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Tv Station - One of the best experts on this subject based on the ideXlab platform.
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Implicit Learning in Language Teaching
Psychological Science, 2004Co-Authors: Tv StationAbstract:On the basis of analyzing the purpose of language teaching and the characteristics of language,the authors of this paper held that Implicit Learning plays and important role in language teaching, especially in Chinese language teaching. Then, they introduced their investigation and experiment on this problem. The experiment showed that there was no difference between the results of Implicit and explicit Learning or the former was superior to the latter. Moreover, in comparison with explicit Learning, Implicit Learning had better retention effects. Accordingly, the authors proposed that Implicit Learning be highly valued in language teaching.
John A. Williams - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Semantic Implicit Learning in Language
Cognitive Science, 2013Co-Authors: Albertyna Paciorek, John A. WilliamsAbstract:Semantic Implicit Learning in Language Albertyna Paciorek (awp23@cam.ac.uk) John N. Williams (jnw12@cam.ac.uk) Department of Theoretical and Applied Linguistics, University of Cambridge 9 West Road, Cambridge CB3 9DP, United Kingdom Abstract (Ellis, 1994) and shared attention (Bloom, 2000) one might suspect not. However, these arguments relate to Learning referential meaning. Others have hypothesised that other aspects of word meaning, such as connotation and collocational behaviour, might be learned Implicitly by the non-declarative system (Paradis, 2004). Here we test this proposal in the context of semantic preferences of verbs. Previous studies of semantic Implicit Learning in language have only examined Learning grammatical form-meaning connections where Learning could have been supported by prior linguistic knowledge. Also, these studies assessed awareness by verbal report, which is arguably not the most reliable measure. Here we target the domain of verb meaning, specifically semantic preferences of novel verbs (e.g. a novel verb takes abstract objects). Using a reaction time methodology we show that after exposure to correct verb-noun combinations, reaction times to incorrect combinations are slowed down even for participants who are unaware of the semantic regularity. This effect was also obtained even when the semantic regularity was irrelevant to the tasks being performed, suggesting that the semantic generalisation is learned and exerts its influence automatically, hence satisfying one criterion for Implicitness. Combined with a lack of verbalisable knowledge in any participant these experiments provide strong evidence for semantic Implicit Learning in language. Previous research on semantic Implicit Learning in language has focused exclusively on article-noun agreement regularities (e.g. Williams, 2005; Leung & Williams, 2012). This work has demonstrated sensitivity to the semantic properties of nouns in Learning about the distribution of articles in miniature semi-artificial languages, and has provided evidence of Implicitness of knowledge through post- experiment verbal report. The present experiments extend this work in terms of generalizability to other aspects of language, and in terms of methodology. We will discuss methodology in Experiment 2, but with regard to generalizability, there was evidence in these earlier experiments that Implicit Learning was dependent upon prior knowledge of article agreement systems in other languages (Williams, 2005). There was also evidence that Learning depended on the semantic regularity in question, since effects were obtained for agreement based on animacy, but not on relative size (Leung & Williams, 2012). Putting these two together one might argue that Learning was dependent both on prior knowledge of the potential for article noun agreement, and on dispositions based on the “potentially encodable distinctions” that can be grammaticised in language (Bickerton, 1999). The question arises, therefore, whether similar effects could be obtained for an aspect of language that falls outside the realm of grammar, and is not so potentially affected by prior dispositions. This motivated the current investigation of Learning the collocational behaviour, specifically semantic preferences, of novel verbs. Keywords: Implicit Learning; consciousness; form- meaning connections; vocabulary Learning; verb Learning, second language acquisition; automaticity. Introduction Most research on Implicit Learning has examined regularities at the level of form, be they in sequences of letters generated by artificial grammars, screen positions in repeating sequences, and in the domain of language, phonological (Dell et al., 2001) and orthographic (Pacton et al., 2001) patterns. This limits generalizability to other aspects of language Learning where regularities might be conditioned by distinctions at the level of meaning, as opposed to form. Some research in visual perception has exposed semantic-based Implicit Learning, notably using the contextual cuing paradigm, where target locations are predicted from semantic properties of contexts, (Goujon, 2007). But can semantic Implicit Learning effects be obtained in the domain of language, especially in the adult language learner? Given arguments that even in children vocabulary acquisition requires declarative, explicit, memory A semantic preference can be understood as a particular type of collocation, where collocation refers to higher than chance co-occurrence of two or more words. Collocates sound natural together and substituting one of them with a near-synonym results
Albertyna Paciorek - One of the best experts on this subject based on the ideXlab platform.
