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      An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects in Visual Word Recognition

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          Abstract

          There is considerable evidence (e.g., Pexman et al., 2008) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks. The present study extends this earlier work by providing the most comprehensive evaluation to date of semantic richness effects on visual word recognition performance. Specifically, using mixed effects analyses to control for the influence of correlated lexical variables, we considered the impact of number of features, number of senses, semantic neighborhood density, imageability, and body–object interaction across five visual word recognition tasks: standard lexical decision, go/no-go lexical decision, speeded pronunciation, progressive demasking, and semantic classification. Semantic richness effects could be reliably detected in all tasks of lexical processing, indicating that semantic representations, particularly their imaginal and featural aspects, play a fundamental role in visual word recognition. However, there was also evidence that the strength of certain richness effects could be flexibly and adaptively modulated by task demands, consistent with an intriguing interplay between task-specific mechanisms and differentiated semantic processing.

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          Most cited references49

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          DRC: a dual route cascaded model of visual word recognition and reading aloud.

          This article describes the Dual Route Cascaded (DRC) model, a computational model of visual word recognition and reading aloud. The DRC is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud. For both tasks, the authors show that a wide variety of variables that influence human latencies influence the DRC model's latencies in exactly the same way. The DRC model simulates a number of such effects that other computational models of reading do not, but there appear to be no effects that any other current computational model of reading can simulate but that the DRC model cannot. The authors conclude that the DRC model is the most successful of the existing computational models of reading.
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            Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes.

            Are words read visually (by means of a direct mapping from orthography to semantics) or phonologically (by mapping from orthography to phonology to semantics)? The authors addressed this long-standing debate by examining how a large-scale computational model based on connectionist principles would solve the problem and comparing the model's performance to people's. In contrast to previous models, the present model uses an architecture in which meanings are jointly determined by the 2 components, with the division of labor between them affected by the nature of the mappings between codes. The model is consistent with a variety of behavioral phenomena, including the results of studies of homophones and pseudohomophones thought to support other theories, and illustrates how efficient processing can be achieved using multiple simultaneous constraints. ((c) 2004 APA, all rights reserved)
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              Orthographic processing in visual word recognition: a multiple read-out model.

              A model of orthographic processing is described that postulates read-out from different information dimensions, determined by variable response criteria set on these dimensions. Performance in a perceptual identification task is simulated as the percentage of trials on which a noisy criterion set on the dimension of single word detector activity is reached. Two additional criteria set on the dimensions of total lexical activity and time from stimulus onset are hypothesized to be operational in the lexical decision task. These additional criteria flexibly adjust to changes in stimulus material and task demands, thus accounting for strategic influences on performance in this task. The model unifies results obtained in response-limited and data-limited paradigms and helps resolve a number of inconsistencies in the experimental literature that cannot be accommodated by other current models of visual word recognition.
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                Author and article information

                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Research Foundation
                1662-5161
                17 April 2012
                2012
                : 6
                : 72
                Affiliations
                [1] 1simpleDepartment of Psychology, Faculty of Arts and Social Sciences, National University of Singapore Singapore
                [2] 2simpleDepartment of Psychology, University of Calgary Calgary, AB, Canada
                Author notes

                Edited by: Paul D. Siakaluk, University of Northern British Columbia, Canada

                Reviewed by: Jon Andoni Duñabeitia, Basque Center on Cognition, Brain and Language, Spain; David Balota, Washington University, USA; Michael J. Cortese, University of Nebraska at Omaha, USA

                *Correspondence: Melvin J. Yap, Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Block AS4, #02-07, Singapore 117570. e-mail: melvin@ 123456nus.edu.sg
                Article
                10.3389/fnhum.2012.00072
                3328122
                22529787
                074649d8-a6ae-4830-975c-f636e6d0210b
                Copyright © 2012 Yap, Pexman, Wellsby, Hargreaves and Huff.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

                History
                : 05 January 2012
                : 15 March 2012
                Page count
                Figures: 1, Tables: 3, Equations: 0, References: 75, Pages: 10, Words: 8619
                Categories
                Neuroscience
                Original Research

                Neurosciences
                lexical decision,semantic richness,semantic classification,semantic neighborhood density,progressive demasking,imageability,visual word recognition,body-object interaction

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