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      Using Monte-Carlo simulation to test predictions about the time-course of semantic and lexical access in reading

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          Abstract

          One of the main theoretical distinctions between reading models is how and when they predict semantic processing occurs. Some models assume semantic activation occurs after word-form is retrieved. Other models assume there is no-word form, and that what people think of as word-form is actually just semantics. These models thus predict semantic effects should occur early in reading. Results showing words with inconsistent spelling-sound correspondences are faster to read aloud if they are imageable/concrete compared to if they are abstract have been used as evidence supporting this prediction, although null-effects have also been reported. To investigate this, I used Monte-Carlo simulation to create a large set of simulated experiments from RTs taken from different databases. The results showed significant main effects of concreteness and spelling-sound consistency, as well as age-of-acquisition, a variable that can potentially confound the results. Alternatively, simulations showing a significant interaction between spelling-sound consistency and concreteness did not occur above chance, even without controlling for age-of-acquisition. These results support models that use lexical form. In addition, they suggest significant interactions from previous experiments may have occurred due to idiosyncratic items affecting the results and random noise causing the occasional statistical error.

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

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            Concreteness ratings for 40 thousand generally known English word lemmas.

            Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 % of the raters. It can be used in future research as a reference list of generally known English lemmas.
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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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2 April 2024
                2024
                : 19
                : 4
                : e0296874
                Affiliations
                [001] Faculty of Health and Medical Sciences, School of Psychology, The University of Adelaide, Adelaide, Australia
                University of Glasgow, UNITED KINGDOM
                Author notes

                Competing Interests: The author has declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-3429-0240
                Article
                PONE-D-23-24150
                10.1371/journal.pone.0296874
                10986942
                38564586
                be422c29-e292-4aeb-8a53-52523ab5456c
                © 2024 Conrad Perry

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 July 2023
                : 21 December 2023
                Page count
                Figures: 4, Tables: 2, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Award ID: DP210100936
                Award Recipient :
                This article was funded by the Australian Research Council: Grant DP210100936. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Social Sciences
                Linguistics
                Semantics
                Research and analysis methods
                Mathematical and statistical techniques
                Statistical methods
                Monte Carlo method
                Physical sciences
                Mathematics
                Statistics
                Statistical methods
                Monte Carlo method
                Social Sciences
                Linguistics
                Neurolinguistics
                Lexical Decision Tasks
                Biology and Life Sciences
                Neuroscience
                Neurolinguistics
                Lexical Decision Tasks
                Research and Analysis Methods
                Simulation and Modeling
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Regression Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Regression Analysis
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Neuroscience
                Reaction Time
                Biology and Life Sciences
                Neuroscience
                Cognitive Neuroscience
                Reaction Time
                Social Sciences
                Linguistics
                Grammar
                Phonology
                Social Sciences
                Linguistics
                Semantics
                Conceptual Semantics
                Custom metadata
                The relevant data and codes are available via OSF at https://osf.io/9qmcs/.

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