About Active learning and decision making

The importance of exploratory behaviors by which agents actively sample information has been long appreciated in a wide range of disciplines ranging from machine learning to neuroscience and psychology. 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  F1000Research collection aims to provide a home for a broad range of studies addressing this topic. The scope includes, but is not limited to:

• Exploration during reward-driven and open-ended (e.g., curiosity driven) tasks
• Learning and plasticity with positive and negative feedback
• Emotional biases during exploration
• The relationship between eye movements, learning and exploration
• How internal models, rewards and uncertainty reduction guide exploration

We welcome full length research articles, brief communications, single figure studies, review/opinion articles, and studies using computational, behavioral or neural approaches.

Read the Editorial by Jacqueline Gottlieb, Manuel Lopes and Pierre-Yves Oudeyer for more details.