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      What happens to large changes? Saltation produces well-liked outputs that are hard to generate

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          Abstract

          Saltatory alternations ‘skip over’ intermediate sounds, as in k~s skipping over [t]. Recent research has argued that saltation is diachronically unstable and documented one possible cause of instability: Learners exposed to saltatory alternations may overgeneralize them to intermediate sounds. However, this research has trained participants to criterion or excluded participants who did not reach criterion accuracy on familiar sounds. In first language acquisition, learners of languages with saltatory patterns cannot hope to receive more exposure to the pattern than those learning non-saltatory patterns. For this reason, we examined learning of saltatory and non-saltatory patterns after a constant amount of training. We compared saltatory labial palatalization to non-saltatory alveolar and velar palatalization. Participants showed overgeneralization of saltatory palatalization in a judgment task. However, saltatory alternations did not result in increased rates of palatalizing similar sounds, compared to non-saltatory alternations. Instead, saltatory alternations were less likely to be produced than non-saltatory alternations. These results suggest that large, saltatory alternations may be diachronically unstable because they are harder to (learn to) produce. Instead of being overgeneralized to intermediate sounds, saltatory alternations may disappear from the language by losing productivity and being replaced with faithful mappings.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            A practical solution to the pervasive problems of p values.

            In the field of psychology, the practice of p value null-hypothesis testing is as widespread as ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of the statistical peculiarities of the p value procedure. In particular, p values are based on data that were never observed, and these hypothetical data are themselves influenced by subjective intentions. Moreover, p values do not quantify statistical evidence. This article reviews these p value problems and illustrates each problem with concrete examples. The three problems are familiar to statisticians but may be new to psychologists. A practical solution to these p value problems is to adopt a model selection perspective and use the Bayesian information criterion (BIC) for statistical inference (Raftery, 1995). The BIC provides an approximation to a Bayesian hypothesis test, does not require the specification of priors, and can be easily calculated from SPSS output.
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              Recognizing spoken words: the neighborhood activation model.

               P Luce,  D Pisoni (1998)
              A fundamental problem in the study of human spoken word recognition concerns the structural relations among the sound patterns of words in memory and the effects these relations have on spoken word recognition. In the present investigation, computational and experimental methods were employed to address a number of fundamental issues related to the representation and structural organization of spoken words in the mental lexicon and to lay the groundwork for a model of spoken word recognition. Using a computerized lexicon consisting of transcriptions of 20,000 words, similarity neighborhoods for each of the transcriptions were computed. Among the variables of interest in the computation of the similarity neighborhoods were: 1) the number of words occurring in a neighborhood, 2) the degree of phonetic similarity among the words, and 3) the frequencies of occurrence of the words in the language. The effects of these variables on auditory word recognition were examined in a series of behavioral experiments employing three experimental paradigms: perceptual identification of words in noise, auditory lexical decision, and auditory word naming. The results of each of these experiments demonstrated that the number and nature of words in a similarity neighborhood affect the speed and accuracy of word recognition. A neighborhood probability rule was developed that adequately predicted identification performance. This rule, based on Luce's (1959) choice rule, combines stimulus word intelligibility, neighborhood confusability, and frequency into a single expression. Based on this rule, a model of auditory word recognition, the neighborhood activation model, was proposed. This model describes the effects of similarity neighborhood structure on the process of discriminating among the acoustic-phonetic representations of words in memory. The results of these experiments have important implications for current conceptions of auditory word recognition in normal and hearing impaired populations of children and adults.
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                Author and article information

                Contributors
                Journal
                1868-6354
                Laboratory Phonology: Journal of the Association for Laboratory Phonology
                Ubiquity Press
                1868-6354
                25 June 2018
                2018
                : 9
                : 1
                10.5334/labphon.93
                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/.

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