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  1. Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor.Anna N. Rafferty, Thomas L. Griffiths & Dan Klein - 2014 - Cognitive Science 38 (7):1406-1431.
    Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling (...)
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  • Functionalism and the competition model.Elizabeth Bates & Brian MacWhinney - 1989 - In Brian MacWhinney & Elizabeth Bates (eds.), The Crosslinguistic study of sentence processing. New York: Cambridge University Press. pp. 3--73.
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  • Maturational Constraints on Language Learning.Elissa L. Newport - 1990 - Cognitive Science 14 (1):11-28.
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  • The myth of language universals: Language diversity and its importance for cognitive science.Nicholas Evans & Stephen C. Levinson - 2009 - Behavioral and Brain Sciences 32 (5):429-448.
    Talk of linguistic universals has given cognitive scientists the impression that languages are all built to a common pattern. In fact, there are vanishingly few universals of language in the direct sense that all languages exhibit them. Instead, diversity can be found at almost every level of linguistic organization. This fundamentally changes the object of enquiry from a cognitive science perspective. This target article summarizes decades of cross-linguistic work by typologists and descriptive linguists, showing just how few and unprofound the (...)
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  • Language as shaped by the brain.Morten H. Christiansen & Nick Chater - 2008 - Behavioral and Brain Sciences 31 (5):489-509.
    It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change (...)
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  • The communicative function of ambiguity in language.Steven T. Piantadosi, Harry Tily & Edward Gibson - 2012 - Cognition 122 (3):280-291.
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  • Cue Effectiveness in Communicatively Efficient Discourse Production.Ting Qian & T. Florian Jaeger - 2012 - Cognitive Science 36 (7):1312-1336.
    Recent years have seen a surge in accounts motivated by information theory that consider language production to be partially driven by a preference for communicative efficiency. Evidence from discourse production (i.e., production beyond the sentence level) has been argued to suggest that speakers distribute information across discourse so as to hold the conditional per-word entropy associated with each word constant, which would facilitate efficient information transfer (Genzel & Charniak, 2002). This hypothesis implies that the conditional (contextualized) probabilities of linguistic units (...)
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  • Eliminating unpredictable variation through iterated learning.Kenny Smith & Elizabeth Wonnacott - 2010 - Cognition 116 (3):444-449.
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  • Relationships Between Language Structure and Language Learning: The Suffixing Preference and Grammatical Categorization.Michelle C. St Clair, Padraic Monaghan & Michael Ramscar - 2009 - Cognitive Science 33 (7):1317-1329.
    It is a reasonable assumption that universal properties of natural languages are not accidental. They occur either because they are underwritten by genetic code, because they assist in language processing or language learning, or due to some combination of the two. In this paper we investigate one such language universal: the suffixing preference across the world’s languages, whereby inflections tend to be added to the end of words. A corpus analysis of child‐directed speech in English found that suffixes were more (...)
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  • Shortlist B: A Bayesian model of continuous speech recognition.Dennis Norris & James M. McQueen - 2008 - Psychological Review 115 (2):357-395.
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  • Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel.Dave F. Kleinschmidt & T. Florian Jaeger - 2015 - Psychological Review 122 (2):148-203.
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  • Learning Phonology With Substantive Bias: An Experimental and Computational Study of Velar Palatalization.Colin Wilson - 2006 - Cognitive Science 30 (5):945-982.
    There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. Participants (...)
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  • Harmonic biases in child learners: In support of language universals.Jennifer Culbertson & Elissa L. Newport - 2015 - Cognition 139 (C):71-82.
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  • Ambiguity Avoidance is Overrated.Thomas Wasow - 2015 - In Susanne Winkler (ed.), Ambiguity: Language and Communication. Boston: De Gruyter. pp. 29-48.
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  • (1 other version)Computational principles of working memory in sentence comprehension.Richard L. Lewis, Shravan Vasishth & Julie A. Van Dyke - 2006 - Trends in Cognitive Sciences 10 (10):447-454.
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  • The kindergarten-path effect: studying on-line sentence processing in young children.John C. Trueswell, Irina Sekerina, Nicole M. Hill & Marian L. Logrip - 1999 - Cognition 73 (2):89-134.
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  • Learning biases predict a word order universal.Jennifer Culbertson, Paul Smolensky & Géraldine Legendre - 2012 - Cognition 122 (3):306-329.
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  • (1 other version)Computational principles of working memory in sentence comprehension.Julie A. Van Dyke Richard L. Lewis, Shravan Vasishth - 2006 - Trends in Cognitive Sciences 10 (10):447.
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