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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 Gao
This 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.

History

Publication status

  • Published

Journal

Cognitive Science

ISSN

0364-0213

Publisher

Cognitive Science

Issue

1

Volume

23

Page range

53-82

ISBN

0364-0213

Department affiliated with

  • Psychology Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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