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      Embodied time: Effect of reading expertise on the spatial representation of past and future

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          Abstract

          How do people grasp the abstract concept of time? It has been argued that abstract concepts, such as future and past, are grounded in sensorimotor experience. When responses to words that refer to the past or the future are either spatially compatible or incompatible with a left-to-right timeline, a space-time congruency effect is observed. In the present study, we investigated whether reading expertise determines the strength of the space-time congruency effect, which would suggest that learning to read and write drives the effect. Using a temporal categorization task, we compared two types of space-time congruency effects, one where spatial incongruency was generated by the location of the stimuli on the screen and one where it was generated by the location of the responses on the keyboard. While the first type of incongruency was visuo-spatial only, the second involved the motor system. Results showed stronger space-time congruency effects for the second type of incongruency (i.e., when the motor system was involved) than for the first type (visuo-spatial). Crucially, reading expertise, as measured by a standardized reading test, predicted the size of the space-time congruency effects. Altogether, these results reinforce the claim that the spatial representation of time is partially mediated by the motor system and partially grounded in spatially-directed movement, such as reading or writing.

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          lmerTest Package: Tests in Linear Mixed Effects Models

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            Estimating the Dimension of a Model

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              Fitting Linear Mixed-Effects Models Using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                27 October 2022
                2022
                : 17
                : 10
                : e0276273
                Affiliations
                [1 ] CNRS, Laboratoire de Psychologie Cognitive (UMR 7290), Aix-Marseille Université, Marseille, France
                [2 ] CNRS, Laboratoire de Neurosciences Cognitive (UMR 7291), Aix-Marseille Université, Marseille, France
                Italian National Research Council, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-7549-7395
                Article
                PONE-D-22-08528
                10.1371/journal.pone.0276273
                9612582
                36301981
                f3bf2b65-d50a-419a-a0ef-0995f6341be8
                © 2022 Grasso et al

                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
                : 25 March 2022
                : 3 October 2022
                Page count
                Figures: 4, Tables: 0, Pages: 15
                Funding
                Funded by: Agence Nationale de la recherche (FR)
                Award ID: ANR-16-CONV-0002
                Camille Grasso was supported by a doctoral fellowship of the French Ministry of Higher Education, Research, and Innovation. This study benefited from support of the Institute of Convergence ILCB (ANR-16-CONV-0002) and the Excellence Initiative of Aix-Marseille University A*MIDEX.
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                Biology and Life Sciences
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