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  1. Learning Harmony: The Role of Serial Statistics.Erin McMullen Jonaitis & Jenny R. Saffran - 2009 - Cognitive Science 33 (5):951-968.
    How do listeners learn about the statistical regularities underlying musical harmony? In traditional Western music, certain chords predict the occurrence of other chords: Given a particular chord, not all chords are equally likely to follow. In Experiments 1 and 2, we investigated whether adults make use of statistical information when learning new musical structures. Listeners were exposed to a novel musical system containing phrases generated using an artificial grammar. This new system contained statistical structure quite different from Western tonal music. (...)
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  • Sequential Expectations: The Role of Prediction‐Based Learning in Language.Jennifer B. Misyak, Morten H. Christiansen & J. Bruce Tomblin - 2010 - Topics in Cognitive Science 2 (1):138-153.
    Prediction‐based processes appear to play an important role in language. Few studies, however, have sought to test the relationship within individuals between prediction learning and natural language processing. This paper builds upon existing statistical learning work using a novel paradigm for studying the on‐line learning of predictive dependencies. Within this paradigm, a new “prediction task” is introduced that provides a sensitive index of individual differences for developing probabilistic sequential expectations. Across three interrelated experiments, the prediction task and results thereof are (...)
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  • Implicit statistical learning in language processing: Word predictability is the key☆.Christopher M. Conway, Althea Bauernschmidt, Sean S. Huang & David B. Pisoni - 2010 - Cognition 114 (3):356-371.
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  • Modeling human performance in statistical word segmentation.Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum - 2010 - Cognition 117 (2):107-125.
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  • (1 other version)Finding Structure in Time.Jeffrey L. Elman - 1990 - Cognitive Science 14 (2):179-211.
    Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...)
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  • The Temporal Dynamics of Regularity Extraction in Non‐Human Primates.Laure Minier, Joël Fagot & Arnaud Rey - 2016 - Cognitive Science 40 (4):1019-1030.
    Extracting the regularities of our environment is one of our core cognitive abilities. To study the fine-grained dynamics of the extraction of embedded regularities, a method combining the advantages of the artificial language paradigm and the serial response time task was used with a group of Guinea baboons in a new automatic experimental device. After a series of random trials, monkeys were exposed to language-like patterns. We found that the extraction of embedded patterns positioned at the end of larger patterns (...)
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  • Working memory and neural oscillations: alpha–gamma versus theta–gamma codes for distinct WM information?Frédéric Roux & Peter J. Uhlhaas - 2014 - Trends in Cognitive Sciences 18 (1):16-25.
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  • Lexical and Sublexical Units in Speech Perception.Ibrahima Giroux & Arnaud Rey - 2009 - Cognitive Science 33 (2):260-272.
    Saffran, Newport, and Aslin (1996a) found that human infants are sensitive to statistical regularities corresponding to lexical units when hearing an artificial spoken language. Two sorts of segmentation strategies have been proposed to account for this early word‐segmentation ability: bracketing strategies, in which infants are assumed to insert boundaries into continuous speech, and clustering strategies, in which infants are assumed to group certain speech sequences together into units (Swingley, 2005). In the present study, we test the predictions of two computational (...)
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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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  • 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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  • Sentence processing in an artificial language: Learning and using combinatorial constraints.Michael S. Amato & Maryellen C. MacDonald - 2010 - Cognition 116 (1):143-148.
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  • Granularity and the acquisition of grammatical gender: How order-of-acquisition affects what gets learned.Inbal Arnon & Michael Ramscar - 2012 - Cognition 122 (3):292-305.
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  • Visual statistical learning in infancy: evidence for a domain general learning mechanism.Natasha Z. Kirkham, Jonathan A. Slemmer & Scott P. Johnson - 2002 - Cognition 83 (2):B35-B42.
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  • Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners.Ruskin H. Hunt & Richard N. Aslin - 2001 - Journal of Experimental Psychology: General 130 (4):658.
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  • The effect of statistical learning on internal stimulus representations: Predictable items are enhanced even when not predicted.Brandon K. Barakat, Aaron R. Seitz & Ladan Shams - 2013 - Cognition 129 (2):205-211.
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  • Statistical learning of tone sequences by human infants and adults.Jenny R. Saffran, Elizabeth K. Johnson, Richard N. Aslin & Elissa L. Newport - 1999 - Cognition 70 (1):27-52.
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  • Implicit Statistical Learning in Language Processing: Word Predictability is the Key.David B. Pisoni Christopher M. Conway, Althea Baurnschmidt, Sean Huang - 2010 - Cognition 114 (3):356.
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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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