Neurocognitive mechanisms of the flow state

Abstract

While the experience of flow is often described in attentional terms—focused concentration or task absorption—specific cognitive mechanisms have received limited interest. We propose that an attentional explanation provides the best way to advance theoretical models and produce practical applications, as well as providing potential solutions to core issues such as how an objectively difficult task can be subjectively effortless. Recent research has begun to utilize brain-imaging techniques to investigate neurocognitive changes during flow, which enables attentional mechanisms to be understood in greater detail. Some tensions within flow research are discussed; including the dissociation between psychophysiological and experiential measures, and the equivocal neuroimaging findings supporting prominent accounts of hypofrontality. While flow has received only preliminary investigation from a neuroscientific perspective, findings already provide important insights into the crucial role played by higher-order attentional networks, and clear indications of reduced activity in brain regions linked to self-referential processing. The manner in which these processes may benefit sporting performance are discussed.

Full text

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Running Head: FLOW IN SPORT
Neurocognitive mechanisms of the flow state
David J. Harris1, Samuel J. Vine1, Mark R. Wilson1
Submission Date: 27/12/2016
1 School of Sport and Health Sciences, University of
Exeter, St. Luke’s Campus, Exeter, UK
Address correspondence to David Harris, School of Sport and Health Sciences, University of
Exeter, St. Luke’s Campus Exeter EX1 2LU, UK. E-mail: D.J.Harris@exeter.ac.uk
Compliance with Ethical Standards
Funding The authors have received no funding for the preparation of this article.
Conflict of interest David Harris, Sam Vine and Mark Wilson declare that they have no
conflicts of interest relevant to the content of this review.
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Abstract
While the experience of flow is often described in attentional terms - focused concentration
or task absorption - specific cognitive mechanisms have received limited interest. We propose
that an attentional explanation provides the best way to advance theoretical models and
produce practical applications, as well as providing potential solutions to core issues such as
how an objectively difficult task can be subjectively effortless. Recent research has begun to
utilise brain-imaging techniques to investigate neurocognitive changes during flow, which
enables attentional mechanisms to be understood in greater detail. Some tensions within flow
research are discussed; including the dissociation between psychophysiological and
experiential measures, and the equivocal neuroimaging findings supporting prominent
accounts of hypofrontality. While flow has received only preliminary investigation from a
neuroscientific perspective, findings already provide important insights into the crucial role
played by higher order attentional networks, and clear indications of reduced activity in brain
regions linked to self-referential processing. The manner in which these processes may
benefit sporting performance are discussed.
Keywords: attention, neuroscience, mechanisms, peak performance, zone
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Neurocognitive mechanisms of the flow state
‘The consciousness of self is the greatest hindrance to the
proper execution of all physical action’
- Bruce Lee (Tao of Jeet Kune Do, 1975)
Introduction
The crucial role that attention plays in shaping the learning of sporting skills,
performing under pressure and facilitating expertise is well established (Mann et al., 2007;
Moore et al., 2012). However, the state of flow, something of an attentional anomaly (Bruya,
2010), provides an alternate perspective from which to examine attention during peak
performance. During flow increased demands are paradoxically met with no apparent
increase in effort (Csikszentmihalyi, 2000), and athletes report a laser-like task focus in the
face of distractions (Jackson & Csikszentmihalyi, 1999). Flow is frequently linked to peak
performances (Jackson et al., 2001; Koehn & Morris, 2012) as well as heightened enjoyment
(Privette, 1983). Therefore understanding the cognitive processes responsible for this state
may inform both attentional models and practical endeavours for finding peak focus during
sporting performance.
Flow, or ‘the zone’, is a phenomenological state where an individual finds an
effortless involvement and deep, task-related focus in the current activity. It is accompanied
by a feeling of control and a loss of self, creating an intrinsically motivated, optimal
experience (Csikszentmihalyi, 1975; 1990; 2000, see figure 1). Athletes in flow often report
reaching the apex of their abilities and levels of fulfilment that are unrivalled in the rest of
their lives (Jackson & Csikszentmihalyi, 1999). While research has described the nature of
the experience and it’s antecedents in sport (Jackson, 1996; Swann et al., 2015), the
underlying mechanisms of flow are not well understood. We propose that a deeper
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consideration of attentional processes may be crucial to this understanding. Therefore we
review current findings related to the role of attention, and the ways in which direct brain
recording can inform this understanding, with the aim of proposing attentional changes as the
fundamental mechanism for creating the state of flow.
In order to understand flow in the context of sporting performance (and the potential
benefits it provides), findings from the sporting literature will be considered alongside
neuroimaging research. Imaging findings are well established across the wider field of
attention (Corbetta & Shulman, 2002; Fan et al., 2005) and will be used to highlight how
attentional changes may provide beneficial outcomes for sport. We do not attempt an
exhaustive review of either sporting flow or neuroimaging studies, but endeavour to give an
overview of findings central to understanding attentional processes during flow.
Figure 1; Schematic representation of the focus of the article. While the experience of flow (box 3.) and
it’s antecedents (box 1.) have been well documented, the focus of our discussion is the attentional
processes (box 2.) that happen alongside, and may be responsible for, the experiential components.
