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HARVARD UNIVERSITY EXTENSION SCHOOL
PSYCE-1609
The Neuroscience of Learning: An Introduction to Mind, Brain, Health and Education
Submission 5
The Neuroscience of Flow
Ria Cheruvu
5/7/2018
Table of Content
2
INTRODUCTION..............................................................................................3
BACKGROUND................................................................................................. ............. ...... 4
Psychology of Flow..................................................................................................... 5
Neuroscience of Flow.................................................................................................. 5
THE PROBLEM....................................................................................................................5
RESEARCH QUESTION.......................................................................................................... 6
LITERATURE REVIEW...................................................................................... 7
LITERATURE GENRES............................................................................................................ 7
The Psychology of Flow...............................................................................................7
Attentional Processes............................................................................................................ 8
Activities and Tools for Measuring/Stimulating Flow...............................................................9
Qualitative Interviews......................................................................................................10
Questionnaires..................................................................................................... ........... 11
Experience Sampling Method.......................................................................................... 11
The Neuroscience of Flow.........................................................................................12
MRI and fMRI imaging and Flow........................................................................................... 13
fNIRS imaging and Flow....................................................................................................... 15
Appreciating the Complexities of Flow Theory.......................................................... 19
Disaggregating Flow into Attention and Motivation............................................................. 20
METHODOLOGY........................................................................................... 22
ANALYSIS....................................................................................................22
DETAILS OF THE ANALYSIS...................................................................................................22
CONCLUSIONS............................................................................................. 26
ANSWER TO THE RESEARCH QUESTION..................................................................................26
LIMITATIONS OF THE STUDY AND RECOMMENDATIONS FOR FUTURE STUDIES.................................27
GENERAL SUMMARY...........................................................................................................28
Introduction
3
Research suggests that flow states can lead to the development of complex patterns of
thought, behavior, and creativity by enabling self-motivated optimal experiences
(Csikszentmihalyi, 2014c). Flow states are states of consciousness involving deep absorption
during the passionate pursuit of an autotelic activity, which involves “doing primarily for the
sake of the experience itself” (Engeser, 2012, p. vi). Flow is differentiated from other forms
of attention and motivation due to its multiple conditions or sub-elements. These conditions
or sub-elements include the construction and achievement of clear goals, reception of
immediate feedback from the environment, a balance between an activity’s challenges and
the flow participant’s skillset, high concentration, distortion of time, negligence of meta-
representations of the self, and transformation of the present experience to an autotelic
activity (Csikszentmihalyi, 1996). Figure 1 summarizes the antecedents, attentional
processes, and experiential components that contribute to the flow experience. The
complexity of flow, which has elements of both motivation and attention, implies that the
measurement of the physiological correlates of flow encompass measurement of numerous
networks and subsystems.
4
Figure 1. Schematic representation of the processes composing the flow experience. (Harris, 2017, p.
223).
Background
Psychology of Flow
Csikszentmihalyi’s contemporary theory of flow, originally established in 1975, is
defined as “intense experiential involvement in moment-to-moment activity”
(Csikszentmihalyi, 2014b, p. 15). Csikszentmihalyi’s definition of flow theory is a well-
accepted concept in psychological literature, and has been employed by psychology
researchers to study the qualities/characteristics of flow states (Engeser, 2012). Psychology
researchers often study how activities stimulate flow using self-report measurement tools that
prompt participants to reflect on flow experiences in order to determine individuals’
behaviors, mental states, and emotions during flow.
Neuroscience of Flow
5
While significant advances have been made in the field of neuroscience for studying
the neurophysiological outcomes of activities similar to flow (e.g., Harris, 2017), such as
meditation, research on the neural correlates of flow is still in the early stages. Neuroscience
researchers are beginning to explain particular sub-elements of flow by measuring brain
activity through neuroimaging (e.g., Yoshida et al., 2014), recording electroencephalographic
dynamics in the brain (e.g., Cheron, 2016), or measuring neurochemical changes (e.g.,
Keeler, et al., 2015). The limited findings in existing literature have led to mixed and
contradictory results on the attentional processes/networks related to flow, key brain hubs for
flow, and the consistency/distinction of neurophysiological outcomes across multiple
autotelic activities and environments.
