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      Universals of word order reflect optimization of grammars for efficient communication

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          Significance

          Human languages share many grammatical properties. We show that some of these properties can be explained by the need for languages to offer efficient communication between humans given our cognitive constraints. Grammars of languages seem to find a balance between two communicative pressures: to be simple enough to allow the speaker to easily produce sentences, but complex enough to be unambiguous to the hearer, and this balance explains well-known word-order generalizations across our sample of 51 varied languages. Our results offer quantitative and computational evidence that language structure is dynamically shaped by communicative and cognitive pressures.

          Abstract

          The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties—those related to word order—result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages.

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          Most cited references51

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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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            Predicting pragmatic reasoning in language games.

            One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.
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              Compression and communication in the cultural evolution of linguistic structure.

              Language exhibits striking systematic structure. Words are composed of combinations of reusable sounds, and those words in turn are combined to form complex sentences. These properties make language unique among natural communication systems and enable our species to convey an open-ended set of messages. We provide a cultural evolutionary account of the origins of this structure. We show, using simulations of rational learners and laboratory experiments, that structure arises from a trade-off between pressures for compressibility (imposed during learning) and expressivity (imposed during communication). We further demonstrate that the relative strength of these two pressures can be varied in different social contexts, leading to novel predictions about the emergence of structured behaviour in the wild.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                4 February 2020
                21 January 2020
                21 January 2020
                : 117
                : 5
                : 2347-2353
                Affiliations
                [1] aDepartment of Linguistics, Stanford University , Stanford, CA 94305;
                [2] bDepartment of Language Science, University of California , Irvine, CA 92697
                Author notes
                1To whom correspondence may be addressed. Email: mhahn2@ 123456stanford.edu .

                Edited by William Croft, University of New Mexico, Albuquerque, NM, and accepted by Editorial Board Member Susan A. Gelman December 16, 2019 (received for review June 30, 2019)

                Author contributions: M.H., D.J., and R.F. designed research; M.H. performed research; and M.H., D.J., and R.F. wrote the paper.

                Article
                201910923
                10.1073/pnas.1910923117
                7007543
                31964811
                98bc2b40-4ce3-41f1-a77a-0286cfbc1df8
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 7
                Categories
                Social Sciences
                Psychological and Cognitive Sciences

                language universals,language processing,computational linguistics

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