Results for 'Artificial grammar'

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  1.  36
    Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.Rebecca L. Gomez & LouAnn Gerken - 1999 - Cognition 70 (2):109-135.
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  2.  6
    Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge.Rebecca L. Gomez & LouAnn Gerken - 1999 - Cognition 70 (2):109-135.
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  3.  34
    Impaired artificial grammar learning in agrammatism.Morten H. Christiansen, M. Louise Kelly, Richard C. Shillcock & Katie Greenfield - 2010 - Cognition 116 (3):382-393.
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  4.  11
    Artificial Grammar Learning Capabilities in an Abstract Visual Task Match Requirements for Linguistic Syntax.Gesche Westphal-Fitch, Beatrice Giustolisi, Carlo Cecchetto, Jordan S. Martin & W. Tecumseh Fitch - 2018 - Frontiers in Psychology 9.
  5.  28
    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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  6.  27
    The P600 in Implicit Artificial Grammar Learning.Susana Silva, Vasiliki Folia, Peter Hagoort & Karl Magnus Petersson - 2016 - Cognitive Science 40 (6):n/a-n/a.
    The suitability of the artificial grammar learning paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences and recorded the EEG before and (...)
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  7. Implicit learning of artificial grammars.Arthur S. Reber - 1967 - Journal of Verbal Learning and Verbal Behavior 6:855-863.
  8.  38
    Measuring strategic control in artificial grammar learning.Elisabeth Norman, Mark C. Price & Emma Jones - 2011 - Consciousness and Cognition 20 (4):1920-1929.
    In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning , we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars , or had the target grammar varying randomly from trial to trial which required a higher degree (...)
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  9.  26
    Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures.Meinou H. de Vries, Padraic Monaghan, Stefan Knecht & Pienie Zwitserlood - 2008 - Cognition 107 (2):763-774.
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  10.  30
    Transfer in artificial grammar learning: A reevaluation.Martin Redington & Nick Chater - 1996 - Journal of Experimental Psychology: General 125 (2):123.
  11.  71
    Rules and similarity processes in artificial grammar and natural second language learning: What is the “default”?Peter Robinson - 2005 - Behavioral and Brain Sciences 28 (1):32-33.
    Are rules processes or similarity processes the default for acquisition of grammatical knowledge during natural second language acquisition? Whereas Pothos argues similarity processes are the default in the many areas he reviews, including artificial grammar learning and first language development, I suggest, citing evidence, that in second language acquisition of grammatical morphology “rules processes” may be the default.
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  12.  17
    Developmental Constraints on Learning Artificial Grammars with Fixed, Flexible and Free Word Order.Iga Nowak & Giosuè Baggio - 2017 - Frontiers in Psychology 8.
  13. Gambling on the unconscious: A comparison of wagering and confidence ratings as measures of awareness in an artificial grammar task☆.Zoltán Dienes & Anil Seth - 2010 - Consciousness and Cognition 19 (2):674-681.
    We explore three methods for measuring the conscious status of knowledge using the artificial grammar learning paradigm. We show wagering is no more sensitive to conscious knowledge than simple verbal confidence reports but is affected by risk aversion. When people wager rather than give verbal confidence they are less ready to indicate high confidence. We introduce a “no-loss gambling” method which is insensitive to risk aversion. We show that when people are just as ready to bet on a (...)
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  14.  27
    Under What Conditions Can Recursion Be Learned? Effects of Starting Small in Artificial Grammar Learning of Center‐Embedded Structure.Fenna H. Poletiek, Christopher M. Conway, Michelle R. Ellefson, Jun Lai, Bruno R. Bocanegra & Morten H. Christiansen - 2018 - Cognitive Science 42 (8):2855-2889.
    It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman, ; Newport, ). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of (...)
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  15.  14
    On Empirical Methodology, Constraints, and Hierarchy in Artificial Grammar Learning.Willem J. M. Levelt - 2020 - Topics in Cognitive Science 12 (3):942-956.
    Levelt, reviewing the AGL field from a psycholinguistic perspective, identifies various gaps and makes a number of concrete suggestions for improving several currently used experimental designs. He raises the question whether artificial (and natural) grammar learning is about detecting ‘rules’, as is commonly assumed, or rather the detection of a set of ‘constraints’. He cautions the community to not ignore ‘semantics’, and recommends to consider less artificial tasks, that may be needed for learning more complex rules by (...)
