When the brain takes a break: A model-based analysis of mind wandering

Abstract

Mind wandering is an ubiquitous phenomenon in everyday life. In the cognitive neurosciences, mind wandering has been associated with several distinct neural processes, most notably increased activity in the default mode network (DMN), suppressed activity within the anti-correlated (task-positive) network (ACN), and changes in neuromodulation. By using an integrative multimodal approach combining machine-learning techniques with modeling of latent cognitive processes, we show that mind wandering in humans is characterized by inefficiencies in executive control (task-monitoring) processes. This failure is predicted by a single-trial signature of (co)activations in the DMN, ACN, and neuromodulation, and accompanied by a decreased rate of evidence accumulation and response thresholds in the cognitive model.