N. Moroshkina, I. Ivanchei, A. D. Karpov, I. Ovchinnikova
{"title":"The verbalization effect on implicit learning","authors":"N. Moroshkina, I. Ivanchei, A. D. Karpov, I. Ovchinnikova","doi":"10.4324/9781315628905-9","DOIUrl":"https://doi.org/10.4324/9781315628905-9","url":null,"abstract":"","PeriodicalId":186117,"journal":{"name":"Implicit Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116515591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elisabeth Norman, R. Scott, Mark C. Price, Emma Jones, Z. Dienes
{"title":"Can unconscious structural knowledge be strategically controlled?","authors":"Elisabeth Norman, R. Scott, Mark C. Price, Emma Jones, Z. Dienes","doi":"10.4324/9781315628905-7","DOIUrl":"https://doi.org/10.4324/9781315628905-7","url":null,"abstract":"","PeriodicalId":186117,"journal":{"name":"Implicit Learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122788994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria Kuvaldina, A. Chetverikov, Alexandr S. Odainic, M. Filippova, N. Andriyanova
{"title":"Implicit learning from one’s mistakes","authors":"Maria Kuvaldina, A. Chetverikov, Alexandr S. Odainic, M. Filippova, N. Andriyanova","doi":"10.4324/9781315628905-5","DOIUrl":"https://doi.org/10.4324/9781315628905-5","url":null,"abstract":"","PeriodicalId":186117,"journal":{"name":"Implicit Learning","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131871969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The mind is deep","authors":"Axel Cleeremans","doi":"10.4324/9781315628905-3","DOIUrl":"https://doi.org/10.4324/9781315628905-3","url":null,"abstract":"","PeriodicalId":186117,"journal":{"name":"Implicit Learning","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125514013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Viktor Allakhverdov, M. Filippova, V. Gershkovich, V. Y. Karpinskaia, T. Scott, N. Vladykina
{"title":"Consciousness, learning, and control","authors":"Viktor Allakhverdov, M. Filippova, V. Gershkovich, V. Y. Karpinskaia, T. Scott, N. Vladykina","doi":"10.4324/9781315628905-4","DOIUrl":"https://doi.org/10.4324/9781315628905-4","url":null,"abstract":"","PeriodicalId":186117,"journal":{"name":"Implicit Learning","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This thesis provides a conceptual and empirical analysis of implicit concept formation. A review of concept formation studies highlights the need for improving existing methodology in establish- ing the claim for implicit concept formation. Eight experiments are reported that address this aim. A review of theoretical issues highlights the need for computational modelling to elucidate the nature of implicit learning. Two chapters address the feasibility of different exemplar and Connectionist models in accounting for how subjects perform on tasks typically employed in the implicit learn- ing literature. The first five experiments use a concept formation task that involves classifying "computer people" as belonging to a particular town or income category. A number of manipulations are made of the underlying rule to be learned and of the cover task given subjects. In all cases, the knowledge underlying classification performance can be elicited both by free recall and by forced choice tasks. The final three experiments employ Reber's (e.g., 1989) grammar learning paradigm. More rigorous methods for eliciting the knowledge underlying classification performance are employed than have been used previously by Reber. The knowledge underlying clas- sification performance is not elicited by free recall, but is elicited by a forced-choice measure. The robustness of the learning in this paradigm is investigated by using a secondary task methodol- ogy. Concurrent random number generation interferes with all knowledge measures. A number of parameter-free Connectionist and exemplar models of artificial grammar learning are tested against the experimental data. The importance of different assumptions regarding the coding of features and the learning rule used is investigated by determin- ing the performance of the model with and without each assumption. Only one class of Connectionist model passes all the tests. Fur- ther, this class of model can simulate subject performance in a different task domain. The relevance of these empirical and theoretical results for understanding implicit learning is discussed, and suggestions are made for future research.
{"title":"Implicit Concept Formation","authors":"Z. Dienes","doi":"10.4324/9781315791227-3","DOIUrl":"https://doi.org/10.4324/9781315791227-3","url":null,"abstract":" This thesis provides a conceptual and empirical analysis of implicit concept formation. A review of concept formation studies highlights the need for improving existing methodology in establish- ing the claim for implicit concept formation. Eight experiments are reported that address this aim. A review of theoretical issues highlights the need for computational modelling to elucidate the nature of implicit learning. Two chapters address the feasibility of different exemplar and Connectionist models in accounting for how subjects perform on tasks typically employed in the implicit learn- ing literature. The first five experiments use a concept formation task that involves classifying \"computer people\" as belonging to a particular town or income category. A number of manipulations are made of the underlying rule to be learned and of the cover task given subjects. In all cases, the knowledge underlying classification performance can be elicited both by free recall and by forced choice tasks. The final three experiments employ Reber's (e.g., 1989) grammar learning paradigm. More rigorous methods for eliciting the knowledge underlying classification performance are employed than have been used previously by Reber. The knowledge underlying clas- sification performance is not elicited by free recall, but is elicited by a forced-choice measure. The robustness of the learning in this paradigm is investigated by using a secondary task methodol- ogy. Concurrent random number generation interferes with all knowledge measures. A number of parameter-free Connectionist and exemplar models of artificial grammar learning are tested against the experimental data. The importance of different assumptions regarding the coding of features and the learning rule used is investigated by determin- ing the performance of the model with and without each assumption. Only one class of Connectionist model passes all the tests. Fur- ther, this class of model can simulate subject performance in a different task domain. The relevance of these empirical and theoretical results for understanding implicit learning is discussed, and suggestions are made for future research.","PeriodicalId":186117,"journal":{"name":"Implicit Learning","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126481265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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