RT Journal Article
SR Electronic
T1 Active Function Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 262394
DO 10.1101/262394
A1 Angela Jones
A1 Eric Schulz
A1 Björn Meder
A1 Azzurra Ruggeri
YR 2018
UL http://biorxiv.org/content/early/2018/05/14/262394.abstract
AB 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.