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The Flow State
Flow, the mental state of complete absorption in the present task, is traditionally
described along Csikszentmihalyi’s (1990) nine dimensions. Three of these dimensions are
described as setting the conditions for flow; a balance of challenges and skills, clear goals and
immediate feedback (Kawabata & Mallett, 2011; Nakamura & Csikszentmihalyi, 2009).
Csikszentmihalyi suggests that flow occurs when challenges and skills are in balance, in a
channel between the states of boredom and anxiety. The remaining six dimensions are
experiential components (Nakamura & Csikszentmihalyi, 2009); intense concentration,
merging of action and awareness, loss of self-consciousness, a sense of control, distortion of
time, and intrinsic rewards (autotelicity). This conceptualisation of flow has received robust
support across a range of domains; including art and science (Csikszentmihalyi, 1996), sport
(Jackson, 1996) and literary writing (Perry, 1999).
Flow in Sport
The sport setting is ideally suited for achieving flow, as sporting activity can provide
the three basic conditions Csikszentmihalyi suggests are necessary; clear goals, immediate
feedback and a balance between challenge and skill. The added effect of physical exertion
may further enhance the experience of total absorption (Dietrich, 2006). Finding flow is
highly desirable, as athletes report effortlessness and fluency of performance when in the
state (Swann et al., 2015). As there is reasonable agreement over what characterises a flow
experience (Swann et al., 2012) research in sport has mainly focused on its antecedents. For
example a systematic review by Swann et al. (2012) found the most common antecedents to
be focus, preparation, motivation, arousal and positive thoughts. Unfortunately research in
sport is yet to identify important mechanisms that underpin the experience and has made
limited use of experimental approaches to studying flow. As such, findings from other
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domains applying neuroscientific approaches will be discussed as they provide both methods
that can drive flow research forwards, and important implications for how attention during
flow may enable superior performance.
Attention
Attention is the cognitive process of selecting discrete aspects of information for
further processing; it allows an animal to behave adaptively in a world containing an
overabundance of information (Knudsen, 2007). Attention therefore shapes our experience
but is also key to selecting our responses to it (Allport, 1989) and is a viable candidate for
facilitating beneficial effects of flow given that effective attentional control has been shown
to have significant benefits for sporting performance (Abernethy et al., 2007). For example,
optimising the target and timing of visual attention can enhance psychophysiological
efficiency, visuomotor coordination and movement kinematics (Moore et al., 2012).
As four of Csikszentmihalyi’s (1990) six experiential components of the flow
experience (intense concentration, merging of action and awareness, loss of self-
consciousness, and distortion of time) allude to changes in attentional processes, these
mechanisms deserve further clarification. Within the sporting literature athletes report focus
as being crucial for finding flow (Jackson, 1992). Swann et al. (2012) also found the most
reported aspects of the experience to be concentration and action-awareness merging, again
suggesting a fundamental role for attention. In order to build an initial picture of attention
during flow, we focus on four topics that emerge from current literature: the degree of
automaticity, the role of effective attentional control, the extent to which flow requires
effortful attention, and the experience of reduced self-awareness.
Automaticity
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A prominent theoretical approach is to describe flow as a state of enhanced
automaticity, essentially an absence of controlled attention. Automated control is generally,
though not always, thought to provide benefits for action in terms of efficiency, fluency and
reduced resource allocation (see Toner et al., 2015). Automatic or implicit processes are fast
and efficient, and circumvent conscious awareness of effort (Moors & DeHouwer, 2006), and
as such may contribute to the frequent perception of ease during flow. As automaticity
involves reduced executive activity, it is characterised by reduced frontal activation (esp.
lateral and dorsolateral PFC), as regions of basal ganglia acquire motor sequence knowledge
(Poldrack et al., 2005). A model of the flow experience centred on a reduction in prefrontal
control is proposed by Arne Dietrich (2003).
Transient Hypofrontality Theory (THT). Dietrich (2003) suggests that the core
components of the flow experience, and other altered states of consciousness, can be
explained by reductions in processing by the prefrontal cortex (PFC). Higher processes like
abstract thinking, self-reflective consciousness and working memory are lost (known as
phenomenological subtraction), creating states of reduced function (common in running,
meditation or hypnosis). These states are suggested to be linked to flow due to a common
mechanism of hypofrontality (i.e. reduced frontal activity and executive functioning).
Dietrich and Stoll (2010) argue that sports are particularly good at engendering flow
states for two reasons. First, bodily motion is extremely complex in computational terms, and
as such uses considerable mental resources (demonstrated by Stoll & Pithan, 2016). When
prolonged, this is sufficient to divert processing away from non-critical cognitive processing
(i.e. higher order function in the PFC). Second, Dietrich suggests that as an action becomes
more automatic, and under control of the basal ganglia (Poldrack et al., 2005), it is easier for
frontal function to decrease and for this altered state of consciousness to arise. Dietrich’s
suggestion that action in flow is more automatic also accounts for the effortless experience
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often associated with flow, as automatic action is controlled through unconscious or implicit
processes, and unavailable to conscious awareness (Moors & De Houwer, 2006). In
attentional terms, they are not constrained by capacity limitations, excluding them from
mental effort (Kahneman, 2011).
