International Journal of Psychophysiology 97 (2015) 1–7
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International Journal of Psychophysiology journal homepage: www.elsevier.com/locate/ijpsycho
Physiological correlates of the flow experience during computer game playing László Harmat a,⁎, Örjan de Manzano a, Töres Theorell a, Lennart Högman b, Håkan Fischer b, Fredrik Ullén a a b
Dept of Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden Dept of Psychology, Stockholm University, SE-106 91 Stockholm, Sweden
a r t i c l e
i n f o
Article history: Received 3 February 2015 Received in revised form 30 April 2015 Accepted 2 May 2015 Available online 6 May 2015 Keywords: Autonomous nervous system Effortless attention Heart rate variability Hypofrontality Functional near-infrared spectroscopy Psychological flow
a b s t r a c t Flow is the subjective experience of effortless attention, reduced self-awareness, and enjoyment that typically occurs during optimal task performance. Previous studies have suggested that flow may be associated with a non-reciprocal coactivation of the sympathetic and parasympathetic systems and, on a cortical level, with a state of hypofrontality and implicit processing. Here, we test these hypotheses, using the computer game TETRIS as model task. The participants (n = 77) played TETRIS under three conditions that differed in difficulty (Easy b Optimal b Difficult). Cardiac and respiratory activities, and the average oxygenation changes of the prefrontal cortex were measured continuously with functional near infrared spectroscopy (fNIRS) during performance. The Optimal condition was characterized by the highest levels of state flow, positive affect, and effortless attention. The associations between self-reported psychological flow and physiological measures were investigated using a series of repeated measures linear mixed model analyses. The results showed that higher flow was associated with larger respiratory depth and lower LF. The higher respiratory depth during high flow is indicative of a more relaxed state with an increased parasympathetic activity, and thus provides partial support for the main hypotheses. There was no association between frontal cortical oxygenation and flow, even at liberal thresholds; i.e. we found no support that flow is related to a state of hypofrontality. © 2015 Elsevier B.V. All rights reserved.
1. Introduction The psychological experience called flow can occur during the performance of challenging activities in which the difficulty of the task is matched to the skill level of the person (Csikszentmihalyi and Csikszentmihalyi, 1988). Characteristics of the flow experience include high but subjectively effortless attention, a sense of control, loss of self-awareness, and altered experience of time and enjoyment (Csikszentmihalyi and Nakamura, 2010). Flow has been studied in widely different activities ranging from e.g. ocean cruising (Macbeth, 1988), motorcycle riding (Sato, 1988) and sports (Jackson et al., 1998; Jackson et al., 2001) to computer gaming (Keller and Bless, 2008; Keller et al., 2011; Moller et al., 2010; Rheinberg and Vollmeyer, 2003), musical performance (de Manzano et al., 2010), and literary writing (Larson, 1988). Although these activities differ in the cognitive, emotional and sensorimotor skills required, the flow experience in itself appears remarkably similar across the tasks (Csikszentmihalyi and Nakamura, 2010). Several investigators have also reported positive associations between subjective flow and
⁎ Corresponding author at: Department of Neuroscience, Retzius väg 8, Karolinska Institute, SE-171 77 Stockholm, Sweden. Tel.: +46 8 52483268. E-mail address: [email protected] (L. Harmat).
http://dx.doi.org/10.1016/j.ijpsycho.2015.05.001 0167-8760/© 2015 Elsevier B.V. All rights reserved.
objective measures of performance (Jackson et al., 2001; Keller and Bless, 2008; Larson, 1988; Moller et al., 2010). In recent years, a number of studies have addressed the biological bases of both state flow and the trait proneness to experience flow in daily life (de Manzano et al., 2010, 2013; Keller et al., 2011; Kivikangas, 2006; Mosing et al., 2012; Peifer et al., 2014; Ullén et al., 2012; Ulrich et al., 2014). We have e.g. previously analyzed physiological correlates of state flow during piano performance in professional pianists (de Manzano et al., 2010). High flow performances, as compared to low flow performances of the same musical piece, were associated with higher heart rate and blood pressure. Firstly, this indicates that flow is linked to an increase in sympathetic nervous system activity. Secondly, higher flow was associated with an increased respiratory depth and increased activity in the smiling muscle m. zygomaticus major, suggesting that flow is also associated with relaxation and positive affect, i.e. possible increase in parasympathetic nervous system activity. A non-reciprocal increase of activity in both branches of the autonomic nervous system may indicate an increased parasympathetic modulation of sympathetic activity. This mode of functioning has been suggested to provide increased control of both response direction, as well as magnitude and fine tuning of target organ function (Berntson et al., 1991, 1997). Interestingly, this specific pattern of activity might also help explain in biological terms, why flow is typically described as a state of effortless attention (Ullén et al., 2010). Thus, we hypothesized
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that a non-reciprocal coactivation of sympathetic and parasympathetic systems could be used as a physiological marker of state flow, as it differentiates from the pattern typically associated with mental effort (Wientjes et al., 1998). Contrary to this hypothesis however, Keller et al. (2011) showed that a measure of vagus-mediated control (derived from heart rate variability) was inversely related to perceived involvement (used as an indicator of flow) during a computerized knowledge task. Thus, in this study we tested the above hypothesis by analyzing how cardiovascular and respiratory responses during trials of computer game playing (TETRIS) covaried with subjective ratings of flow after each trial. Difficulty (required speed of performance) was manipulated in three experimental conditions (Easy b Optimal b Difficult). As flow would be assumed show an inverted u-shaped relation to task demand, we sought to distinguish between measures linked primarily to one or the other of these variables. We additionally explored oxygenation changes of the prefrontal cortex, as measured with functional nearinfrared spectroscopy (fNIRS), as a potential marker for effortless attention. It has been suggested that flow is associated with reduced activity in prefrontal brain regions where activity typically increases with mental effort (Dietrich, 2004; Ullén et al., 2010). This latter hypothesis has recently found some support in a functional magnetic resonance imaging study by Ulrich et al. (2014), in which the experience of flow during mental arithmetic tasks was associated with relative decreases in neural activity in the medial prefrontal cortex and the amygdala. 2. Materials and methods 2.1. Participants Eighty individuals were recruited through posters at the Karolinska Institute and Stockholm University campuses. One participant was excluded because of a technical failure with the electrocardiogram and two further participants were excluded because of technical problems with the fNIRS. Four participants were excluded from the analysis of respiratory depth due to technical issues. Thus, 77 healthy, right-handed individuals (M = 27.8 ± 5.4 years old; 40 female) were included in the study. Thirty-five of these individuals performed the experiment while monitored with fNIRS. All experimental procedures were ethically approved by the Regional Ethical Review Board in Stockholm (Dnr 2012/198-32/4 and Dnr 2013/949-32) and undertaken with the understanding and written consent of each participant. Participation was reimbursed with one movie ticket voucher. 2.2. Experimental task The experimental task consisted of an adapted version of the popular computer game TETRIS. The original source code was retrieved from http://www.percederberg.net under the GNU General Public License and implemented on a conventional PC. In this game, geometrical objects called tetrominoes, consisting of four squares that are joined edge to edge in different configurations (Golomb, 1994), fall vertically from the top of the computer screen, one at a time. While a tetromino piece falls, the player can move it sideways and rotate it, using keys on the computer keyboard. The goal is to fit the pieces together so as to create complete horizontal rows of squares, which then disappear and earn the participant points. In the present implementation of the game, the speed of the falling pieces could be varied in 13 discrete steps, which corresponded to different levels of difficulty. In order to ensure a variation in psychological flow during the experiment, participants played TETRIS in three trials which differed in difficulty (Easy b Optimal b Difficult). In the Optimal condition, the initial speed was adapted to the skill level of each participant (see Experimental procedure). In this condition, the player could also use the down arrow key to instantly drop a falling tetromino, allowing for
