Active learning and decision making: an introduction to the collection

Abstract

The importance of exploratory behaviors by which agents actively sample information has been long appreciated in a wide range of disciplines ranging from machine and robot learning to neuroscience and psychology.  Given the complexity of these behaviors, progress in understanding them will require a confluence of ideas from these multiple fields.  This collection of articles in F1000Research aims to provide a home for a broad range of studies addressing this topic, including full length research articles, brief communications, single figure studies, and review/opinion articles, and studies using computational, behavioral or neural approaches.  Here, we provide an introduction to the collection which we hope will grow and become a valuable resource for the researchers exploring this topic.