Literature DB >> 12412895

The impact of feedback semantics in visual word recognition: number-of-features effects in lexical decision and naming tasks.

Penny M Pexman1, Stephen J Lupker, Yasushi Hino.   

Abstract

The notion of feedback activation from semantics to both orthography and phonology has recently been used to explain a number of semantic effects in visual word recognition, including polysemy effects (Hino & Lupker, 1996; Pexman & Lupker, 1999) and synonym effects (Pecher, 2001). In the present research, we tested an account based on feedback activation by investigating a new semantic variable: number of features (NOF). Words with high NOF (e.g., LION) should activate richer semantic representations than do words with low NOF (e.g., LIME). As a result, the feedback activation from semantics to orthographic and phonological representations should be greater for high-NOF words, which should produce superior lexical decision task (LDT) and naming task performance. The predicted facilitory NOF effects were observed in both LDT and naming.

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Mesh:

Year:  2002        PMID: 12412895     DOI: 10.3758/bf03196311

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  21 in total

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Authors:  Yasushi Hino; Stephen J Lupker; Penny M Pexman
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2002-07       Impact factor: 3.051

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Journal:  J Exp Psychol Hum Percept Perform       Date:  1994-12       Impact factor: 3.332

Review 8.  Visual word recognition: a multistage activation model.

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Journal:  J Exp Psychol Learn Mem Cogn       Date:  1993-07       Impact factor: 3.051

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Journal:  Brain Lang       Date:  1985-01       Impact factor: 2.381

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  43 in total

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Authors:  Penny M Pexman; Gregory G Holyk; Marie-H Monfils
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Journal:  Mem Cognit       Date:  2003-06

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5.  Typicality effects in contingency-shaped generalized equivalence classes.

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Journal:  J Exp Anal Behav       Date:  2004-11       Impact factor: 2.468

6.  Does a pear growl? Interference from semantic properties of orthographic neighbors.

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Journal:  Mem Cognit       Date:  2009-07

7.  There are many ways to be rich: effects of three measures of semantic richness on visual word recognition.

Authors:  Penny M Pexman; Ian S Hargreaves; Paul D Siakaluk; Glen E Bodner; Jamie Pope
Journal:  Psychon Bull Rev       Date:  2008-02

8.  Automatic semantic feedback during visual word recognition.

Authors:  Jason F Reimer; Thomas C Lorsbach; Dana M Bleakney
Journal:  Mem Cognit       Date:  2008-04

9.  Semantic Richness and Aging: The Effect of Number of Features in the Lexical Decision Task.

Authors:  Christelle Robert; Liliana Rico Duarte
Journal:  J Psycholinguist Res       Date:  2016-04

10.  The Effects of Learning and Retrieval Contexts on Cross-situational Word Learning.

Authors:  Chi-Hsin Chen; Chen Yu
Journal:  IEEE Int Conf Dev Learn Epigenetic Robot       Date:  2015-12-07
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