# Synchronisation Theory of Flow Experiences **Author:** René Weber and Jacob T. Fisher. --------------- **Reference:** The Handbook of Communication Science and Biology. Edited by Kory Floyd, René Weber. Page 157 to 176. https://books.google.com.au/books?hl=en&lr=&id=NE8PEAAAQBAJ&oi=fnd&pg=PA157&dq=Synchronization+Theory+of+Flow+Experiences&ots=ipwgNQ9D--&sig=ZGZKDHEG2FZZx8nnSrR3YhWLMRk#v=onepage&q&f=false ----------- Flow is a psychological state characterised by full absorption in the task at hand (Csikszentmih-alyi, 1990; Nakamura & Csikszentmihalyi, 2014). Individuals in a state of flow lose track of time, experience reduced self-consciousness, and feel a sense that their skills are matched to the challenges of the task. Flow states have been investigated in a broad variety of domains, including sports (Harris, Vine, & Wilson, 2017; Jackson, Ford, Kimiecik, & Marsh, 1998; Stein, Kimiecik, Daniels, & Jackson, 1995), music and art (MacDonald, Byrne, & Carlton, 2006), work (Hallberg & Schaufeli, 2006; Salanova, Bakker, & Lorens, 2006), and media (Ghani & Deshpande, 1994; Sherry, 2004; Weber, Tamborini, Westcott-Baker, & Kantor, 2009). In each of these domains, flow research has been characterised by conceptual broadness and methodological heterogeneity, leading to a flood of findings regarding the precursors, correlates, and outcomes of flow, but little clarity as to how these diverse findings can be integrated into a general understanding of the state itself (Harris et al., 2017). The Synchronisation Theory of Flow (STF) (Weber et al., 2009) aimed to rectify this issue by proposing specific neurophysiological processes underlying the experience of flow, leading to greater theoretical and methodological clarity (Weber, Sherry, & Mathiak, 2008). Nearly twelve years have now passed since the publication of the original premises and predictions of STF. In the intervening time, STF has garnered theoretical and empirical support from within communication and cognate fields, bolstering its core propositions. At the same time, advances in neuroscience have augmented our understanding of the neurophysiological mechanisms underlying flow experiences, allowing for more refined predictions. These two developments necessitate a return to the original theory to review its premises and to refine its core hypotheses. In this chapter, we begin with a brief overview of the core tenets of STF, followed by a review of the burgeoning empirical evidence for its predictions. On this foundation we propose a selection of theoretical and methodological advances that clarify and extend the theory, and we provide future research avenues for scholars. Finally, we discuss a selection of domains in which STF is particularly useful for guiding the design of messages, games, and tools that contribute to health and well-being. Much of communication scholarship adopts the perspective that psychological and communicative phenomena of interest can be understood using theories and methods that solely consider a portion of human experience (Floyd, 2014; Sherry, 2004. Recent work in communication science considers behavioural, sociocultural, and psychological phenomena that are the traditional provenance of communication research, but also emphasises relevant chemical, neural, physiological, and evolutionary factors. These approaches more accurately capture the dynamic, multilevel nature of media psychology phenomena than do approaches that ignore the human processing system (Lang, 2013, 2014; Lang & Ewoldsen, 2010; Sherry, 2015; Weber et al., 2008). STF follows in this tradition. In prying open "the black box" of the brain, STF leverages a wealth of research from biology, neuroscience, and communication to generate accurate and falsifiable predictions and explanations at multiple levels (Weber et al., 2009). Communication phenomena of interest are often emergent properties of complex, net-worked, dynamic, multilevel systems (Lang & Ewoldsen, 2010). Observing and attempting to explain these high-level phenomena often result in a preponderance of difficult-to-falsify theories that may even be logically inconsistent with one another (Watts, 2017; Weber et al., 2008; Yarkoni & Westfall, 2017). Specifying the neural substrates of these phenomena constrains the sorts of predictions available to researchers and affords an increased depth of understanding related to how the brain enables certain behaviors. These factors increase explanatory and predictive power as well as the likelihood that independent researchers will arrive at similar conclusions regarding the same behavioral phenomena (Ashby & Helie, 2011). Weber and colleagues propose five key premises of STF regarding the neurophysiological substrates of flow (Weber, Huskey, & Craighead, 2017; Weber et al., 2009): 1. Brains are oscillating systems that rely on metabolic energy. Information is encoded in the firing of single neurons, but also in the synchronized activity of large networks acting in unison (Breakspear, Heitmann, & Daffertshofer, 2010; Buzsáki, 2006; Buzsáki, Logothetis, & Singer, 2013). 2. Synchronized systems are energy-efficient. Oscillating systems that are out of sync with one another expend more metabolic energy (and are therefore less efficient) than oscillating systems that are in syne (Arenas, Diaz-Guilera, Kurths, Moreno, & Zhou, 2008; Strogatz, 2004). 3. Brain states are discrete. Transitions into and out of specific neural states are not continuous and as such cannot be characterized as "more" or "less" of that state. Brain systems are either in sync or they are not- -there is no "in between." Transitions between phases follow a power law distribution, meaning that there are many more small-phase changes than large ones. The dynamic coupling of these systems comprises a quasi-critical system (Beggs, 2008; Priesemann et al., 2014; Wilting & Priesemann, 2018). 