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  1. 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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  • 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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  • 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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  • Concurrent Statistical Learning of Ignored and Attended Sound Sequences: An MEG Study.Tatsuya Daikoku & Masato Yumoto - 2019 - Frontiers in Human Neuroscience 13.
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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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  • What do we do with what we learn? Statistical learning of orthographic regularities impacts written word processing.Fabienne Chetail - 2017 - Cognition 163 (C):103-120.
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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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 under incidental versus intentional conditions.Joanne Arciuli, Janne von Koss Torkildsen, David J. Stevens & Ian C. Simpson - 2014 - Frontiers in Psychology 5.
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  • Statistical Regularities Attract Attention when Task-Relevant.Andrea Alamia & Alexandre Zénon - 2016 - Frontiers in Human Neuroscience 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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  • 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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  • 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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  • Statistical regularities reduce perceived numerosity.Jiaying Zhao & Ru Qi Yu - 2016 - Cognition 146:217-222.
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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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  • 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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  • Explicit and implicit memory representations in cross-situational word learning.Felix Hao Wang - 2020 - Cognition 205 (C):104444.
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  • Cross-Domain Statistical–Sequential Dependencies Are Difficult to Learn.Anne M. Walk & Christopher M. Conway - 2016 - Frontiers in Psychology 7.
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  • Adding statistical regularity results in a global slowdown in visual search.Anna Vaskevich & Roy Luria - 2018 - Cognition 174:19-27.
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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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  • 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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  • Concurrent Movement Impairs Incidental But Not Intentional Statistical Learning.David J. Stevens, Joanne Arciuli & David I. Anderson - 2015 - Cognitive Science 39 (5):1081-1098.
    The effect of concurrent movement on incidental versus intentional statistical learning was examined in two experiments. In Experiment 1, participants learned the statistical regularities embedded within familiarization stimuli implicitly, whereas in Experiment 2 they were made aware of the embedded regularities and were instructed explicitly to learn these regularities. Experiment 1 demonstrated that while the control group were able to learn the statistical regularities, the resistance-free cycling group and the exercise group did not demonstrate learning. This is in contrast with (...)
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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • Learning what to expect.Peggy Seriès & Aaron R. Seitz - 2013 - Frontiers in Human Neuroscience 7.
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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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  • 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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  • 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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  • Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning.Martin A. Rohrmeier & Ian Cross - 2014 - Consciousness and Cognition 27:155-167.
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  • Implicit Learning and Acquisition of Music.Martin Rohrmeier & Patrick Rebuschat - 2012 - Topics in Cognitive Science 4 (4):525-553.
    Implicit learning is a core process for the acquisition of a complex, rule‐based environment from mere interaction, such as motor action, skill acquisition, or language. A body of evidence suggests that implicit knowledge governs music acquisition and perception in nonmusicians and musicians, and that both expert and nonexpert participants acquire complex melodic, harmonic, and other features from mere exposure. While current findings and computational modeling largely support the learning of chunks, some results indicate learning of more complex structures. Despite the (...)
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  • Incidental Learning of Melodic Structure of North Indian Music.Martin Rohrmeier & Richard Widdess - 2017 - Cognitive Science 41 (5):1299-1327.
    Musical knowledge is largely implicit. It is acquired without awareness of its complex rules, through interaction with a large number of samples during musical enculturation. Whereas several studies explored implicit learning of mostly abstract and less ecologically valid features of Western music, very little work has been done with respect to ecologically valid stimuli as well as non-Western music. The present study investigated implicit learning of modal melodic features in North Indian classical music in a realistic and ecologically valid way. (...)
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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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  • 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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  • Context influences conscious appraisal of cross situational statistical learning.Timothy J. Poepsel & Daniel J. Weiss - 2014 - Frontiers in Psychology 5.
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  • What Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning.Pierre Perruchet - 2019 - Topics in Cognitive Science 11 (3):520-535.
    In 2006, Perruchet and Pacton (2006) asked whether implicit learning and statistical learning represent two approaches to the same phenomenon. This article represents an important follow‐up to their seminal review article. As in the previous paper, the focus is on the formation of elementary cognitive units. Both approaches favor different explanations on what these units consist of and how they are formed. Perruchet weighs up the evidence for different explanations and concludes with a helpful agenda for future research.
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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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  • 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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  • Developmental Constraints on Learning Artificial Grammars with Fixed, Flexible and Free Word Order.Iga Nowak & Giosuè Baggio - 2017 - Frontiers in Psychology 8.
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