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Active Function Learning

Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri
doi: https://doi.org/10.1101/262394
Angela Jones
1Max Planck Research Group iSearch, Max Planck Institute for Human Development, Berlin, Germany
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  • For correspondence: jones@mpib-berlin.mpg.de
Eric Schulz
2Department of Psychology, Harvard University, Cambridge, Massachusetts, USA
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Björn Meder
1Max Planck Research Group iSearch, Max Planck Institute for Human Development, Berlin, Germany
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Azzurra Ruggeri
1Max Planck Research Group iSearch, Max Planck Institute for Human Development, Berlin, Germany
3School of Education, Technical University of Munich, Germany
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 14, 2018.
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Active Function Learning
Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri
bioRxiv 262394; doi: https://doi.org/10.1101/262394
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Active Function Learning
Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri
bioRxiv 262394; doi: https://doi.org/10.1101/262394

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