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  1. Individual Differences in Learning Abilities Impact Structure Addition: Better Learners Create More Structured Languages.Tamar Johnson, Noam Siegelman & Inbal Arnon - 2020 - Cognitive Science 44 (8):e12877.
    Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non‐structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of change. Here, we identify and test two contrasting predictions: The first sees learning as a pre‐requisite for structure addition, and predicts a positive correlation between learning accuracy and structure addition, (...)
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  • Musicians’ Online Performance during Auditory and Visual Statistical Learning Tasks.Pragati R. Mandikal Vasuki, Mridula Sharma, Ronny K. Ibrahim & Joanne Arciuli - 2017 - Frontiers in Human Neuroscience 11.
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  • (1 other version)Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
    I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...)
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  • A cortical network for semantics: (de)constructing the N400.E. Lau, C. Phillips & D. Poeppel - 2008 - Nature Reviews Neuroscience 9:920-933.
    Measuring event-related potentials (ERPs) has been fundamental to our understanding of how language is encoded in the brain. One particular ERP response, the N400 response, has been especially influential as an index of lexical and semantic processing. However, there remains a lack of consensus on the interpretation of this component. Resolving this issue has important consequences for neural models of language comprehension. Here we show that evidence bearing on where the N400 response is generated provides key insights into what it (...)
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  • Implicit Statistical Learning: A Tale of Two Literatures.Morten H. Christiansen - 2019 - Topics in Cognitive Science 11 (3):468-481.
    In this review article, Christiansen provides a historical perspective on the two research traditions, implicit learning and statistical learning, thus nicely setting the scene for this special issue of Topics in Cognitive Science. In this “tale of two literatures”, he first traces the history of both literatures before sketching a framework that provides a basis for understanding implicit learning and statistical learning as a unified phenomenon.
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  • Can Chunk Size Differences Explain Developmental Changes in Lexical Learning?Eleonore H. M. Smalle, Louisa Bogaerts, Morgane Simonis, Wouter Duyck, Michael P. A. Page, Martin G. Edwards & Arnaud Szmalec - 2015 - Frontiers in Psychology 6.
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  • Maternal stress predicts neural responses during auditory statistical learning in 26-month-old children: An event-related potential study.Lara J. Pierce, Erin Carmody Tague & Charles A. Nelson - 2021 - Cognition 213 (C):104600.
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  • Visual statistical learning is facilitated in Zipfian distributions.Ori Lavi-Rotbain & Inbal Arnon - 2021 - Cognition 206 (C):104492.
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  • Development of Different Forms of Skill Learning Throughout the Lifespan.Ágnes Lukács & Ferenc Kemény - 2015 - Cognitive Science 39 (2):383-404.
    The acquisition of complex motor, cognitive, and social skills, like playing a musical instrument or mastering sports or a language, is generally associated with implicit skill learning . Although it is a general view that SL is most effective in childhood, and such skills are best acquired if learning starts early, this idea has rarely been tested by systematic empirical studies on the developmental pathways of SL from childhood to old age. In this paper, we challenge the view that childhood (...)
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  • Understanding the Neural Bases of Implicit and Statistical Learning.Laura J. Batterink, Ken A. Paller & Paul J. Reber - 2019 - Topics in Cognitive Science 11 (3):482-503.
    This article provides a much‐needed review of the neural bases of implicit statistical learning. Batterink, Paller and Reber focus on the neural processes that underpin performance in experimental paradigms employed in implicit learning and statistical learning research. An important insight is that learning across all paradigms is supported by interactions between the declarative and nondeclarative memory systems of the brain. They conclude with a helpful discussion of future directions of research.
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  • Statistical Learning Is Not Age‐Invariant During Childhood: Performance Improves With Age Across Modality.Amir Shufaniya & Inbal Arnon - 2018 - Cognitive Science 42 (8):3100-3115.
    Humans are capable of extracting recurring patterns from their environment via statistical learning (SL), an ability thought to play an important role in language learning and learning more generally. While much work has examined statistical learning in infants and adults, less work has looked at the developmental trajectory of SL during childhood to see whether it is fully developed in infancy or improves with age, like many other cognitive abilities. A recent study showed modality‐based differences in the effect of age (...)
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  • Linguistic entrenchment: Prior knowledge impacts statistical learning performance.Noam Siegelman, Louisa Bogaerts, Amit Elazar, Joanne Arciuli & Ram Frost - 2018 - Cognition 177 (C):198-213.
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  • Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?Noam Siegelman, Louisa Bogaerts, Ofer Kronenfeld & Ram Frost - 2018 - Cognitive Science 42 (S3):692-727.
    From a theoretical perspective, most discussions of statistical learning have focused on the possible “statistical” properties that are the object of learning. Much less attention has been given to defining what “learning” is in the context of “statistical learning.” One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization (...)
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  • Visual statistical learning in children and young adults: how implicit?Julie Bertels, Emeline Boursain, Arnaud Destrebecqz & Vinciane Gaillard - 2014 - Frontiers in Psychology 5.
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  • Assessing Visual Statistical Learning in Early-School-Aged Children: The Usefulness of an Online Reaction Time Measure.Merel van Witteloostuijn, Imme Lammertink, Paul Boersma, Frank Wijnen & Judith Rispens - 2019 - Frontiers in Psychology 10.
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  • Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us?Jerome Daltrozzo & Christopher M. Conway - 2014 - Frontiers in Human Neuroscience 8.
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  • Not All Words Are Equally Acquired: Transitional Probabilities and Instructions Affect the Electrophysiological Correlates of Statistical Learning.Ana Paula Soares, Francisco-Javier Gutiérrez-Domínguez, Margarida Vasconcelos, Helena M. Oliveira, David Tomé & Luis Jiménez - 2020 - Frontiers in Human Neuroscience 14.
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  • Surface features can deeply affect artificial grammar learning.Luis Jiménez, Helena Mendes Oliveira & Ana Paula Soares - 2020 - Consciousness and Cognition 80:102919.
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