Hypothesisen_US
dc.subject.otherStatistical Learningen_US
dc.subject.otherConnectionismen_US
dc.titleHow Many Mechanisms Are Needed to Analyze Speech? A Connectionist Simulation of Structural Rule Learning in Artificial Language Acquisitionen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelNeurosciencesen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Behavioral Sciences, University of Michigan‐Dearbornen_US
dc.contributor.affiliationotherPhilosophy Department, University of Murciaen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/86908/1/j.1551-6709.2011.01191.x.pdf
dc.identifier.doi10.1111/j.1551-6709.2011.01191.xen_US
dc.identifier.sourceCognitive Scienceen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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