further control and skill-challenge adjustment. Finally, in Optimal, the speed was adjusted during the game to match the participant's performance which was evaluated at every 30th piece (as in Keller and Bless, 2008). If the player had succeeded in creating five or more complete rows using the previous 30 pieces, the speed was increased by one step. If the player had only managed to create three or fewer complete rows, the speed was instead decreased by one step. In the Easy condition, the speed was set three steps below the participant's original optimal level; in the Difficult condition, the speed level was set to three steps above the original optimal level. In both Easy and Difficult, the speed remained constant throughout the condition, regardless of the participant's performance. 2.3. Physiological measures and instruments Physiological recordings were performed continuously during the three experimental conditions as well as during an initial baseline period (see Experimental procedure), using the BIOPAC MP 150 System (Biopac Systems, Inc., Santa Barbara, CA) with the AcqKnowledge 4.3 software. A sampling rate of 1000 Hz was used for all channels. For all measurements, we used the recommended standard filter settings for the BioNomadix systems. For the electrocardiogram, this included a high pass filter at 1 Hz and a low pass filter at 35 Hz. For respiration, we used a low pass filter at 1 Hz. The following measurements were performed: Cardiac activity. Cardiac activity was recorded with bipolar EL 504 Cloth Base Electrodes from the left and right chest, using an Electro Lead 3 × 30 cm (BN-EL30-LEAD3) connected to the BIOPAC BioNomadix RSP & ECG amplifier. The recorded electrocardiographic (ECG) data was imported to the Kubios Heart Rate Analysis Software 2.1 (University of Eastern Finland) to calculate R waves to R waves (R–R) intervals and associated variability (HRV) (Tarvainen et al., 2014). Generally, the quality of the recorded data was high, with little interference due to movement. Careful examination of the ECG and the tachogram ensured that the autonomic R-wave detection procedure had been performed correctly. Artifact removal for the HRV was performed manually using the artifact correction tool to detect R–R intervals “differing abnormally” from the local mean R–R interval provided by the Kubios HRV Analysis Software. When the corrections were applied, detected artifact beats were replaced using cubic spline interpolation (Tarvainen et al., 2014). Spectrum analysis was performed using FFT routine provided by the software. The power of the low frequency (LF; 0.04–0.15 Hz) and the high frequency components (HF; 0.15–0.40 Hz) of the HRV. Because of the skewed distribution of the HRV variables (LF and HF) these were log2 transformed before analyses. Heart rate (HR) was also calculated. Respiration. Chest and abdominal respiratory movements were recorded with a Bionomadix Respiration Transducer (BN-RESPXDCR) connected to the BioNomadix 2 channel RSP Amplifier. We measured respiratory rates (RR) in cycles per minutes (cpm) from the thoracic respiration. Measures of respiratory depth were obtained both from the thoracic (RDT) and the abdominal (RDA) respiration belts. Cortical oxygenation. Thirty-five of the participants were monitored with fNIRS. For these individuals, regional variation in the mean blood oxygenation of the frontal cortex were recorded during at a sample rate 2 Hz using the functional near infrared spectrometer fNIR100 (Biopac®) equipped with 16 optodes and acquisition software Cobi Studio® (Ayaz et al., 2011b). fNIRS uses light, introduced at the scalp, to measure changes in blood oxygenation as oxy-hemoglobin (HbO2) converts to deoxy-hemoglobin (HbR)
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during neural activity, i.e. the cerebral hemodynamic response (Ayaz et al., 2006, 2012) (Fig. 1). After collection, the data for each optode was firstly normalized and then the average oxygenation change (HbO2–HbR) was calculated over the conditions and used as dependent measure (Izzetoglu et al., 2004). For each participant, raw fNIRS data (16 optodes × 2 wavelengths) were low-pass filtered with a finite impulse response, linear phase filter with order 20 and cutoff frequency of 0.1 Hz to attenuate the high frequency noise, respiration and cardiac cycle effects (Ayaz et al., 2011b; Izzetoglu et al., 2005). Saturated channels, in which light intensity at the detector was higher than the analog-to-digital converter limit were excluded. Oxygenation changes for each optode were calculated separately using the Modified Beer Lambert Law (MBLL) for task periods with respect to a proceeding 5 s baseline period (Ayaz et al., 2011a,b). 2.4. Psychological measures The following three brief paper-and-pencil questionnaires reflecting different aspects of psychological state/subjective experience were administered after each trial: State flow. State flow (Flow) was measured using a subset of nine items from the Event Experience Scale (FSS-2) (Jackson and Eklund, 2004). Good psychometric properties of the FSS-2, as well as of the shorter English 9-item version of the test, similar to the one employed here, have been demonstrated in several studies on different samples (Jackson and Eklund, 2002, 2004; Jackson et al., 2008; Kawabata et al., 2008). Items are formulated as statements about subjective experiences during a previous performance (e.g., “I had total concentration.”), with which the respondent should agree or disagree. Answers are given on a Likert scale with nine steps ranging from 1 (strongly disagree) to 9 (strongly agree).
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Subjective concentration. Two items were included to measure selfestimated degree of concentration (Concentration; “How well were you concentrating?”) and attentional effort (Attentional effort; “Was it hard to concentrate?”). These items have been used in several earlier studies on flow experiences (e.g. Csikszentmihalyi and Larson, 1984; Csikszentmihalyi and Nakamura, 2010). Answers were given on a 9-step Likert-scale, ranging from 1 (not at all) to 9 (very much). Affective state. Affective state was measured using the affect grid, which is a quick single-item test designed to assess affect along the Valence (pleasure–displeasure) and Arousal (arousal–sleepiness) dimensions (Gray and Watson, 2007; Russell et al., 1989). Participants indicate their affective state in a two-dimensional grid of 9 × 9 squares, where the vertical axis represents arousal, ranging from 1 (very sleepy) to 9 (very aroused), and the horizontal axis represents valence, ranging from 1 (very unpleasant) to 9 (very pleasant). 2.5. Experimental procedure Participants were asked not to consume caffeine or alcoholic beverages during 24 h before the experiment. All experiments were carried out between 2 p.m. and 6 p.m. in the afternoon to minimize the effect of circadian variations on the physiological parameters. The participants were tested individually. After arrival at the laboratory, they were briefed on the experimental procedures and fitted with the electrodes and fNIR100 sensors. During the experiment the participant was sitting upright on a chair in front of a PC. The experiment started with a calibration phase to find the optimal TETRIS difficulty level (speed) for the participant. During this calibration phase, the participant played TETRIS for 6 min in the adaptive playing mode, i.e. a playing mode where the game speed increased or decreased depending on the performance of the participant. The final difficulty level of this calibration phase was used for the Optimal condition as well as to determine the difficulty levels of the Easy and Difficult conditions, which were set to three levels below and above the optimal level, respectively (see Experimental task). After the calibration, baseline physiological measurements (Rest condition) were recorded while the participant was sitting in silence for 4 min. The order of the three experimental conditions (Easy, Difficult, and Optimal) was counterbalanced between participants and the duration of each condition was 6 min. The duration was 4 min for the 35 subjects who were monitored with fNIRS, to avoid discomfort associated with wearing the sensors for a longer time. The state flow, subjective attention, and affective state questionnaires were completed after each condition. The entire experimental session was completed in around 40 min. 2.6. Statistical analysis
Fig. 1. Optode location. The approximate location of the 16 optodes of the fNIR sensor pad in relation to the brain is indicated schematically. RS = right side, LS = left side.
