Skip to main content
Open Access Publications from the University of California


UCLA Electronic Theses and Dissertations bannerUCLA

Understanding and Optimizing the Inductive Learning of Categories and Concepts


Inductive learning refers to learning categories and concepts from exemplars of those categories or concepts. Such learning, which is a fundamental component of human cognition, enables us to classify never-before-encountered examples as instances of a given category or concept. How, though, should the exemplars of different categories, such as pictures of different species of butterflies, be presented in order to enhance inductive learning? The prevailing view has been that exemplars of a given category should be presented close together in time to highlight the commonalities that define that category, but Kornell and Bjork (2008)--to their surprise and against participants' intuitions--found that interleaving, not blocking, the exemplars of separate to-be-learned categories enhanced inductive learning. These findings, together with results from subsequent studies, suggest that the opportunity interleaving provides to contrast exemplars of different categories, such as an Admiral butterfly versus an Elfin butterfly, may be key to optimizing inductive learning.

The eight experiments reported in this dissertation, which involved having participants learn species of butterflies, families of birds, the handwriting styles of different individuals, and the styles of different artists, were designed to clarify the roles of contrast processing (encoding differences between exemplars of different categories) and commonality processes (encoding commonalities across the exemplars within a category).

Overall, the results obtained suggest that contrast processing and commonality processing are both critical, but that contrast processes occur automatically, whereas noticing commonalities is a deliberate and conscious process in category learning. Importantly, interleaving exemplars of different categories appears not only to facilitate contrast processing, but also, under some circumstances, commonality processing. Finally, it appears that learners--even when they know about the benefits of interleaving and the importance of contrast processing for achieving category learning--cannot effectively engage in contrast processing as a self-initiated study strategy without the aid of an interleaved presentation schedule.

The present findings help to provide a more complete picture of the processes involved in feature extraction and category generalization. From a practical standpoint, the results also have implications for enhancing category learning in educational settings, where such learning is prevalent and critical to mastery and achievement.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View