The Lab is excited to present our latest work on the neural basis of flow at this year’s meeting of the Cognitive Neuroscience Society! This work investigates the brain network dynamics of flow relative to boredom and frustration. It finds that there are specific brain network topologies related to flow, but the topological architecture of these experiences changes over time.

In addition to downloading the poster, you can watch Xuanjun (Jason) Gong present our main findings. The full abstract is posted below. Send us a message if you have any questions! We look forward to your feedback.

A Network Neuroscience Investigation of the Psychological State of Flow

Richard Huskey(1), Justin Robert Keene(2), Shelby Wilcox(3), Xuanjun (Jason) Gong(1), Robyn Adams(4), and Christina Jimenez Najera(2)

(1) Cognitive Communication Science Lab, C2 Lab, Center for Mind and Brain, Department of Communication, University of California Davis

(2) Cognition and Emotion Lab, College of Media and Communication, Texas Tech University

(3) Neuroscience of Messages Lab, Department of Communication, Michigan State University

(4) Department of Advertising and Public Relations, Michigan State University

Abstract Flow is a positively valenced psychological state characterized by high levels of intrinsic reward during goal-directed behavior. Flow occurs when there is a high level of task difficulty as well as when an individual has a high level of ability at the task. Empirical evidence shows that, when task difficulty and individual ability are both high, participants self-report the highest levels of flow and behavioral studies show that flow requires high levels of attention. Neurally, flow is associated with increased functional connectivity between fronto-parietal control and subcortical reward networks. Network neuroscience results show that flow is characterized by a brain-network topology that is energetically efficient and studies using tDCS demonstrate that default mode network down-regulation is causally implicated in the flow experience. However, little is known about the network dynamics that underpin flow, or how the network topology that characterizes flow experiences emerges over time. In this fMRI study (n=35), we use multi-layer network analyses to address this gap (GitHub: https://github.com/cogcommscience-lab/flow-dynamic). We apply a multi-layer community detection algorithm to investigate node flexibility – how many times a node changes community – in the network. We show that nodes in the fronto-parietal control network are characterized by a high level of flexibility early on, but that this flexibility stabilizes over time. By comparison, subcortical reward network nodes exhibit relatively low flexibility during task. These results provide support for the Synchronization Theory of Flow by demonstrating that the discrete brain network topology characterizing flow emerges and becomes stable over time. Asteroid Impact, the stimulus used in this study, is also available for download on GitHub (https://github.com/cogcommscience-lab/asteroid_impact).