The Problem
The complexity of flow states is caused by the strong correlations between the
components, antecedents, and consequences of flow. Furthermore, since flow is a subjective
experience (Harmat, Andersen, Ullén, Wright, & Sadlo, 2016), the components and
conditions of flow differ depending on the type of autotelic activity (Csikszentmihalyi,
2014b) and the participant’s interests, history, and skillset (Engeser, 2012). The complexity of
flow leads to challenges related to capturing/quantifying the multifaceted and subjective flow
experiences. While psychology researchers are capable of leveraging the delineation of the
sub-elements of flow to measure the dimensions/conditions of flow (Harmat, Andersen,
Ullén, Wright, & Sadlo, 2016), the complex nature of flow with multiple, overlapping
networks/subsystems makes the physiological correlates of flow challenging to measure with
a single tool.
6
Research Question
How and to what extent can flow be explained through neuroscience using
neuroimaging techniques? Technologies such as brain imaging allow neuroscience
researchers to assess key indicators and brain activity during flow. This approach may
produce a more reliable measurement that can be used to validate psychological measures of
flow (Engeser, 2012). By complementing psychological literature with concrete
neurophysiological outcomes, flow researchers may be able to form a bio-behavioral theory
that integrates cognitive, neurological, and behavioral variables to understand the
characteristics of flow (Engeser, 2012).
Literature Review
Literature Genres
The research was performed using Google Scholar and Harvard University’s online
library catalog to find relevant comprehensive literature reviews, journal articles on flow and
neuroimaging from peer-reviewed journals, and books from prominent authors in the field.
As most articles and books on flow coming from psychology literature emerged from before
2014, no parameters were set for the years of publication during the search. Most studies
discussed in this paper consider psychology and neuroscientific literature and observe the
effects of flow on 26-year-old to 50-year-old female and male individuals. The research was
used to establish common definitions of terms such as flow and describe the empirical and
theoretical support behind psychological/neuroscientific findings. The goal of the chosen
literature is to map findings from existing neuroscientific literature to validate psychological
claims regarding flow experiences.
7
To answer this research question, this literature review is organized into three topics:
The Psychology of Flow, the Neuroscience of Flow, and Appreciating the Complexities of
Flow Theory.
The Psychology of Flow
Precursors of flow from Western psychological traditions include theories on self-
actualization (Maslow, 1959) from the humanistic tradition of psychology and research on
intrinsic motivation (Ryan & Deci, 2000). Flow theory considers an individual’s interaction
with his/her environment through the expression of intense concentration on the task being
performed, lack of self-consciousness, and feelings of distortion of time during participation
in specific activities (e.g. ironing clothes or mountain-climbing), depending on the challenge
level and participant’s skill set (Csikszentmihalyi, 2014b). Psychology researchers often
study how activities stimulate flow to determine individuals’ behaviors, mental states, and
emotions during flow. Researchers from psychology characterize flow as a state when an
individual has high concentration and strikes a balance between skill set and challenge level
(Csikszentmihalyi, 2014b; Havitz & Mannell, 2005; Yeh, Lai, & Lin, 2016; Zaman,
Anandarajan, & Dai, 2010). Activities as varied as gaming (Yeh, Lai, Lin, 2016), learning in
a classroom setting (Csikszentmihalyi, 2014c), and sports (Harris, 2017) yield the same
conditions of flow, including deep absorption, loss of self-consciousness, and distortion of
time. The theory of flow authored by Csikszentmihalyi (1996) and considered by many
psychology researchers (e.g., Havitz & Mannell, 2005; Yeh, Lai, & Lin, 2016; Zaman,
Anandarajan, & Dai, 2010) involves nine sub-elements/conditions. This section will focus on
one of those sub-elements, the attentional processes of flow.
Attentional Processes
8
The flow experience necessitates undivided attention directed at the task at hand and
exclusion of distractions (tangential thoughts that direct attention away from the task). During
flow, an individual’s perception of the difficulty of a task is greatly reduced. Dormashev
(2010) explains that the flow model involves attention selectivity, characterized as the
direction of attentional resources to a particular task, and intensity, characterized as a feeling
of deep absorption. Consequently, extreme focus and lack of perceived effort during flow
necessitates the direction of attentional resources towards relevant stimuli rather than
thoughts/events that are tangential to the task at hand. Csikszentmihalyi and Nakamura
(2010) suggest that flow involves striking a balance between attentional effort and automatic
processing of sequences, enabling the direction of more attention towards important details.