Imaging studies have provided some support for a reduction in frontal activity, but
results suggest a very specific pattern, that does not constitute a general deactivation. Ulrich
et al. (2016) and Ulrich et al. (2014) found reduced activity of the medial prefrontal cortex,
an important structure in self-referential processing. Conversely, however, research by
Yoshida et al. (2014) using functional near infrared spectroscopy (fNIRS) found increased
ventrolateral PFC activation during a flow level of a gaming task. Additionally research by
Harmat et al. (2015) again used fNIRS to test prefrontal activation during flow in a widely
used paradigm utilising continual adjustment of difficulty in the game ‘Tetris’. There was
found to be no reduction in frontal activity, suggesting that a general mechanism of
hypofrontality may be overly simplistic. Support for hypofrontality comes mainly from
studies finding reductions in cognitive function as a result of prolonged exercise (see Stoll &
Pithan, 2016). Whether this state is representative of a general flow mechanism is
questionable. Harmat et al. (2015) suggest that the demands on executive control, even during
a relatively simple Tetris task, make a reduction in prefrontal activity unlikely.
Reduced verbal-analytic processing. Initial electroencephalogram (EEG) research
does provide support for a move towards automaticity, and the avoidance of deliberative
control of motor responses (e.g. Reinvestment theory, Masters & Maxwell, 2008). EEG
assesses cortical activity through electrodes placed on the scalp, which give information
regarding rhythmic neural oscillations, measured in Hertz (Hz), and may be useful for
identifying brain areas that are particularly active or inactive during flow. Of particular
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interest is the alpha band (8-12 Hz), which reflects relaxed wakefulness and may signpost
areas that are inhibited during flow (Pfurtscheller et al., 1996).
Wolf et al. (2015) demonstrated that flow during table tennis imagery was related to a
reduced influence of verbal-analytic processing on motor control, which was also related to
expertise. EEG recordings showed relative deactivation (higher alpha power) of a left
temporal site (T3), associated with verbal-analytic processing, compared to the corresponding
right site (T4) which reflects visuospatial processing. Their results suggest that the flow
experience may involve a shift away from verbal-analytic influence to a more automatic
mode of operating; the same shift that has been found with a progression from novice to
expert performance (Deeny et al., 2003), and from more explicit to more implicit forms of
motor performance (Zhu et al., 2011). Further success of EEG in identifying markers of flow
may provide opportunities for training flow experiences (see Cheron, 2016), due to previous
success with EEG in neurofeedback training (Ring et al., 2015).
This conceptualisation of flow based on automation is appealing and indeed seems
sufficiently entrenched to have permeated areas like philosophy; from the ancient Chinese
concept of ‘wu-wei’ (Slingerland, 2014) to Dreyfus’ anti-representationalist account of
choking and coping under pressure (see Dreyfus, 2007; Gottlieb, 2015). Automated processes
provide benefits for action and are a hallmark of expertise (Fitts & Posner, 1967; Singer,
2002), so whilst the mechanism of hypofrontality may be questionable (based on
neuroimaging and mental effort findings, see below), the benefits of automated action for
avoiding conscious disruption of well-learned movement sequences are clear. As such, if flow
enables an enhanced degree of implicitness in motor control we can expect to see benefits for
sport. Whether this degree of automaticity extends to aspects of performance like decision-
making, selective attention, creativity1 and problem solving seems unlikely (Harmat et al.
2015).
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A mixed pattern of automatic and controlled processing has also been found in the
study of musical improvisation, a flow-like activity where musicians display high levels of
creativity, spontaneously producing complex musical structures (Nisenson, 1995). Limb and
Braun (2008) found that during improvisation, in contrast to over-learned sequences, jazz
pianists displayed a dissociated pattern of frontal activity with deactivation of lateral
prefrontal areas associated with effortful problem solving and monitoring of goal-directed
behaviours (Ashby, Isen & Turken, 1999). However, several other studies of improvisation
have supported the involvement of monitoring and cognitive control processes (Bengtsson,
Csikszentmihalyi & Ullén, 2007) suggesting a greater coupling between creative and
controlled (executive) processes (see Beaty, 2015 for a review). Losing yourself in the
performance may not be as simple as a complete absence of cognitive control, but a more
balanced cooperation between spontaneous and deliberative processing.
Similarly, Csikszentmihalyi and Nakamura (2010) postulate that the importance of
automaticity during flow lies in allowing automated sequences to take care of themselves, so
that more attention can be paid to essential aspects of the activity. They suggest that effortless
attention is rarely fully automatic, and a person is often more open, alert and flexible within
the structure of the activity. Such a balance between attentional effort and automatic
processing perhaps provides the benefits for sporting performance that accompany automated
action sequences (Singer, 2002) and the flexibility and problem solving that come with more
effortful, controlled processing (Miyake et al., 2000).
