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  1. Tuning in to non-adjacencies: Exposure to learnable patterns supports discovering otherwise difficult structures.Martin Zettersten, Christine E. Potter & Jenny R. Saffran - 2020 - Cognition 202 (C):104283.
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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 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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  • 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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  • Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta‐Analysis Approach.Antony S. Trotter, Padraic Monaghan, Gabriël J. L. Beckers & Morten H. Christiansen - 2020 - Topics in Cognitive Science 12 (3):875-893.
    Studies of AGL have frequently used training and test stimuli that might provide multiple cues for learning, raising the question what subjects have actually learned. Using a selected subset of studies on humans and non‐human animals, Trotter et al. demonstrate how a meta‐analysis can be used to identify relevant experimental variables, providing a first step in asssessing the relative contribution of design features of grammars as well as of species‐specific effects on AGL.
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  • Simultaneous Acquisition of Words and Syntax: Effects of Exposure Condition and Declarative Memory.Simón Ruiz, Kaitlyn M. Tagarelli & Patrick Rebuschat - 2018 - Frontiers in Psychology 9.
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  • Modeling the Influence of Language Input Statistics on Children's Speech Production.Ingeborg Roete, Stefan L. Frank, Paula Fikkert & Marisa Casillas - 2020 - Cognitive Science 44 (12):e12924.
    We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, (...)
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  • Learning vocabulary and grammar from cross-situational statistics.Patrick Rebuschat, Padraic Monaghan & Christine Schoetensack - 2021 - Cognition 206 (C):104475.
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  • Aligning Developmental and Processing Accounts of Implicit and Statistical Learning.Michelle S. Peter & Caroline F. Rowland - 2019 - Topics in Cognitive Science 11 (3):555-572.
    In this article, Peter and Rowland explore the role of implicit statistical learning in syntactic development. It is often accepted that the processes observed in classic implicit learning or statistical learning experiments play an important role in the acquisition of natural language syntax. As Peter and Rowland point out, however, the results from neither research strand can be used to fully explain how children's syntax becomes adult‐like. They propose to address this shortcoming by using the structural priming paradigm.
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  • Canalization of Language Structure From Environmental Constraints: A Computational Model of Word Learning From Multiple Cues.Padraic Monaghan - 2017 - Topics in Cognitive Science 9 (1):21-34.
    There is substantial variation in language experience, yet there is surprising similarity in the language structure acquired. Constraints on language structure may be external modulators that result in this canalization of language structure, or else they may derive from the broader, communicative environment in which language is acquired. In this paper, the latter perspective is tested for its adequacy in explaining robustness of language learning to environmental variation. A computational model of word learning from cross-situational, multimodal information was constructed and (...)
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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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  • Children’s Learning of Non-adjacent Dependencies Using a Web-Based Computer Game Setting.Mireia Marimon, Andrea Hofmann, João Veríssimo, Claudia Männel, Angela D. Friederici, Barbara Höhle & Isabell Wartenburger - 2021 - Frontiers in Psychology 12.
    Infants show impressive speech decoding abilities and detect acoustic regularities that highlight the syntactic relations of a language, often coded via non-adjacent dependencies. It has been claimed that infants learn NADs implicitly and associatively through passive listening and that there is a shift from effortless associative learning to a more controlled learning of NADs after the age of 2 years, potentially driven by the maturation of the prefrontal cortex. To investigate if older children are able to learn NADs, Lammertink et (...)
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  • Non‐adjacent Dependencies Processing in Human and Non‐human Primates.Raphaëlle Malassis, Arnaud Rey & Joël Fagot - 2018 - Cognitive Science 42 (5):1677-1699.
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  • Dynamic Motion and Human Agents Facilitate Visual Nonadjacent Dependency Learning.Helen Shiyang Lu & Toben H. Mintz - 2023 - Cognitive Science 47 (9):e13344.
    Many events that humans and other species experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co‐occurrences between temporally adjacent stimuli, others involve tracking the co‐occurrences between temporally distant stimuli (i.e., nonadjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here, (...)
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  • Perceiving structure in unstructured stimuli: Implicitly acquired prior knowledge impacts the processing of unpredictable transitional probabilities.Andrea Kóbor, Kata Horváth, Zsófia Kardos, Dezso Nemeth & Karolina Janacsek - 2020 - Cognition 205 (C):104413.
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  • Statistically Induced Chunking Recall: A Memory‐Based Approach to Statistical Learning.Erin S. Isbilen, Stewart M. McCauley, Evan Kidd & Morten H. Christiansen - 2020 - Cognitive Science 44 (7):e12848.
    The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short‐term memory is fundamentally shaped by long‐term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to (...)
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  • Evidence of ordinal position encoding of sequences extracted from continuous speech.Ana Fló - 2021 - Cognition 213 (C):104646.
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  • Role of implicit learning abilities in metaphor understanding.Luc Drouillet, Nicolas Stefaniak, Christelle Declercq & Alexandre Obert - 2018 - Consciousness and Cognition 61:13-23.
  • Concurrent Learning of Adjacent and Nonadjacent Dependencies in Visuo-Spatial and Visuo-Verbal Sequences.Joanne A. Deocampo, Tricia Z. King & Christopher M. Conway - 2019 - Frontiers in Psychology 10.
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  • Does bilingual experience influence statistical language learning?Jose A. Aguasvivas, Jesús Cespón & Manuel Carreiras - 2024 - Cognition 242 (C):105639.
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