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  1. Darwin's mistake: Explaining the discontinuity between human and nonhuman minds.Derek C. Penn, Keith J. Holyoak & Daniel J. Povinelli - 2008 - Behavioral and Brain Sciences 31 (2):109-130.
    Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...)
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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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  • 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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  • Superior learning in synesthetes: Consistent grapheme-color associations facilitate statistical learning.Tess Allegra Forest, Alessandra Lichtenfeld, Bryan Alvarez & Amy S. Finn - 2019 - Cognition 186:72-81.
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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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  • Learning what to expect.Peggy Seriès & Aaron R. Seitz - 2013 - Frontiers in Human Neuroscience 7.
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  • Control of automated behavior: insights from the discrete sequence production task.Elger L. Abrahamse, Marit F. L. Ruitenberg, Elian de Kleine & Willem B. Verwey - 2013 - Frontiers in Human Neuroscience 7.
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  • Modeling cross-situational word–referent learning: Prior questions.Chen Yu & Linda B. Smith - 2012 - Psychological Review 119 (1):21-39.
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  • Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech.Rebecca L. A. Frost & Padraic Monaghan - 2016 - Cognition 147 (C):70-74.
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  • Learning non-local dependencies.Gustav Kuhn & Zoltán Dienes - 2008 - Cognition 106 (1):184-206.
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  • Explicit pre-training instruction does not improve implicit perceptual-motor sequence learning.Daniel J. Sanchez & Paul J. Reber - 2013 - Cognition 126 (3):341-351.
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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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  • Predictive uncertainty in auditory sequence processing.Niels Chr Hansen & Marcus T. Pearce - 2014 - Frontiers in Psychology 5:88945.
    Previous studies of auditory expectation have focused on the expectedness perceived by listeners retrospectively in response to events. In contrast, this research examines predictive uncertainty —a property of listeners' prospective state of expectation prior to the onset of an event. We examine the information-theoretic concept of Shannon entropy as a model of predictive uncertainty in music cognition. This is motivated by the Statistical Learning Hypothesis, which proposes that schematic expectations reflect probabilistic relationships between sensory events learned implicitly through exposure. Using (...)
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  • Statistical learning is constrained to less abstract patterns in complex sensory input.Lauren L. Emberson & Dani Y. Rubinstein - 2016 - Cognition 153 (C):63-78.
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  • (1 other version)The curse of knowledge: First language knowledge impairs adult learners’ use of novel statistics for word segmentation.Amy S. Finn & Carla L. Hudson Kam - 2008 - Cognition 108 (2):477-499.
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  • What’s magic about magic numbers? Chunking and data compression in short-term memory.Fabien Mathy & Jacob Feldman - 2012 - Cognition 122 (3):346-362.
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  • Exploring the hierarchical structure of human plans via program generation.Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw & Thomas L. Griffiths - 2025 - Cognition 255 (C):105990.
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  • Statistical Learning, Implicit Learning, and First Language Acquisition: A Critical Evaluation of Two Developmental Predictions.Inbal Arnon - 2019 - Topics in Cognitive Science 11 (3):504-519.
    In this article, Arnon explores the link between implicit learning, statistical learning and language development. She focuses on two central themes, namely the issue of age invariance and the question of variation in learning outcomes. Arnon suggests that the two literatures are studying a fundamentally similar phenomenon and argues in favor of a closer alignment. However, she also raises important methodological concerns.
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  • Implicit learning of predictable sound sequences modulates human brain responses at different levels of the auditory hierarchy.Françoise Lecaignard, Olivier Bertrand, Gérard Gimenez, Jérémie Mattout & Anne Caclin - 2015 - Frontiers in Human Neuroscience 9.
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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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  • 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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  • Reconsidering the role of orthographic redundancy in visual word recognition.Fabienne Chetail - 2015 - Frontiers in Psychology 6.
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  • Attentional effects on rule extraction and consolidation from speech.Diana López-Barroso, David Cucurell, Antoni Rodríguez-Fornells & Ruth de Diego-Balaguer - 2016 - Cognition 152:61-69.
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  • Statistical regularities reduce perceived numerosity.Jiaying Zhao & Ru Qi Yu - 2016 - Cognition 146:217-222.
