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  1. No need to forget, just keep the balance: Hebbian neural networks for statistical learning.Ángel Eugenio Tovar & Gert Westermann - 2023 - Cognition 230 (C):105176.
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  • Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion.Ashley L. Ruba, Seth D. Pollak & Jenny R. Saffran - 2022 - Topics in Cognitive Science 14 (3):432-450.
    In this article, we consider infants’ acquisition of foundational aspects of language and emotion through the lens of statistical learning. By taking a comparative developmental approach, we highlight ways in which the learning problems presented by input from these two rich communicative domains are both similar and different. Our goal is to encourage other scholars to consider multiple domains of human experience when developing theories in developmental cognitive science.
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  • Eyetracking evidence for heritage speakers’ access to abstract syntactic agreement features in real-time processing.Zuzanna Fuchs - 2022 - Frontiers in Psychology 13.
    This paper presents the results of an eyetracking study that uses the Visual World Paradigm to determine whether heritage speakers of Polish can use grammatical gender cues to facilitate lexical retrieval of the subsequent noun during real time processing. Previous work has investigated this question for heritage speakers of Spanish with gender cues located on definite articles, which are highly frequent in Spanish; the results are therefore consistent both with a grammatical account, wherein heritage speakers access abstract syntactic gender features (...)
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  • Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution.Christopher I. Petkov & Carel ten Cate - 2020 - Topics in Cognitive Science 12 (3):828-842.
    Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in (...)
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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 12:734877.
    Infants show impressive speech decoding abilities and detect acoustic regularities that highlight the syntactic relations of a language, often codedvianon-adjacent dependencies (NADs, e.g.,issinging). 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 al. (...)
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  • Arc-shaped pitch contours facilitate item recognition in non-human animals.Juan M. Toro & Paola Crespo-Bojorque - 2021 - Cognition 213 (C):104614.
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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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  • 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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  • The Value of Statistical Learning to Cognitive Network Science.Elisabeth A. Karuza - 2022 - Topics in Cognitive Science 14 (1):78-92.
    Topics in Cognitive Science, Volume 14, Issue 1, Page 78-92, January 2022.
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  • Infants' developing sensitivity to native language phonotactics: A meta-analysis.Megha Sundara, Z. L. Zhou, Canaan Breiss, Hironori Katsuda & Jeremy Steffman - 2022 - Cognition 221 (C):104993.
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  • 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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