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Semantic Implicit Learning
2015Co-Authors: Albertyna Paciorek, John N. WilliamsAbstract:Much previous research on Implicit Learning has examined form-based sequential regularities over letters and syllables. Recently, however, researchers have begun to examine Implicit Learning of systems in which the regularities are described at the level of meaning. We review existing work in this area, primarily from vision research and natural language. These studies suggest that meaning-based generalisations can be learned without intent and without awareness of what those generalisations are. In the case of language we review work on Learning semantic constraints on determiner usage, and the acquisition of semantic preferences of verbs. We discuss outstanding issues: whether noticing of meaning, as well as form, is necessary, whether the effects reflect Learning of new form-meaning connections as opposed to tuning of existing ones, and whether some semantic distinctions are more available to the Implicit Learning process than others.
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CogSci - Semantic Implicit Learning in Language
Cognitive Science, 2013Co-Authors: Albertyna Paciorek, John A. WilliamsAbstract:Semantic Implicit Learning in Language Albertyna Paciorek (awp23@cam.ac.uk) John N. Williams (jnw12@cam.ac.uk) Department of Theoretical and Applied Linguistics, University of Cambridge 9 West Road, Cambridge CB3 9DP, United Kingdom Abstract (Ellis, 1994) and shared attention (Bloom, 2000) one might suspect not. However, these arguments relate to Learning referential meaning. Others have hypothesised that other aspects of word meaning, such as connotation and collocational behaviour, might be learned Implicitly by the non-declarative system (Paradis, 2004). Here we test this proposal in the context of semantic preferences of verbs. Previous studies of semantic Implicit Learning in language have only examined Learning grammatical form-meaning connections where Learning could have been supported by prior linguistic knowledge. Also, these studies assessed awareness by verbal report, which is arguably not the most reliable measure. Here we target the domain of verb meaning, specifically semantic preferences of novel verbs (e.g. a novel verb takes abstract objects). Using a reaction time methodology we show that after exposure to correct verb-noun combinations, reaction times to incorrect combinations are slowed down even for participants who are unaware of the semantic regularity. This effect was also obtained even when the semantic regularity was irrelevant to the tasks being performed, suggesting that the semantic generalisation is learned and exerts its influence automatically, hence satisfying one criterion for Implicitness. Combined with a lack of verbalisable knowledge in any participant these experiments provide strong evidence for semantic Implicit Learning in language. Previous research on semantic Implicit Learning in language has focused exclusively on article-noun agreement regularities (e.g. Williams, 2005; Leung & Williams, 2012). This work has demonstrated sensitivity to the semantic properties of nouns in Learning about the distribution of articles in miniature semi-artificial languages, and has provided evidence of Implicitness of knowledge through post- experiment verbal report. The present experiments extend this work in terms of generalizability to other aspects of language, and in terms of methodology. We will discuss methodology in Experiment 2, but with regard to generalizability, there was evidence in these earlier experiments that Implicit Learning was dependent upon prior knowledge of article agreement systems in other languages (Williams, 2005). There was also evidence that Learning depended on the semantic regularity in question, since effects were obtained for agreement based on animacy, but not on relative size (Leung & Williams, 2012). Putting these two together one might argue that Learning was dependent both on prior knowledge of the potential for article noun agreement, and on dispositions based on the “potentially encodable distinctions” that can be grammaticised in language (Bickerton, 1999). The question arises, therefore, whether similar effects could be obtained for an aspect of language that falls outside the realm of grammar, and is not so potentially affected by prior dispositions. This motivated the current investigation of Learning the collocational behaviour, specifically semantic preferences, of novel verbs. Keywords: Implicit Learning; consciousness; form- meaning connections; vocabulary Learning; verb Learning, second language acquisition; automaticity. Introduction Most research on Implicit Learning has examined regularities at the level of form, be they in sequences of letters generated by artificial grammars, screen positions in repeating sequences, and in the domain of language, phonological (Dell et al., 2001) and orthographic (Pacton et al., 2001) patterns. This limits generalizability to other aspects of language Learning where regularities might be conditioned by distinctions at the level of meaning, as opposed to form. Some research in visual perception has exposed semantic-based Implicit Learning, notably using the contextual cuing paradigm, where target locations are predicted from semantic properties of contexts, (Goujon, 2007). But can semantic Implicit Learning effects be obtained in the domain of language, especially in the adult language learner? Given arguments that even in children vocabulary acquisition requires declarative, explicit, memory A semantic preference can be understood as a particular type of collocation, where collocation refers to higher than chance co-occurrence of two or more words. Collocates sound natural together and substituting one of them with a near-synonym results
Priya Kalra - One of the best experts on this subject based on the ideXlab platform.