4. Brains are functionally organized. Different brain regions and networks are semi-specialized for the performance of certain tasks and are dynamically recruited for processing tasks based on a set of simple rules (Betzel & Bassett, 2017; Kanwisher, 2010; Khambhati, Mattar, Wymbs, Grafton, & Bassett, 2018). 5. Brains are hierarchically organized. Higher-order cognitive states emerge from lower-order cognitive processes (Bassett & Gazzaniga, 2011). These processes unfold across the brain on multiple timescales. Emergence of higher-order cognitive states and representations from these lower-level processes are associated with conscious attention (Regev et al., 2018). These premises lead to three primary predictions regarding the biological, behavioural, and phenomenological aspects of flow (for more detail, see Weber et al., 2009, 2017). First, from a phenomenological perspective, attention and reward networks should be recruited during flow experiences and, given premise I, they should be synchronised with one another. Second, according to premises 2 and 3, the synchronisation of attention and reward networks observed during flow states should be sudden at critical time points and correspond to energetic optimisation in brain networks. Finally, premises 4 and 5 suggest that the observed network synchronisation spans large-scale attention and reward networks that are hierarchically organised and that this pattern of network synchronisation should be predictive of behavioural aspects of flow, including enjoyment and increased performance in the task at hand. **Networks of Attention** Before reviewing extant evidence for these predictions, we provide a brief overview of the neurobiological underpinnings of attention and reward in the human brain. Attention in STF was initially conceptualised as a tripartite process relying on separable but closely interlinked neural substrates (Petersen & Posner, 2012; Posner & Petersen, 1990; Raz & Buhle, 2006). More recent treatments of STF focus on connectivity between large-scale cortical and subcortical networks involved in task performance, reward, and task disengagement (Huskey, Craighead, Miller, & Weber, 2018; Huskey, Wilcox, & Weber, 2018). The first of these processes, alerting, is conceptualised as a generalised state of vigilance that increases the brain's readiness to perform an impending task. Alerting is primarily dependent on the neurotransmitter norepinephrine (NE), and involves a network of brain regions centred on the locus coeruleus (Aston-Jones & Cohen, 2005) and extending to prefrontal and parietal areas. Alerting is related to (but not commensurate with) arousal, an emotional state associated with broad activation in the parasympathetic nervous system leading to physiological responses such as increased heart rate, greater skin conductance levels, and wide-ranging modulations to perceptual and cognitive processes (Dolcos, Wang, & Mather, 2015; Lee, Itti, & Mather, 2012; Mather & Sutherland, 2011). The flow state is associated with increased physiological arousal and greater self-reported alertness (Peifer, Schulz, Schächinger, Baumann, & Antoni, 2014) but also with reduced activation in the amygdala, a pattern often associated with reduced arousal (Ulrich, Keller, & Grön, 2016; Ulrich, Keller, Hoenig, Waller, & Grön, 2013). The orienting subprocess involves selecting information from the sensory field and modulating sensory and cognitive systems in order to maximize processing of the selected information (Corbetta & Shulman, 2002; Sokolov, 1963). A rich research area within media psychology is concerned with elucidating how and when the human processing system orients to stimuli within a mediated environment (Francuz & Zabielska-Mendyk, 2013; Lang, Bradley, Park, Shin, & Chung, 2006; Potter, Lang, & Bolls, 2008; Thorson & Lang, 1992). In the brain, orienting is primarily associated with a cholinergic network involving the frontal eye fields (FEF), the temporopa-rietal junction (TPJ), the superior parietal lobule (SPL), and ventral portions of the frontal lobes (Corbetta & Shulman, 2002; Davidson & Marrocco, 2000; Petersen & Posner, 2012). The executive control subprocess is implicated in situations wherein stimuli must be selected or inhibited based on the parameters of an individual's goals. Executive control is perhaps the most widely researched of the three subprocesses, as it is heavily involved in theoretically related processes and models such as the dual mechanisms of cognitive control framework (Braver, Gray, & Burgess, 2007; Miller & Cohen, 2001), working memory (Baddeley, 1992; Baddeley & Hitch, 1974), and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP; Fisher, Huskey, Keene, & Weber, 2018; Fisher, Keene, Huskey, & Weber, 2018; Lang, 2000, 2009) (see Chapter 29 by Lang, in this volume). The executive control network in the brain consists of frontoparietal regions and the anterior cingulate cortex (ACC; Petersen & Posner, 2012). Frontoparietal structures especially the lateral and inferior portions of the prefrontal cortex and the lateral intraparietal area (LIP)-seem to work together to control attention in important ways (Aron, Robbins, & Poldrack, 2004, 2014; Buschman & Miller, 2007). Most notably, evidence suggests that these regions work together to create a salience map of the visual field that directs top-down or bottom-up orienting processes (Bisley & Goldberg, 2010; Buschman & Miller, 2007; Sestieri, Shulman, & Corbetta, 2009). **EMERGING EMPIRICAL EVIDENCE** Since the publication of STF, multiple studies have been undertaken to test its core predictions and to compare them to predictions