Similarity-based Ordering of Instances for Efficient Concept Learning

Erik WeitnauerIU Bloomington
Paulo CarvalhoIU Bloomington
Robert GoldstoneIU Bloomington
Helge RitterCITEC, Bielefeld University

Abstract

Theories in concept learning predict that interleaving instances of different concepts is especially beneficial if the concepts are highly similar to each other, whereas blocking instances belonging to the same concept provides an advantage for learning low-similarity concept structures. This suggests that the performance in concept learning tasks can be improved by grouping the instances of given concepts based on their similarity. To explore this hypothesis, we use Physical Bongard Problems, a rich categorization task with an open feature space, to analyze the combined effects of comparing dissimilar and similar instances within and across categories. We manipulate the within- and between-category similarity of instances presented close to each other in blocked, interleaved and simultaneous presentation schedules. The results show that grouping instances to promote dissimilar within- and similar between-category comparisons improves the learning results, to a degree depending on the strategy used by the learner.

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Similarity-based Ordering of Instances for Efficient Concept Learning (521 KB)



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