Does opposition logic provide evidence for conscious and unconscious processes in artificial grammar learning?
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Consciousness and Cognition 12 (2):201-218 (2003)
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 Higham, Vokey, and Pritchard that dissociated conscious and unconscious influences on classification. The simulations demonstrate that opposition logic is insufficient to distinguish between single- and multiple-system models
|Keywords||*Artificial Intelligence *Classification (Cognitive Process) *Consciousness States *Grammar *Logic (Philosophy) Machine Learning Models Neural Networks|
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Zoltán Dienes & Anil Seth (2010). Gambling on the Unconscious: A Comparison of Wagering and Confidence Ratings as Measures of Awareness in an Artificial Grammar Task☆. Consciousness and Cognition 19 (2):674-681.
Qiufang Fu, Zoltán Dienes & Xiaolan Fu (2010). Can Unconscious Knowledge Allow Control in Sequence Learning? Consciousness and Cognition 19 (1):462-474.
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