Active Function Learning

Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri

Research output: Contribution to Book/Report typesConference contributionpeer-review

Abstract (may include machine translation)

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.

Original languageEnglish
Title of host publicationProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PublisherThe Cognitive Science Society
Pages578-583
Number of pages6
ISBN (Electronic)9780991196784
StatePublished - 2018
Externally publishedYes
Event40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 - Madison, United States
Duration: 25 Jul 201828 Jul 2018

Publication series

NameProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018

Conference

Conference40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Country/TerritoryUnited States
CityMadison
Period25/07/1828/07/18

Keywords

  • Active learning
  • Function learning
  • Information search
  • Rule learning
  • Self-directed sampling

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