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dc.contributor.authorYeates, F
dc.contributor.authorWills, Andy
dc.contributor.authorJones, FW
dc.contributor.authorMcLaren, IPL
dc.date.accessioned2016-01-04T13:03:02Z
dc.date.available2016-01-04T13:03:02Z
dc.date.issued2015-01-01
dc.identifier.issn0364-0213
dc.identifier.issn1551-6709
dc.identifier.urihttp://hdl.handle.net/10026.1/4157
dc.description.abstract

<jats:title>Abstract</jats:title><jats:p>Some argue the common practice of inferring multiple processes or systems from a dissociation is flawed (Dunn, 2003). One proposed solution is state‐trace analysis (Bamber, 1979), which involves plotting, across two or more conditions of interest, performance measured by either two dependent variables, or two conditions of the same dependent measure. The resulting analysis is considered to provide evidence that either (a) a single process underlies performance (one function is produced) or (b) there is evidence for more than one process (more than one function is produced). This article reports simulations using the simple recurrent network (<jats:styled-content style="fixed-case">SRN</jats:styled-content>; Elman, 1990) in which changes to the learning rate produced state‐trace plots with multiple functions. We also report simulations using a single‐layer error‐correcting network that generate plots with a single function. We argue that the presence of different functions on a state‐trace plot does not necessarily support a dual‐system account, at least as typically defined (e.g. two separate autonomous systems competing to control responding); it can also indicate variation in a single parameter within theories generally considered to be single‐system accounts.</jats:p>

dc.format.extent1047-1061
dc.format.mediumPrint-Electronic
dc.languageen
dc.language.isoen
dc.publisherWiley
dc.subjectState-trace analysis
dc.subjectMultiple systems
dc.subjectModel evaluation
dc.subjectConnectionist network
dc.subjectDual processes
dc.subjectComputer simulation
dc.titleState trace analysis: Dissociable processes in a connectionist network?
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000357893800008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue5
plymouth.volume39
plymouth.publication-statusPublished
plymouth.journalCognitive Science
dc.identifier.doi10.1111/cogs.12185
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plymouth.organisational-group/Plymouth/Faculty of Health/School of Psychology
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA04 Psychology, Psychiatry and Neuroscience
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dc.publisher.placeUnited States
dcterms.dateAccepted2014-04-14
dc.identifier.eissn1551-6709
dc.rights.embargoperiodNo embargo
rioxxterms.versionofrecord10.1111/cogs.12185
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2015-01-01
rioxxterms.typeJournal Article/Review


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