Table 1.

Summary of studies.

Study	Description
Ulrich et al. (2014)	Used perfusion fMRI (arterial spin labeling) to compare brain activation during an arithmetic task under “flow” (difficulty matched to skill) vs. “boredom” (easy) and “overload” (too hard) conditions. Focused on identifying brain regions whose activity decreases or increases during flow.
Ulrich et al. (2016)	Replicated and extended Ulrich 2014 using BOLD fMRI with shorter task blocks (30 s) in a within-subject design. Examined whether flow-related brain activation patterns (especially in DMN and ECN regions) could be detected with typical fMRI timing and confirmed physiological markers (electrodermal activity) of flow.
Ulrich et al. (2022a)	Employed a combined task-based activation and connectivity fMRI analysis to investigate the right anterior insula’s role as a salience network hub during flow. Flow was induced via a dynamic task (similar to challenge-skill balancing). The study specifically looked at changes in connectivity between the right insula and nodes of the ECN (DLPFC) and the DMN (mPFC) during flow vs. boredom/overload.
Huskey et al. (2018)	Tested the “synchronization theory of flow” using a naturalistic video game. Manipulated the balance between task difficulty and player skill in an open-source game and measured brain activity with fMRI. Focused on whether the flow condition (balanced difficulty) produces increased functional connectivity between cognitive control (ECN) regions and the reward network, compared to imbalanced conditions, which were hypothesized to activate the DMN (associated with disengagement).
de Sampaio Barros et al. (2018)	Investigated attentional resource mobilization during flow using near-infrared spectroscopy (NIRS) and psychophysiology. Participants played simple video games (Tetris, Pong) at easy, optimal (flow), hard, and self-chosen difficulty levels. Measured self-reported flow, attentional lapses, autonomic activity, and cortical oxygenation in frontoparietal regions to see how optimal challenge (flow) affects frontal and parietal activation and attention.
Beaty et al. (2016)	Examined whole-brain functional connectivity during a divergent thinking task to test if creativity is supported by cooperation between default mode and executive networks. Using fMRI, they compared a creative idea generation condition to a control condition, analyzing connectivity between key regions. Focus was on whether higher creative performance is associated with increased DMN–ECN coupling.
Beaty et al. (2018)	Used resting-state fMRI in a large sample to identify brain network predictors of trait creative ability. Although not an induced flow study, it was included for relevance to creativity. Found that individuals with higher creativity showed a whole-brain network linking default, salience, and executive regions working in concert.
Rosen et al. (2024)	EEG in 32 jazz guitarists (varying expertise) as they engaged in musical improvisation. Aimed to isolate brain oscillation patterns unique to the creative flow state. Compared experts (who more readily achieve flow) to less experienced players. Focused on neural signatures of “letting go” of executive control (transient hypofrontality) and the engagement of specialized creative networks when players experienced high-flow vs. low-flow moments.
Ulrich et al. (2022b)	Replication study of the established mental-arithmetic flow paradigm (boredom–flow–overload) using BOLD fMRI in a fresh sample (N = 41 healthy male participants). The study quantified replication evidence using the replication Bayes factor, reporting strong replication evidence for electrodermal activation and decisive replication evidence for both canonical neural “flow effects.” Inverted U-shaped activation was observed in regions including dorsolateral prefrontal cortex, anterior insula, and parietal cortex, while U-shaped activation was predominant in regions including medial prefrontal cortex, ventral striatum, amygdala, and cingulate cortex.