Limitations to optimal search in naturalistic active learning.

Abstract

Optimality in active learning is under intense debate in numerous disciplines. We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Participants in our task can ask questions and learn about hundreds of everyday items but must retrieve queried items from memory. To maximize information gain, participants need to retrieve sequences of dissimilar items. In eight experiments (N = 795), we find that participants are unable to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality in question asking) is unaffected by task instructions and incentives, though participants can identify efficient query sequences when given a choice between query sequences. Overall, our results indicate that participants can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes may place critical restrictions on people's ability to ask good questions in naturalistic active learning tasks. Public Significance StatementThe ability to guide our own learning is one of the greatest developmental tools we have. A common theory of such self-guided or "active" learning is that people tend to ask questions that gather the most information. However, most work supporting this theory is based on research with simple domains where questions need not be generated from memory. We show that in more complex domains, where more realistic questions must be recalled, subjects are far less able to generate optimal queries. This suggests that optimal question asking may be more limited in naturalistic, everyday domains than previously thought.