made by competing models of the neuronal underpinnings of flow (such as the hypofrontality model proposed by Dietrich, 2004). These studies have largely bolstered the predictions of STF. Using state-of-the-art methodological tools afforded by network neuroscience (Bassett & Sporns, 2017; Medaglia, Lynall, & Bassett, 2015), this research has found compelling evidence for the presence of synchronization during flow states and for the energetic efficiency of these states. Furthermore, emerging research suggests promising future avenues for advancing STF into new theoretical and methodological domains and illuminates pathways for potential applications in media design and health communication contexts. **Hypothesis 1: Synchronization** STF predicts that attention and reward networks in the brain will be active during flow states and that these networks will operate in sync with one another (Weber et al., 2009). It has long been suggested that synchronisation of neural oscillations is the core means through which the brain integrates influences from disparate brain regions. The synchronous activation of cortical regions during perceptual processing is theorised to be a solution to the binding problem--how the brain combines perceived object features into a coherent representation of the object itself (Crick & Koch, 1990). Various neurons throughout the cortex and subcortical regions are specialised for the processing of certain object features, such as color, shape, motion, and orientation (for an overview of visual processing, see Nassi & Callaway, 2009). These neurons oscillate at certain frequencies and are connected either directly to one another or indirectly via various nuclei in the thalamus and hippocampus (Buzsáki, 1991, 2006; Crick & Koch, 1990; Singer, 2001). This network synchronisation is proposed to be the means by which disparate perceptual representations are "linked" to one another to form a coherent representation of an object (Stryker, 1989). Network synchronisation is critical for the effective performance of a number of cognitive tasks, including sustained attention (Clayton, Yeung, & Cohen Kadosh, 2015). This research has shown that a network composed of the posterior portion of the medial prefrontal cortex (pMFC), the lateral prefrontal cortex (IFC), and the lower-level sensory processing regions serves to direct attentional processing (MacDonald, Cohen, Stenger, & Carter, 2000; Rid-derinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). This network largely overlaps with the executive control network discussed earlier in this chapter (Buschman & Miller, 2007). Impor-tantly, the nodes within this network have been found to phase-synchronize within the theta oscillatory band (4-7Hz) following commission of errors in an attentional task (Cavanagh, Cohen, & Allen, 2009; van de Vijver, Ridderinkhof, & Cohen, 2011; van Driel, Ridderinkhof, & Cohen, 2012). This theta-synchronization between nodes in the executive control network is theorized to subserve error-correction processes and has been shown to predict learning and cognitive control outcomes (Cavanagh et al., 2009; Cavanagh, Zambrano-Vazquez, & Allen, 2012). STF does not specify which oscillatory band(s) will be used for internode communication within attention and reward networks, but it is likely that synchronization within different bands is associated with different communication processes between nodes. For example, in attention research, gamma oscillations have been linked to active but unfocused attention, beta oscillations with focused attention, and alpha oscillations with passive unfocused attention (Basar, 2012; Ward, 2003). Interestingly, a recent study found a significant power increase only in the alpha band during flow (Núñez Castellar, Antons, Marinazzo, & Van Loo, 2019). However, these separate oscillations also seem to function as separate attentional "refresh rates" for stimuli in central and peripheral attention, rhythmically sampling various portions of the visual field for the presence of salient stimuli (Fiebelkorn, Saalmann, & Kastner, 2013; Lakatos, Karmos, Mehta, Ulbert, & Schroeder, 2008; Landau & Fries, 2012). These varying oscillatory refresh rates may serve as a mechanism for flexibly gating the reallocation of attentional resources (Buschman & Kastner, 2015). Research in this area has produced broad support for Hypothesis 1 of STF. Several studies have shown that the neural correlates of flow include robust activation in frontoparietal attention and reward networks (Huskey, Craighead et al., 2018; Huskey, Wilcox et al., 2018; Klasen, Weber, Kircher, Mathiak, & Mathiak, 2012; Ulrich et al., 2016, 2013; Yoshida et al., 2014). Furthermore, activation patterns in these networks are correlated with one another during flow states, a pattern not observed when skills and reward are mismatched (Huskey, Craighead et al., 2018). These findings have been augmented by recent work leveraging methods from network neuroscience (Bassett & Sporns, 2017) to investigate many-to-many connectivity within the frontoparietal attention network during flow states. This work has revealed that during flow, the average degree of nodes within the frontoparietal attention network is highest during flow (Huskey, Wilcox et al., 2018). **Hypothesis 2: Optimization** The second core hypothesis of STF is that the flow state should be reflected in an energetic optimisation of cortical networks in the brain. This hypothesis is based on extensive evidence that synchronized systems in nature are more energy-efficient than are non-synchronized systems (Laufs et al., 2003; Sporns, 2011; Strogatz, 2004). Neural systems have structurally evolved to maximize information processing ability while minimizing wiring costs (Achard & Bullmore, 2007; Bassett et al., 2009). Short-range connections are energetically cheaper than long-range connections, but long-range connections are required in order to effectively integrate information from different parts of the network (Sporns, 2011). This has resulted in widespread "small-world" topology wherein most connections in the brain are short-range (within a small brain region) but a small number of connections extend to greater distances (Bassett & Sporns, 2017). The cost efficiency of functional connections can be measured as the average of the inverse of the shortest path length between all nodes in a brain network (Bassett et al., 2009). The cost-efficiency of functional connectivity is associated with cognitive performance in a variety of domains (Bassett et al., 2009; Gießing, Thiel, Alexander-Bloch, Patel, & Bullmore, 2013) and with general intelligence (Langer et al., 2012). Less cost-efficient network topologies are associated with high performance in effortful tasks that demand high levels of control whereas more cost-efficient topologies are associated with high performance in less control-demanding, more automatic tasks (Bullmore & Sporns, 2012). In STF, cost-efficient synchronization of brain networks is proposed to be an underlying factor in the perceived effortlessness of flow experi-ences, despite the objective difficulty of the tasks being performed (Huskey, Wilcox et al., 2018; Weber, Huskey, & Craighead, 2016). Emerging support for the energetic efficiency hypothesis is provided in one recent study (Huskey, Wilcox et al., 2018). In this study, participants engaged in a media task (playing a video game) while undergoing brain imaging. The video game stimulus was programmed to be either very easy (skill >> difficulty), very difficult (skill << difficulty), or approximately matched to participant ability (skill~ difficulty). As matching of difficulty and ability is proposed to be a precursor of flow (Nakamura & Csikszentmihalyi, 2014), it was hypothesized that brain networks would be energetically optimized in the skill ~ ability condition as compared to the other two conditions. This was found to be the case across various edge thresholding cutoffs, supporting the second hypothesis of STF. **Hypothesis 3: Outcomes** The final prediction of STF is that synchronization of brain networks observed during flow experiences should be associated with behavioral and self-reported outcomes such as higher performance and greater enjoyment (Weber et al., 2009). This hypothesis has been broadly supported in a variety of different research paradigms, including video games (Huskey, Craighead et al., 2018; Huskey, Wilcox et al., 2018; Keller & Bless, 2008; Klasen et al., 2012), and cognitive tasks (Ulrich et al., 2013, 2016). In each of these paradigms, the matching of skill and difficulty leads to activation in flow-related brain regions as well as higher self-reported flow and enjoyment. Flow experiences in video games have been measured using both MRI and functional near-infrared spectroscopy (fNIRS). In one study (Yoshida et al., 2014), brain activity was measured using fIRS during video game play in a boredom condition (skill » difficulty) and in a flow condition (skill = difficulty). The flow condition was associated with activation in the frontal pole, ventrolateral PFC, and ventromedial PFC (nodes in the frontoparietal attentional network) as well as with higher self-reported flow (Yoshida et al., 2014). Huskey and colleagues (Huskey, Craighead et al., 2018; Huskey, Wilcox et al., 2018) used Asteroid Impact,' an open-source video game stimulus, to manipulate the balance of difficulty and reward during three behavioral experiments and during MRI scanning. In these studies, self-reported reward-ingness of the video game was highest when skill~ difficulty. Flow experiences were also associated with activation in cognitive control and reward-related brain regions. Ulrich and colleagues (2013, 2016) observed brain activation while participants solved math problems that were either easy (skill » difficulty), difficult (skill « difficulty), or matched to their skill level (skill~difficulty). In both of these studies, the flow condition was associated with activation in cognitive control and reward-related brain regions as well as higher self-reported enjoyment and willingness to do the task again. **ADVANCING THE SYNCHRONISATION THEORY OF FLOW** Evidence is mounting for all three core hypotheses of STF. Flow experiences have been shown to be associated with activity in cognitive control and reward networks, and to result in efficient synchronization in the brain. In addition, these brain activation patterns are associated with behavioral outcomes such as enjoyment and performance. Now, almost 12 years after the publication of the original theory, theoretical and methodological advances in neuroscience and communication research allow for refinement of the assumptions and predictions of STF. In this section, we introduce four key ways in which STF can be pushed forward. Two of these advances are theoretical, reflecting key insights from neuroscience regarding the dynamic networks undergirding attention, reward, and cognitive control. The last two proposed advances are methodological: (1) introducing novel methods from network neuroscience, and (2) incorporating recent work concerning the social nature of flow-inducing tasks. **Theoretical Advance: Beyond the Tripartite Network Model** In the original conceptualisation of STF, attention was proposed to rely on three primary sub-processes: alerting, orienting, and executive control (Petersen & Posner, 2012; Posner & Petersen, 1990). More recent treatments of the model (Weber et al., 2017) expand STF beyond the