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  16.  10
    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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  17.  72
    Subjective measures of consciousness in artificial grammar learning task.Michał Wierzchoń, Dariusz Asanowicz, Borysław Paulewicz & Axel Cleeremans - 2012 - Consciousness and Cognition 21 (3):1141-1153.
    Consciousness can be measured in various ways, but different measures often yield different conclusions about the extent to which awareness relates to performance. Here, we compare five different subjective measures of awareness in the context of an artificial grammar learning task. Participants expressed their subjective awareness of rules using one of five different scales: confidence ratings , post-decision wagering , feeling of warmth , rule awareness , and continuous scale . All scales were equally sensitive to conscious knowledge. (...)
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  18.  64
    Does opposition logic provide evidence for conscious and unconscious processes in artificial grammar learning?Richard J. Tunney & David R. Shanks - 2003 - Consciousness and Cognition 12 (2):201-218.
    The question of whether studies of human learning provide evidence for distinct conscious and unconscious influences remains as controversial today as ever. Much of this controversy arises from the use of the logic of dissociation. The controversy has prompted the use of an alternative approach that places conscious and unconscious influences on memory retrieval in opposition. Here we ask whether evidence acquired via the logic of opposition requires a dual-process account or whether it can be accommodated within a single similarity-based (...)
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  19.  39
    Learning simple and complex artificial grammars in the presence of a semantic reference field: effects on performance and awareness.Esther Van den Bos & Fenna H. Poletiek - 2015 - Frontiers in Psychology 6.
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  20.  35
    Conscious and unconscious thought in artificial grammar learning.Andy David Mealor & Zoltan Dienes - 2012 - Consciousness and Cognition 21 (2):865-874.
    Unconscious Thought Theory posits that a period of distraction after information acquisition leads to unconscious processing which enhances decision making relative to conscious deliberation or immediate choice . Support thus far has been mixed. In the present study, artificial grammar learning was used in order to produce measurable amounts of conscious and unconscious knowledge. Intermediate phases were introduced between training and testing. Participants engaged in conscious deliberation of grammar rules, were distracted for the same period of time, (...)
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  21.  17
    Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.Willem Zuidema, Robert M. French, Raquel G. Alhama, Kevin Ellis, Timothy J. O'Donnell, Tim Sainburg & Timothy Q. Gentner - 2020 - Topics in Cognitive Science 12 (3):925-941.
    Zuidema et al. illustrate how empirical AGL studies can benefit from computational models and techniques. Computational models can help clarifying theories, and thus in delineating research questions, but also in facilitating experimental design, stimulus generation, and data analysis. The authors show, with a series of examples, how computational modeling can be integrated with empirical AGL approaches, and how model selection techniques can indicate the most likely model to explain experimental outcomes.
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  22.  39
    Connectionist and Memory‐Array Models of Artificial Grammar Learning.Zoltan Dienes - 1992 - Cognitive Science 16 (1):41-79.
    Subjects exposed to strings of letters generated by a finite state grammar can later classify grammatical and nongrammatical test strings, even though they cannot adequately say what the rules of the grammar are (e.g., Reber, 1989). The MINERVA 2 (Hintzman, 1986) and Medin and Schaffer (1978) memory‐array models and a number of connectionist outoassociator models are tested against experimental data by deriving mainly parameter‐free predictions from the models of the rank order of classification difficulty of test strings. The (...)
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  23.  17
    Behavioral and Imaging Studies of Infant Artificial Grammar Learning.Judit Gervain, Irene la Cruz-Pavía & LouAnn Gerken - 2020 - Topics in Cognitive Science 12 (3):815-827.
    Gervain et al. discuss both behavioral and neurophysiological AGL studies that investigate rule and structure learning processes in infants. The paper provides an overview of all the major AGL paradigms used to date to investigate infant learning abilities at the level of morpho‐phonology and syntax from a very early age onwards. Gervain et al. also discuss the implications of the results for a general theory of natural language acquisition.
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  24.  23
    Do Humans Really Learn A n B n Artificial Grammars From Exemplars?Jean-Rémy Hochmann, Mahan Azadpour & Jacques Mehler - 2008 - Cognitive Science 32 (6):1021-1036.
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  25.  15
    Behavioral and Imaging Studies of Infant Artificial Grammar Learning.Judit Gervain, Irene de la Cruz-Pavía & LouAnn Gerken - 2018 - Topics in Cognitive Science 12 (3):815-827.
