Journal article
Subu Kandaswamy, Kenneth D. Forbus, D. Gentner
Annual Meeting of the Cognitive Science Society, 2014
Annual Meeting of the Cognitive Science Society, 2014
Semantic Scholar
DBLP
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Cite
APA
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Kandaswamy, S., Forbus, K. D., & Gentner, D. (2014). Modeling Learning via Progressive Alignment using Interim Generalizations. Annual Meeting of the Cognitive Science Society.
Chicago/Turabian
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Kandaswamy, Subu, Kenneth D. Forbus, and D. Gentner. “Modeling Learning via Progressive Alignment Using Interim Generalizations.” Annual Meeting of the Cognitive Science Society (2014).
MLA
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Kandaswamy, Subu, et al. “Modeling Learning via Progressive Alignment Using Interim Generalizations.” Annual Meeting of the Cognitive Science Society, 2014.
BibTeX Click to copy
@article{subu2014a,
title = {Modeling Learning via Progressive Alignment using Interim Generalizations},
year = {2014},
journal = {Annual Meeting of the Cognitive Science Society},
author = {Kandaswamy, Subu and Forbus, Kenneth D. and Gentner, D.}
}
Abstract
There is ample empirical evidence that children can sometimes learn during the course of even a few experimental trials. We propose that one mechanism for this is the use of analogical generalizations constructed in working memory, producing what we call interim generalizations. Prior research suggests that such generalizations can be constructed when there is high similarity between closely spaced items. This paper describes how structure-mapping simulations can be adapted to capture this phenomenon, using automatically encoded stimuli. It is an advance over prior models in that it automatically detects when rerepresentation should be tried and carries it out to improve its performance.