First, in order to investigate how the psychological state measures varied across experimental conditions we performed a series of general linear models, using the statistical software STATISTICA 12. The psychological variables (Flow, Concentration, Attentional effort, Arousal and Valence) were used as dependent variables and condition was used as a within-participant categorical variable with three levels (Easy, Optimal, and Difficult). Post-hoc analyses were performed using Tukey's HSD test. Considering that the sample could be divided into two groups, i.e. with (n = 35) and without (n = 42) fNIRS recordings, a categorical nuisance variable Group was introduced, along with Age and Sex. Second, the associations between self-reported psychological flow and physiological measures were investigated using a series of repeated measures linear mixed model analyses. Mixed model analyses were performed using the packages lme4 and lmerTest in R (Bates et al.,
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2014; Kuznetsova et al., 2014). Difference scores for the physiological variables were created by subtracting the baseline Rest measurement from Easy, Optimal and Difficult, respectively. A full model was constructed regressing Flow on the physiological measures HR, RR, RDT, RDA, LF, and HF. In addition, in order to control for differences in difficulty between conditions, an additional fixed effect Task demand was created as a continuous variable: The conditions were coded as 1, 2, and 3 (Easy b Optimal b Difficult), according to their relation in starting speed (see Experimental procedure). This was entered as nuisance covariate together with Age, Sex, and Group. ‘Subject’ was entered as a random effect in the model. After analyzing the full model we performed a backward elimination of non-significant effects (Kuznetsova et al., 2014) and compared goodness-of-fit between the reduced and full models using the Akaike and Bayesian information criteria (AIC and BIC). Third, the association between self-reported Flow and frontal cortical oxygenation was explored, also using repeated measures linear mixed effects modeling. The oxygenation measures were based on within-participant difference scores for each of the 16 optodes (Rest subtracted from Easy, Optimal, and Difficult) and analyzed separately. Age and Sex were entered as nuisance covariates and Subject as a random effect. These analyses were as previously, performed using the modeling packages lme4 and lmerTest in R (Bates et al., 2014; Kuznetsova et al., 2014). For exploratory purposes we also made additional analyses in which channels on the left side (LS) (channels 1–8) and on the right side (RS) (channels 9–16) were averaged and analyzed separately. We also estimated the laterality index ((LS − RS) / (LS + RS)) (Seghier, 2008). As a manipulation check, we further compared the mean cortical oxygenation across optodes during TETRISplaying to Rest in a general linear model where condition was used as a within-participant categorical variable with four levels (Rest, Easy, Optimal, and Difficult). Age and Sex were entered as covariates of no interest. Post-hoc testing was done with Tukey's HSD tests. The manipulation check was performed using the statistical software STATISTICA 12. 3. Results 3.1. Psychological variables Descriptive statistics of both psychological and physiological variables are provided in Table 1. The results from the analysis of differences in psychological state between conditions are summarized in Table 2 and in Fig. 2A–E. There were no conditions by Age/Sex/Group Table 1 Descriptive statistics for measured psychological and physiological variables in the different conditions. Values are means with standard deviations within parentheses. Measure
Psychological variables State flow – Concentration – Attentional effort – Arousal – Valence –
Variable
Main effect of condition
Post-hoc comparisons (p values)
F value
p value
E–O
E–D
O–D
Event Experience Scale (FSS-2) State flow 189.57
b0.0001
b0.0001
n.s.
b0.0001
Subjective attention Concentration Attentional effort
103.37 11.62
b0.0001 0.001
b0.0001 b0.0001
b0.0001 n.s.
n.s. 0.01
Affect grid Arousal Valence
169.62 48.87
b0.0001 b0.0001
b0.0001 b0.0001
b0.0001 n.s.
0.002 b0.0001
n.s. = not significant, E = Easy, O = Optimal, D = Difficult.
interactions for any of the psychological variables. Flow was significantly higher in Optimal than in Easy and Difficult (Fig. 2A). Valence showed a similar pattern (Fig. 2D). Attentional effort showed an opposite pattern to Valence and Flow, being equally high in Easy and Difficult and significantly lower in Optimal (Fig. 2E). Concentration did not differ between Optimal and Difficult, but was higher in these conditions than in Easy (Fig. 2B). Arousal increased significantly with difficulty level (Easy b Optimal b Difficult) (Fig. 2C). 3.2. Associations between flow and physiological measures We initially examined the full model where Flow was regressed on all physiological measures, i.e. HR, RR, RDT, RDA, LF, and HF, modeled as repeated measures fixed effects. In this model, the physiological measure associated with Flow was RDT (β = .20, CI = .04–.36, SE = .09, t = 2.31). We subsequently employed a backwards elimination procedure, which resulted in a final reduced model that in addition to RDT also included LF and Age as fixed effects, as well as Subject as an estimated random effect. Results from the reduced model showed significant relations between Flow and RDT (β = .20, CI = .04–.35, SE = .08, t = 2.51), as well as LF (β = − .26, CI = − .42 to − .11, SE = .08, t = − 3.28) and Age (β = − .19, CI = − .36 to − .02, SE = .09, t = − 2.21). Comparing the reduced model to the full model, the AIC was greater but the BIC smaller (560 vs. 543 and 580 vs. 585, respectively). None of the fNIRS analyses revealed associations between frontal cortical oxygenation and Flow, even uncorrected for multiple comparisons; i.e. prefrontal brain activity was not related to subjective ratings of flow. The manipulation check could nonetheless confirm a significant elevation in oxygenation during active conditions compared to Rest (p b 0.001) as revealed in Tukey's HSD test ( Fig. 3). 4. Discussion
Condition Rest
Table 2 Differences in psychological state across the three experimental conditions.
Easy
Optimal 6.12 (0.96) 5.71 (1.89) 4.32 (1.33) 4.32 (2.33) 5.49 (1.27)
6.98 (0.92) 7.35 (1.38) 2.94 (1.65) 6.19 (1.45) 6.45 (1.36)
Difficult 5.85 (1.25) 7.00 (1.38) 3.79 (2.09) 6.80 (1.29) 5.42 (1.48)
Physiological variables HR (bpm) 69.55 (10.43) 69.93 (10.86) 71.22 (11.23) 71.75 (10.91) 9.78 (1,39) 9.39 (1,14) 8.87 (1,15) 9.20 (1,11) LF (log2 s2/Hz) 8. 78 (1.62) 8. 42 (1.55) 8. 49 (1.55) HF (log2 s2/Hz) 9.16 (1,55) RR (cpm) 13.84 (3.4) 16.92 (2.85) 19.35 (2.56) 20.44 (2.8) RDT (V) 1.16 (0.71) 1.24 (0.79) 1.21 (0.73) 1.09 (0.66) RDA (V) 1.82 (1.06) 1.71 (1.21) 1.65 (1.09) 1.57 (1.04) OXY (μmolar) −0.61 (0.16) −0.0006 (0.07) 0.27 (0.09) 0.33 (0.10) HR = heart rate, LF = low frequency power of heart rate variability, HF = high frequency power of heart rate variability, RDT = thoracic respiratory depth, RDA = abdominal respiratory depth, OXY = normalized oxygenation changes.