    Gervain et al. discuss both behavioral and neurophysiological AGL studies that investigate rule and structure learning processes in infants. The paper provides an overview of all the major AGL paradigms used to date to investigate infant learning abilities at the level of morpho‐phonology and syntax from a very early age onwards. Gervain et al. also discuss the implications of the results for a general theory of natural language acquisition.
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  26.  58
    Intentional control based on familiarity in artificial grammar learning.Lulu Wan, Zoltán Dienes & Xiaolan Fu - 2008 - Consciousness and Cognition 17 (4):1209-1218.
    It is commonly held that implicit learning is based largely on familiarity. It is also commonly held that familiarity is not affected by intentions. It follows that people should not be able to use familiarity to distinguish strings from two different implicitly learned grammars. In two experiments, subjects were trained on two grammars and then asked to endorse strings from only one of the grammars. Subjects also rated how familiar each string felt and reported whether or not they used familiarity (...)
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  27.  73
    Learning and Liking of Melody and Harmony: Further Studies in Artificial Grammar Learning.Psyche Loui - 2012 - Topics in Cognitive Science 4 (4):554-567.
    Much of what we know and love about music is based on implicitly acquired mental representations of musical pitches and the relationships between them. While previous studies have shown that these mental representations of music can be acquired rapidly and can influence preference, it is still unclear which aspects of music influence learning and preference formation. This article reports two experiments that use an artificial musical system to examine two questions: (1) which aspects of music matter most for learning, (...)
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  28.  19
    Independent judgment-linked and motor-linked forms of artificial grammar learning.Carol A. Seger - 1998 - Consciousness and Cognition 7 (2):259-284.
    Three experiments investigated whether a motor-linked measure (string typing speed) and an judgment-linked measure (grammatical judgment of strings) accessed the same implicit learning mechanisms in the artificial grammar learning task. Participants first studied grammatical strings through observation or through responding to each letter by typing it and then performed typing and grammatical judgment tests. Grammatical judgment test performance was better after observation than after respond learning, whereas typing test performance on higher order relations was worse after observation than (...)
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  29.  22
    Fluency does not express implicit knowledge of artificial grammars.Ryan B. Scott & Zoltan Dienes - 2010 - Cognition 114 (3):372-388.
  30.  15
    Methodological vs. strategic control in artificial grammar learning: A commentary on Norman, Price and Jones (2011).Luis Jiménez - 2011 - Consciousness and Cognition 20 (4):1930-1932.
    Norman et al. reported that participants exposed in succession to two artificial grammars could be able to learn implicitly about them, and could apply their knowledge strategically to select which string corresponds to one of these two grammars. In this commentary, I identify an artifact that could account for the learning obtained not only in this study, but also in some previous studies using the same procedures. I claim that more methodological control is needed before jumping to conclusions on (...)
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  31.  31
    Opposition logic and neural network models in artificial grammar learning.J. Vokey - 2004 - Consciousness and Cognition 13 (3):565-578.
    Following neural network simulations of the two experiments of Higham, Vokey, and Pritchard , Tunney and Shanks argued that the opposition logic advocated by Higham et al. was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al.
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  32.  43
    Volitional control in the learning of artificial grammars.Peter A. Bibby & Geoffrey Underwood - 1999 - Behavioral and Brain Sciences 22 (5):757-758.
    Dienes & Perner argue that volitional control in artificial grammar learning is best understood in terms of the distinction between implicit and explicit knowledge representations. We maintain that direct, explicit access to knowledge organised in a hierarchy of implicitness/explicitness is neither necessary nor sufficient to explain volitional control. People can invoke volitional control when their knowledge is implicit, as in the case of artificial grammar learning, and they can invoke volitional control when any part of their (...)
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  33.  75
    Mindfulness reduces habitual responding based on implicit knowledge: Evidence from artificial grammar learning.Stephen Whitmarsh, Julia Uddén, Henk Barendregt & Karl Magnus Petersson - 2013 - Consciousness and Cognition 22 (3):833-845.
    Participants were unknowingly exposed to complex regularities in a working memory task. The existence of implicit knowledge was subsequently inferred from a preference for stimuli with similar grammatical regularities. Several affective traits have been shown to influence AGL performance positively, many of which are related to a tendency for automatic responding. We therefore tested whether the mindfulness trait predicted a reduction of grammatically congruent preferences, and used emotional primes to explore the influence of affect. Mindfulness was shown to correlate negatively (...)