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Evidence of stable individual differences in Implicit Learning.
Cognition, 2019Co-Authors: Priya Kalra, John D. E. Gabrieli, Amy S. FinnAbstract:Abstract There is a fundamental psychological and neuropsychological distinction between explicit and Implicit memory, and it has been proposed that whereas there are stable trait individual differences in explicit memory ability, there are not such differences across people for Implicit Learning. There is, however, little evidence about whether or not there are stable trait differences in Implicit Learning. Here we performed a test-retest reliability study with healthy young adults in which they performed four Implicit Learning tasks (artificial grammar Learning, probabilistic classification, serial response, and Implicit category Learning) twice, about a week apart. We found medium (by Cohen’s guidelines) test-retest reliability for three of the tasks: probabilistic classification, serial response, and Implicit category Learning, suggesting that differences in Implicit Learning ability are more stable than originally thought. In addition, Implicit Learning on all tasks was unrelated to explicit measures: we did not find any correlation between Implicit Learning measures and independent measures of IQ, working memory, or explicit Learning ability. These findings indicate that Implicit Learning, like explicit Learning, varies reliably across individuals.
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Implicit Learning: Development, Individual Differences, and Educational Implications
2015Co-Authors: Priya KalraAbstract:iii Chapter One: Overview 1 Chapter Two: Introduction to Implicit Learning 3 What is Implicit Learning? 4 Operationalizing Implicit Learning: The Tasks 17 Chapter Three: Implicit Learning and Education 24 The Representation of Procedural Memory 24 Procedural Knowledge and Conceptual Knowledge in Education Research 26 Relevance to Education 32 Chapter Four: Developmental Differences in Implicit LLearning 40 Background 40 Methods 47 Results 54 Discussion 56 Chapter Five: Individual Differences in Implicit Learning 62 Background 63 Methods 69 Results 78 Discussion 90 Chapter Six: Conclusion and Directions for Future Study 97 Appendix 1: Tables and Figures 104 Tables 104 Figures 127 References 132 Vita 150
Amy S. Finn - One of the best experts on this subject based on the ideXlab platform.
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Evidence of stable individual differences in Implicit Learning.
Cognition, 2019Co-Authors: Priya Kalra, John D. E. Gabrieli, Amy S. FinnAbstract:Abstract There is a fundamental psychological and neuropsychological distinction between explicit and Implicit memory, and it has been proposed that whereas there are stable trait individual differences in explicit memory ability, there are not such differences across people for Implicit Learning. There is, however, little evidence about whether or not there are stable trait differences in Implicit Learning. Here we performed a test-retest reliability study with healthy young adults in which they performed four Implicit Learning tasks (artificial grammar Learning, probabilistic classification, serial response, and Implicit category Learning) twice, about a week apart. We found medium (by Cohen’s guidelines) test-retest reliability for three of the tasks: probabilistic classification, serial response, and Implicit category Learning, suggesting that differences in Implicit Learning ability are more stable than originally thought. In addition, Implicit Learning on all tasks was unrelated to explicit measures: we did not find any correlation between Implicit Learning measures and independent measures of IQ, working memory, or explicit Learning ability. These findings indicate that Implicit Learning, like explicit Learning, varies reliably across individuals.