Harris (2017) describes that an individual in flow neglects meta-representations of the self
(self-awareness) since attentional resources are directed towards the autotelic activity, rather
than towards awareness of the self. The suggestion that flow involves lack of awareness of
the self has also been demonstrated through the quantitative reviews conducted by
Csikszentmihalyi (1996), who reported that flow participants reported lack of feeling
conscious willing during flow. The dimensions of flow often overlap and contain numerous
contradictions, since they attempt to characterize the changing feelings of an individual in
flow. For example, intense involvement in an activity entails the sense of control over
actions, which contradicts the theory that total concentration cannot involve control over
one’s actions due to the consequent splitting of attention between performance and feelings of
control (Csikszentmihalyi, 1996).
Activities and Tools for Measuring/Stimulating Flow
9
Most theories and studies on the training and measurement of flow have remained in
the psychological realm (e.g., Havitz & Mannell, 2005; Zaman, Anandarajan, & Dai, 2010)
and involve the assessment of qualities and behavioral outcomes of the flow experience by
using self-report instruments after an individual’s participation in the activity. However,
measuring outcomes after the flow state, as done in psychological literature, may not allow
researchers to observe meaningful information regarding the microprocesses (e.g. intensity
and stability) and the effects of flow (Engeser, 2012). Csikszentmihalyi (2014b) breaks down
commonly used methods for measuring flow in psychology into three categories: a)
qualitative interviews encouraging subjects to provide practical examples of flow
experiences, b) questionnaires for measuring sub-elements of flow experiences in specific
contexts, and c) the Experience Sampling Method for collecting and studying systematic self-
reports of flow experiences. The following section will explain the usage of the three tools for
measuring flow:
Qualitative Interviews
The qualitative interview method (e.g. Csikszentmihalyi, 1996; Swann, Keegan,
Crust, & Piggott, 2016) measures the dimensions, characteristics, and emotions associated
with the flow experience as perceived by the participant in real-life situations and activities
by asking participants to recall their experiences. Csikszentmihalyi (2014b) claims that semi-
structured qualitative interviews are commonly used by researchers seeking holistic
descriptions/accounts of the flow experience. The benefits of qualitative interviews include
the ability to acquire a detailed description of antecedents/consequences of flow experiences,
the degree of control over the task, and other factors that psychology researchers can use to
understand the factors that impact and are impacted by the flow experience. The disadvantage
10
of interviews is that the methods prompt the participant to recollect past experiences, which
leaves the possibility for individuals to erroneously recollect false qualities/characteristics of
the flow state. Biased recall of experiences and vague definitions of terms such as ‘ego’ in
interviews can cause researchers to form incomplete conclusions on the
qualities/characteristics of the flow state.
Questionnaires
Questionnaires, such as the Flow State Scale, are commonly used by psychology
researchers studying flow in applications such as sports (e.g. Jackson & Eklund, 2002) and
gaming (Yoshida et al., 2014). The tool measures differences of flow states across diverse
contexts, past participation in the flow state, and the frequency and intensity of past flow
experiences. Measurements are taken through paper-and-pencil measures asking participants
about the characteristics of past participation in flow states and the frequency of an individual
experiencing each of the sub-elements/conditions of flow (Csikszentmihalyi, 2014b). The
benefits of the method include the reliability of measurements of flow across multiple
autotelic activities and the ability to investigate potential relationships between flow and
concepts such as creativity (Harmat, Andersen, Ullén, Wright, & Sadlo, 2016). The
disadvantages of the method include that questionnaires attempt to categorize individuals’
thoughts and feelings into agreement/disagreement with commonly defined statements, which
do not take into account mental states and behaviors specific to a particular
individual/task/context (Harmat, Andersen, Ullén, Wright, & Sadlo, 2016). Furthermore,
similar to qualitative interviews, questionnaires also prompt the participant to recollect past
experiences after the activity, which could potentially result in collection of erroneous data
(Harmat, Andersen, Ullén, Wright, & Sadlo, 2016).
11
Experience Sampling Method
The Experience Sampling Method (ESM), used by researchers to measure flow in
everyday activities (e.g. Havitz & Mannell, 2005), measures individual’s mental states and
health at particular times throughout the day. The ESM measures flow by prompting the
subject to complete a questionnaire with a paging device when the conditions for flow exist.
