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Mapping across domains without feedback: a neural network model of transfer of implicit knowledge
journal contribution
posted on 2023-06-07, 18:10 authored by Zoltan DienesZoltan Dienes, Gerry TM Altmann, Shi-Ji GaoThis paper shows how a neural network can model the way people who have acquired knowledge of an artificial grammar in one perceptual domain (e.g., sequences of tones differing in pitch) can apply the knowledge to a quite different perceptual domain (e.g., sequences of letters). It is shown that a version of the Simple Recurrent Network (SRN) can transfer its knowledge of artificial grammars across domains without feedback. The performance of the model is sensitive to at least some of the same variables that affect subjects' performance—for example, the model is responsive to both the grammaticality of test sequences and their similarity to training sequences, to the cover task used during training, and to whether training is on bigrams or larger sequences.
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Publication status
- Published
Journal
Cognitive ScienceISSN
0364-0213Publisher
Cognitive ScienceExternal DOI
Issue
1Volume
23Page range
53-82ISBN
0364-0213Department affiliated with
- Psychology Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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