Literature DB >> 12763005

Does opposition logic provide evidence for conscious and unconscious processes in artificial grammar learning?

Richard J Tunney1, David R Shanks.   

Abstract

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 account. We report simulations using a simple neural network model of two artificial grammar learning experiments reported by that dissociated conscious and unconscious influences on classification. The simulations demonstrate that opposition logic is insufficient to distinguish between single- and multiple-system models.

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Year:  2003        PMID: 12763005     DOI: 10.1016/s1053-8100(02)00068-5

Source DB:  PubMed          Journal:  Conscious Cogn        ISSN: 1053-8100


  9 in total

1.  Attentional load and implicit sequence learning.

Authors:  David R Shanks; Lee A Rowland; Mandeep S Ranger
Journal:  Psychol Res       Date:  2005-04-23

Review 2.  Kinds of access: different methods for report reveal different kinds of metacognitive access.

Authors:  Morten Overgaard; Kristian Sandberg
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2012-05-19       Impact factor: 6.237

3.  Chunking or not chunking? How do we find words in artificial language learning?

Authors:  Ana Franco; Arnaud Destrebecqz
Journal:  Adv Cogn Psychol       Date:  2012-05-21

4.  Visual statistical learning in children and young adults: how implicit?

Authors:  Julie Bertels; Emeline Boursain; Arnaud Destrebecqz; Vinciane Gaillard
Journal:  Front Psychol       Date:  2015-01-08

5.  Side effects of being blue: influence of sad mood on visual statistical learning.

Authors:  Julie Bertels; Catherine Demoulin; Ana Franco; Arnaud Destrebecqz
Journal:  PLoS One       Date:  2013-03-26       Impact factor: 3.240

6.  Negative affect reduces performance in implicit sequence learning.

Authors:  Junchen Shang; Qiufang Fu; Zoltan Dienes; Can Shao; Xiaolan Fu
Journal:  PLoS One       Date:  2013-01-22       Impact factor: 3.240

7.  The development of a sense of control scale.

Authors:  Mia Y Dong; Kristian Sandberg; Bo M Bibby; Michael N Pedersen; Morten Overgaard
Journal:  Front Psychol       Date:  2015-11-06

8.  Learning to predict: exposure to temporal sequences facilitates prediction of future events.

Authors:  Rosalind Baker; Matthew Dexter; Tom E Hardwicke; Aimee Goldstone; Zoe Kourtzi
Journal:  Vision Res       Date:  2013-11-11       Impact factor: 1.886

9.  Modeling test learning and dual-task dissociations.

Authors:  Tobias Johansson
Journal:  Psychon Bull Rev       Date:  2020-10
  9 in total

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