Csikszentmihalyi’s research (2014b) demonstrates that the benefits of ESM include that it
allows researchers to overcome the disadvantages of interviews and questionnaires, and study
the mechanisms and characteristics of flow in daily life both during and after the flow
experience. The disadvantages of ESM include that it might interrupt the participant during
the flow experience by causing the individual to stop task execution, self-reflect, and record
thoughts and emotions, which makes the usage of this method in natural environments (e.g. a
sports competition) difficult (Harmat, Andersen, Ullén, Wright, & Sadlo, 2016).
The Neuroscience of Flow
Researchers have attempted to deconstruct the psychological theory of flow into
neural processes that can be detected by functional magnetic resonance imaging (fMRI) (e.g.,
Klasen, Weber, Kircher, Mathiak, & Mathiak, 2011), magnetic resonance imaging (MRI)
(e.g., Ulrich, Keller, Hoenig, Waller, & Grön, 2014), and functional near-infrared
spectroscopy (fNIRS) (e.g., Yoshida et al., 2014). Neuroscientific literature involves the study
of flow theory using a variety of autotelic activities and measurement tools, encompassing
fMRI, MRI, and fNIRS technologies, to stimulate and observe the brain mechanisms
associated with flow (e.g., Cheron, 2016; Keeler, et al., 2015). Neuroscience differs in the
units of analysis of psychology, since psychology researchers study subjective measurements
of flow based on an individual’s feelings and emotions. However, neuroscience researchers
12
attempt to objectivize measurements of flow by measuring the neural patterns underlying the
observable aspects of flow that are not specific to an individual and might be similar across
multiple autotelic activities (Engeser, 2012). A thorough review of the neuroscientific
research on flow revealed that the emergence of flow during participation in an activity
cannot be directly measured using existing neuroimaging technologies and research methods.
The neural correlates of flow experiences have only been studied in periods of time where
probability of the emergence of flow was increased (which was confirmed by asking
participants to complete self-report measures) after the flow activity.
MRI and fMRI imaging and Flow
MRI imaging involves the use of a magnetic field to produce detailed images of the
brain (Ulrich, Keller, Hoenig, Waller, & Grön, 2014). fMRI imaging uses MRI in real-time by
measuring blood oxygenation level-dependent (BOLD) signals in the brain (Harmat,
Andersen, Ullén, Wright, & Sadlo, 2016). Researchers studied 27 male participants using
MRI imaging technologies during the performance of mental arithmetic tasks (Ulrich, Keller,
Hoenig, Waller, & Grön, 2014). The authors found deactivation of the medial prefrontal
cortex and the amygdala. The results of the study also demonstrated increased activation of
the inferior frontal gyri, anterior insula, and putamen. The authors confirmed that the study
measured the neural correlates of flow as opposed to measuring brain activity in response to
mental arithmetic by demonstrating that findings such as lower rCBF levels in regions of the
prefrontal cortex were not present in non-flow experiences (low task difficulty or boredom
and high task difficulty or overload conditions).
Klasen, Weber, Kircher, Mathiak, and Mathiak (2011) conducted fMRI research on 13
male volunteers participating in a first-person shooter game. The authors demonstrated that
13
there is a high likelihood that the data on the neural correlates of flow was not corrupted by
the confounding activities (gaming) by measuring brain activity during periods of time when
the probability for the appearance of flow was increased. They found deactivation of the
orbitofrontal cortex and the anterior cingulate cortex, regions of the prefrontal cortex, as well
as increased activation of the putamen, located at the base of the forebrain. Ulrich, Keller, and
Gron (2016b) measured the neural correlates of flow during 23 male participants’
performance of mental arithmetic tasks using fMRI imaging technologies. They found that
deactivation of the medial prefrontal cortex; reduced activity in the amygdala, orbitofrontal
regions, and anterior cingulate cortex; and activation of the inferior frontal gyri was prevalent
in flow conditions, but was not prevalent in non-flow experiences (low task difficulty or
boredom and high task difficulty or overload conditions). The authors claim the mental
arithmetic tasks are autotelic activities they designed to stimulate flow through varying
difficulty levels in response to a participant’s skillset. Furthermore, Ulrich, Keller, and Gron
(2016) utilized the same experimental setup as the study described above by Ulrich, Keller,
and Gron (2016b) and demonstrated deactivation of the medial prefrontal cortex.