4.1. Flow and autonomic nervous activity In this study we investigated physiological markers of flow using TETRIS playing as model behavior. An analysis of the psychological data supported that the experimental manipulation, i.e. varying the speed of falling tetrominoes, induced a variation in flow between conditions. The Optimal condition was characterized by the highest levels of state flow, positive affect, and effortless attention. The main purpose of the study was to test whether increases in selfreported psychological flow would be associated with a non-reciprocal coactivation of the sympathetic and parasympathetic branches of the autonomic nervous system. The results showed that higher flow was associated with larger respiratory depth and lower LF. The higher respiratory depth during high flow is indicative of a more relaxed state, possibly reflecting an increased parasympathetic activity, and thus provides partial support for the main hypotheses. These findings also
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Fig. 2. Psychological measures across the experimental conditions. Mean values (markers) and standard errors (whiskers) are shown for all variables for which there was a significant effect of condition. Statistical results are summarized in Table 2. (A) State flow, (B) concentration, (C) arousal, (D) affective valence, and (E) attentional effort. E = Easy, O = Optimal, D = Difficult.
Fig. 3. Average oxygenation changes of the prefrontal cortex. Mean values (markers) and standard errors (whiskers) are shown for each experimental condition and Rest. There was a significant effect of condition. R = Rest, E = Easy, O = Optimal, D = Difficult.
in part replicate those of our previous study on piano playing where we concluded that flow was associated with an increase of sympathetic activation in combination with deep respiration (de Manzano et al., 2010). Interestingly, this increased respiratory depth accompanying effortless attention during flow contrasts with the fast and shallow respiration commonly seen during mental effort (Wientjes, 1992). However, given that HF — our most direct measure of parasympathetic activation (Berntson et al., 1997) — was unrelated to flow, the findings taken together do not unambiguously support the main hypothesis. LF was not significantly related to flow in the full model, which could be due to a slight colinearity between LF and Task demand. Comparing the reduced model to the full model, the AIC was greater but the BIC smaller. This is likely because the BIC penalizes the models more for the number of estimated parameters than does the AIC. In earlier studies, it has commonly been assumed that LF power provides an index of cardiac sympathetic tone and that the ratio of LF to HF indicates sympathovagal balance (Malliani et al., 1991; Pagani et al., 1986). However, LF may represent both sympathetic and vagal influences (Berntson et al., 1997) and is also associated with blood pressure regulation via baroreceptor activity (Goldstein et al., 2011; Hjortskov et al., 2004; Kamath
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and Fallen, 1993; Schächinger et al., 2001). Few previous studies have investigated within-person associations between flow and LF, but to some extent in line with the present outcomes, Krygier et al. (2013) showed that LF (Traube–Hering–Mayer waves) was significantly reduced during meditation compared to rest after 10 days of Vipassana mindfulness meditation training. The reduction in LF during meditation was suggested to indicate deeper immersion in a flow-like state of attention and positive affect, an interpretation which might fit also in the present context. 4.2. Flow and the hypofrontality hypothesis Dietrich (2004) suggested that a neural substrate of the flow experience is a transient state of hypofrontality. This theory was based on the idea that the subjectively effortless performance during flow relies on automated skills that require little explicit control. However, the present fNIRS data do not support the hypothesis that flow during computer game playing is associated with decreased activity in frontal brain regions. While this is a negative finding, which should be interpreted carefully, we note that we did not observe any relation between flow and frontal activity, or laterality of frontal activity, even at very liberal statistical thresholds. This suggests that frontal deactivation is unlikely to be an essential generic mechanism for flow (Dietrich, 2004; Ullén et al., 2010). An alternative hypothesis could be that flow is more related to activity in deeper brain regions involved in emotional control and autonomous regulation than to frontal systems (de Manzano et al., 2013). Secondly, it appears plausible that the neural substrates of flow may vary depending on task. Computer games like TETRIS involve a high level of unpredictability and rapid online decision making. Presumably, even skilled performance on such tasks require certain explicit control and, accordingly, frontal brain activity. It is certainly possible that flow experiences during more predictable and automated tasks are accompanied by lower activity in executive cognitive systems. 4.3. Limitations and future directions Though we here find certain physiological measures as markers of psychological flow, it is somewhat difficult to relate these measures, i.e. respiratory depth and LF, to underlying physiological mechanisms. An interesting option for future studies is therefore to use measurements which constitute more unambiguous indices of specific physiologic processes. As an example, the ‘pre-ejection period’ might serve as a good index of sympathetic influences on the heart (Berntson et al., 2008). Further considerations might also include task specific moderations on the relationship between flow and physiology. As pointed out by Peifer et al. (2014), computer games which are played at high demand level may e.g. not lead to high arousal and stress in the physiological sense. A conceivable limitation in the present study is that we used a single baseline condition, rather than separate baselines between active conditions. However, given that the trial order was randomized between participants, carry-over effects should not have introduced a systematic confound in the analyses. 5. Conclusion The present analyses indicated that subjective flow was negatively associated with LF power and positively associated with respiratory depth. The higher respiratory depth during high flow is indicative of a more relaxed state with an increased parasympathetic activity, and thus provides partial support for the main hypotheses. There was no association between frontal cortical oxygenation and flow, even at liberal thresholds; i.e. we found no support that flow is related to a state of hypofrontality.
Acknowledgments This work was supported by the Sven and Dagmar Salén Foundation, the Swedish Scientific Council (521-2010-3195), the Freemasons in Sweden Foundation for Children's Welfare, and the Bank of Sweden Tercentenary Foundation (M11-0451:1). We are thankful to Miriam Mosing for comments on an earlier version of the manuscript and to Rita Almeida for the discussions on statistics.