Attentional control
Attentional control involves the ability to direct attention to only those stimuli that are
relevant to our current goals, minimizing the extent to which bottom-up influences capture
our attention (Corbetta & Shulman, 2002). Sarter et al. (2006) describe top-down control as
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the ‘biasing of attentional resources toward the detection and processing of target stimuli’
(p.148) which they link to attentional effort. While flow has not traditionally been associated
with controlled processing (Dietrich, 2006), the characterization of flow as an extreme focus
with immunity to distraction (Jackson & Csikszentmihalyi, 1999) suggests a strong influence
of top-down attentional control. Effective attentional control might explain the performance
advantages reported for flow, as limited attentional resources are optimised through becoming
highly task focused.
Synchronisation theory. A theory of flow proposed by Weber et al. (2009) suggests
that widespread synchronization between neural attention networks may provide the basis for
the complete absorption apparent during a flow state. The theory is based on Posner, et al.’s
(1987) tripartite theory of attention; involving executive, alerting and orienting networks. The
alerting network is responsible for initiating and maintaining attentiveness, while the
orienting network directs attention to a stimulus. The executive network modulates both and
plays a crucial role in top-down control. Optimal attentional control depends upon directing
attention to relevant stimuli, and if alerting and orienting networks were optimally
synchronized with executive control we would expect attention to be highly goal-directed
(Petersen & Posner, 2012).
Weber and colleagues propose that this organisation is achieved through synchronized
firing rates of neurons within attentional networks. Neurons fire at varying rates at rest, but
oscillatory activity in groups of neurons can arise from feedback connections between them,
particularly in the alpha (8-12Hz) and theta (4-7Hz) bands. Synchronised neural activity is
proposed as a solution to the binding problem of consciousness (Crick & Koch, 1990), as
groups of neurons that have become synchronized may coordinate their processing. Weber et
al. suggest that coordinated firing of alerting, orienting and executive attention networks,
alongside reward networks (Schultz, 2006), gives rise to the experience of flow, as all
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attention and reward systems are working harmoniously. This optimal organization (which
Csikszentmihalyi terms negentropy) is highly efficient, creating mental ease, which is often
reported during flow (Bruya, 2010). In effect, this theory suggests that flow enables an
optimal, highly efficient organisation of attention, with an important role for executive
function and top-down maintenance of goal directed attention.
Top-down attention networks. The importance of higher attentional processes during
the flow experience is supported by neuroimaging research. Ulrich et al. (2016) have
identified activation of brain areas associated with the Multiple Demand (MD) network
during flow in an arithmetic task. The MD system (Duncan, 2010) reflects activity of a
functionally general network of brain regions associated with a variety of cognitive
challenges. The MD network involves areas of the prefrontal and parietal cortex, including:
the inferior frontal sulcus, anterior insula, pre-supplementary motor area and in and around
the intraparietal sulcus. Duncan (2010; 2013) links the MD system to goal-directed
behaviour, fluid intelligence and selective visual attention. In particular it plays a function in
organizing multi-step behaviour. Interestingly, Ulrich et al. found brain areas related to the
MD network to be activated more during a flow level of difficulty than a harder level of
difficulty, with activation associated with performance.
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Figure 2; The ventral network (blue), responsible for reorienting attention to salient stimuli,
projects from the temporo-parietal junction (TPJ) towards inferior frontal gyrus (IFG) and middle
frontal gyrus (MFG). The dorsal network (orange), responsible for top-down voluntary allocation of
attention, projects from the superior parietal lobe (SPL) towards the frontal eye fields (FEF). The MD
system includes overlapping fronto-parietal areas, from the SPL to the premotor cortex and inferior
frontal sulcus (IFS) (Figure reproduced from Aboitiz et al., 2014).
Alternatively the areas identified by Ulrich et al. (2016) could also be interpreted as
reflecting increased activity of the dorsal stream of attention (Corbetta & Shulman, 2002, see
figure 2). The MD system depends on areas of parietal and frontal cortex which are identified
by Corbetta and Shulman (2002) as part of the fronto-parietal dorsal stream. Indeed Duncan
(2013) notes the overlap between MD and dorsal networks, and therefore the findings of
Ulrich et al. may also be instructive of the important role played by the dorsal stream in
promoting goal-directed control of attention during flow. Either way, these findings are
indicative of top-down control of attention, as the MD system serves to coordinate a series of
multi-step behaviours, guide selective focus to task-relevant information and provide
cognitive control. These findings strongly challenge views of flow as a state of automaticity,
with reduced frontal influence (Dietrich, 2004), and highlight that during flow supervisory
attentional and cognitive control systems of the brain are highly active.
Dopamine pathways. Attentional processes during flow may also be affected by
neurotransmitter activity, as findings highlight a role for dopamine. Dopamine pathways are
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primarily associated with reward networks in the brain (Schultz, 2006), but also modulate
attentional focusing (Nieoullon, 2002), error monitoring (Holroyd & Coles, 2002;
Ridderinkhof et al., 2004) and response inhibition (Congdon et al., 2008; Chambers et al.,
2009). De Manzano et al. (2013) have demonstrated that flow prone individuals have
increased availability of dopamine D2 receptors in the striatum, which is functionally related
to selective attention (Nieoullon, 2002). Gyurkovics et al. (2016) have identified flow
proneness to be related to a D2 receptor coding gene, and suggest that the relationship
between dopamine and flow may be through reduced impulsivity and more effective response
inhibition.