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  • Exploiting Multiple Sources of Information in Learning an Artificial Language: Human Data and Modeling.Pierre Perruchet & Barbara Tillmann - 2010 - Cognitive Science 34 (2):255-285.
    This study investigates the joint influences of three factors on the discovery of new word‐like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word‐likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word‐like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of different (...)
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  • Implicit Transfer of Reversed Temporal Structure in Visuomotor Sequence Learning.Kanji Tanaka & Katsumi Watanabe - 2014 - Cognitive Science 38 (3):565-579.
    Some spatio-temporal structures are easier to transfer implicitly in sequential learning. In this study, we investigated whether the consistent reversal of triads of learned components would support the implicit transfer of their temporal structure in visuomotor sequence learning. A triad comprised three sequential button presses ([1][2][3]) and seven consecutive triads comprised a sequence. Participants learned sequences by trial and error, until they could complete it 20 times without error. Then, they learned another sequence, in which each triad was reversed ([3][2][1]), (...)
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  • (1 other version)The P600 in Implicit Artificial Grammar Learning.Susana Silva, Vasiliki Folia, Peter Hagoort & Karl Magnus Petersson - 2017 - Cognitive Science 41 (1):137-157.
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  • Auditory Pattern Representations Under Conditions of Uncertainty—An ERP Study.Maria Bader, Erich Schröger & Sabine Grimm - 2021 - Frontiers in Human Neuroscience 15.
    The auditory system is able to recognize auditory objects and is thought to form predictive models of them even though the acoustic information arriving at our ears is often imperfect, intermixed, or distorted. We investigated implicit regularity extraction for acoustically intact versus disrupted six-tone sound patterns via event-related potentials. In an exact-repetition condition, identical patterns were repeated; in two distorted-repetition conditions, one randomly chosen segment in each sound pattern was replaced either by white noise or by a wrong pitch. In (...)
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  • Editors’ Introduction: Aligning Implicit Learning and Statistical Learning: Two Approaches, One Phenomenon.Patrick Rebuschat & Padraic Monaghan - 2019 - Topics in Cognitive Science 11 (3):459-467.
    In their editors’ introduction, Rebuschat and Monaghan provide the background to the special issue. They outline the rationale for bringing together, in a single volume, leading researchers from two distinct, yet related research strands, implicit learning and statistical learning. The editors then introduce the new contributions solicited for this special issue and provide their perspective on the agenda setting that results from combining these two approaches.
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  • Regularity Extraction Across Species: Associative Learning Mechanisms Shared by Human and Non‐Human Primates.Arnaud Rey, Laure Minier, Raphaëlle Malassis, Louisa Bogaerts & Joël Fagot - 2019 - Topics in Cognitive Science 11 (3):573-586.
    One of the themes that has been widely addressed in both the implicit learning and statistical learning literatures is that of rule learning. While it is widely agreed that the extraction of regularities from the environment is a fundamental facet of cognition, there is still debate about the nature of rule learning. Rey and colleagues show that the comparison between human and non‐human primates can contribute important insights to this debate.
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  • Information‐Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory.Kat Agres, Samer Abdallah & Marcus Pearce - 2018 - Cognitive Science 42 (1):43-76.
    A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard (...)
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  • The time course and characteristics of procedural learning in schizophrenia patients and healthy individuals.Yael Adini, Yoram S. Bonneh, Seva Komm, Lisa Deutsch & David Israeli - 2015 - Frontiers in Human Neuroscience 9.
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  • (1 other version)The curse of knowledge: First language knowledge impairs adult learners’ use of novel statistics for word segmentation.Amy S. Finn & Carla L. Hudson Kam - 2008 - Cognition 108 (2):477-499.
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  • Beyond prejudice: Are negative evaluations the problem and is getting us to like one another more the solution?John Dixon, Mark Levine, Steve Reicher, Kevin Durrheim, Dominic Abrams, Mark Alicke, Michal Bilewicz, Rupert Brown, Eric P. Charles & John Drury - 2012 - Behavioral and Brain Sciences 35 (6):411-425.
    For most of the history of prejudice research, negativity has been treated as its emotional and cognitive signature, a conception that continues to dominate work on the topic. By this definition, prejudice occurs when we dislike or derogate members of other groups. Recent research, however, has highlighted the need for a more nuanced and “inclusive” (Eagly 2004) perspective on the role of intergroup emotions and beliefs in sustaining discrimination. On the one hand, several independent lines of research have shown that (...)