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Peifer, C., Schulz, A., Schächinger, H., Baumann, N., Conny, H.A., 2014. The relation of flowexperience and physiological arousal under stress — can u shape it? J. Exp. Soc. Psychol. 53, 62–69. Rheinberg, F., Vollmeyer, R., 2003. Flow-erleben in einem computerspiel unter experimentell variierten bedingungen. Z. Psychol. 211, 161–170. Russell, J.A., Weiss, A., Mendelsohn, G.A., 1989. Affect grid: a single-item scale of pleasure and arousal. J. Pers. Soc. Psychol. 57 (3), 493–502. Sato, I., 1988. Bosozoku: flow in Japanese motorcycle gangs. In: Csikszentmihalyi, M., Csikszentmihalyi, I.S. (Eds.), Optimal Experience: Psychological Studies of Flow in Consciousness. Cambridge University Press, Cambridge, UK, pp. 92–117. Schächinger, H., Weinbacher, M., Kiss, A., Ritz, R., Langewitz, W., 2001. Cardiovascular indices of peripheral and central sympathetic activation. Psychosom. Med. 63, 788–796. Seghier, M.L., 2008. Laterality index in functional MRI: methodological issues. Magn. Reson. Imaging 26, 594–601. Tarvainen, M.P., Niskanen, J.P., Lipponen, J.A., Ranta-aho, P.O., Karjalainen, P.A., 2014. Kubios HRV — heart rate variability analysis software. Comput. Methods Programs Biomed. 113, 210–220. Ullén, F., de Manzano, Ö., Theorell, T., Harmat, L., 2010. The physiology of effortless attention: correlates of sate flow and flow proneness. In: Bruya, B. (Ed.), Effortless Attention. The MIT Press, Cambridge, MA, USA, pp. 205–218. Ullén, F., de Manzano, Ö., Almeida, R., Magnussson, P.K.E., Pedersen, N.L., Nakamura, J., Csikszentmihalyi, M., Madison, G., 2012. Proneness for psychological flow in everyday life: associations with personality and intelligence. Personal. Individ. Differ. 52, 167–172. Ulrich, M., Keller, J., Hoenig, K., Waller, C., Gron, G., 2014. Neural correlates of experimentally induced flow experiences. NeuroImage 86, 194–202. Wientjes, C.J., 1992. Respiration in psychophysiology: methods and applications. Biol. Psychol. 34 (2–3), 179–203. Wientjes, C.J., Grossman, P., Gaillard, A.W., 1998. Influence of drive and timing mechanisms on breathing pattern and ventilation during mental task performance. Biol. Psychol. 49, 53–70.
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International Journal of Psychophysiology journal homepage: www.elsevier.com/locate/ijpsycho
Physiological correlates of the flow experience during computer game playing László Harmat a,⁎, Örjan de Manzano a, Töres Theorell a, Lennart Högman b, Håkan Fischer b, Fredrik Ullén a a b
Dept of Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden Dept of Psychology, Stockholm University, SE-106 91 Stockholm, Sweden
a r t i c l e
i n f o
Article history: Received 3 February 2015 Received in revised form 30 April 2015 Accepted 2 May 2015 Available online 6 May 2015 Keywords: Autonomous nervous system Effortless attention Heart rate variability Hypofrontality Functional near-infrared spectroscopy Psychological flow
a b s t r a c t Flow is the subjective experience of effortless attention, reduced self-awareness, and enjoyment that typically occurs during optimal task performance. Previous studies have suggested that flow may be associated with a non-reciprocal coactivation of the sympathetic and parasympathetic systems and, on a cortical level, with a state of hypofrontality and implicit processing. Here, we test these hypotheses, using the computer game TETRIS as model task. The participants (n = 77) played TETRIS under three conditions that differed in difficulty (Easy b Optimal b Difficult). Cardiac and respiratory activities, and the average oxygenation changes of the prefrontal cortex were measured continuously with functional near infrared spectroscopy (fNIRS) during performance. The Optimal condition was characterized by the highest levels of state flow, positive affect, and effortless attention. The associations between self-reported psychological flow and physiological measures were investigated using a series of repeated measures linear mixed model analyses. The results showed that higher flow was associated with larger respiratory depth and lower LF. The higher respiratory depth during high flow is indicative of a more relaxed state with an increased parasympathetic activity, and thus provides partial support for the main hypotheses. There was no association between frontal cortical oxygenation and flow, even at liberal thresholds; i.e. we found no support that flow is related to a state of hypofrontality. © 2015 Elsevier B.V. All rights reserved.
1. Introduction The psychological experience called flow can occur during the performance of challenging activities in which the difficulty of the task is matched to the skill level of the person (Csikszentmihalyi and Csikszentmihalyi, 1988). Characteristics of the flow experience include high but subjectively effortless attention, a sense of control, loss of self-awareness, and altered experience of time and enjoyment (Csikszentmihalyi and Nakamura, 2010). Flow has been studied in widely different activities ranging from e.g. ocean cruising (Macbeth, 1988), motorcycle riding (Sato, 1988) and sports (Jackson et al., 1998; Jackson et al., 2001) to computer gaming (Keller and Bless, 2008; Keller et al., 2011; Moller et al., 2010; Rheinberg and Vollmeyer, 2003), musical performance (de Manzano et al., 2010), and literary writing (Larson, 1988). Although these activities differ in the cognitive, emotional and sensorimotor skills required, the flow experience in itself appears remarkably similar across the tasks (Csikszentmihalyi and Nakamura, 2010). Several investigators have also reported positive associations between subjective flow and
⁎ Corresponding author at: Department of Neuroscience, Retzius väg 8, Karolinska Institute, SE-171 77 Stockholm, Sweden. Tel.: +46 8 52483268. E-mail address: [email protected] (L. Harmat).
http://dx.doi.org/10.1016/j.ijpsycho.2015.05.001 0167-8760/© 2015 Elsevier B.V. All rights reserved.
objective measures of performance (Jackson et al., 2001; Keller and Bless, 2008; Larson, 1988; Moller et al., 2010). In recent years, a number of studies have addressed the biological bases of both state flow and the trait proneness to experience flow in daily life (de Manzano et al., 2010, 2013; Keller et al., 2011; Kivikangas, 2006; Mosing et al., 2012; Peifer et al., 2014; Ullén et al., 2012; Ulrich et al., 2014). We have e.g. previously analyzed physiological correlates of state flow during piano performance in professional pianists (de Manzano et al., 2010). High flow performances, as compared to low flow performances of the same musical piece, were associated with higher heart rate and blood pressure. Firstly, this indicates that flow is linked to an increase in sympathetic nervous system activity. Secondly, higher flow was associated with an increased respiratory depth and increased activity in the smiling muscle m. zygomaticus major, suggesting that flow is also associated with relaxation and positive affect, i.e. possible increase in parasympathetic nervous system activity. A non-reciprocal increase of activity in both branches of the autonomic nervous system may indicate an increased parasympathetic modulation of sympathetic activity. This mode of functioning has been suggested to provide increased control of both response direction, as well as magnitude and fine tuning of target organ function (Berntson et al., 1991, 1997). Interestingly, this specific pattern of activity might also help explain in biological terms, why flow is typically described as a state of effortless attention (Ullén et al., 2010). Thus, we hypothesized
