Submitted 7 February 2018
Accepted 6 March 2019
Published 26 April 2019
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2019 Chen et al.
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Impact of emotional and motivational
regulation on putting performance: a
frontal alpha asymmetry study
Tai-Ting Chen1, Kuo-Pin Wang1, Ming-Yang Cheng2, Yi-Ting Chang1,
Chung-Ju Huang3and Tsung-Min Hung4
1Departement of Physical Education, National Taiwan Normal University, Taipei, Taiwan
2Faculty of Psychology and Sport Science, Universität Bielefeld, Bielefeld, Germany
3Graduate Institute of Sport Pedagogy, University of Taipei, Taipei, Taiwan
4Department of Physical Education & Institute for Research Excellence in Learning Science, National Taiwan
Normal University, Taipei, Taiwan
Background. The efficacy of emotional and motivational regulation can determine
athletic performance. Giving the short duration and fast changing nature of emotions
experienced by athletes in competition, it is important to examine the temporal
dynamics of emotional and motivational regulation. The aim of this study was to
investigate emotional and motivational regulation as measured by frontal alpha
asymmetry in skilled golfers during putting performance after a performance failure.
Methods. Twenty skilled university golfers were recruited and requested to perform
40 putts at an individualized difficulty level of 40–60% successful putting rate. Trials
immediately after a failed putt were selected for analysis. Successful performances were
those trials where a hole was and unsuccessful performances were those that failed. The
frontal alpha asymmetry index of LnF4-LnF3 was derived for statistical analysis.
Results. (1) Successful performance was preceded by a larger frontal alpha asymmetry
index at T2 than that of T1, and (2) a larger frontal alpha asymmetry index was observed
for unsuccessful performance than for successful performance at T1.
Discussion. The results suggest that successful emotional and motivational regulation
was characterized by a progressive increase of frontal alpha asymmetry, which led to
subsequent putting success when facing an emotionally provocative putting failure.
These findings shed light on the application of frontal alpha asymmetry for the
understanding and enhancement of emotional and motivational regulation during
Subjects Neuroscience, Kinesiology
Keywords Golf, Anxiety, Attention, Self-regulation
‘‘Emotional regulation’’ is a term generally used to describe a person’s ability to use
strategies to initiate, maintain, modify, or display emotions (Gross & Thompson, 2007).
Emotions play important roles, as they ready necessary behavioral responses, tune decision
making, enhance memory for important events, and facilitate interpersonal interactions
(Gross & Thompson, 2007). Athletes constantly attempt to regulate emotions if they believe
How to cite this article Chen T-T, Wang K-P, Cheng M-Y, Chang Y-T, Huang C-J, Hung T-M. 2019. Impact of emotional and motiva-
tional regulation on putting performance: a frontal alpha asymmetry study. PeerJ 7:e6777 http://doi.org/10.7717/peerj.6777
that doing so will facilitate performance (Lane et al., 2012). For example, an athlete might
seek advice from a coach, take a deep breath, and visualize successful outcomes to reduce
their anxiety to regain the feelings associated with winning. On the contrary, when athletes
make a mistake during a game, they might subsequently suffer from anxiety arising from
fear of making the same mistake. If they do not have the ability to regulate that emotion,
their performance worsens. Therefore, the efficacy of emotional regulation may be a key
differentiating factor for elite athletes.
The depletion of emotional self-regulation resources influences performance. Wagstaff
(2014) showed that compared with a control group (that received no video treatment)
and a nonsuppression group (that was given no self-regulation instructions during video
watching), participants who suppressed their emotional reactions to an upsetting video
completed a 10-km cycling task more slowly, generated lower mean power outputs, reached
a lower maximum heart rate, and perceived greater physical exertion. The findings suggest
that excessive suppression of emotion may deplete self-regulation resources (Baumeister,
Vohs & Tice, 2007), which subsequently results in impaired physical performance and
increased mental fatigue. Although past studies have used films (Dennis & Solomon, 2010),
pictures (Pérez-Edgar et al., 2013), or words (Kessler et al., 2009) with negative, neural, or
positive valence as emotional stimuli, which are effective at inducing emotion, emotional
induction from stimuli that are frequently encountered by athletes during real life settings
would increase the ecological validity of these findings in the sport context.
Failure is common in sport performance, and it is also one of the most emotionally
laden stimuli during competition. When a failure occurs, there is still little known about
how the athlete regulates their emotions, which affects subsequent performance. Moreover,
emotion is a state with relatively short duration. It has been suggested that the few seconds
prior to skill execution is critical for subsequent sport performance (Kao, Huang & Hung,
2013). As the emotional state during competition can fluctuate as a result of various
environmental and psycho-social influences, it is important to understand the dynamic
nature of emotional regulation right before performance start.
