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      The role of predictability in shaping phonological patterns

      , , ,
      Linguistics Vanguard
      Walter de Gruyter GmbH

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          Abstract

          A diverse set of empirical findings indicate that word predictability in context influences the fine-grained details of both speech production and comprehension. In particular, lower predictability relative to similar competitors tends to be associated with phonetic enhancement, while higher predictability is associated with phonetic reduction. We review evidence that these in-the-moment biases can shift the prototypical pronunciations of individual lexical items, and that over time, these shifts can promote larger-scale phonological changes such as phoneme mergers. We argue that predictability-associated enhancement and reduction effects are based on predictability at the level of meaning-bearing units (such as words) rather than at sublexical levels (such as segments) and present preliminary typological evidence in support of this view. Based on these arguments, we introduce a Bayesian framework that helps generate testable predictions about the type of enhancement and reduction patterns that are more probable in a given language.

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          Most cited references19

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          The effect of word predictability on reading time is logarithmic.

          It is well known that real-time human language processing is highly incremental and context-driven, and that the strength of a comprehender's expectation for each word encountered is a key determinant of the difficulty of integrating that word into the preceding context. In reading, this differential difficulty is largely manifested in the amount of time taken to read each word. While numerous studies over the past thirty years have shown expectation-based effects on reading times driven by lexical, syntactic, semantic, pragmatic, and other information sources, there has been little progress in establishing the quantitative relationship between expectation (or prediction) and reading times. Here, by combining a state-of-the-art computational language model, two large behavioral data-sets, and non-parametric statistical techniques, we establish for the first time the quantitative form of this relationship, finding that it is logarithmic over six orders of magnitude in estimated predictability. This result is problematic for a number of established models of eye movement control in reading, but lends partial support to an optimal perceptual discrimination account of word recognition. We also present a novel model in which language processing is highly incremental well below the level of the individual word, and show that it predicts both the shape and time-course of this effect. At a more general level, this result provides challenges for both anticipatory processing and semantic integration accounts of lexical predictability effects. And finally, this result provides evidence that comprehenders are highly sensitive to relative differences in predictability - even for differences between highly unpredictable words - and thus helps bring theoretical unity to our understanding of the role of prediction at multiple levels of linguistic structure in real-time language comprehension. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.
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            Contributions of ideal observer theory to vision research.

            An ideal observer is a hypothetical device that performs optimally in a perceptual task given the available information. The theory of ideal observers has proven to be a powerful and useful tool in vision research, which has been applied to a wide range of problems. Here I first summarize the basic concepts and logic of ideal observer analysis and then briefly describe applications in a number of different areas, including pattern detection, discrimination and estimation, perceptual grouping, shape, depth and motion perception and visual attention, with an emphasis on recent applications. Given recent advances in mathematical statistics, in computational power, and in techniques for measuring behavioral performance, neural activity and natural scene statistics, it seems certain that ideal observer theory will play an ever increasing role in basic and applied areas of vision science. Copyright © 2010 Elsevier Ltd. All rights reserved.
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              Prediction During Natural Language Comprehension.

              The notion of prediction is studied in cognitive neuroscience with increasing intensity. We investigated the neural basis of 2 distinct aspects of word prediction, derived from information theory, during story comprehension. We assessed the effect of entropy of next-word probability distributions as well as surprisal A computational model determined entropy and surprisal for each word in 3 literary stories. Twenty-four healthy participants listened to the same 3 stories while their brain activation was measured using fMRI. Reversed speech fragments were presented as a control condition. Brain areas sensitive to entropy were left ventral premotor cortex, left middle frontal gyrus, right inferior frontal gyrus, left inferior parietal lobule, and left supplementary motor area. Areas sensitive to surprisal were left inferior temporal sulcus ("visual word form area"), bilateral superior temporal gyrus, right amygdala, bilateral anterior temporal poles, and right inferior frontal sulcus. We conclude that prediction during language comprehension can occur at several levels of processing, including at the level of word form. Our study exemplifies the power of combining computational linguistics with cognitive neuroscience, and additionally underlines the feasibility of studying continuous spoken language materials with fMRI.
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                Author and article information

                Journal
                Linguistics Vanguard
                Walter de Gruyter GmbH
                2199-174X
                September 25 2018
                September 13 2018
                September 25 2018
                September 13 2018
                : 4
                : s2
                Article
                10.1515/lingvan-2017-0027
                b592f028-2f53-43d0-84cc-71e2e76d96e5
                © 2018
                History

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