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that a non-reciprocal coactivation of sympathetic and parasympathetic systems could be used as a physiological marker of state flow, as it differentiates from the pattern typically associated with mental effort (Wientjes et al., 1998). Contrary to this hypothesis however, Keller et al. (2011) showed that a measure of vagus-mediated control (derived from heart rate variability) was inversely related to perceived involvement (used as an indicator of flow) during a computerized knowledge task. Thus, in this study we tested the above hypothesis by analyzing how cardiovascular and respiratory responses during trials of computer game playing (TETRIS) covaried with subjective ratings of flow after each trial. Difficulty (required speed of performance) was manipulated in three experimental conditions (Easy b Optimal b Difficult). As flow would be assumed show an inverted u-shaped relation to task demand, we sought to distinguish between measures linked primarily to one or the other of these variables. We additionally explored oxygenation changes of the prefrontal cortex, as measured with functional nearinfrared spectroscopy (fNIRS), as a potential marker for effortless attention. It has been suggested that flow is associated with reduced activity in prefrontal brain regions where activity typically increases with mental effort (Dietrich, 2004; Ullén et al., 2010). This latter hypothesis has recently found some support in a functional magnetic resonance imaging study by Ulrich et al. (2014), in which the experience of flow during mental arithmetic tasks was associated with relative decreases in neural activity in the medial prefrontal cortex and the amygdala. 2. Materials and methods 2.1. Participants Eighty individuals were recruited through posters at the Karolinska Institute and Stockholm University campuses. One participant was excluded because of a technical failure with the electrocardiogram and two further participants were excluded because of technical problems with the fNIRS. Four participants were excluded from the analysis of respiratory depth due to technical issues. Thus, 77 healthy, right-handed individuals (M = 27.8 ± 5.4 years old; 40 female) were included in the study. Thirty-five of these individuals performed the experiment while monitored with fNIRS. All experimental procedures were ethically approved by the Regional Ethical Review Board in Stockholm (Dnr 2012/198-32/4 and Dnr 2013/949-32) and undertaken with the understanding and written consent of each participant. Participation was reimbursed with one movie ticket voucher. 2.2. Experimental task The experimental task consisted of an adapted version of the popular computer game TETRIS. The original source code was retrieved from http://www.percederberg.net under the GNU General Public License and implemented on a conventional PC. In this game, geometrical objects called tetrominoes, consisting of four squares that are joined edge to edge in different configurations (Golomb, 1994), fall vertically from the top of the computer screen, one at a time. While a tetromino piece falls, the player can move it sideways and rotate it, using keys on the computer keyboard. The goal is to fit the pieces together so as to create complete horizontal rows of squares, which then disappear and earn the participant points. In the present implementation of the game, the speed of the falling pieces could be varied in 13 discrete steps, which corresponded to different levels of difficulty. In order to ensure a variation in psychological flow during the experiment, participants played TETRIS in three trials which differed in difficulty (Easy b Optimal b Difficult). In the Optimal condition, the initial speed was adapted to the skill level of each participant (see Experimental procedure). In this condition, the player could also use the down arrow key to instantly drop a falling tetromino, allowing for
further control and skill-challenge adjustment. Finally, in Optimal, the speed was adjusted during the game to match the participant's performance which was evaluated at every 30th piece (as in Keller and Bless, 2008). If the player had succeeded in creating five or more complete rows using the previous 30 pieces, the speed was increased by one step. If the player had only managed to create three or fewer complete rows, the speed was instead decreased by one step. In the Easy condition, the speed was set three steps below the participant's original optimal level; in the Difficult condition, the speed level was set to three steps above the original optimal level. In both Easy and Difficult, the speed remained constant throughout the condition, regardless of the participant's performance. 2.3. Physiological measures and instruments Physiological recordings were performed continuously during the three experimental conditions as well as during an initial baseline period (see Experimental procedure), using the BIOPAC MP 150 System (Biopac Systems, Inc., Santa Barbara, CA) with the AcqKnowledge 4.3 software. A sampling rate of 1000 Hz was used for all channels. For all measurements, we used the recommended standard filter settings for the BioNomadix systems. For the electrocardiogram, this included a high pass filter at 1 Hz and a low pass filter at 35 Hz. For respiration, we used a low pass filter at 1 Hz. The following measurements were performed: Cardiac activity. Cardiac activity was recorded with bipolar EL 504 Cloth Base Electrodes from the left and right chest, using an Electro Lead 3 × 30 cm (BN-EL30-LEAD3) connected to the BIOPAC BioNomadix RSP & ECG amplifier. The recorded electrocardiographic (ECG) data was imported to the Kubios Heart Rate Analysis Software 2.1 (University of Eastern Finland) to calculate R waves to R waves (R–R) intervals and associated variability (HRV) (Tarvainen et al., 2014). Generally, the quality of the recorded data was high, with little interference due to movement. Careful examination of the ECG and the tachogram ensured that the autonomic R-wave detection procedure had been performed correctly. Artifact removal for the HRV was performed manually using the artifact correction tool to detect R–R intervals “differing abnormally” from the local mean R–R interval provided by the Kubios HRV Analysis Software. When the corrections were applied, detected artifact beats were replaced using cubic spline interpolation (Tarvainen et al., 2014). Spectrum analysis was performed using FFT routine provided by the software. The power of the low frequency (LF; 0.04–0.15 Hz) and the high frequency components (HF; 0.15–0.40 Hz) of the HRV. Because of the skewed distribution of the HRV variables (LF and HF) these were log2 transformed before analyses. Heart rate (HR) was also calculated. Respiration. Chest and abdominal respiratory movements were recorded with a Bionomadix Respiration Transducer (BN-RESPXDCR) connected to the BioNomadix 2 channel RSP Amplifier. We measured respiratory rates (RR) in cycles per minutes (cpm) from the thoracic respiration. Measures of respiratory depth were obtained both from the thoracic (RDT) and the abdominal (RDA) respiration belts. Cortical oxygenation. Thirty-five of the participants were monitored with fNIRS. For these individuals, regional variation in the mean blood oxygenation of the frontal cortex were recorded during at a sample rate 2 Hz using the functional near infrared spectrometer fNIR100 (Biopac®) equipped with 16 optodes and acquisition software Cobi Studio® (Ayaz et al., 2011b). fNIRS uses light, introduced at the scalp, to measure changes in blood oxygenation as oxy-hemoglobin (HbO2) converts to deoxy-hemoglobin (HbR)
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during neural activity, i.e. the cerebral hemodynamic response (Ayaz et al., 2006, 2012) (Fig. 1). After collection, the data for each optode was firstly normalized and then the average oxygenation change (HbO2–HbR) was calculated over the conditions and used as dependent measure (Izzetoglu et al., 2004). For each participant, raw fNIRS data (16 optodes × 2 wavelengths) were low-pass filtered with a finite impulse response, linear phase filter with order 20 and cutoff frequency of 0.1 Hz to attenuate the high frequency noise, respiration and cardiac cycle effects (Ayaz et al., 2011b; Izzetoglu et al., 2005). Saturated channels, in which light intensity at the detector was higher than the analog-to-digital converter limit were excluded. Oxygenation changes for each optode were calculated separately using the Modified Beer Lambert Law (MBLL) for task periods with respect to a proceeding 5 s baseline period (Ayaz et al., 2011a,b). 2.4. Psychological measures The following three brief paper-and-pencil questionnaires reflecting different aspects of psychological state/subjective experience were administered after each trial: State flow. State flow (Flow) was measured using a subset of nine items from the Event Experience Scale (FSS-2) (Jackson and Eklund, 2004). Good psychometric properties of the FSS-2, as well as of the shorter English 9-item version of the test, similar to the one employed here, have been demonstrated in several studies on different samples (Jackson and Eklund, 2002, 2004; Jackson et al., 2008; Kawabata et al., 2008). Items are formulated as statements about subjective experiences during a previous performance (e.g., “I had total concentration.”), with which the respondent should agree or disagree. Answers are given on a Likert scale with nine steps ranging from 1 (strongly disagree) to 9 (strongly agree).
