Active learning and decision making: an introduction to the collection

The importance of exploratory behaviors by which agents actively sample information has been long appreciated in a range of disciplines. However, because of their complexity and cognitive nature, these behaviors have been difficult to characterize. In recent years, a resurgence of interest in this question has been based on a confluence of ideas from multiple fields, including machine learning, development, perceptual learning and attention and decision making.  This collection of articles in F1000Research aims to provide a home for a broad range of studies addressing this topic. We welcome 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.