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  34.  24
    Can we do without distributed models? Not in artificial grammar learning.Annette Kinder - 2000 - Behavioral and Brain Sciences 23 (4):484-484.
    Page argues that localist models can be applied to a number of problems that are difficult for distributed models. However, it is easy to find examples where the opposite is true. This commentary illustrates the superiority of distributed models in the domain of artificial grammar learning, a paradigm widely used to investigate implicit learning.
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  35.  37
    Recollection, fluency, and the explicit/implicit distinction in artificial grammar learning.Annette Kinder, David R. Shanks, Josephine Cock & Richard J. Tunney - 2003 - Journal of Experimental Psychology: General 132 (4):551.
  36.  26
    Surface features can deeply affect artificial grammar learning.Luis Jiménez, Helena Mendes Oliveira & Ana Paula Soares - 2020 - Consciousness and Cognition 80:102919.
  37.  31
    Implicit learning of conjunctive rule sets: An alternative to artificial grammars.Greg J. Neil & Philip A. Higham - 2012 - Consciousness and Cognition 21 (3):1393-1400.
  38.  17
    Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning.Martin A. Rohrmeier & Ian Cross - 2014 - Consciousness and Cognition 27:155-167.
  39.  14
    The effect of subjective awareness measures on performance in artificial grammar learning task.Ivan I. Ivanchei & Nadezhda V. Moroshkina - 2018 - Consciousness and Cognition 57:116-133.
  40.  8
    Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks.Rachel Schiff & Pesia Katan - 2014 - Frontiers in Psychology 5.
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  41.  28
    Role of prior knowledge in implicit and explicit learning of artificial grammars.Eleni Ziori, Emmanuel M. Pothos & Zoltán Dienes - 2014 - Consciousness and Cognition 28:1-16.
  42.  9
    Commentary: Developmental Constraints on Learning Artificial Grammars with Fixed, Flexible, and Free Word Order.Aniello De Santo - 2018 - Frontiers in Psychology 9.
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  43. Abstractness of implicitly versus explicitly acquired knowledge of artificial grammars.Rc Mathews, F. Blanchardfields, L. Norris & Lg Roussel - 1991 - Bulletin of the Psychonomic Society 29 (6):500-500.
  44. Forgetting is learning-evaluation of 3 induction algorithms for learning artificial grammars.Rc Mathews, B. Druhan & L. Roussel - 1989 - Bulletin of the Psychonomic Society 27 (6):516-516.
  45. Confidence judgements, performance, and practice, in artificial grammar learning.Martin Redington, Matt Friend & Nick Chater - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum.
  46.  20
    The relationship between strategic control and conscious structural knowledge in artificial grammar learning.Elisabeth Norman, Ryan B. Scott, Mark C. Price & Zoltan Dienes - 2016 - Consciousness and Cognition 42:229-236.
  47.  28
    Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language.Pyeong Whan Cho, Emily Szkudlarek & Whitney Tabor - 2016 - Frontiers in Psychology 7.
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  48.  21
    Implicit learning in aphasia: Evidence from serial reaction time and artificial grammar tasks.Schuchard Julia & Thompson Cynthia - 2014 - Frontiers in Psychology 5.
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  49.  17
    Artificial syntactic violations activate Broca's region.K. Petersson - 2004 - Cognitive Science 28 (3):383-407.
    In the present study, using event-related functional magnetic resonance imaging, we investigated a group of participants on a grammaticality classification task after they had been exposed to well-formed consonant strings generated from an artificial regular grammar. We used an implicit acquisition paradigm in which the participants were exposed to positive examples. The objective of this studywas to investigate whether brain regions related to language processing overlap with the brain regions activated by the grammaticality classification task used in the (...)
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  50.  47
    Artificial syntactic violations activate Broca's region.Karl Magnus Petersson, Christian Forkstam & Martin Ingvar - 2004 - Cognitive Science 28 (3):383-407.
    In the present study, using event-related functional magnetic resonance imaging, we investigated a group of participants on a grammaticality classification task after they had been exposed to well-formed consonant strings generated from an artificial regular grammar. We used an implicit acquisition paradigm in which the participants were exposed to positive examples. The objective of this studywas to investigate whether brain regions related to language processing overlap with the brain regions activated by the grammaticality classification task used in the (...)
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