Reduced dopamine action is associated with impulsive behaviour (Dalley & Roiser,
2012) evidenced by its therapeutic effects on impulsivity in ADHD (Kollins & March, 2007),
so individuals with enhanced D2 availability are predisposed to the behavioural control and
monitoring benefits of dopamine. The findings related to dopamine fit with our wider
discussion of attentional control, which requires response inhibition and impulse control
(Miyake et al., 2000), that are modulated by dopamine action (Dalley & Roiser, 2012;
Nieoullon, 2002). These findings also have important implications for individual differences
in flow and the ‘autotelic personality’ (Csikszentmihalyi, 2000), which may therefore have a
biological basis.
The findings discussed here point strongly to an important role for higher order
attention control mechanisms during flow, although this is perhaps not the dominant approach
within the flow literature (Dietrich, 2006; Jackson, 1996; Swann et al., 2016). Effective
control of attention is established as crucial for optimal sporting performance. For example,
optimal attention control, as indexed by the quiet eye (Vickers, 1996), provides significant
inter- and intra-individual performance benefits (see meta-analysis by Lebeau et al., 2016).
The quiet eye is a final fixation to a target prior to movement execution, which provides task
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focus and allows organisation of neural networks for controlling movement (Vickers, 2009).
The importance of optimal control of visual attention is highlighted by its negative corollary;
as disrupted attentional control under pressure has been shown to impair motor performance
(see Eysenck & Wilson, 2016 for a review). For example, there is ample evidence that
conditions designed to increase anxiety and reduce flow can impair attention (indexed via
quiet eye) and subsequent performance in a range of sport skills; including basketball free-
throw shooting (Wilson et al., 2009), soccer penalties (Wood & Wilson, 2010) golf putting
(Vine et al., 2013); and biathlon (Vickers & Williams, 2007). The use of technology, such as
mobile eye trackers, to measure objective, task-relevant indices of attentional control (e.g.,
quiet eye) may therefore provide useful insights into the flow process while it unfolds.
Attentional effort
Traditional models of attention suggest that as a task becomes more difficult,
requiring cognitive or executive control, more mental energy (or effort) is required to meet
demands (McGuire & Botvinick, 2010). Mental effort, the motivated activation of attentional
systems (Sarter et al., 2006), has a physical basis, leading to changes in both central (neural)
and peripheral (e.g., cardiovascular) psychophysiological indicators (Berntson et al., 1997).
However flow presents a challenge for attention researchers, as difficult tasks are met with a
perceived decrease in felt effort (Bruya, 2010). This type of effortless attention has been
referred to as ‘post-voluntary’: neither voluntary (effortful) nor involuntary (automatic), but
captured by an absorbing activity (Dobrynin, 1966).
During flow, individuals report that task control becomes effortless as they become
fully absorbed. Therefore either the task has become easier, and does not require mental
effort, or the subjective experience has become dissociated from the objective level of mental
work. Several studies utilising objective markers of mental effort during flow, would suggest
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the latter. For example, Gaggioli et al. (2013) revealed that everyday flow experiences were
indexed by increased sympathetic activity (increased heart rate), suggesting that the
perception of ease is not reliably reflected in physiological activity.
Psychophysiological measures of attentional effort. Heart rate variability (HRV), the
fluctuation in beat-to-beat interval, provides an objective measure of mental effort (Berntson
et al., 1997) and attention (Backs, 1997). Mental effort reduces HRV (particularly in the low
frequency, 0.04-0.15 Hz band) due to increased sympathetic regulation and decreased
influence of the baroreflex (Berntson et al., 1997). Keller et al (2011) found that a balance
between challenge and skills (a precursor to flow) produced positive subjective reports, but
decreased HRV, indicating increased mental effort. Likewise, Tozman et al. (2015) found that
a flow-task reduced HRV in the low frequency component (i.e. required more mental effort)
compared to an objectively easy task. Finally, Peifer et al. (2014) found an inverted-U
relationship between flow and low frequency HRV; suggesting that once a moderate level of
mental effort is reached, additional mental load is detrimental.
Conflict monitoring. The psychophysiological research provides a consistent picture
that flow does demand attentional effort, and hence cannot be considered ‘automatic’. A
possible explanation for the lack of felt effort can be found in the work of Botvinick and
colleagues on the conflict-monitoring hypothesis (Botvinick et al. 2001), which indicates how
a task may feel easier during flow. Within the conflict monitoring hypothesis, subjective
mental effort arises from a change in cognitive control, when the demands of the situation are
appraised as not being met. During flow, challenges and skills balance, performance is fluent
and no change in cognitive control is required (McGuire & Botvinick, 2010). As highlighted
by Dobrynin (1966), effortless attention occurs when the task is fully absorbing, and hence
cognitive control is not required to maintain focus. The anterior cingulate cortex (ACC, see
figure 3) is proposed as a key structure within cognitive control and conflict monitoring
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(Botvinick et al., 2001; Botvinick et al., 2004; Van Veen & Carter, 2002), serving to monitor
processing and determine when attention needs to be refocused or redirected. The importance
of the ACC in the perception of effort is demonstrated by Naccache et al. (2005), who found a
patient with ACC lesion did not experience effort in a Stroop task, in spite of normal
executive control. In addition, Posner et al. (2010) suggest that early stages of meditation
may depend on ACC to exert control, with activity receding as it becomes more effortless.
