Active function learning

Abstract

How do people actively explore to learn about functional relationships, that is, how continuous inputs map onto continuous outputs? We introduce a novel paradigm to investigate information search in continuous, multi-feature function learning scenarios. Participants either actively selected or passively observed information to learn about an underlying linear function. We develop and compare different variants of rule-based (linear regression) and non-parametric (Gaussian process regression) active learning approaches to model participants’ active learning behavior. Our results show that participants’ performance is best described by a rule-based model that attempts to efficiently learn linear functions with a focus on high and uncertain outcomes. These results advance our understanding of how people actively search for information to learn about functional relations in the environment.

Publication
In Kalish, C., Rau, M., Zhu, J., Rogers, T. T. (Eds.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society (pp. 580–585). Austin, TX: Cognitive Science Society.

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