Questionnaires have been widely used for assessing emotional state in sports. However,
they are of limited use in assessing the fast fluctuating emotional states during the
short pre-performance period. Alternatively, psychophysiological measurement such
as electroencephalography (EEG) can be used to ameliorate this limitation. In addition
to the strength of high temporal resolution (milliseconds), which is suitable for capturing
the dynamic nature of mental state prior to performance, several EEG components have
been associated with emotion. For example, delta (Meerwijk, Ford & Weiss, 2015), theta
(Uusberg, Thiruchselvam & Gross, 2014), alpha (Quaedflieg et al., 2015), beta (Morillas-
Romero et al., 2015), and gamma (Balconia & Lucchiari, 2008) in the frontal area have been
implicated in affective states. Among these measurements, the frontal alpha asymmetry
has been suggested for assessing emotional regulation. Frontal alpha asymmetry is the
measure of differences in alpha frequency (8–12 Hz) band power between the left (F3) and
right (F4) side of the frontal lobe (Hugdahl & Davidson, 2003). Alpha power represents
the inverse of cortical activation (Klimesch, Sauseng & Hanslmayr, 2007). According to the
valence hypothesis (Tomarken et al., 1992), positive emotions are associated with higher left
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 2/16
prefrontal cortex activation (e.g., lower alpha power) whereas negative emotions are related
to higher right prefrontal cortex activation. Papousek et al. (2014) showed that reduced
alpha at right compared to left frontal lobe was associated with negative emotions (sadness)
after unpleasant film viewing, whereas reduced alpha at left compared to right frontal lobe
was accompanied by positive emotion (pleasantness) after positive film viewing (Wheeler,
Davidson & Tomarken, 1993). Moreover, Dennis & Solomon (2010) suggested that frontal
alpha asymmetry is also observed in event-related stimuli, which might reflect the ability to
regulate emotions in specific contexts. In addition to the valence hypothesis, Harmon-Jones
& Gable (2017) maintained that frontal alpha asymmetry reflects increased approach
motivation. Relatively increased frontal asymmetry (i.e., higher alpha power in the right
relative to the left frontal lobe) may serve as approach motivation or related emotion.
In contrast, relatively decreased frontal asymmetry may serve as withdrawal motivation.
Evidently, frontal alpha asymmetry activity is not only a reflection of positive/negative
emotions, but also an ability to regulate functional approach/withdrawal motivation and
emotion depending on the demands of the situation.
A recently developed model for exploring the relationship between emotional regulation
and optimal performance is the multi-action plan model (MAP model; Robazza et al.,
2016). MAP depicts four performance categories, specifically, optimal-automated (Type
1), optimal-controlled (Type 2), sub optimal-controlled (Type 3), and suboptimal-
automated (Type 4), derived from a hypothesized interaction of optimal/suboptimal
and automatic/controlled performance dimensions. The MAP model could serve as a
useful theoretical framework for the examination of emotional regulation prior to motor
performance (Fronso et al., 2017). In particular, when an athlete is facing a difficult task
(e.g., a distance of 40–60% holed rate), they need to exert conscious effort to focus on
the individual’s core components of action to ‘‘control’’ performance. This is particularly
challenging after a previous putting failure. If the athlete can regulate the emotion that
leads to the following successful putting performance, they are considered to be in a Type
2 state. On the contrary, if the subsequent performance failed, they are in a Type 3 state.
Giving the scant research regarding emotional regulation during sport performance and
the potential of frontal alpha asymmetry measurement for exploring this issue, the purpose
of this study was to examine the temporal dynamic of frontal alpha asymmetry with an
ecologically relevant emotional stimuli (i.e., facing failure). We hypothesized that after
failed putting, subsequent successful putting would be preceded by progressively reduced
alpha power in the left compared to the right frontal lobe, whereas subsequent unsuccessful
putting would be preceded by progressively reduced alpha power in the right compared to
the left frontal lobe.
MATERIALS & METHODS
Sixteen male and four female skilled golfers (all right handed) ranging in age from 18 to 29
(mean age =20.33 ±2.54) volunteered to participate in the study. They practiced at least
five times a week and often participated in national or international competitions (mean
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 3/16
year =7.70 ±2.41, mean best handicap =−2.95 ±2.42). Moreover, participants were
screened with a health history questionnaire to ensure that all were free of neurological
disorders or not taking any related medicine. All participants were asked to read and
sign an informed consent form and were free to withdraw from the study at any time.