Figure3; Key prefrontal areas and some of their functions. The ACC may contribute to
perceptions of effort during flow, while imaging has suggested reduced mPFC activity (Ulrich et al. 2016)
but increased activity within IFG (Ulrich et al. 2014) and dlPFC (Yoshida et al. 2014). Reprinted by
permission from Macmillan Publishers Ltd: Nature Reviews Neuroscience (Amodio, 2014), copyright
(2014) www.nature.com.
Neuroimaging studies provide evidence that the same process may be occurring
during flow. Klasen et al. (2011) found reductions in rostral ACC activity associated with
increased focus, and reductions in dorsal ACC activity associated with clear goals in the task,
in participants experiencing flow while playing a video game. Ulrich et al. (2016) also found
reduced neural activation in the right anterior cingulate during a flow level of an arithmetic
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task. These findings indicate how the nature of a flow task, providing clear goals and holding
an individual’s focus, may allow reductions in the need for cognitive control through reduced
activity of the ACC, and subsequent reductions in felt effort.
The predictions of the CMH identify how felt effort may dissociate from the
physiological markers of attentional effort discussed previously. This dissociation has been
demonstrated experimentally by Harris et al. (2017), who directly tested opposing predictions
for felt and physiological effort in a simulated driving task. While greatest physiological
effort was found during a matched-to-skills (flow) condition, subjects reported effort to be
highest in a difficult drive and only moderate in the matched level. Harris et al. (2017)
propose a model of effort during flow whereby (low) felt effort relates to the conflict
monitoring mechanism, but invested physiological effort is greatest under optimal challenge
(based on the predictions of motivational intensity theory, Wright, 1996). These findings
indicate that despite greater investment of resources, flow felt comparatively easy, as conflict
was minimised. This effect may help to explain the effort paradox during flow.
The research discussed demonstrates that attentional effort is required during flow in
order to meet the demands of a challenging task, which fits with our preceding consideration
of top-down attention. It has been illustrated that while these processes can be highly
effortful, during flow this is not necessarily the case, as when goals direct attention there is
little psychological cost (Schmeichel & Baumeister, 2010) and goal-directed control may be
maintained with little effort (Land, 2006). Optimal attentional control during flow is perhaps
more dependent upon the absence of stimulus-driven disruption and monitoring processes,
than of effortful top-down control. Subsequent reductions in cognitive control may be
beneficial for sporting performance through avoidance of deliberative control of implicit
processes which can lead to reinvestment and disrupted performance (Masters, 1992; Masters
& Maxwell, 2008). Feelings of ease may further contribute to performance through
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promoting confidence and positive emotions. While potential explanations have been outlined
here, this paradoxical aspect of attentional effort during flow requires further consideration
and should be considered a significant challenge for attention researchers (Bruya, 2010).
Self-awareness
Whilst there appears to be an increased deployment of attentional resources during
flow, they appear to be largely directed away from the self (Csikszentmihalyi, 1975; 1990).
When fully absorbed in a flow inducing activity, attention is directed towards the goal and the
self recedes; bodily actions are reported to feel as though they are moving on their own,
without conscious willing and where self-awareness does exist, it seems to be pre-reflective
(see Toner et al., 2016 for discussion). Phenomenologist Jean-Paul Sartre (1957) recognized
the reduction in awareness of the self that comes from complete absorption in an activity. He
noted that ‘When I run after a streetcar, when I look at the time, when I am absorbed in
contemplating a portrait, there is no I’ (p.48). The role of self-awareness links strongly to the
preceding discussion of automaticity, as a switch from internal to external attentional focus
provides performance benefits through enhanced automaticity (Zachry et al., 2005). This
reduction in self-consciousness during flow is also strongly supported by recent
neuroimaging findings.
For instance, Ulrich et al. (2014) utilised magnetic resonance perfusion imaging to
investigate flow during differing levels of arithmetic challenge. Reduced relative cerebral
blood flow (rCBF) was found in the medial prefrontal cortex (see figure 4), an area strongly
linked to self-referential processing (Jenkins & Mitchell, 2011; Northoff et al., 2006) and an
important part of the brains Default Mode Network (DMN, Buckner et al., 2008; Raichle et
al., 2001). The DMN is an interacting system of brain regions including the posterior
cingulate cortex, medial prefrontal cortex and angular gyrus, which are active during passive
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states. Activity in the DMN is associated with mind-wandering and thinking about the self,
past and future, and is known to be reduced during goal-directed behaviours (Raichle et al.,
2001). As well as medial PFC, Ulrich et al. found reduced activity in other regions associated
with the DMN (e.g. angular gyrus, supramarginal gyrus, parahippocampal cortex). While
decreased activity in this network is not unique to flow, it highlights the importance of
reductions in self-awareness and internal focus during the state.