tripartite network model to consider the role of cognitive control in flow experiences. Cognitive control is an overarching term for a set of neural processes that enable the brain to focus on certain stimuli in the environment that are relevant for individual goals while inhibiting responses to less-relevant stimuli (Botvinick, Braver, Barch, Carter, & Cohen, 2001; Miller & Cohen, 2001). The process of cognitive control is enabled by three large and well-researched networks in the brain: the task-positive network, the task-negative network, and the salience network (Menon, 2011; see also Chapter 13 by Fisher & Keene, in this volume). In this section, we review these three networks, discussing their relevance for understanding the neural underpinnings of flow. Cognitive control requires recruitment of excitatory and inhibitory neurons throughout wide swaths of the cortex and subcortical regions (Botvinick & Braver, 2015). These connections comprise the task-positive (central executive) network. The task-positive network is anchored in the dorsolateral prefrontal cortex (DLPFC) and lateral portions of the posterior parietal cortex (Seeley et al., 2007). Nodes in this network are activated during flow and also highly synchronized with other nodes in the task-positive network (Huskey, Craighead et al., 2018; Huskey Wilcox, et al., 2018). Consistent activation of the task-positive network during flow points to its importance for the maintenance of flow states in the presence of potentially salient distractors. Thus, it can be expected that the task-positive network will exhibit three primary characteristics during flow: (1) activation nodes in the network should exhibit higher activation during flow than during conditions of skill/difficulty/reward mismatch; (2) synchronization edge weights between nodes in the network should be high, reflecting large correlation coefficients between nodes; and (3) allegiance connections between nodes in the task-positive network should be more connected to one another than to nodes outside of the network. In contrast to the task-positive network, the task-negative network (also called the default mode network; DMN consists of a collection of brain regions that have been observed to become more active whenever someone is not engaged in the task at hand (Greicius, Krasnow, Reiss, & Menon, 2003; Raichle et al., 2001; Seeley et al., 2007). This network is involved in mind-wandering, self-referential thought, planning, mentalizing, reward evaluation, and a wide selection of other behaviors (Raichle, 2015; Spreng, Mar, & Kim, 2009). Flow is associated with reduced activation of nodes in the DMN (Huskey, Craighead et al., 2018; Ulrich et al., 2013). An important exception to this pattern is the precuneus. The precuneus is a core member of the task-negative network but is also highly active during flow (Huskey, Craighead et al., 2018). The precuneus seems to serve as a hub node connecting the DMN and the task-positive network (Cavanna & Trimble, 2006; Utevsky, Smith, & Huettel, 2014). Thus, during flow, the DMN seems to serve to update representations of cognitive effort and commensurate rewards, cueing the individual that the current behavior is rewarding and should be continued. A third network the salience network- serves as a bridge between the task-positive and the default mode network (Menon, 2011, 2015; Seeley et al., 2007). The salience network serves to detect important stimuli in the internal or external environment and direct attention toward those stimuli as a function of their sensory salience and motivational priority. The primary hubs of this network are the anterior insula (AI; Menon & Uddin, 2010) and dorsal portions of the anterior cingulate cortex (dACC). Activation in the salience network is often coupled with activity in the dopaminergic midbrain, including the nucleus accumbens (NAc), the caudate nucleus, and the putamen (Pessoa, 2009). These brain regions have long been known to be involved in the processing of rewards and threats, and are of critical importance for network models of emotion and motivation (Pessoa, 2017). The salience network is proposed to undergird the initiation of the flow experience, serving as the inflection point between non-flow and flow experiences. Drawing on the metaphor of car driving in previous work (Weber, Sherry, & Mathiak, 2008), we envision the salience network serving as a clutch that transitions an idling car (default mode) into a driving car (executive processing) at system-critical time-points. System criticality is determined by the value of external stimuli and once reached leads to a sudden change of the system (from idle to driving). Huskey et al. (2018) provide preliminary evidence for the clutch model of flow experiences. **Theoretical Advance: Individual Differences in Capacity and Control** In STF, flow experiences are proposed to be reliant on synchronized activity between cognitive control networks and reward networks in the brain. The reward network consists of a web of connections between dopaminergic regions of the midbrain and the cortex. These connections transmit dopamine both tonically (over a long period of time) and phasically (over shorter periods of time; Bromberg-Martin, Matsumoto, & Hikosaka, 2010; Grace, Floresco, Goto, & Lodge, 2007). Tonic dopamine release serves to maintain homeostasis of dopamine levels in cortical regions, whereas phasic dopamine release results in sharp increases/ decreases in dopamine levels that influence specific behaviors (Schultz, 1998, 2007; Schultz, Dayan, & Montague, 1997). These dopamine afferents have been shown to exhibit substantial individual differences in both tonic levels of activation and responsivity to motivational stimuli, and are