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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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  • When learning goes beyond statistics: Infants represent visual sequences in terms of chunks.Lauren K. Slone & Scott P. Johnson - 2018 - Cognition 178 (C):92-102.
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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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  • Changing Structures in Midstream: Learning Along the Statistical Garden Path.Andrea L. Gebhart, Richard N. Aslin & Elissa L. Newport - 2009 - Cognitive Science 33 (6):1087-1116.
    Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low‐level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit cue (...)
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  • All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language.Alexa R. Romberg & Jenny R. Saffran - 2013 - Cognitive Science 37 (7):1290-1320.
    Natural languages contain many layers of sequential structure, from the distribution of phonemes within words to the distribution of phrases within utterances. However, most research modeling language acquisition using artificial languages has focused on only one type of distributional structure at a time. In two experiments, we investigated adult learning of an artificial language that contains dependencies between both adjacent and non-adjacent words. We found that learners rapidly acquired both types of regularities and that the strength of the adjacent statistics (...)
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  • Explicit Instructions Do Not Enhance Auditory Statistical Learning in Children With Developmental Language Disorder: Evidence From Event-Related Potentials.Ana Paula Soares, Francisco-Javier Gutiérrez-Domínguez, Helena M. Oliveira, Alexandrina Lages, Natália Guerra, Ana Rita Pereira, David Tomé & Marisa Lousada - 2022 - Frontiers in Psychology 13.
    A current issue in psycholinguistic research is whether the language difficulties exhibited by children with developmental language disorder [DLD, previously labeled specific language impairment ] are due to deficits in their abilities to pick up patterns in the sensory environment, an ability known as statistical learning, and the extent to which explicit learning mechanisms can be used to compensate for those deficits. Studies designed to test the compensatory role of explicit learning mechanisms in children with DLD are, however, scarce, and (...)
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  • Explicit and implicit memory representations in cross-situational word learning.Felix Hao Wang - 2020 - Cognition 205 (C):104444.
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  • What exactly is learned in visual statistical learning? Insights from Bayesian modeling.Noam Siegelman, Louisa Bogaerts, Blair C. Armstrong & Ram Frost - 2019 - Cognition 192 (C):104002.
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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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  • Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning.Martin A. Rohrmeier & Ian Cross - 2014 - Consciousness and Cognition 27 (C):155-167.
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  • Unattended exposure to components of speech sounds yields same benefits as explicit auditory training.Aaron R. Seitz, Athanassios Protopapas, Yoshiaki Tsushima, Eleni L. Vlahou, Simone Gori, Stephen Grossberg & Takeo Watanabe - 2010 - Cognition 115 (3):435-443.
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  • Segmenting dynamic human action via statistical structure.Dare Baldwin, Annika Andersson, Jenny Saffran & Meredith Meyer - 2008 - Cognition 106 (3):1382-1407.
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  • Towards a universal model of reading.Ram Frost, Christina Behme, Madeleine El Beveridge, Thomas H. Bak, Jeffrey S. Bowers, Max Coltheart, Stephen Crain, Colin J. Davis, S. Hélène Deacon & Laurie Beth Feldman - 2012 - Behavioral and Brain Sciences 35 (5):263.
    In the last decade, reading research has seen a paradigmatic shift. A new wave of computational models of orthographic processing that offer various forms of noisy position or context-sensitive coding have revolutionized the field of visual word recognition. The influx of such models stems mainly from consistent findings, coming mostly from European languages, regarding an apparent insensitivity of skilled readers to letter order. Underlying the current revolution is the theoretical assumption that the insensitivity of readers to letter order reflects the (...)
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  • Statistical Learning Is Related to Reading Ability in Children and Adults.Joanne Arciuli & Ian C. Simpson - 2012 - Cognitive Science 36 (2):286-304.
    There is little empirical evidence showing a direct link between a capacity for statistical learning (SL) and proficiency with natural language. Moreover, discussion of the role of SL in language acquisition has seldom focused on literacy development. Our study addressed these issues by investigating the relationship between SL and reading ability in typically developing children and healthy adults. We tested SL using visually presented stimuli within a triplet learning paradigm and examined reading ability by administering the Wide Range Achievement Test (...)
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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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