Keywords

1 Introduction

Gaming has become a realizable form of entertainment for players of all ages and background [1]. Video games can evoke strong feelings due to their simulated environments, and such feelings are characterized by activations of the temporal, parietal, and dorsolateral areas [2,3,4,5]. Some studies have linked brain activities to video game play. An example of such studies is the use of a content-based event-related analysis for investigating the neural correlates of circumscribed gameplay events [6]. In this review paper, we identify the neural and psychophysiological correlates of the state of flow.

Flow is an optimal experience where people are so involved in an activity that they lose track of time and nothing else seems to matter. It is a subjective experience of effortless attention, reduced self-awareness, and enjoyment that typically occurs during optimal task performance [7]. Flow can occur when the challenge posed by a task matches the skill of the individual carrying out the task [7,8,9]. However, it is still not known what exactly happens in the brain when the flow state is achieved.

The human brain acts as a duty director for the human nervous system. It is composed of many interconnected neurons that form a complex system, from which thought, behavior, and creativity emerge. It receives information from the sensory organs and delivers output to the muscles. The cerebrum is largely responsible for the execution of cognitive functions. The frontal lobes of the cerebrum are responsible for executive control actions [10]. The frontal lobes are a large brain region representing 30% of the cortical surface. Brain activities can differ from one state to another; EEG is a technique/tool that can be used to record such activities using waveforms [11]. Research has shown that during the state of flow, the cortical activity is reduced [12].

2 Literature Review

Flow represents a psychological state where people are completely absorbed or engaged in an activity and are performing on the edge of their ability [13]. Flow has been conceptualized to comprise the following nine components [7]:

  1. 1.

    Balance of challenge and skill: A key aspect of the state of flow is that the skill of the individual and the challenge of the activity need to be in balance with each other. If the challenge is less than the skill, boredom occurs. If the challenge is substantially higher than the skill, anxiety can arise.

  2. 2.

    Clear goals: The goals/objectives of the task or activity must be clear and unambiguous.

  3. 3.

    Immediate feedback: The performance feedback on the task or activity should be clear, immediate, and unambiguous.

  4. 4.

    Paradox of control: The individual perceives control of his/her actions and the environment.

  5. 5.

    Loss of self-consciousness: Because of the pre-occupied activity, the individual “loses” oneself and experiences a sense of separation from the world around him/her.

  6. 6.

    Concentration on task at hand: The individual focuses or pays complete attention on the task or activity, such that all other distractions are blocked from his/her awareness.

  7. 7.

    Transformation of time: Time no longer seems to pass the way it normally does. The individual loses track of time and the perception of time is distorted.

  8. 8.

    Merging of action and awareness: The individual is so involved in the activity that his/her actions become spontaneous, just like automatic responses.

  9. 9.

    Autotelic nature: The activity that consumes the individual is intrinsically rewarding and motivating to him/her.

The relationship between skill and challenge lays the foundation for the psychological state or concept of flow [14]. An opportunity to perform an action is considered as challenge, and the capability to perform that action is known as skill. In the state of flow, attention is effortless [9]. When one is in flow, “one is given over to the activity so thoroughly that action and attention seem effortless” [15, p. 1].

3 Neuropsychophysiological Correlates of Flow

This review focuses on synthesizing the literature on neuropsychophysiological correlates of flow. The following databases have been utilized for identifying relevant published articles: ACM Digital Library, Scopus, PsycINFO, ABI/Inform, and IEEE. We have used various combinations of search keywords: ‘neural’, ‘physiological’, ‘psychophysiological’, ‘correlates’, ‘flow’, ‘brain activity’, ‘cortex activity’, ‘gaming’, ‘electroencephalogram (EEG)’, ‘brain imaging’. The set of articles included in the review is comprised of a combination of conceptual and empirical papers. The findings are presented in Table 1.

Table 1. Summary of findings
Full size table

The nervous system is the core component of the brain, and neurons are the core components of the nervous system. Neurons operate on electrical impulses and chemical signals [12]. The human brain can be generally divided into three parts: (1) Forebrain (2) Midbrain (3) Hindbrain [10]. The cerebrum is responsible for most cognitive functions. The cerebrum, or cortex, is channeled into four lobes: frontal, parietal, occipital, and temporal. Each lobe has its specific predefined set of functions to perform. The functions of the lobes are: frontal – planning, motor/physical movement, emotion, problem solving; parietal – perception of stimuli, associated with movement and recognition; occipital – visual processing; temporal – memory, speech, perception and recognition of auditory stimuli. The largest portion of the brain is the cerebrum, which is widely channeled into two parts, the left and right hemispheres [10].