implicated in cognitive disorders such as ADHD (LaHoste et al., 1996; Swanson et al., 2007). Since the original publication of STF, research in this area has progressed rapidly, allowing for more informed predictions as to how individual differences in flow proneness and in the robustness of flow states are contingent on tonic and phasic activation in dopaminergic circuits. Tonic levels of dopamine in the brain are regulated by spontaneously firing neurons in the midbrain (Grace et al., 2007). These neurons act like a pacemaker, firing at regular intervals regardless of external or internal stimuli in order to maintain homeostasis of dopamine levels and to enact changes in dopamine concentrations at relatively long time periods (seconds to minutes). In a recent model it has been proposed that tonic changes in dopamine levels in the midbrain serve to "prepare" a set of responses to the environment that would likely result in reward based on sensory inputs and on memory. This creates a limited workspace wherein more fine-grained actions can be chosen and enacted (Keeler, Pretsell, & Robbins, 2014). Thus, individual differences in tonic dopamine levels are likely associated with proneness to certain behavioral patterns such as flow. Support for this idea has been found in one study showing that flow proneness is correlated with the availability of dopamine D, receptors in the ventral portion of the striatum (de Manzano et al., 2013). In contrast, phasic dopamine responses are mediated by rapid bursts of neuronal firing regulated by inputs from the hippocampus, the frontal regions, and other areas (Grace et al., 2007). These bursts occur in response to motivationally relevant stimuli and release a large amount of dopamine in a short time (Berridge, 2000; Dayan & Balleine, 2002; Schultz et al., 1997). Phasic dopamine responses bias the action selection process by interacting with the task-positive, default mode, and salience networks to guide the processing system toward actions that are predicted to be the most rewarding (Braver et al., 2014; Westbrook & Frank, 2018). In this framework, phasic changes in dopamine levels help the brain "select" appropriate responses from the set prepared by tonic activity. Incorporating a more thorough account of the reward processes that undergird action preparation and selection in the brain allows for several additional falsifiable predictions regarding individual differences in flow proneness and the robustness of flow in the presence of salient distractors. First. flow proneness should be associated with firing rates of dopaminergic neurons in the ventral tegmental area (VTA) of the midbrain as well as receptor availability. Emerging support for this prediction has already been identified (de Manzano et al., 2013). Second, those with known deficits in dopaminergic pathways (such as in ADHD) should evidence higher variance in the task parameters that induce flow and should have less efficient network activation during flow. Those with ADHD have been shown to have abnormally low dopamine receptor availability in the VTA, as well as genetic mutations that produce underactivity in cortico-midbrain dopamine pathways (Ilgin, Senol, Gucuyener, Gokcora, & Sener, 2001; LaHoste et al., 1996). Those with ADHD are also known to exhibit high variance in attention behaviors, ranging from extreme inability to focus attention to a hyper-focused state (wherein ADHD individuals perform a repetitive task without interruption for hours on end; Bush, 2010). **Methodological Advance: Dynamic Brain Networks** Another methodological update that is changing the face of STF is the development and rapid expansion of network neuroscience, especially methods that use dynamic and multilayer networks (Bassett & Sporns, 2017; Sporns, 2014). The hypotheses of STF are undeniably network hypotheses. Testing the primary hypotheses of ST involves measuring the synchronization between brain regions and networks, measuring metabolic efficiency of active brain networks, and predicting behavioral outcomes using network models. These hypotheses could be tested in a rudimentary fashion using methods available at the time, including psychophysiological interaction analysis (PPI; Friston et al., 1997), and GLM-based brain imaging methods, but rigorous testing of these hypotheses required development of more refined computational and statistical techniques. The field of network neuroscience has developed rapidly since the publication of STF, especially in the creation and application of tools and models that can be used in naturalistic environments, such as video games and media viewing tasks (Vanderwal et al., 2017; Weber, Alicea, Huskey, & Mathiak, 2018). The most widespread of these approaches utilizes a parcel-lation atlas to divide the brain into a certain number of regions (e.g., Power et al., 2011). Edges between nodes are typically defined as the average correlation between activity patterns of each node over time (Fornito, Zalesky, & Bullmore, 2016). Dynamic approaches consider edge weights as the correlation or coherence between nodes within a sliding window, allowing for comparisons of network characteristics (such as average path length, node degree, or edge weight) over time during a task (Zalesky, Fornito, Cocchi, Gollo, & Breakspear, 2014). In the tradition of method-theory synergy in communication neuroscience (Weber, Fisher, Hopp, & Lonergan, 2018), these methodological developments have allowed for testing existing hypotheses in new ways, but also for the development of more refined predictions. To this end, an especially salient metric for flow researchers is that of network allegiance- the amount of times a node within a network changes module membership over time (Bassett, Wymbs, & Porter, 2011; Cooper et al., 2018; Khambhati et al., 2018). As highly modular brain networks are metabolically efficient (Bullmore & Sporns, 2012), one would expect that during flow, nodes in the task-positive network would exhibit high connection with one another, but also high allegiance within the network. The allegiance of nodes within the task-positive network during flow states should be compromised in those with cognitive disorders such as ADHD and should dynamically predict variables of interest such as enjoyment and performance. **Methodological Advance: Social Demand Tasks vs. Visuo-Motor Demand Tasks** In the original conceptualization of flow theory, Csikszentmihalyi (Csikszentmihalyi, 1990) described flow as an almost completely domain-general concept. Flow could happen in essentially any task so long as it contained the right balance of difficulty and reward. In most flow research, investigation of flow states has been limited to a narrow range of cognitive and visuo-motor tasks such as solving mathematical problems or playing puzzle games. The neural substrates and cognitive outcomes of flow in these sorts of tasks have been well explicated (Harris et al., 2017; Huskey, Craighead et al., 2018; Huskey, Wilcox et al., 2018; Ulrich et al., 2013, 2016). Less is known, though, about how social demands and rewards may contribute to flow experiences. The social aspect of many media experiences is frequently posited to be one of the primary motivations for using media (Sherry, 2004) and social rewards have been shown to be a primary motivator guiding choices to play particular video games (Weber & Shaw, 2009). Recent work has developed a framework to understand how the social demands and rewards present in video games and other tasks can be incorporated into STF (Kryston, Novotny, Schmälzle, & Tamborini, 2018). In this framework, tasks that incorporate a social element--such as collaborating with a team or competing against another individual--have different sets of demands/rewards than do simple visuomotor or cognitive tasks. This could lead to differences in the precursors or outcomes of flow in social vs. nonsocial tasks. Behavioral work has shown that teams that had higher interaction and higher levels of flow outperformed teams that interacted less or had lower levels of flow (Aubé, Brunelle, & Rousseau, 2014). It has been proposed that social interaction may catalyze the neural synchrony that undergirds flow experiences (Harris et al., 2017), but this assertion has not yet been tested. Cooperative success in social tasks requires dynamically adapting one's behavior based on the behaviors and perceived intentions of others (Dale, Fusaroli, Duran, & Richardson, 2013). As in neural systems, evidence suggests that increased synchrony of actors within social systems serves to increase the efficiency and effectiveness of the system as a whole (Fusaroli & Tylén, 2016; Miles, Nind, & Macrae, 2009) and to increase cooperative performance on specific tasks. Interpersonal synchrony also has social rewards. Interaction partners who show increased gestural, lexical, or neural synchrony show increased positive affect and bonding (Koehne, Hatri, Cacioppo, & Dziobek, 2016; Wheatley, Kang, Parkinson, & Looser, 2012), and are more likely to become friends (Parkinson, Kleinbaum, & Wheatley, 2018). Methodological developments in brain imaging and in network science have allowed for a proliferation of recent studies investigating synchrony both within and between brains (Hasson, Ghazanfar, Galantucci, Garrod, & Keysers, 2012). This emerging field at the intersection of brain networks and social networks provides key tools for scaffolding predictions regarding flow in social environments (Falk & Bassett, 2017). Rather than considering one brain network in isolation, the brains of those co-participating in a task (e.g., playing a video game are considered as a four-dimensional hypergraph. Edge weights are calculated at each time point for each node within one brain, but also for the corresponding nodes in other brains (see Schmälzle et al., 2017). This allows for calculations of synchrony both within and between brains. Emerging evidence has shown that more effective media messages elicit higher synchrony between individuals (Schmäl-zle, Häcker, Honey, & Hasson, 2015) and that synchrony between individual brains during media viewing can predict shared memories during a recall task (Chen et al., 2017). With these things in mind, the three core hypotheses of STF can be outlined in view of social tasks. First, flow within cooperative partners or teams should be reflected as synchronization within and between task-positive and reward networks in each individual brain, but also as increased synchronization across brains. This should be observable at the neural level (e.g., as increased average coherence between the brain networks of interactants) but also at the behavioral level (e.g., as increased reciprocity, gestural entrainment, or lexical similarity; see Fusaroli, Konvalinka, & Wallot, 2014). Second, this flow state should be energetically optimized within each brain, and should also be observable at the behavioral level using measures of communication efficiency (as a ratio of words used or time spent communicating per unit of task per-formance; see Fay, Arbib, & Garrod, 2013). Finally, this synchronized state within and between brains should be associated with cognitive, affective, and social outcomes of interest, such as higher performance, more positive affect, and a greater desire to perform the same sorts of tasks with the same sorts of individuals in the future. **APPLYING SYNC THEORY** The concept of flow has been widely utilized to illuminate the antecedents and consequences