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  1. Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross‐Species, and Computational Approaches.Carel ten Cate, Judit Gervain, Clara C. Levelt, Christopher I. Petkov & Willem Zuidema - 2020 - Topics in Cognitive Science 12 (3):804-814.
    Artificial grammar learning (AGL) is used to study how human adults, infants, animals or machines learn various sorts of rules defined over sounds or visual items. Ten Cate et al. introduce the topic and provide a critical synthesis of this important interdisciplinary area of research. They identify the questions that remain open and the challenges that lie ahead, and argue that the limits of human, animal and machine learning abilities have yet to be found.
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  • A Single Paradigm for Implicit and Statistical Learning.Padraic Monaghan, Christine Schoetensack & Patrick Rebuschat - 2019 - Topics in Cognitive Science 11 (3):536-554.
    This article focuses on the implicit statistical learning of words and syntax. Monaghan, Schoetensack and Rebuschat introduce a novel paradigm that combines theoretical and methodological insights from the two research traditions, implicit learning and statistical learning. Their cross‐situational learning paradigm has been used in the statistical learning literature, while their measures of awareness have widely been used in implicit learning research. They illustrate how the two literatures can be conjoined in a single paradigm to explore implicit statistical learning.
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  • Individual differences in artificial and natural language statistical learning.Erin S. Isbilen, Stewart M. McCauley & Morten H. Christiansen - 2022 - Cognition 225 (C):105123.
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  • Statistical Learning of Language: A Meta‐Analysis Into 25 Years of Research.Erin S. Isbilen & Morten H. Christiansen - 2022 - Cognitive Science 46 (9):e13198.
    Cognitive Science, Volume 46, Issue 9, September 2022.
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