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      Exploring the “ anchor word” effect in infants: Segmentation and categorisation of speech with and without high frequency words

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          Abstract

          High frequency words play a key role in language acquisition, with recent work suggesting they may serve both speech segmentation and lexical categorisation. However, it is not yet known whether infants can detect novel high frequency words in continuous speech, nor whether they can use them to help learning for segmentation and categorisation at the same time. For instance, when hearing “ you eat the biscuit”, can children use the high-frequency words “ you” and “ the” to segment out “ eat” and “ biscuit”, and determine their respective lexical categories? We tested this in two experiments. In Experiment 1, we familiarised 12-month-old infants with continuous artificial speech comprising repetitions of target words, which were preceded by high-frequency marker words that distinguished the targets into two distributional categories. In Experiment 2, we repeated the task using the same language but with additional phonological cues to word and category structure. In both studies, we measured learning with head-turn preference tests of segmentation and categorisation, and compared performance against a control group that heard the artificial speech without the marker words (i.e., just the targets). There was no evidence that high frequency words helped either speech segmentation or grammatical categorisation. However, segmentation was seen to improve when the distributional information was supplemented with phonological cues (Experiment 2). In both experiments, exploratory analysis indicated that infants’ looking behaviour was related to their linguistic maturity (indexed by infants’ vocabulary scores) with infants with high versus low vocabulary scores displaying novelty and familiarity preferences, respectively. We propose that high-frequency words must reach a critical threshold of familiarity before they can be of significant benefit to learning.

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          Most cited references 103

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          lmerTest Package: Tests in Linear Mixed Effects Models

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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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              Random effects structure for confirmatory hypothesis testing: Keep it maximal.

              Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 December 2020
                2020
                : 15
                : 12
                Affiliations
                [1 ] Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
                [2 ] Lancaster University, Lancaster, United Kingdom
                [3 ] Cornell University, Ithaca, New York, United States of America
                [4 ] University of Arizona, Tucson, Arizona, United States of America
                CNRS - Université d’Aix-Marseille, FRANCE
                Author notes

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

                Article
                PONE-D-20-02917
                10.1371/journal.pone.0243436
                7746152
                33332419
                30dce5ae-d477-4666-8ebe-f2a3b9b699a2
                © 2020 Frost 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.

                Page count
                Figures: 6, Tables: 8, Pages: 28
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
                Award ID: ES/L008955/1
                Award Recipient :
                This work was supported by the International Centre for Language and Communicative Development (LuCiD). The support of the Economic and Social Research Council [ES/L008955/1] is gratefully acknowledged. 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
                Speech
                Social Sciences
                Linguistics
                Semantics
                People and Places
                Population Groupings
                Age Groups
                Children
                Infants
                People and Places
                Population Groupings
                Families
                Children
                Infants
                Social Sciences
                Linguistics
                Grammar
                Phonology
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Language
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Language
                Social Sciences
                Psychology
                Cognitive Psychology
                Language
                Social Sciences
                Linguistics
                Language Acquisition
                Social Sciences
                Linguistics
                Grammar
                Syntax
                Social Sciences
                Linguistics
                Phonetics
                Vowels
                Custom metadata
                The data underlying this study are available at the OSF repository, https://osf.io/4qx6s/.

                Uncategorized

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