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      Lexical representation and processing of word-initial morphological alternations: Scottish Gaelic mutation

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          Abstract

          When hearing speech, listeners begin recognizing words before reaching the end of the word. Therefore, early sounds impact spoken word recognition before sounds later in the word. In languages like English, most morphophonological alternations affect the ends of words, but in some languages, morphophonology can alter the early sounds of a word. Scottish Gaelic, an endangered language, has a pattern of ‘initial consonant mutation’ that changes initial consonants: Pòg ‘kiss’ begins with [p h], but phòg ‘kissed’ begins with [f]. This raises questions both of how listeners process words that might begin with a mutated consonant during spoken word recognition, and how listeners relate the mutated and unmutated forms to each other in the lexicon. We present three experiments to investigate these questions. A priming experiment shows that native speakers link the mutated and unmutated forms in the lexicon. A gating experiment shows that Gaelic listeners usually do not consider mutated forms as candidates during lexical recognition until there is enough evidence to force that interpretation. However, a phonetic identification experiment confirms that listeners can identify the mutated sounds correctly. Together, these experiments contribute to our understanding of how speakers represent and process a language with morphophonological alternations at word onset.

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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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            The TRACE model of speech perception

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              Shortlist B: a Bayesian model of continuous speech recognition.

              A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; D. Norris, J. M. McQueen, A. Cutler, & S. Butterfield, 1997) and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward architecture with no online feedback, and a lexical segmentation algorithm based on the viability of chunks of the input as possible words. Shortlist B is radically different from its predecessor in two respects. First, whereas Shortlist was a connectionist model based on interactive-activation principles, Shortlist B is based on Bayesian principles. Second, the input to Shortlist B is no longer a sequence of discrete phonemes; it is a sequence of multiple phoneme probabilities over 3 time slices per segment, derived from the performance of listeners in a large-scale gating study. Simulations are presented showing that the model can account for key findings: data on the segmentation of continuous speech, word frequency effects, the effects of mispronunciations on word recognition, and evidence on lexical involvement in phonemic decision making. The success of Shortlist B suggests that listeners make optimal Bayesian decisions during spoken-word recognition.
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                Author and article information

                Contributors
                Journal
                1868-6354
                Laboratory Phonology
                Ubiquity Press
                1868-6354
                12 April 2017
                : 8
                : 1
                : 8
                Affiliations
                [1 ]Department of Linguistics, University of Arizona, Tucson, US
                [2 ]Department of English, University of Nevada, Reno, US
                [3 ]Department of Linguistics, University of Alberta, Edmonton, CA
                Article
                10.5334/labphon.22
                e4bc92d9-3abb-4c57-a2de-a21b85288a53
                Copyright: © 2017 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

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                Categories
                Journal article

                Applied linguistics,General linguistics,Linguistics & Semiotics
                spoken word recognition,Gaelic,morphophonology

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