Machine Generalization and Human Categorization: An
  Information-Theoretic View
  In designing an intelligent system that must be able to explain its reasoning
to a human user, or to provide generalizations that the human user finds
reasonable, it may be useful to take into consideration psychological data on
what types of concepts and categories people naturally use. The psychological
literature on concept learning and categorization provides strong evidence that
certain categories are more easily learned, recalled, and recognized than
others. We show here how a measure of the informational value of a category
predicts the results of several important categorization experiments better
than standard alternative explanations. This suggests that information-based
approaches to machine generalization may prove particularly useful and natural
for human users of the systems.