Figure 4; Medial areas of the default mode network. Medial prefrontal cortex (mPFC), posterior
cingulate cortex (PCC) and precuneus (PC) are active when the individual is engaged in mind wandering
and thoughts about the self. The DMN also includes lateral parietal and medial temporal areas (Figure
reproduced from Aboitiz et al., 2014).
Further research by Ulrich et al. (2016) using fMRI again found reductions in medial
prefrontal areas and brain regions associated with the DMN. Goldberg et al. (2006) found
reduced activity in self-related structures during sensory processing, likening it to ‘losing
yourself in the act’, which is strongly suggestive of Csikszentmihalyi’s (1990) dimension of
action awareness merging. Ulrich et al. (2016) suggest that this reduced self-awareness may
also contribute to another facet of flow; the positive nature of the experience. As self-
referential processing is associated with negative affectivity (Lemogne et al., 2011),
reductions in activity of the medial PFC and DMN may contribute to a positive flow
experience. Further, an fMRI study by Garrison et al. (2013) applying real time
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neurofeedback in experienced meditators found reductions in areas of the DMN to link to
effortless doing and contentment.
Reduced awareness of the self may contribute, not just to the experience of flow, but
to its benefits for performance. In sport, awareness of, and focus on, the self is associated
with impaired skill learning and performance (Baumeister, 1984; Beilock & Carr, 2001;
Masters, 1992). Wulf and Lewthwaite (2010) outline the self-invoking trigger hypothesis to
describe how activation of the self-schema by environmental triggers (e.g. instructions,
presence of others) can account for a variety of findings on motor learning and performance.
Wulf and Lewthwaite (2010) link this self-schema system to the functional network of
cortical mid-line structures found to be inactive during flow. As such, during flow the athlete
may be resistant to the self-related triggers and their negative consequences. The reduced
activity of the DMN also points to a reduction in internal focus, in favour of focusing
externally, on the goal (Nideffer, 1976). Wulf (2013) reports how external focus has been
shown to enhance effective and efficient movement, something that is often described in
flow, but is yet to be measured empirically. Training target-related, external focus is therefore
a potential route for enhancing flow (e.g. Moore et al., 2012). As such, a reduction in activity
of self-referential neural structures and the DMN may be highly beneficial for sporting
performance, as well as underlying key features of the flow experience.
Attention summary
We have considered four issues related to attention; attention control, mental effort,
automaticity and self-awareness that have implications for understanding the experience and
maintenance of flow. While there are certainly tensions, for example between automaticity
and mental effort, all these features seem important in providing a full picture of the flow
state. A combination of efficient attention and automated action control would seem to
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account for many of the features identified by Csikszentmihalyi (1990) as well as providing
benefits for sporting performance. Based on flow reports we might expect attention during
flow to be more external, less self-conscious, less prone to distraction and more task directed,
thus leading to improved performance (see Figure 1). Further empirical testing is needed in
order to understand the extent of these attentional benefits.
Discussion
This chapter set out to use recent neuroimaging findings to illustrate how attentional
processes may provide the best way to understand the mechanisms behind the flow
experience. The studies discussed support our contention that attentional processes can
explain the key features of flow (e.g. Gyurkovics et al., 2016; Klasen et al., 2012; Ulrich et
al., 2016). Findings suggest a reduction in self-awareness, through reductions in medial
prefrontal areas and the default mode network (Ulrich et al., 2014); improvements in impulse
control related to dopamine activity (de Manzano et al., 2013); and considerable activity in
networks related to higher order attentional processing in the Multiple Demand system or
dorsal stream of attention (Ulrich et al., 2016). Together these findings account for many of
the key features of the flow experience (Csikszentmihalyi, 2000). In addition, these features
have implications for sporting performance. For example the activation of higher attentional
processes like the MD network support selective and goal-directed attention, which have
beneficial performance effects in many sports (Abernethy et al., 2007; Vine & Wilson, 2011;
Williams & Davids, 1998). Additionally, reduced verbal-analytic influence (Wolf et al., 2015)
and automated performance (Dietrich, 2003) have been linked to sporting expertise (Beilock
et al., 2002). Overall, these findings suggest a mental state where improved performance is
almost inevitable.
These results also have implications for theories of flow. Dietrich’s (2003)
hypofrontality theory has received mixed support, suggestive of a more nuanced pattern of
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activity across the PFC. While unnecessary or even detrimental activity such as self-
awareness may be limited, areas of dorsolateral and ventrolateral PFC involved in response
selection and inhibition are highly active. As such, current findings suggest that Weber et al.’s
(2009) synchronisation theory may more accurately reflect neural activity during flow, as
areas related to higher order attentional networks appear to be crucial. The overall pattern of
activity during flow suggests a highly efficient ‘switching on’ of networks for goal-directed
activity and a ‘switching off’ of areas related to the self and conscious control of movement.
