How to navigate everyday distractions: Leveraging optimal feedback to train attention control
- Maria Wirzberger, Institute of Educational Science, University of Stuttgart, Stuttgart, Germany
- Anastasia Lado, Max Planck Institute for Intelligent Systems, Tuebingen , Germany
- Lisa Eckerstorfer, Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Ivan Oreshnikov, Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Jean-Claude Passy, Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Adrian Stock, Max Planck Institute for Intelligent Systems, Tübingen, Germany
- Amitai Shenhav, Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island, United States
- Falk Lieder, Max Planck Institute for Intelligent Systems, Tübingen, Germany
AbstractTo stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people’s productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p <.01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.