Challenging the widespread assumption that connectionism and distributed representations go hand-in-hand.
One of the central claims associated with the parallel distributed processing approach popularized by D.E. Rumelhart, J.L. McClelland and the PDP Research Group is that knowledge is coded in a distributed fashion. Localist representations within this perspective are widely rejected. It is important...
| Publicado en: | Cognitive Psychology Vol. 45; no. 3; pp. 413 - 446 |
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| Formato: | Artículo |
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Academic Press Inc.
November 2002
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=507786192&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507786192 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00100285 COP jtl: Cognitive Psychology issn: 00100285 maglogo: N pubinfo: dt: November 2002 vid: 45 iid: 3 pid: 735 pub: Academic Press Inc. artinfo: ui: 507786192 10.1016/S0010-0285(02)00506-6 ppf: 413 ppct: 33 formats: tig: atl: Challenging the widespread assumption that connectionism and distributed representations go hand-in-hand. aug: au: Bowers, Jeffrey S. su: Psycholinguistics Human information processing sug: subj: Psycholinguistics Human information processing ab: One of the central claims associated with the parallel distributed processing approach popularized by D.E. Rumelhart, J.L. McClelland and the PDP Research Group is that knowledge is coded in a distributed fashion. Localist representations within this perspective are widely rejected. It is important to note, however, that connectionist networks can learn localist representations and many connectionist models depend on localist coding for their functioning. Accordingly, a commitment to distributed representations should be considered a specific theoretical claim regarding the structure of knowledge rather than a core principle, as often assumed. In this paper, it is argued that there are fundamental computational and empirical challenges that have not yet been addressed by distributed connectionist theories that are readily accommodated within localist approaches. This is highlighted in the context of modeling word and nonword naming, the domain in which some of the strongest claims have been made. It is shown that current PDP models provide a poor account of naming monosyllable items, and that distributed representations make it difficult for these models to scale up to more complex language phenomena. At the same time, models that learn localist representations are shown to hold promise in supporting many of the core reading and language functions on which PDP models fail. It is concluded that the common rejection of localist coding schemes within connectionist architectures is premature. |Sc 2002 Elsevier Science (USA) pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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