Opposition logic and neural network models of artificial grammar learning
Opposition logic and neural network models of artificial grammar learning
Following neural network simulations of the two experiments of Higham, Vokey, and Pritchard (2000), Tunney and Shanks (2003) argued that the opposition logic advocated by Higham et al. (2000) 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. (2000).
conscious, unconscious, controlled, automatic, implicit learning, opposition logic, dissociation logic, connectionist modelling, simple recurrent network, autoassociative network
575-578
Vokey, John R.
32b95583-7d67-451a-be57-616009a63580
Higham, Philip A.
4093b28f-7d58-4d18-89d4-021792e418e7
2004
Vokey, John R.
32b95583-7d67-451a-be57-616009a63580
Higham, Philip A.
4093b28f-7d58-4d18-89d4-021792e418e7
Vokey, John R. and Higham, Philip A.
(2004)
Opposition logic and neural network models of artificial grammar learning.
Consciousness and Cognition, 13 (3), .
(doi:10.1016/j.concog.2004.05.008).
Abstract
Following neural network simulations of the two experiments of Higham, Vokey, and Pritchard (2000), Tunney and Shanks (2003) argued that the opposition logic advocated by Higham et al. (2000) 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. (2000).
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Published date: 2004
Keywords:
conscious, unconscious, controlled, automatic, implicit learning, opposition logic, dissociation logic, connectionist modelling, simple recurrent network, autoassociative network
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Local EPrints ID: 18324
URI: http://eprints.soton.ac.uk/id/eprint/18324
ISSN: 1053-8100
PURE UUID: 7f38058e-9847-49ff-8d4f-2a553206f2de
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Date deposited: 13 Jan 2006
Last modified: 16 Mar 2024 03:18
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Author:
John R. Vokey
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