The task had no physical or mental risk. To respect privacy, the information and data
from participants were processed with confidentiality and anonymity. This study was
approved by the institutional review board of National Taiwan University (NTU-REC No.:
In accordance with the international 10–20 system, electrode sites of brain waves were
recorded on F3, F4, C3, C4, P3, P4, T3, T4, O1 and O2. Electrical reference was located
on the left and right ear mastoids (A1, A2), and the ground electrode was located at FPz.
Vertical and horizontal electrooculograms were recorded with bipolar configurations
located superior and inferior to the left eye and on the left and right orbital canthi.
Neuroscan software 4.3 was used to collect data, with a band pass filter setting from DC
to 100 Hz. A 60 Hz Notch filter was kept on during the data recording, and the sampling
frequency was 500 Hz. Electrode impedance was kept below 5 k.
Forty straight putts were taken to a regulation hole (10.80 cm diameter) on an artificial
putting green (length =7 m, width =0.9 m) and the putting distance was determined
based on a 40–60% successful putting rate. All participants used their own golf putters and
standard size (4.27 cm diameter) white golf balls. The putting performance subsequent to a
failed putt was the trial of interest. These trials of interest were classified into successful and
unsuccessful performances depending on whether they were putted into the hole or not.
All participants were asked to refrain from drinking coffee and alcoholic beverages the
day before testing. Next, they were informed of their right to withdraw from the study
at any time during the data collection process, and then provided informed consent.
They were fitted with a Lycra electrode cap (Quick-cap; Neuroscan, Charlotte, NC, USA).
Electroencephalogram signals and resistance were checked. Participants attempted to
keep their eyes open without blinking and kept their body stable for at least 2 s before
the backswing. Putting distance was designated 40–60% putting success rate and 300
centimeters was the beginning distance they putted. They performed 10 putts and the
distance was adjusted relying on whether the average of 10 putting success rate was
40–60% or not. If the success rate was between 40 to 60%, then the putting distance was
set at 300 cm. If the success rate was above 60%, then the putting distance would increase
30 cm and then they performed extra 10 putts until the success rate reached 40–60%. On
the contrary, if the success rate was below 40%, then the putting distance would decrease
30 cm and then they performed extra 10 putts until the success rate reached 40–60%. After
the appropriate putting distance was decided, the participant performed 40 putts to the
best of their ability. Each block encompassed 10 putts and participants could rest for 2 min
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 4/16
between blocks. In each trial the backswing movement was detected by an infrared sensor
as an event marker. To increase the ecological validity of the experiment, participants
were instructed to maintain their routine and fix their eyes on the ball during the last two
seconds before putting action. The balls were prepared and retrieved by the experimenter
for the golfers after each putt so that the green was clear from any putted balls.
Neuroscan Edition 4.3 software was used to perform offline EEG data processing such as
baseline correction, EOG correction, and artifact rejection. Ocular artifacts such as eye
blinks and lateral eye movements were automatically removed using regression methods
(Gratton, Coles & Donchin, 1983) and other artifacts were visually checked and manually
rejected. To reduce interference from the participants’ pre-performance routine and
preparatory tempo and increasing ecological validity, they were told to keep their bodies
static and their eyes on the ball for at least 2 s before the backswing. To analyze the
pre-putt emotional state, at least 2 s before the backswing was needed for each trial. Each
2-s epoch was baseline-corrected based on the entire sweep and was then segmented into
two 1-s epochs (T1:-2- 1 s. and T2:-1- 0 s.). The baseline correction was performed after
excluding those trials with amplitude exceeding ±100 mv. In addition, band-pass filter
was set at 1–30 Hz with a slope of 12 db/oct. EEG were processed through the fast Fourier
transform with Hanning window. 8–12 Hz was selected for the alpha frequency band. The
averaged alpha power (µV2) at F3 and F4 sites was derived. Asymmetry score =ln (F4
alpha power)–ln (F3 alpha power).
With a sample of 20 participants, an alpha of 0.05, and power =0.8, a sensitivity analysis was
conducted, which showed that sample size was powered to detect a medium to large effect
size ES =0.66. Kolmogorov–Smirnov was used to do the test of normality. The result was
0.2, larger than 0.05; the data could thus be considered normally distributed. Additionally,
the equation of emotional regulation was based on simple models used in previous EEG
Asymmetry research. Specifically, the Frontal alpha asymmetry index was obtained by
applying the equation of ln (F4) - ln (F3) (Jaworska et al., 2012). Because alpha power is
often interpreted as inversely related to cortical activity, higher values on this index reflect
greater left frontal activation and lower values reflect greater right frontal activation (Allen,
Coan & Nazarian, 2004). Two-way repeated measures analysis of variance 2 (performance:
successful, unsuccessful) ×2 (time: T1, T2) was applied to the Frontal alpha asymmetry
index. When interaction occurred, simple main effects were tested. Alpha was set to .05 for
all analyses, and effect sizes were calculated using partial eta squared (η2
Participants missed 26.50 ±4.86 putts at the 40–60% difficulty level. Following these
misses, participants made the next putt 8.1 ±2.27 times, on average; and missed their next
putt 18.4 ±5.92 times on average.
