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A backwards glance at words: Using reversed-interior masked primes to test models of visual word identification.
Davis, Colin J; Lupker, Stephen J.
Afiliación
  • Davis CJ; University of Bristol, Bristol, United Kingdom.
  • Lupker SJ; University of Western Ontario, London, Ontario, Canada.
PLoS One ; 12(12): e0189056, 2017.
Article en En | MEDLINE | ID: mdl-29244824
The experiments reported here used "Reversed-Interior" (RI) primes (e.g., cetupmor-COMPUTER) in three different masked priming paradigms in order to test between different models of orthographic coding/visual word recognition. The results of Experiment 1, using a standard masked priming methodology, showed no evidence of priming from RI primes, in contrast to the predictions of the Bayesian Reader and LTRS models. By contrast, Experiment 2, using a sandwich priming methodology, showed significant priming from RI primes, in contrast to the predictions of open bigram models, which predict that there should be no orthographic similarity between these primes and their targets. Similar results were obtained in Experiment 3, using a masked prime same-different task. The results of all three experiments are most consistent with the predictions derived from simulations of the Spatial-coding model.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reconocimiento Visual de Modelos / Enmascaramiento Perceptual / Lectura / Semántica Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adolescent / Adult / Female / Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reconocimiento Visual de Modelos / Enmascaramiento Perceptual / Lectura / Semántica Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adolescent / Adult / Female / Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2017 Tipo del documento: Article País de afiliación: Reino Unido Pais de publicación: Estados Unidos