The frontal lobe functions are related to central executive processes [31]. Some research studies have shown that executive actions do not depend on frontal cortical activation but rely on the frontal-parietal network [32]. Prefrontal Cortex (PFC) in the front lobe of the brain is responsible for the execution of cognitive functions [32]. Most of the user-game engagement brain activities occur in Dorsolateral Prefrontal Cortex (DLPFC), which is the prefrontal cortex of the brain [26]. Previous research has shown that DLPFC is responsible for executive functions such as cognitive flexibility, planning, inhibition, and reasoning [32,33,34]. Medial prefrontal cortex (mPFC) plays a role in the integration of emotional and cognitive processes by incorporating emotional biasing signals or markers into the decision-making process [18]. EEG recordings are converted into spectral band frequencies (delta, theta, beta, alpha, and gamma) [35].

The alpha, low-beta, and mid-beta bands show greatest differences between three states of user experience, namely, flow, boredom, and anxiety. The frontal and parietal regions have low activity when one is in flow [28]. In addition, the alpha band is positively correlated with flow, whereas the beta band is negatively correlated with flow. The theta oscillations from the left side of the DLPFC can explain engagement [29].

EEG oscillations resemble the synchronized activities in the neuronal population in the brain [36]. These oscillations represent a subset of the brain’s electrical activities at a point in time. We can record these activities on the surface of the scalp with the help of electrodes (EEG headset). Many neurons will need to be synchronously active to detect oscillations of an activity at the scalp level [35]. Researchers have estimated that tens of thousands of synchronously activated pyramidal cortical neurons are involved for an EEG oscillation to emerge [37, 38]. In EEG research, the oscillations are classified into low-frequency oscillations and high-frequency oscillations. Delta and theta waves are considered low frequency, whereas beta, alpha, and gamma waves are considered high frequency.

Delta waves (1–4 Hz) are primarily associated with deep sleep (sometimes coma) but can also be present in the waking state. The power of the theta oscillations increases when information is retrieved from working memory [39, 40] because theta is related to memory performance [33, 41]. Theta waves (4–8 Hz) are associated with creative inspiration and deep meditation, and they arise when consciousness slips. An increase in game engagement implies a reduction in the density of theta oscillations [42].

Alpha (8–12 Hz) usually appears over the occipital region (posterior regions). Alpha represents relaxed awareness. Alpha waves are produced with the eyes closed and they signal a scanning or waiting pattern [43]. Alpha waves are reduced or eliminated by opening the eyes, by hearing unfamiliar sounds, or by anxiety [44]. Alpha oscillations are also considered indicators of the notion of happiness. Alpha oscillations increase with focus or concentration [45]. The upper alpha oscillations are associated mainly with long-term memory processes [12]. For the upper alpha band, the connectivity decreases in a condition with higher executive demands [46].

Beta (12–30 Hz) primarily deals with active thinking, active attention, focus on the outside world, and solving concrete problems. A very high beta represents a person’s panic state, and is found in frontal and central regions. Gamma (30–32 Hz) represent arousal. The rate of gamma wave occurrence is very low [35].

In summary, brain activity in the cortical regions, especially in frontal and parietal lobes, decreases during the state of flow. The alpha band is positively correlated with flow, the beta band tends to be negatively correlated with flow, and the theta oscillations from the left side of the DLPFC correlate with flow.

4 Conclusion and Future Research

In our ongoing and future research, we are interested to assess the neuropsychophysiological correlates of different states of user experience including flow, boredom, and anxiety, as well as the degree to which user states can be explained by or assessed using their neuropsychophysiological correlates. We plan to use time frequency decomposition and machine learning techniques to address the above questions. One of the main goals of our research is to improve the assessment and evaluation of user experience in human-computer interaction research. We are also interested to utilize both the questionnaire approach and the neuropsychophysiological correlates of flow to test and assess a theoretical framework for flow in gaming developed by Nah and her colleagues [47]. Another implication of this research is to contribute to improving brain-computer interfaces of smart devices by having computers respond to users based on the users’ states of experience.