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Subjective concentration. Two items were included to measure selfestimated degree of concentration (Concentration; “How well were you concentrating?”) and attentional effort (Attentional effort; “Was it hard to concentrate?”). These items have been used in several earlier studies on flow experiences (e.g. Csikszentmihalyi and Larson, 1984; Csikszentmihalyi and Nakamura, 2010). Answers were given on a 9-step Likert-scale, ranging from 1 (not at all) to 9 (very much). Affective state. Affective state was measured using the affect grid, which is a quick single-item test designed to assess affect along the Valence (pleasure–displeasure) and Arousal (arousal–sleepiness) dimensions (Gray and Watson, 2007; Russell et al., 1989). Participants indicate their affective state in a two-dimensional grid of 9 × 9 squares, where the vertical axis represents arousal, ranging from 1 (very sleepy) to 9 (very aroused), and the horizontal axis represents valence, ranging from 1 (very unpleasant) to 9 (very pleasant). 2.5. Experimental procedure Participants were asked not to consume caffeine or alcoholic beverages during 24 h before the experiment. All experiments were carried out between 2 p.m. and 6 p.m. in the afternoon to minimize the effect of circadian variations on the physiological parameters. The participants were tested individually. After arrival at the laboratory, they were briefed on the experimental procedures and fitted with the electrodes and fNIR100 sensors. During the experiment the participant was sitting upright on a chair in front of a PC. The experiment started with a calibration phase to find the optimal TETRIS difficulty level (speed) for the participant. During this calibration phase, the participant played TETRIS for 6 min in the adaptive playing mode, i.e. a playing mode where the game speed increased or decreased depending on the performance of the participant. The final difficulty level of this calibration phase was used for the Optimal condition as well as to determine the difficulty levels of the Easy and Difficult conditions, which were set to three levels below and above the optimal level, respectively (see Experimental task). After the calibration, baseline physiological measurements (Rest condition) were recorded while the participant was sitting in silence for 4 min. The order of the three experimental conditions (Easy, Difficult, and Optimal) was counterbalanced between participants and the duration of each condition was 6 min. The duration was 4 min for the 35 subjects who were monitored with fNIRS, to avoid discomfort associated with wearing the sensors for a longer time. The state flow, subjective attention, and affective state questionnaires were completed after each condition. The entire experimental session was completed in around 40 min. 2.6. Statistical analysis
Fig. 1. Optode location. The approximate location of the 16 optodes of the fNIR sensor pad in relation to the brain is indicated schematically. RS = right side, LS = left side.
First, in order to investigate how the psychological state measures varied across experimental conditions we performed a series of general linear models, using the statistical software STATISTICA 12. The psychological variables (Flow, Concentration, Attentional effort, Arousal and Valence) were used as dependent variables and condition was used as a within-participant categorical variable with three levels (Easy, Optimal, and Difficult). Post-hoc analyses were performed using Tukey's HSD test. Considering that the sample could be divided into two groups, i.e. with (n = 35) and without (n = 42) fNIRS recordings, a categorical nuisance variable Group was introduced, along with Age and Sex. Second, the associations between self-reported psychological flow and physiological measures were investigated using a series of repeated measures linear mixed model analyses. Mixed model analyses were performed using the packages lme4 and lmerTest in R (Bates et al.,
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2014; Kuznetsova et al., 2014). Difference scores for the physiological variables were created by subtracting the baseline Rest measurement from Easy, Optimal and Difficult, respectively. A full model was constructed regressing Flow on the physiological measures HR, RR, RDT, RDA, LF, and HF. In addition, in order to control for differences in difficulty between conditions, an additional fixed effect Task demand was created as a continuous variable: The conditions were coded as 1, 2, and 3 (Easy b Optimal b Difficult), according to their relation in starting speed (see Experimental procedure). This was entered as nuisance covariate together with Age, Sex, and Group. ‘Subject’ was entered as a random effect in the model. After analyzing the full model we performed a backward elimination of non-significant effects (Kuznetsova et al., 2014) and compared goodness-of-fit between the reduced and full models using the Akaike and Bayesian information criteria (AIC and BIC). Third, the association between self-reported Flow and frontal cortical oxygenation was explored, also using repeated measures linear mixed effects modeling. The oxygenation measures were based on within-participant difference scores for each of the 16 optodes (Rest subtracted from Easy, Optimal, and Difficult) and analyzed separately. Age and Sex were entered as nuisance covariates and Subject as a random effect. These analyses were as previously, performed using the modeling packages lme4 and lmerTest in R (Bates et al., 2014; Kuznetsova et al., 2014). For exploratory purposes we also made additional analyses in which channels on the left side (LS) (channels 1–8) and on the right side (RS) (channels 9–16) were averaged and analyzed separately. We also estimated the laterality index ((LS − RS) / (LS + RS)) (Seghier, 2008). As a manipulation check, we further compared the mean cortical oxygenation across optodes during TETRISplaying to Rest in a general linear model where condition was used as a within-participant categorical variable with four levels (Rest, Easy, Optimal, and Difficult). Age and Sex were entered as covariates of no interest. Post-hoc testing was done with Tukey's HSD tests. The manipulation check was performed using the statistical software STATISTICA 12. 3. Results 3.1. Psychological variables Descriptive statistics of both psychological and physiological variables are provided in Table 1. The results from the analysis of differences in psychological state between conditions are summarized in Table 2 and in Fig. 2A–E. There were no conditions by Age/Sex/Group Table 1 Descriptive statistics for measured psychological and physiological variables in the different conditions. Values are means with standard deviations within parentheses. Measure
Psychological variables State flow – Concentration – Attentional effort – Arousal – Valence –
Variable
Main effect of condition
Post-hoc comparisons (p values)
F value
p value
E–O
E–D
O–D
Event Experience Scale (FSS-2) State flow 189.57
b0.0001
b0.0001
n.s.
b0.0001
Subjective attention Concentration Attentional effort
103.37 11.62
b0.0001 0.001
b0.0001 b0.0001
b0.0001 n.s.
n.s. 0.01
Affect grid Arousal Valence
169.62 48.87
b0.0001 b0.0001
b0.0001 b0.0001
b0.0001 n.s.
0.002 b0.0001
n.s. = not significant, E = Easy, O = Optimal, D = Difficult.
interactions for any of the psychological variables. Flow was significantly higher in Optimal than in Easy and Difficult (Fig. 2A). Valence showed a similar pattern (Fig. 2D). Attentional effort showed an opposite pattern to Valence and Flow, being equally high in Easy and Difficult and significantly lower in Optimal (Fig. 2E). Concentration did not differ between Optimal and Difficult, but was higher in these conditions than in Easy (Fig. 2B). Arousal increased significantly with difficulty level (Easy b Optimal b Difficult) (Fig. 2C). 3.2. Associations between flow and physiological measures We initially examined the full model where Flow was regressed on all physiological measures, i.e. HR, RR, RDT, RDA, LF, and HF, modeled as repeated measures fixed effects. In this model, the physiological measure associated with Flow was RDT (β = .20, CI = .04–.36, SE = .09, t = 2.31). We subsequently employed a backwards elimination procedure, which resulted in a final reduced model that in addition to RDT also included LF and Age as fixed effects, as well as Subject as an estimated random effect. Results from the reduced model showed significant relations between Flow and RDT (β = .20, CI = .04–.35, SE = .08, t = 2.51), as well as LF (β = − .26, CI = − .42 to − .11, SE = .08, t = − 3.28) and Age (β = − .19, CI = − .36 to − .02, SE = .09, t = − 2.21). Comparing the reduced model to the full model, the AIC was greater but the BIC smaller (560 vs. 543 and 580 vs. 585, respectively). None of the fNIRS analyses revealed associations between frontal cortical oxygenation and Flow, even uncorrected for multiple comparisons; i.e. prefrontal brain activity was not related to subjective ratings of flow. The manipulation check could nonetheless confirm a significant elevation in oxygenation during active conditions compared to Rest (p b 0.001) as revealed in Tukey's HSD test ( Fig. 3). 4. Discussion
Condition Rest
Table 2 Differences in psychological state across the three experimental conditions.