Additionally models of effort derived from the conflict monitoring hypothesis (Botvinick et
al., 2001) such as that of Harris et al. (2017) have received initial support in describing the
effort paradox during flow by addressing mechanisms for subjective and objective effort
separately.
The important role of top-down focusing and higher attentional networks in flow that
has been highlighted here has clear practical implications. To achieve an optimal task focus,
the engaging and challenging nature of the task is clearly crucial (Csikszentmihalyi, 1990),
but enhancing personal skills and capacities in attention control may also provide substantial
benefits. Attention and self-regulation are trainable (Tang & Posner, 2009) and the efficacy of
attentional training for sporting skills has recently been demonstrated (Ducrocq et al., 2016).
While the transferability of the trained attention skills may require further validation
(Shipstead et al., 2012), training sport specific attention skills through methods like quiet eye
training may enable more frequent flow experience in the given task. As discussed, the quiet
eye is an instance of optimal attention control in an aiming task (Vickers, 1996), but is also
trainable, promoting external, goal-directed attention with subsequent performance benefits.
Flow experiences are known to be fleeting, and are not a prerequisite for performing well, but
given the important role of attentional mechanisms, developing the ability to control attention
may be the best way to find flow more often.
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Significant research questions lie ahead in understanding the role of attention in
creating and maintaining flow. For example, do those in flow experience enhanced perceptual
abilities? Do they always demonstrate an external focus of attention? And does flow prevent
skill disruption through reinvestment (Swann, Crust & Vella, 2017)? Future studies may wish
to examine attentional benefits, like immunity to distraction or improved focus on task
relevant stimuli. Initially inquiry may have to rely on creative laboratory research, where
internal control is high and objective measures can be reliably assessed, even if this is
unrepresentative of real sport environments. However, the need to disentangle cause and
effect makes such an approach a crucial step in furthering our understanding of this complex
phenomenon. Some of these questions can be answered by utilising eye tracking techniques
which provide an objective measure of how attention is being directed and controlled
(Corbetta, 1998).
For example, improvements in attentional control can be easily measured through
assessing the extent to which individuals direct attention to goal-relevant over irrelevant
stimuli in the environment. Attentional probes, attentional bias and unexpected events can
also be utilised to see how strongly visual attention is held by the goals of the current task.
Brain measurement should continue to be utilised, whilst psychophysiological methods may
prove a useful, more accessible first step in sport, due to the difficulties of direct brain
recordings during sporting performance. Intermediate steps like movement controlled gaming
(see Thin et al., 2011), simulators (Harris et al., 2017; Tozman et al., 2015) and virtual reality
may be important in applying imaging techniques to tasks representative of sport.
Conclusions
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In this chapter we hoped to provide an overview of findings from direct measurement
techniques regarding the nature of the flow experience and its underlying attentional
mechanisms. Imaging findings have indicated that flow is a state where attention is focused,
self-awareness is reduced, positive emotions are elicited and automatic actions are allowed to
take control. A greater focus on the specific attentional processes and networks discussed will
allow clearer theoretical predictions that will drive flow research forward, as well as making
use of flow as an attentional anomaly that can inform wider theories of attention.
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Figure legends
Fig 1; Schematic representation of the focus of the article. While the experience of flow (box 3.) and it’s
antecedents (box 1.) have been well documented, the focus of our discussion is the attentional processes
(box 2.) that happen alongside, and may be responsible for, the experiential components.
Figure 2; The ventral network (blue), responsible for reorienting attention to salient stimuli, projects
from the temporo-parietal junction (TPJ) towards inferior frontal gyrus (IFG) and middle frontal gyrus
(MFG). The dorsal network (orange), responsible for top-down voluntary allocation of attention, projects
from the superior parietal lobe (SPL) towards the frontal eye fields (FEF). The MD system includes
overlapping fronto-parietal areas, from the SPL to the premotor cortex and inferior frontal sulcus (IFS)
(Figure reproduced from Aboitiz et al., 2014).
Figure3; Key prefrontal areas and some of their functions. The ACC may contribute to perceptions of
effort during flow, while imaging has suggested reduced mPFC activity (Ulrich et al. 2016) but increased
activity within IFG (Ulrich et al. 2014) and dlPFC (Yoshida et al. 2014). Reprinted by permission from
Macmillan Publishers Ltd: Nature Reviews Neuroscience (Amodio, 2014), copyright (2014)
www.nature.com.
Figure 4; Medial areas of the default mode network. Medial prefrontal cortex (mPFC), posterior
cingulate cortex (PCC) and precuneus (PC) are active when the individual is engaged in mind wandering
and thoughts about the self. The DMN also includes lateral parietal and medial temporal areas (Figure
reproduced from Aboitiz et al., 2014).
Footnotes
1. Flow is often associated with creativity (Csikszentmihalyi, 1996), which has itself received
neuroimaging interest. However findings from this area were not included as methods,
definitions and results are sufficiently diverse as to create additional confusion. For a review,
see Dietrich and Kanso, (2010).
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