27
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Auditory disruption improves word segmentation: A functional basis for lenition phenomena

      research-article

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper presents evidence that spirantization, a cross-linguistically common lenition process, affects English listeners’ ease of segmenting novel “words” in an artificial language. The cross-linguistically common spirantization pattern of initial stops and medial continuants (e.g. [ɡuβa]) results in improved word segmentation compared to the inverse “anti-lenition” pattern of initial continuants and medial stops (e.g. [ɣuba]). The study also tests the effect of obstruent voicing, another common lenition pattern, but finds no significant differences in segmentation performance. There are several points of broader interest in these studies. Most of the phonetic factors influencing word segmentation in past studies have been language-specific and/or prosodic in nature: stress, intonation, final lengthening, etc. Spirantization, while often prosodically conditioned, is different from all of these patterns in that it concerns a segmental alternation. Moreover, the effects reported here are for speakers of a language, American English, that only sporadically displays spirantization, and not in the phonological contexts used in the experiment. This suggests that the results may reflect more general properties of speech perception and word boundary detection, rather than a perceptual processing strategy transferred directly from English. As such, the studies offer partial support for theories of lenition rooted in notions of perceptual-acoustic continuity and disruption.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: found
          • Article: not found
          Is Open Access

          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
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              OpenSesame: An open-source, graphical experiment builder for the social sciences

              In the present article, we introduce OpenSesame, a graphical experiment builder for the social sciences. OpenSesame is free, open-source, and cross-platform. It features a comprehensive and intuitive graphical user interface and supports Python scripting for complex tasks. Additional functionality, such as support for eyetrackers, input devices, and video playback, is available through plug-ins. OpenSesame can be used in combination with existing software for creating experiments.
                Bookmark

                Author and article information

                Contributors
                Journal
                2397-1835
                Glossa: a journal of general linguistics
                Ubiquity Press
                2397-1835
                23 March 2018
                2018
                : 3
                : 1
                : 38
                Affiliations
                [1 ]West Virginia University, US
                [2 ]University of Pittsburgh, US
                Article
                10.5334/gjgl.443
                df150fdd-99b9-4fe9-99d3-dcb035ef4615
                Copyright: © 2018 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/.

                History
                : 02 June 2017
                : 09 January 2018
                Categories
                Research

                General linguistics,Linguistics & Semiotics
                spirantization,statistical learning,word segmentation,phonetics,lenition,phonology

                Comments

                Comment on this article