AccScience Publishing / GHES / Online First / DOI: 10.36922/ghes.4345
REVIEW

Enhancing flow states in neurodivergent individuals through cognitive network integration

Piper Hutson1* James Hutson1
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1 Department of Art History, AI, and Visual Culture, College of Arts and Humanities, Lindenwood University, Saint Charles, Missouri, United States of America
Submitted: 27 July 2024 | Accepted: 30 August 2024 | Published: 8 October 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

This article explores the concept of “flow” in the context of neurodivergence, focusing on the interaction between the default mode network (DMN) and task-positive networks (TPNs), particularly in conditions such as autism spectrum condition (ASC) and attention-deficit/hyperactivity disorder (ADHD). Flow, a state of deep immersion and focused engagement, is associated with enhanced performance and satisfaction across activities. The DMN, typically active during rest and self-referential thinking, interacts uniquely with TPNs during flow states, creating a dynamic balance crucial for sustained focus and reduced self-referential thinking. Neurodivergent individuals often exhibit distinct DMN activity patterns, which affect their capacity for achieving flow. For instance, individuals with ADHD may experience flow in stimulating tasks, whereas those with ASC may achieve it in areas of deep interest due to their intense focus. This paper examines the expertise-and-release model in creative flow and transient hypofrontality as a potential mechanism facilitating flow in neurodivergent individuals. It also proposes strategies to enhance flow through personalized cognitive training, specialized task environments, and technology-assisted interventions, aiming to harness neurodivergent strengths and accommodate unique cognitive profiles. This comprehensive analysis deepens understanding of neurodiversity and offers practical applications for improving quality of life and performance in neurodivergent populations.

Keywords
Neurodivergence
Flow state
Default model network
Task-positive networks
Transient hypofrontality
Expertise-plus-experience model
Funding
None.
Conflict of interest
The authors declare no conflicts of interest.
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