Encoder: A Connectionist Model of How Learning to Visually Encode Fixated Text Images Improves Reading Fluency.
This article proposes that visual encoding learning improves reading fluency by widening the span over which letters are recognized from a fixated text image so that fewer fixations are needed to cover a text line. Encoder is a connectionist model that learns to convert images like the fixated text...
| Publicado en: | Psychological Review Vol. 111; no. 3; pp. 617 - 640 |
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| Formato: | Artículo |
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American Psychological Association
Jul2004
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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=507916695&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507916695 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: Jul2004 vid: 111 iid: 3 pid: 34 pub: American Psychological Association artinfo: ui: 507916695 10.1037/0033-295X.111.3.617 ppf: 617 ppct: 23 formats: tig: atl: Encoder: A Connectionist Model of How Learning to Visually Encode Fixated Text Images Improves Reading Fluency. aug: au: Martin, Gale L. su: Human information processing Psychology of learning Reading level of students Psychological research Computer software sug: subj: Human information processing Psychology of learning Reading level of students Psychological research Computer software ab: This article proposes that visual encoding learning improves reading fluency by widening the span over which letters are recognized from a fixated text image so that fewer fixations are needed to cover a text line. Encoder is a connectionist model that learns to convert images like the fixated text images human readers encode into the corresponding letter sequences. The computational theory of classification learning predicts that fixated text-image size makes this learning difficult but that reducing image variability and biasing learning should help. Encoder confirms these predictions. It fails to learn as image size increases but achieves humanlike visual encoding accuracy when image variability is reduced by regularities in fixation positions and letter sequences and when learning is biased to discover mapping functions based on the sequential, componential structure of text. After training, Encoder exhibits many humanlike text familiarity effects. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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