The long-term effect of relational information in classification learning

Abstract

This study examines the long-term effect of mutual information in the learning of Shepardian classifications. Mutual information is a measure of the complexity of the relationship between features because it quantifies how the features relate to each other. For instance, in various categorisation models, Type VI concepts—originally studied by Shepard, Hovland, and Jenkins (1961)—are unanimously judged to be the most complex kind of 3-D Boolean concepts. This has been largely confirmed by empirical data. Yet, it is apparently inconsistent with the fact that this concept entails the greatest amount of mutual information of all the 3-D Boolean concepts. The present study was aimed at verifying whether individuals can use relational information, in the long run, to devise easier strategies for category learning. Subject performance was measured repeatedly for 1 hour on either successive Type VI concepts (using different features between problems) or successive Type IV concepts. The results showed that shortly after the second problem, Type VI concepts became easier to learn than Type IV ones. The gap between the mean per-problem error rates of the two concepts continued to increase as the number of problems increased. Two other experiments tended to confirm this trend. The discussion brings up the idea of combining different metrics in categorisation models in order to include every possible way for subjects to simplify the categorisation process.