Landau and Limb (2017) assert that during musical improvisation, musicians enter
into flow states. Limb and Braun (2008) conducted research on six healthy male professional
musicians participating in musical improvisation. The authors found evidence of deactivation
of the dorsolateral prefrontal cortex (PFC), ventrolateral PFC, amygdala, and orbitofrontal
regions of the brain. They found activation of regions of the anterior cingulate cortex during
flow. The literature survey of MRI and fMRI studies suggest that deactivation of regions of
the frontal cortex is characteristic of flow states, although there is no conclusive evidence
demonstrating common patterns of deactivation/activation among the studies. Furthermore,
the literature suggests that the inferior frontal gyri and the medial frontal regions might be
14
key hubs for flow, and reduced activity in regions such as the amygdala and regions of the
orbitofrontal cortex might be potential neural correlates of flow.
fNIRS imaging and Flow
fNIRS imaging involves studying cerebral hemodynamics in the brain. fNIRS is a
technology that is not susceptible to errors due to head and body motion as is common in
fMRI studies, and is also favored as it has high temporal resolution (Yoshida et al., 2014).
Research using fNIRS technologies such as Yoshida and colleagues’ study (2014) and Harmat
and colleagues’ study (2015) demonstrate that overall deactivation of the frontal cortex might
not be true during flow states. Yoshida and colleagues (2014) used fNIRS to study the neural
correlates of flow in 20 university students during participation in a Tetris® computer gaming
task that was set at a challenge level matching the participant’s skillset. The authors detected
increased activation in the dorsolateral PFC and the inferior frontal gyri. The authors also
administered the Flow State Scale (a questionnaire designed to measure psychological
outcomes of the flow experience), and the results showed a high score on the Flow State
Scale during the flow condition. These results confirmed that the gaming task specifically
induced flow and the measured brain activity was prevalent in flow conditions but was not
prevalent in non-flow experiences (low task difficulty or boredom conditions). This is
relevant for the research question, since findings from Yoshida and colleagues’ study (2014)
contradict the deactivation of the frontal cortex identified in fMRI studies, as the authors
claim attentional mechanisms of flow could be associated with functions of the prefrontal
cortex. In another revealing study on the brain activity associated with flow, Harmat and
colleagues (2015) conducted an fNIRS imaging study on 77 individuals participating in a
Tetris® computer gaming task. The authors found no relationship between activity in the
15
frontal cortex and flow. They confirmed that the study’s findings were focused on flow by
demonstrating distinct brain activity patterns when participants participated in optimal flow
conditions as opposed to participating in easy/difficult gaming tasks (non-flow experiences).
The authors evaluated individuals’ flow experiences by reporting the balance between the
subjects’ skillset and challenge level. Table 1 illustrates findings on activated/deactivated
brain regions during flow based on the experiments discussed in the literature review.
Regions of the brain that were not studied in the papers discussed in the literature review are
marked as “Not considered” in the table.
Table 1. Potential activated/deactivated brain regions during flow.
16
Source: Author, based on Harmat, Andersen, Ullén, Wright, and Sadlo (2016), Klasen, Weber,
Kircher, Mathiak, and Mathiak (2011), Limb and Braun (2008), Ulrich, Keller, and Gron (2016b),
Ulrich, M., Keller, J., Hoenig, K., Waller, C., and Grön, G. (2014), and Yoshida et al. (2014).
In summary, findings regarding key brain hubs for flow (e.g. the inferior frontal gyri
and the medial frontal regions) and reduced activity in regions such as the amygdala seem to
vary according to the type of autotelic activity being performed and the associated attentional
effort demanded by the task. fMRI and MRI studies support the belief that deactivation of
certain regions of the prefrontal cortex is associated with flow. However, fNIRS studies
indicate that regions of the prefrontal cortex, such as the dorsolateral prefrontal cortex, are
key hubs for flow.
17
Appreciating the Complexities of Flow Theory
Psychological literature takes into account the complex nature associated with the
multiple, overlapping subsystems/networks of flow theory by adopting the lens of complexity
theory. Complexity theory involves the study of emergent events (e.g., creativity or learning)
arising from interactions between multiple interconnected units/elements (Ambrose,
Sriraman, & Pierce, 2014). The theory proposes that emergent events cannot be understood
by simplifying phenomena into individual components/units, and that the interplay between
the individual units is more significant than the units themselves (Poutanen, 2013).
Researchers from psychology have employed complexity theory during the measurement of
flow by emphasizing the importance of identifying and studying the interplay of the
conditions/sub-elements of flow, as opposed to separately studying the components of flow.