of flow experiences in a variety of contexts, including education (Shernoff, Csikszentmihalyi, Sch-neider, & Shernoff, 2014), business (Ghani & Deshpande, 1994; Salanova et al., 2006), art (MacDonald et al., 2006), athletics (Harris et al., 2017; Jackson et al., 1998; Stein et al., 1995), and many others. The clear physiological operationalization of flow employed in STF provides further specification as to how, when, and why the human brain enters these discrete periods of efficient, synchronized, goal-focused activity. This makes STF uniquely amenable to understanding flow states, but also for contributing to practical and humanitarian outcomes outside of the "ivory tower." In this section we will review three potentially fruitful applications of STF: (1) designing media that are challenging, engaging, and enjoyable; (2) creating innovative treatments for cognitive processing disorders, such as ADHD; and (3) understanding how and why teams are successful in fast-paced cooperative tasks. First, STF can be used to design media that are engaging and enjoyable while still being chal-lenging. Media that induce efficient synchronization of cognitive control and reward networks can be said to be more flow-inducing than media that do not elicit this physiological response pattern. Thus, physiological responses can be used to index flow states in situations when self-report measures may be undesirable due to their intrusiveness (as in a video game or other interactive media) or their lack of reliability. Media designers can use STF to construct pilot tests of films, video games, or other stimuli to understand which structural or content features elicit flow states reliably and which do not. This is especially salient for those designing educational multimedia (such as learning games or educational television shows) or public service announcements. These efforts are informed by the brain-as-predictor approach (Berkman & Falk, 2013), wherein brain activation patterns in a small group of individuals are used to predict real-world outcomes. Second, STF can be used to explore and develop novel treatments for cognitive and behavioral disorders, such as ADHD. Brain imaging approaches have provided notable insights into individual differences in the structure and function of human brain networks in health and disease (Braun, Muldoon, & Bassett, 2015). These insights allow for the development of brain-imaging-based approaches to cognitive and behavioral change that are personalized to the brains of those undergoing treatment (Gabriel, Ghosh, & Whitfield-Gabrieli, 2015), and provide a promising way forward for remedying the notable ineffectiveness of "brain training" games (Lindenberger, Wenger, & Lövdén, 2017). For example, flow states are characterized by high levels of synchronization, efficiency, and network allegiance in cognitive control networks. This synchronization is robust to distraction in a curvilinear fashion (Weber, Alice et al., 2018). Current work in our lab investigates how structural features of video games (e.g., perceptual load) can be used to elicit flow (and thus increased performance outcomes) in those with ADHD (Fisher, Hopp, & Weber, 2018). Individual differences in the robustness of this attentional network in the face of distraction can be used to inform and adapt treatments aimed at ameliorating symptoms of distractibility and impulsiveness observed in ADHD. Finally, STF-with the social components outlined in this manuscript can be used to predict and explain performance differences in pairs and small teams. If flow in team environments is characterized by a state of increased synchronization both within and between brains, this increased synchrony can be used to predict performance on complex tasks. Brain imaging and electrophysiological approaches are more cost-effective and widely available than in the past. Technologies such as functional near-infrared spectroscopy (fIRS; Ayaz et al., 2013) and electroencephalography (EEG; Cohen, 2017) allow for measurement of brain activity outside of an MRI scanner, allowing for measurement of interactive dynamics in a much more flexible fashion. These measures have also been shown to be useful for measurement of interpersonal synchrony (Cacioppo et al., 2014; Liu et al., 2017). Thus, STF would predict that optimal team performance would be associated with high synchrony both between and within brains. **CONCLUSION** By defining the neurophysiological substrates of flow experiences, STF moves beyond descriptive research and provides a way forward to investigate the "why" of flow experiences. In this manuscript, we have reviewed an upwelling of recent support for its core hypotheses driven by advances in network science and computational methodologies. We have also outlined five ways in which STF can be pushed forward into the next ten years of research: (1) redefinition of the core networks of cognitive control and reward; (2) incorporating an increased understanding of cognitive and neural individual differences; (3) updating core methodologies in light of advances in network modeling and dynamic approaches; (4) including a proper concep-tualization and operationalization of the social components of flow experiences; and (5) outlining a path toward conducting sync theory research on a broad scale. We also included a selection of applications of STF in the non-academic sphere: informing the design of challenging and engaging media messages, contributing to the development of more effective cognitive and behavioral treatments and training applications, and increasing practical understanding of how and why effective teams synchronize with one another to complete complex tasks. 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