Modeling Learning via Progressive Alignment using Interim Generalizations

Subu KandaswamyNorthwestern University, Evanston, Illinois, United States
Ken ForbusNorthwestern University
Dedre GentnerNorthwestern University

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.

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Modeling Learning via Progressive Alignment using Interim Generalizations (329 KB)



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