Neuroscience researchers face numerous challenges and mixed results when using
traditional research methods that do not measure emergent events at appropriate levels of
complexity. Therefore, the application of complexity theory to appreciate the complex nature
of emergent events has applications in neuroscience. In the context of flow, most
neuroscientific research attempting to define the neural correlates of the flow experience
involves using approaches focusing on only one mechanism of measuring flow and/or one
sub-element of flow. However, these approaches may be insufficient for explaining flow
states (Engeser, 2012; Harmat, Andersen, Ullén, Wright, and Sadlo). Based on the approach
taken by psychology researchers, it is possible that by disaggregating flow into separate
constructs and analyzing the interplay between the elements, the difficulties associated with
the measurement of flow could be avoided.
Disaggregating Flow into Attention and Motivation
18
The sub-elements of flow can be disaggregated into at least two main global domains,
attention (e.g., Harris, 2017) and motivation (e.g., Csikszentmihalyi, 2014b), as shown in
Table 2.
Table 2
The attentional and motivational elements of flow
Attentional elements of Flow Motivational elements of
Flow
High concentration
Construction and
achievement of clear
goals
Distortion of time
Reception of immediate
feedback from the
environment
Exclusion of distractions and worry of failure
Coordination of skills and
challenges
Loss of self-consciousness
Transformation of the
present experience to an
autotelic activity
Source: Author, based on Connolly & Tenenbaum (2010), Domenico and Ryan (2017),
Csikszentmihalyi (2014a), Csikszentmihalyi (2014b), Keller & Bless (2007), Kowal & Fortier (2000),
Swann, Keegan, Piggott, & Crust (2012).
The disaggregation of flow into attention and motivation stems from the definition of
the flow experience as a “prototypical experience of intrinsic motivation [behavior driven by
internal rewards]” (Csikszentmihalyi, 2014a, p. 24) and the importance of attention for
entering/maintaining flow states (Csikszentmihalyi, 2014b). Figure 1 outlines key attentional
processes of flow that contribute to flow experiences in conjunction with experiential
components. The attentional and motivational processes of flow contribute to the experiential
components of the flow experience (e.g., lack of self-consciousness and distortion of time)
identified through psychological self-report instruments. Psychology studies (e.g., Connolly
19
& Tenenbaum, 2010; Swann, Keegan, Piggott, & Crust, 2012) and neuroimaging studies
(e.g., Ulrich, Keller, & Gron, 2016b) focusing on the attentional processes of flow
demonstrate that effective attentional control might be a key facilitator of experiential
components of the flow experience, such as high concentration and elimination of distracting,
tangential thoughts. Harris (2017) suggests that identifying the attentional mechanisms of
flow using findings from neuroscientific research can help explain the key features of flow
and allow researchers to understand the complexities of flow.
Psychology studies focusing on the motivational mechanisms of flow (e.g., Keller &
Bless, 2007; Kowal & Fortier, 2000), such as coordination of skills and challenges and
transformation of the present experience to an autotelic activity, suggest that there is a strong
correlation between motivational elements (e.g. intrinsic motivation or self-determined
motivation) and flow; the studies suggest studying the sub-elements of flow related to
motivation can lead to meaningful conclusions on the emergence of flow. In terms of
neuroscientific literature on the motivational processes of flow, Domenico and Ryan (2017)
suggest that the activity in certain regions of the brain associated with intrinsic motivation
could be consistent with the neural correlates of flow. The authors suggest studying the neural
correlates of intrinsic motivation in relation to flow research is an important agenda for future
research. While the neurophysiological basis of attentional and motivational mechanisms
have been studied in detail, a deeper understanding of the relationship/interplay between
these processes could help researchers identify measurable elements of flow.
Methodology
20
A search regarding the different forms/expressions of creativity in a variety of
contexts (e.g. education, poetry, sports) gave way to further research on the topic of optimal
experiences or states of high concentration that might be related to creative processes. The
search was expanded to consider neuroscientific literature considering multiple types of
activities/contexts for stimulating flow, as the number of journal articles on the neural
correlates of flow are limited. The search progressed as the concept of flow was
disaggregated into sub-elements related to Attention and Motivation, and a transdisciplinary
vision of flow theory was gleaned through knowledge obtained from psychology and
neuroscientific literature on flow.
