Systematic minds, unsystematic models: Learning transfer in humans and networks [Book Review]

Minds and Machines 9 (3):383-398 (1999)
Abstract
Minds are said to be systematic: the capacity to entertain certain thoughts confers to other related thoughts. Although an important property of human cognition, its implication for cognitive architecture has been less than clear. In part, the uncertainty is due to lack of precise accounts on the degree to which cognition is systematic. However, a recent study on learning transfer provides one clear example. This study is used here to compare transfer in humans and feedforward networks. Simulations and analysis show, that while feedforward networks with shared weights are capable of exhibiting transfer, they cannot support the same degree of transfer as humans. One interpretation of these results is that common connectionist models lack explicit internal representations permitting rapid learning.
Keywords classicism  connectionism  Klein group  learning transfer  normalization  systematicity  weight sharing
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Reprint years 2004
DOI 10.1023/A:1008308002377
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