Repetition priming of words, pseudowords, and nonwords.
In 5 experiments, the authors assessed repetition priming for words, pseudowords, and nonwords using a task that combines an implicit perceptual fluency measure and a recognition memory assessment for each list item. Words and pseudowords generated a consistently strong repetition effect even when...
| Publicado en: | Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 26; no. 4; pp. 945 - 973 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
July 2000
|
| Materias: | |
| 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=507705037&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507705037 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02787393 EXL jtl: Journal of Experimental Psychology. Learning, Memory & Cognition issn: 02787393 maglogo: N pubinfo: dt: July 2000 vid: 26 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 507705037 10.1037/0278-7393.26.4.945 ppf: 945 ppct: 28 formats: tig: atl: Repetition priming of words, pseudowords, and nonwords. aug: au: Stark, Craig E. L. McClelland, James L. su: Recognition (Psychology) Priming (Psychology) Repetition (Learning process) Word recognition sug: subj: Recognition (Psychology) Priming (Psychology) Repetition (Learning process) Word recognition ab: In 5 experiments, the authors assessed repetition priming for words, pseudowords, and nonwords using a task that combines an implicit perceptual fluency measure and a recognition memory assessment for each list item. Words and pseudowords generated a consistently strong repetition effect even when there was a failure to recognize the stimulus. In 2 of the experiments, the repetition effect for nonwords was reliably above chance even when there was a failure to recognize the stimulus. The authors propose a parallel distributed processing (PDP) model based on the work of J. McClelland and D. Rumelhart (1985) as a way to understand the mechanisms potentially responsible for the pattern of findings. Although the error-driven nature of learning in the model results in a poor fit to the nonword priming data, this is not endemic to all PDP models. Using a model based on Hebbian learning, the authors instantiate a property that they believe is characteristic of implicit memory—that learning is primarily based on the strengthening of connections between units that become active during the processing of a stimulus. This model provides a far more satisfactory account of the data than does the error-driven model. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|