@inproceedings{3f3138a2150a40e28bf87d3b4ae36fc5,
title = "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.",
keywords = "Active learning, Function learning, Information search, Rule learning, Self-directed sampling",
author = "Angela Jones and Eric Schulz and Bj{\"o}rn Meder and Azzurra Ruggeri",
note = "Publisher Copyright: {\textcopyright} 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.; 40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 ; Conference date: 25-07-2018 Through 28-07-2018",
year = "2018",
language = "English",
series = "Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018",
publisher = "The Cognitive Science Society",
pages = "578--583",
booktitle = "Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018",
}