The Neural Correlates of Flow Experiences

Our lab recently pilot tested a study that looks at the structural assumptions central to a synchronization theory of flow. The initial results (reported below) were submitted to the Communication Science – Evolution, Biology, and Brains 2.0: Innovation in Theory and Methods pre-conference.


Flow experiences are characterized by unique affective and attentional states including feelings of motivation, high energy, immersion, and complete attentional focus resulting from certain experiences and activities (Csíkszentmihályi, 1990). These psychological states can be elicited by media exposure and serve as a motivator for subsequent media use (Sherry, 2004). Central to flow theory is the assumption that optimal experience is a product of a balance between the perceived challenges presented by an activity and the skills an individual brings to that activity (Nakamura & Csíkszentmihályi, 2002). The synchronization theory of flow (Weber, Tamborini, Westcott-Baker, & Kantor, 2009) attempts to identify the neurological basis of flow experiences.

Five assumptions are central to synchronization theory. First, neural networks can oscillate at the same frequency and networks oscillating at the same frequency are said to be in sync. This synchronization is central to the communication of information between neural networks. Second, synchronization is a discrete state. Networks cannot be more or less in sync just as an individual cannot be more or less in flow. Third, the synchronization of neural networks is energetically cheap. This energetic efficiency may be related to the perception of flow experiences as not taxing. Fourth, the combined effect of networks in sync is thought to be greater than the sum of their individual parts. This may explain why flow experiences are regarded as qualitatively different from the individual components of each antecedent. Finally, flow experiences are thought to occur as the result of a synchronization of attentional and reward networks under conditions of a balance between challenge and skill. Together, these assumptions account for the wholly absorptive and highly rewarding nature of flow experiences. This pilot test used functional magnetic resonance imaging (fMRI) to test the fifth (structural) assumption of sync theory and predicted:

H1: Increased activation in alerting and orienting networks during flow compared to boredom and frustration.

H2: Increased activation in reward networks during flow compared to boredom and frustration.

One right-handed healthy female subject (age = 24) participated in this pilot study. The subject had 20 years of video game experience, played video games 5 times in the past month, played for an average of 30-minutes per session, self reported video game skill as 4 on a 7-point Likert-type scale, and scored 105 out of a possible 160 on a two-dimensional spatial rotation task (ETS, 1975). The stimulus was a Flash-based point-and-click style video game called Star Reaction (ABiGames). Star reaction offers 13 levels of difficulty and varies game difficulty by manipulating the number of stars a player needs to collect, the number of rings to be avoided, and the rate at which the rings move around the screen (see Figure 1).

This study adapted an experimental procedure previously demonstrated to result in three distinct affective states – (1) boredom, (2) flow, and (3) frustration (Weber & Huskey, 2013) – to an fMRI context. In the boredom condition, the subject played level 1 repeatedly. Similarly, the subject repeatedly played the last level in the frustration condition. In the flow condition, the subject started at level 2 and progressed through subsequent levels at her own pace. Previous work has demonstrated that secondary task reaction time (STRT) tasks can be used to measure flow unobtrusively (Kantor & Weber, 2009; Weber & Huskey, 2013). During each experimental condition, the subject completed a secondary task reaction time (STRT) measure by responding to an auditory distractor. See figure 2 for block design.

When testing H1 and H2, we used cluster-based thresholding corrected for multiple comparisons to identify significant voxels (Z > 2.3, p < .05) (Worsley, 2001). Activation in attentional (inferior parietal lobe, secondary somatosensory cortex, cerebellum) and reward networks (thalamus) was observed in the flow condition above and beyond activation observed in the boredom condition. Activations were observed in the visual and lateral occipital cortex when contrasting activation in the flow condition in excess to the frustration condition.

The joint activation of attentional and reward networks in the flow > boredom contrast accords with what synchronization theory would predict. Puzzlingly, the flow > frustration contrast revealed activation in attentional networks but not in areas associated with reward. This may be a function of this pilot study’s one case design. It may also be that the frustration (high task challenge) condition was not experienced as frustrating by the test participant. Still, these results provide preliminary support for the structural assumptions central to a synchronization theory of flow.


Csíkszentmihályi, M. (1990). Flow: The psychology of optimal experience. New York, NY: HarperCollins Publishers.

Kantor, B., & Weber, R. (November, 2009). Flow theory in media contexts: Advances in experimental research. Paper presented at the annual meeting of the National Communication Association, Chicago IL.

Nakamura, J., & Csíkszentmihályi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). New York, NY: Oxford University Press.

Sherry, J. (2004). Flow and media enjoyment. Communication Theory, 14(4), 328–347. doi:10.1111/j.1468-2885.2004.tb00318.x

Weber, R., & Huskey, R. (June, 2013). Flow theory and media exposure: Advances in experimental manipulation and measurement. Paper accepted to the annual conference of the International Communication Association, London, United Kingdom.

Weber, R., Tamborini, R., Westcott-Baker, A., & Kantor, B. (2009). Theorizing flow and media enjoyment as cognitive synchronization of attentional and reward networks. Communication Theory, 19(4), 397–422. doi:10.1111/j.1468-2885.2009.01352.x

Worsley, K. J. (2001). Statistical analysis of activation images. In P. Jezzard, P. M. Matthews, & S. M. Smith (Eds.), Functional MRI: An Introduction to Methods (pp. 251–270). Oxford, United Kingdom: Oxford University Press.

Figure 1:
Screen capture of level 2 in Star Reaction. The red and white circle is the subject’s cursor. The subject’s goal is to collect blue and white stars by moving the cursor to the location of each star while avoiding the blue ring.


Figure 2:
Block design for each functional run. In each functional run, subjects always experienced the same experimental order: boredom, flow, frustration.


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