Go with the flow: A neuroscientific view on being fully engaged. (9th November 2020)

• Record Type: Journal Article
• Authors: van der Linden, Dimitri; Tops, Mattie; Bakker, Arnold B.
• Is Part Of: European journal of neuroscience. Volume 53:Number 4(2021)
• Page Start: 947
• Page End: 963
• Publication Date: 2020-11-09
• Subjects: brain networks -- flow -- neuroscience of optimal performance -- task engagement; Nervous system -- Periodicals; 612.8
• DOI: 10.1111/ejn.15014
• Languages: English
• ISSNs: 0953-816X

Abstract

Abstract: Flow is a state of full task absorption, accompanied with a strong drive and low levels of self‐referential thinking. Flow is likely when there is a match between a person's skills and the task challenge. Despite its relevance for human performance and the vast body of research on flow, there is currently still relatively little insight in its underlying neurocognitive mechanisms. In this paper, we discuss a set of large brain networks that may be involved in establishing the core dimensions of flow. We propose that dopaminergic and noradrenergic systems mediate the intrinsic motivation and activate mood states that are typical for flow. The interaction between three large‐scale attentional networks, namely the Default Mode Network, Central Executive Network and the Salience Network is proposed to play a role in the strong task engagement, low self‐referential thinking, feedback and feelings of control in flow. The proposed relationships between flow and the brain networks may support the generation of new hypotheses and can guide future research in this field. Abstract : Flow is the experience of full task engagement and low self‐referential thinking. It plays a relevant role in human performance and well‐being. We propose a neuroscientific model in which dopaminergic and norepinephrine networks underlie the motivational aspects of flow. The interplay between the default mode network, the salience network and the central executive network subsequently regulate the attentional properties of flow. The model may guide future neuroscientific research on flow.