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      Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche

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          Abstract

          Human languages encode similar average information rates (~39 bits/s) despite their remarkable differences.

          Abstract

          Language is universal, but it has few indisputably universal characteristics, with cross-linguistic variation being the norm. For example, languages differ greatly in the number of syllables they allow, resulting in large variation in the Shannon information per syllable. Nevertheless, all natural languages allow their speakers to efficiently encode and transmit information. We show here, using quantitative methods on a large cross-linguistic corpus of 17 languages, that the coupling between language-level (information per syllable) and speaker-level (speech rate) properties results in languages encoding similar information rates (~39 bits/s) despite wide differences in each property individually: Languages are more similar in information rates than in Shannon information or speech rate. These findings highlight the intimate feedback loops between languages’ structural properties and their speakers’ neurocognition and biology under communicative pressures. Thus, language is the product of a multiscale communicative niche construction process at the intersection of biology, environment, and culture.

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          Turn-taking in Human Communication--Origins and Implications for Language Processing.

          Most language usage is interactive, involving rapid turn-taking. The turn-taking system has a number of striking properties: turns are short and responses are remarkably rapid, but turns are of varying length and often of very complex construction such that the underlying cognitive processing is highly compressed. Although neglected in cognitive science, the system has deep implications for language processing and acquisition that are only now becoming clear. Appearing earlier in ontogeny than linguistic competence, it is also found across all the major primate clades. This suggests a possible phylogenetic continuity, which may provide key insights into language evolution.
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            Adult height, nutrition, and population health.

            In this review, the potential causes and consequences of adult height, a measure of cumulative net nutrition, in modern populations are summarized. The mechanisms linking adult height and health are examined, with a focus on the role of potential confounders. Evidence across studies indicates that short adult height (reflecting growth retardation) in low- and middle-income countries is driven by environmental conditions, especially net nutrition during early years. Some of the associations of height with health and social outcomes potentially reflect the association between these environmental factors and such outcomes. These conditions are manifested in the substantial differences in adult height that exist between and within countries and over time. This review suggests that adult height is a useful marker of variation in cumulative net nutrition, biological deprivation, and standard of living between and within populations and should be routinely measured. Linkages between adult height and health, within and across generations, suggest that adult height may be a potential tool for monitoring health conditions and that programs focused on offspring outcomes may consider maternal height as a potentially important influence.
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              Optimal isn't good enough.

              The notion that biological systems come to embody optimal solutions seems consistent with the competitive drive of evolution. It has been used to interpret many examples of sensorimotor behavior. It is attractive from the viewpoint of control engineers because it solves the redundancy problem by identifying the one optimal motor strategy out of many similarly acceptable possibilities. This perspective examines whether there is sufficient basis to apply the formal engineering tools of optimal control to a reductionist understanding of biological systems. For an experimental biologist, this translates into whether the theory of optimal control generates nontrivial and testable hypotheses that accurately predict novel phenomena, ideally at deeper levels of structure than the observable behavior. The methodology of optimal control is applicable when there is (i) a single, known cost function to be optimized, (ii) an invertible model of the plant, and (iii) simple noise interfering with optimal performance. None of these is likely to be true for biological organisms. Furthermore, their motivation is usually good-enough rather than globally optimal behavior. Even then, the performance of a biological organism is often much farther from optimal than the physical limits of its hardware because the brain is continuously testing the acceptable limits of performance as well as just performing the task. This perspective considers an alternative strategy called "good-enough" control, in which the organism uses trial-and-error learning to acquire a repertoire of sensorimotor behaviors that are known to be useful, but not necessarily optimal. This leads to a diversity of solutions that tends to confer robustness on the individual organism and its evolution. It is also more consistent with the capabilities of higher sensorimotor structures, such as cerebral cortex, which seems to be designed to classify and recall complex sets of information, thereby allowing the organism to learn from experience, rather than to compute new strategies online. Optimal control has been a useful metaphor for understanding some superficial aspects of motor psychophysics. Reductionists who want to understand the underlying neural mechanisms need to move on.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                September 2019
                04 September 2019
                : 5
                : 9
                Affiliations
                [1 ]Laboratoire Dynamique du Langage UMR5596, CNRS, University of Lyon, 14 Avenue Berthelot, 69007 Lyon, France.
                [2 ]Department of Linguistics, The University of Hong Kong, Pokfulam Road, Hong Kong.
                [3 ]New Zealand Institute of Language, Brain and Behaviour, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand.
                [4 ]Department of French Language and Literature, Ajou University, Suwon, 16499 Gyeonggi-do, South Korea.
                [5 ]Collegium de Lyon, University of Lyon, 24 rue Baldassini, 69007 Lyon, France.
                Author notes
                [*]

                These authors contributed equally to this work.

                []Corresponding author. Email: francois.pellegrino@ 123456univ-lyon2.fr
                Article
                aaw2594
                10.1126/sciadv.aaw2594
                6984970
                3d379542-1e70-430a-b241-89208b2112ca
                Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                Funding
                Funded by: doi http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-10-LABX-0081
                Funded by: doi http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-11-IDEX-0007
                Funded by: doi http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-11-IDEX-0007
                Funded by: European Institutes for Advanced Study (EURIAS);
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Psychology
                Social Sciences
                Social Sciences
                Custom metadata
                Monica Bilog

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