Analysis
Details of the Analysis
The literature review reveals that existing neuroimaging studies do not show a
measurable method for evaluating flow, as existing studies measure the neural correlates of
flow states in different autotelic activities and provide contradictory findings. However, there
seems to be evidence to justify a new model of flow measurement involving disaggregation
of flow into attentional and motivational processes. The following section describes key
findings from the literature that provide insight regarding what is now understood about the
research question, in addition to important implications for researchers, practitioners, and the
field of research. The perspective purported by psychological literature is that the flow
experience and the associated attentional processes and lack of self-awareness is pervasive
across culture, age, gender, and multiple types of autotelic activities and use of tools/methods
for measuring flow (Csikszentmihalyi, 2014b). Neuroimaging studies demonstrate that the
21
neural correlates of experimentally induced flow experiences might not be similar across
multiple autotelic activities. Consequently, it appears that while overall deactivation of
certain regions of the frontal cortex could be characterized as a neural correlate of flow, the
specific regions of the brain that are deactivated are not consistent across multiple autotelic
activities and environments. For example, flow experienced through participation in tasks
such as gaming might require different attentional processes, more vigilance, and rapid
decision making (Yoshida et al., 2014) compared to flow in activities such as musical
improvisation, which requires the use of spontaneous artistic creativity (Limb and Braun,
2008).
The varying autotelic activities and associated attentional and motivational processes
lead to activation/deactivation of different regions of the brain in relation to participation in
different tasks. There is limited evidence proving that the data gathered by neuroimaging is
not corrupted by the confounding autotelic activities that are studied. Furthermore, existing
neuroscientific studies use small sample sizes that lend to the questionability of the authors’
claims. For example, studies on musical improvisation (e.g., Limb & Braun, 2008) do not
indicate how the findings on the neural correlates of flow are not corrupted by the effects of
musical improvisation. This finding is relevant to researchers since it illustrates that it is
important to confirm that the autotelic activities are successfully stimulating flow, and prove
that data gathered by neuroimaging is not corrupted by the confounding activities that are
studied. Practitioners should consider structuring interventions targeted at facilitating flow
based on environmental conditions/situations and an individual’s history, interests, and
expertise (Engeser, 2012).
The literature review revealed that current neuroscientific studies only consider single
research methods (e.g. fMRI vs. fNIRS) rather than employing a combination of research
22
tools. The research fails to provide conclusive evidence for brain patterns/hubs for flow, and
effectively capture the complexity associated with the multiple subsystems of flow. For
example, while measuring flow during a gaming task, Ulrich, Keller, and Gron (2016b) did
not consider the distortion of time and feelings of deep absorption/immersion associated with
the flow experience. Furthermore, while the authors confirmed the balance between the
participants’ skillset and the activity’s challenge level, the authors did not consider other
motivational elements of flow, such as the construction and achievement of clear goals, in
their study. It is important for the literature to capture flow theory’s complexity in order to
explain flow states and help elucidate conclusions from psychological literature by
establishing concrete bio-behavioral/physiological evidence for the sub-elements of flow
(Engeser, 2012).
An acceptable measurement of flow could be determined by accepting the
complexities of flow theory and considering the measurement of flow through the lens of
complexity theory. By disaggregating the attentional and motivational processes central to
flow and considering the interconnections between these two domains that gives rise to flow
experiences, researchers can develop measurement tools and protocols that efficiently
capture/quantify the dynamic complexity of flow in terms of physiological outcomes.
Practitioners can then leverage these measurement tools and protocols to effectively manage
and enhance flow participants’ experiences, performance, and learning. For example, if a
measure of flow was the motivational measure of construction and achievement of clear
goals, then researchers could identify neurophysiological outcomes and form conclusions on
the extent to which conscious control and decision making in conjunction with feelings of
task difficulty and enjoyment contribute to the flow experience. Another example related to
attention: If a measure of flow was the attentional measure of reduced self-referential
23
processing and automated action control, then researchers might be able to solve the
anomalies/paradoxes of flow theory, such as the contradiction between greater use of
attentional resources in the brain and the perceived effortlessness associated with flow
(Harris, 2017). Furthermore, the disaggregation of flow could bridge the gap between
psychological units of analysis (related to mental states) and neuroscientific units of analysis
(related to molecular changes) of flow by allowing researchers to study how the interplay of
attentional and motivational processes contribute to the experiential components of flow. This
finding is relevant to research-practitioners since it illustrates the importance of considering a
transdisciplinary perspective integrating approaches from multiple disciplines (e.g., cognitive,
neurological, and behavioral perspectives) when measuring task-specific neural correlates of
flow and devising flow-enhancing interventions. The finding also demonstrates the
importance of using multiple imaging technologies and psychological measures to confirm
that the autotelic activities are successfully stimulating flow, since sub-elements of flow such
as high concentration and coordination between skillset and challenge level are challenging to
measure using a single neuroimaging technology.