Easy
Optimal 6.12 (0.96) 5.71 (1.89) 4.32 (1.33) 4.32 (2.33) 5.49 (1.27)
6.98 (0.92) 7.35 (1.38) 2.94 (1.65) 6.19 (1.45) 6.45 (1.36)
Difficult 5.85 (1.25) 7.00 (1.38) 3.79 (2.09) 6.80 (1.29) 5.42 (1.48)
Physiological variables HR (bpm) 69.55 (10.43) 69.93 (10.86) 71.22 (11.23) 71.75 (10.91) 9.78 (1,39) 9.39 (1,14) 8.87 (1,15) 9.20 (1,11) LF (log2 s2/Hz) 8. 78 (1.62) 8. 42 (1.55) 8. 49 (1.55) HF (log2 s2/Hz) 9.16 (1,55) RR (cpm) 13.84 (3.4) 16.92 (2.85) 19.35 (2.56) 20.44 (2.8) RDT (V) 1.16 (0.71) 1.24 (0.79) 1.21 (0.73) 1.09 (0.66) RDA (V) 1.82 (1.06) 1.71 (1.21) 1.65 (1.09) 1.57 (1.04) OXY (μmolar) −0.61 (0.16) −0.0006 (0.07) 0.27 (0.09) 0.33 (0.10) HR = heart rate, LF = low frequency power of heart rate variability, HF = high frequency power of heart rate variability, RDT = thoracic respiratory depth, RDA = abdominal respiratory depth, OXY = normalized oxygenation changes.
4.1. Flow and autonomic nervous activity In this study we investigated physiological markers of flow using TETRIS playing as model behavior. An analysis of the psychological data supported that the experimental manipulation, i.e. varying the speed of falling tetrominoes, induced a variation in flow between conditions. The Optimal condition was characterized by the highest levels of state flow, positive affect, and effortless attention. The main purpose of the study was to test whether increases in selfreported psychological flow would be associated with a non-reciprocal coactivation of the sympathetic and parasympathetic branches of the autonomic nervous system. The results showed that higher flow was associated with larger respiratory depth and lower LF. The higher respiratory depth during high flow is indicative of a more relaxed state, possibly reflecting an increased parasympathetic activity, and thus provides partial support for the main hypotheses. These findings also
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Fig. 2. Psychological measures across the experimental conditions. Mean values (markers) and standard errors (whiskers) are shown for all variables for which there was a significant effect of condition. Statistical results are summarized in Table 2. (A) State flow, (B) concentration, (C) arousal, (D) affective valence, and (E) attentional effort. E = Easy, O = Optimal, D = Difficult.
Fig. 3. Average oxygenation changes of the prefrontal cortex. Mean values (markers) and standard errors (whiskers) are shown for each experimental condition and Rest. There was a significant effect of condition. R = Rest, E = Easy, O = Optimal, D = Difficult.
in part replicate those of our previous study on piano playing where we concluded that flow was associated with an increase of sympathetic activation in combination with deep respiration (de Manzano et al., 2010). Interestingly, this increased respiratory depth accompanying effortless attention during flow contrasts with the fast and shallow respiration commonly seen during mental effort (Wientjes, 1992). However, given that HF — our most direct measure of parasympathetic activation (Berntson et al., 1997) — was unrelated to flow, the findings taken together do not unambiguously support the main hypothesis. LF was not significantly related to flow in the full model, which could be due to a slight colinearity between LF and Task demand. Comparing the reduced model to the full model, the AIC was greater but the BIC smaller. This is likely because the BIC penalizes the models more for the number of estimated parameters than does the AIC. In earlier studies, it has commonly been assumed that LF power provides an index of cardiac sympathetic tone and that the ratio of LF to HF indicates sympathovagal balance (Malliani et al., 1991; Pagani et al., 1986). However, LF may represent both sympathetic and vagal influences (Berntson et al., 1997) and is also associated with blood pressure regulation via baroreceptor activity (Goldstein et al., 2011; Hjortskov et al., 2004; Kamath
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and Fallen, 1993; Schächinger et al., 2001). Few previous studies have investigated within-person associations between flow and LF, but to some extent in line with the present outcomes, Krygier et al. (2013) showed that LF (Traube–Hering–Mayer waves) was significantly reduced during meditation compared to rest after 10 days of Vipassana mindfulness meditation training. The reduction in LF during meditation was suggested to indicate deeper immersion in a flow-like state of attention and positive affect, an interpretation which might fit also in the present context. 4.2. Flow and the hypofrontality hypothesis Dietrich (2004) suggested that a neural substrate of the flow experience is a transient state of hypofrontality. This theory was based on the idea that the subjectively effortless performance during flow relies on automated skills that require little explicit control. However, the present fNIRS data do not support the hypothesis that flow during computer game playing is associated with decreased activity in frontal brain regions. While this is a negative finding, which should be interpreted carefully, we note that we did not observe any relation between flow and frontal activity, or laterality of frontal activity, even at very liberal statistical thresholds. This suggests that frontal deactivation is unlikely to be an essential generic mechanism for flow (Dietrich, 2004; Ullén et al., 2010). An alternative hypothesis could be that flow is more related to activity in deeper brain regions involved in emotional control and autonomous regulation than to frontal systems (de Manzano et al., 2013). Secondly, it appears plausible that the neural substrates of flow may vary depending on task. Computer games like TETRIS involve a high level of unpredictability and rapid online decision making. Presumably, even skilled performance on such tasks require certain explicit control and, accordingly, frontal brain activity. It is certainly possible that flow experiences during more predictable and automated tasks are accompanied by lower activity in executive cognitive systems. 4.3. Limitations and future directions Though we here find certain physiological measures as markers of psychological flow, it is somewhat difficult to relate these measures, i.e. respiratory depth and LF, to underlying physiological mechanisms. An interesting option for future studies is therefore to use measurements which constitute more unambiguous indices of specific physiologic processes. As an example, the ‘pre-ejection period’ might serve as a good index of sympathetic influences on the heart (Berntson et al., 2008). Further considerations might also include task specific moderations on the relationship between flow and physiology. As pointed out by Peifer et al. (2014), computer games which are played at high demand level may e.g. not lead to high arousal and stress in the physiological sense. A conceivable limitation in the present study is that we used a single baseline condition, rather than separate baselines between active conditions. However, given that the trial order was randomized between participants, carry-over effects should not have introduced a systematic confound in the analyses. 5. Conclusion The present analyses indicated that subjective flow was negatively associated with LF power and positively associated with respiratory depth. The higher respiratory depth during high flow is indicative of a more relaxed state with an increased parasympathetic activity, and thus provides partial support for the main hypotheses. There was no association between frontal cortical oxygenation and flow, even at liberal thresholds; i.e. we found no support that flow is related to a state of hypofrontality.
Acknowledgments This work was supported by the Sven and Dagmar Salén Foundation, the Swedish Scientific Council (521-2010-3195), the Freemasons in Sweden Foundation for Children's Welfare, and the Bank of Sweden Tercentenary Foundation (M11-0451:1). We are thankful to Miriam Mosing for comments on an earlier version of the manuscript and to Rita Almeida for the discussions on statistics.
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