Shultz, T. R. (2001). Assessing generalization in connectionist and rule-based models under the learning constraint. Proceedings of the Twenty-third Annual Conference of the Cognitive Science Society (pp. 922-927). Mahwah, NJ: Erlbaum.



Although it is commonly assumed that rule-based models generalize more effectively than do connectionist models, the comparison is often confounded by pitting hand-written rules against learned connections. Three case studies from cognitive development show that, under the constraint that both types of models learn their representations from equivalent examples, generalization is consistently superior in connectionist models.


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