Proceedings of the Annual Meeting of the Cognitive Science Society

The enormous scale of the available information and productson the Internet has necessitated the development of algorithmsthat intermediate between options and human users. These al-gorithms do not select information at random, but attempt toprovide the user with relevant information. In doing so, thealgorithms may incur potential negative consequences relatedto, for example, “filter bubbles.” Building from existing al-gorithms, we introduce a parametrized model that unifies andinterpolates between recommending relevant information andactive learning. In a concept learning paradigm, we illustratethe trade-offs of optimizing prediction and recommendation,show that there is a broad parameter region of stable perfor-mance that optimizes for both, identify a specific regime thatis most robust to human variability, and identify the cause ofthis optimized performance. We conclude by discussing im-plications for the cognitive science of concept learning and thepractice of machine learning in the real world.