Conclusions
Answer to the Research Question
This paper sought to answer the question, “How and to what extent can flow be
explained through neuroscience using neuroimaging techniques?” The evidence from the
literature supports the idea that key hubs for flow in the brain that correspond to the sub-
elements of flow established in the psychological literature are not commonly established in
neuroimaging studies. Neuroscientific literature offers contradictory evidence regarding
24
activity in particular regions of the brain and neural processes associated with flow. For
example, fMRI and MRI studies (e.g. Klasen et al., 2011; Ulrich, Keller, Hoenig, Waller, and
Grön, 2014) support the theory that flow involves an overall deactivation of multiple regions
of the frontal cortex contra fNIRS studies, which indicate that attentional mechanisms of flow
involve specific activation of certain regions of the frontal cortex serving as brain hubs for
attention (Yoshida et al., 2014). This paper proposes that complexity theory can be applied
towards the measurement of neurophysiological outcomes of flow through the disaggregation
of flow into attentional and motivational processes. Evidence from psychology and
neuroscience research (e.g., Domenico and Ryan, 2017; Swann, Keegan, Piggott, & Crust,
2012; Harris, 2017) suggests that this new model of flow measurement would allow
researchers to understand the complexities/paradoxes associated with multiple
subsystems/networks of flow, such as the participant’s perception of participation in
objectively difficult tasks as effortless (Harris, 2017). The model could potentially lead to
more accurate measurements/interpretations of the sub-elements of the multifaceted and
subjective flow experience.
Limitations of the Study and Recommendations for Future Studies
The neuroscientific literature on the neural correlates of specific sub-elements of flow
(e.g. Klasen et al., 2011) is limited, which impacts the scope of neuroscientific literature this
paper can cover. This paper did not cover studies on the neurochemistry of flow; growing
research on this topic (e.g., Manzano, Cervenka, Jucaite, Hellenäs, Farde, & Ullén, 2013;
Keeler, et al., 2015) suggests that neurochemicals play an important role in an individual’s
subjective participation in an autotelic activity or unique proneness to flow, which is beyond
the scope of this paper. This paper did not cover research conducted on the
25
electroencephalography dynamics associated with flow, which offers an additional
neuroscientific perspective on the psychological construct of flow, through observation of
brain oscillations related to attention during flow (e.g. Cheron, 2016; Wang & Hsu, 2014). In
order to confirm that the neural activity identified in research correlates with the sub-
elements of flow theory, researchers have linked activity in key brain hubs for flow to
behavioral outcomes. For example, Ulrich, Keller, Hoenig, Waller, & Grön (2014) links
deactivation of the prefrontal cortex, which the authors claim is a hub for self-referential
processing, to lack of self-consciousness during flow. Therefore, it is recommended that
future studies consider validating claims regarding certain regions of the brain serving as key
structures/hubs for outcomes such as redirection of attention.
General Summary
Flow is a state of deep absorption when individuals are able to function at their
optimal capacity and tackle challenging endeavors with ease (Csikszentmihalyi, 1996).
Empirical research demonstrating neural correlates of the flow state has developed over the
past decade (Harmat, L., Andersen, F. Ø, Ullén, F., Wright, J., & Sadlo, G., 2016), and
attempts to provide evidence of changes in neural activity in response to participation in flow.
This study sought to summarize what is known about neuroscientific literature on flow by
mapping findings from existing neuroscientific literature to explain psychological claims
regarding flow experiences. It was found that there is no conclusive evidence for the
neuroscience of flow, as neuroimaging studies demonstrate contradictory findings on key
brain hubs for flow that vary according to the activity being performed during flow. The
paper proposes that the difficulties associated with measuring the neurophysiological
correlates of multiple sub-systems/networks associated with flow states could be avoided by
26
adopting the lens of complexity theory and disaggregating flow into attentional and
motivational processes. Future studies should include validation of claims regarding the
behavioral outcomes associated with activation/deactivation of key hubs for flow in the brain.
The growing neuroscientific research addressed in this paper proposes exciting discoveries
targeted at answering questions on the neural correlates of flow. The answers to these
questions will catalyze a greater understanding of the brain’s unique responses to optimal
experiences.
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