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  1. Non‐adjacent Dependency Learning in Humans and Other Animals.Benjamin Wilson, Michelle Spierings, Andrea Ravignani, Jutta L. Mueller, Toben H. Mintz, Frank Wijnen, Anne van der Kant, Kenny Smith & Arnaud Rey - 2018 - Topics in Cognitive Science 12 (3):843-858.
    Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning (...)
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  • Segmentation of the speech stream in a non-human primate: statistical learning in cotton-top tamarins.Marc D. Hauser, Elissa L. Newport & Richard N. Aslin - 2001 - Cognition 78 (3):B53-B64.
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  • Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.Rebecca L. Gomez & LouAnn Gerken - 1999 - Cognition 70 (2):109-135.
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  • Segmentation of the speech stream in a non-human primate: statistical learning in cotton-top tamarins.Marc D. Hauser, Elissa L. Newport & Richard N. Aslin - 2001 - Cognition 78 (3):B53-B64.
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  • Non‐adjacent Dependency Learning in Humans and Other Animals.Benjamin Wilson, Michelle Spierings, Andrea Ravignani, Jutta L. Mueller, Toben H. Mintz, Frank Wijnen, Anne Kant, Kenny Smith & Arnaud Rey - 2020 - Topics in Cognitive Science 12 (3):843-858.
    Wilson et al. focus on one class of AGL tasks: the cognitively demanding task of detecting non‐adjacent dependencies (NADs) among items. They provide a typology of the different types of NADs in natural languages and in AGL tasks. A range of cues affect NAD learning, ranging from the variability and number of intervening elements to the presence of shared prosodic cues between the dependent items. These cues, important for humans to discover non‐adjacent dependencies, are also found to facilitate NAD learning (...)
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  • Behavioral and Imaging Studies of Infant Artificial Grammar Learning.Judit Gervain, Irene de la Cruz-Pavía & LouAnn Gerken - 2018 - Topics in Cognitive Science 12 (3):815-827.
    Gervain et al. discuss both behavioral and neurophysiological AGL studies that investigate rule and structure learning processes in infants. The paper provides an overview of all the major AGL paradigms used to date to investigate infant learning abilities at the level of morpho‐phonology and syntax from a very early age onwards. Gervain et al. also discuss the implications of the results for a general theory of natural language acquisition.
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  • Decisions, decisions: infant language learning when multiple generalizations are possible.LouAnn Gerken - 2006 - Cognition 98 (3):B67-B74.
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  • Centre-embedded structures are a by-product of associative learning and working memory constraints: Evidence from baboons ( Papio Papio ).Arnaud Rey, Pierre Perruchet & Joël Fagot - 2012 - Cognition 123 (1):180-184.
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  • Grammatical pattern learning by human infants and cotton-top tamarin monkeys.Fiery Cushman Jenny Saffran, Marc Hauser, Rebecca Seibel, Joshua Kapfhamer, Fritz Tsao - 2008 - Cognition 107 (2):479.
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  • Implicit Acquisition of Grammars With Crossed and Nested Non-Adjacent Dependencies: Investigating the Push-Down Stack Model.Julia Uddén, Martin Ingvar, Peter Hagoort & Karl M. Petersson - 2012 - Cognitive Science 36 (6):1078-1101.
    A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central (...)
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  • A Comparative Perspective on the Role of Acoustic Cues in Detecting Language Structure.Jutta L. Mueller, Carel ten Cate & Juan M. Toro - 2018 - Topics in Cognitive Science 12 (3):859-874.
    Mueller et al. discuss the role of acoustic cues in detecting language structure more generally. Across languages, there are clear links between acoustic cues and syntactic structure. They show that AGL experiments implementing analogous links demonstrate that prosodic cues, as well as various auditory biases, facilitate the learning of structural rules. Some of these biases, e.g. for auditory grouping, are also present in other species.
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  • Rule learning over consonants and vowels in a non-human animal.Daniela M. de la Mora & Juan M. Toro - 2013 - Cognition 126 (2):307-312.
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  • Working memory for patterned sequences of auditory objects in a songbird.Jordan A. Comins & Timothy Q. Gentner - 2010 - Cognition 117 (1):38-53.
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  • Behavioral and Imaging Studies of Infant Artificial Grammar Learning.Judit Gervain, Irene la Cruz-Pavía & LouAnn Gerken - 2020 - Topics in Cognitive Science 12 (3):815-827.
    Gervain et al. discuss both behavioral and neurophysiological AGL studies that investigate rule and structure learning processes in infants. The paper provides an overview of all the major AGL paradigms used to date to investigate infant learning abilities at the level of morpho‐phonology and syntax from a very early age onwards. Gervain et al. also discuss the implications of the results for a general theory of natural language acquisition.
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  • Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.Rebecca L. Gomez & LouAnn Gerken - 1999 - Cognition 70 (2):109-135.
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  • Grammatical pattern learning by human infants and cotton-top tamarin monkeys.Jenny Saffran, Marc Hauser, Rebecca Seibel, Joshua Kapfhamer, Fritz Tsao & Fiery Cushman - 2008 - Cognition 107 (2):479-500.
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  • What baboons can (not) tell us about natural language grammars.Fenna H. Poletiek, Hartmut Fitz & Bruno R. Bocanegra - 2016 - Cognition 151 (C):108-112.
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