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 5/16
Figure 1 Frontal alpha asymmetry between successful and unsuccessful performance at T1 (-2∼-1s)
and T2 (-1∼0s).
Full-size DOI: 10.7717/peerj.6777/fig-1
Frontal alpha asymmetry
The 2 ×2 (performance ×time) ANOVA revealed a significant performance ×time
interaction effect, F(1, 19) =14.583, p=.001, η2
p=.434. The post hoc simple main effect
analysis showed that successful performance was preceded by a progressively increased
frontal alpha asymmetry index T1 to T2, t(19) =2.420, p=.026, Cohen’s d=.733.
Main effect of performance is F(1,19) =2.469, p=.133, η2
p=.115; time F(1,19) =.194,
p=.010. Mean frontal alpha asymmetry of successful performance is -.40; Mean
frontal alpha asymmetry of unsuccessful performance is .010. In addition, the frontal alpha
asymmetry index for successful performance was smaller than unsuccessful performance
at T1, t(19) = −3.920, p=.001, Cohen’s d=.798 (See Fig. 1). There were no performance
or time main effects.
Three additional analyses were performed to provide more support for using the frontal
alpha asymmetry index as a measure of emotion.
Regional speciﬁcity of frontal alpha asymmetry index
In order to demonstrate that the alpha asymmetry index is specific to the frontal region,
alpha asymmetry indices were computed for the central, parietal, temporal, and occipital
regions. Several performance X time two-way ANOVAs were employed to separately
assess the alpha asymmetry index in central, temporal, parietal, and occipital regions.
Results showed that neither performance main effects (central region, F(1,19) =.000,
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 6/16
p=.000; parietal region, F(1,19) =.021, p=.885, η2
p=.001; occipital region,
F(1,19) =.004, p=.948, η2
p=.000; temporal region, F(1,19) =.199, p=.660, η2
nor interaction effects (central region, F(1,19) =3.573, p=.074, η2
region, F(1,19) =.788, p=.386, η2
p=.040; occipital region, F(1,19) =1.408, p=.250,
p=.069; temporal region, F(1,19) =2.447, p=.134, η2
p=.114) were significant.
Frequency speciﬁcity of frontal alpha asymmetry index
The alpha band in the frontal area was the dominant frequency band related to putting
performance. Neighboring frequencies of alpha in the frontal region were examined.
Specifically, the frequency bands of 4–8 Hz and 12–16 Hz were subjected to a performance
X time two-way ANOVA separately. Results showed there were neither interaction effects
in 4–8 Hz, F(1,19) =3.919, p=.062, η2
p=.171, and 12–16 Hz, F(1,19) =.609, p=.445,
p=.031, nor performance main effects in 4–8 Hz, F(1,19) =.540, p=.471, η2
and 12–16 Hz, F(1,19) =.262, p=.614, η2
Equality of baseline frontal alpha asymmetry
The association of frontal alpha asymmetry during putting with putting performance can
be further supported by ruling out the inequality of frontal alpha asymmetry at baseline.
The one-sample ttest, t(19) =1.094, p=.288, showed that participants were in the
emotionally neutral state (mean =40.04 ±0.16) before the putting task began.
Alpha power analysis
Alpha power analysis was performed to determine the hemispheric contribution to
the frontal asymmetry observed during the preparatory period right after a failed putt.
A 2 ×2×2 Performance (successful, unsuccessful) ×Time (T1, T2) ×Hemi (Left,
Right) ANOVA showed a significant Performance X Time X Hemi interaction effect,
F(1,19) =16.118, p=.001, η2
p=.446. The follow up simple interaction effect analysis
1. On successful performance condition, the Time main effect, F(1,19) =10.405,
p=.354, and the Hemi X Time interaction effect, F(1,19) =5.859, p=.026,
p=.236 were significant. Given this interaction effect, a subsequent simple main
effect analysis was performed and showed a progressive reduction of alpha power from
T1 to T2, t(19) =4.272, p=.000, Cohen’s d=2.774, in the left hemisphere, and at
T1 alpha power was significantly lower in the right compared to the left hemisphere,
t(19) =2.383, p=.028, Cohen’s d=1.052. However, in unsuccessful performance
conditions, only the Time main effect, F(1,19) =35.661, p=.000, η2
significance (See Table 1).
2. In the left hemisphere, the result showed the Time main effect F(1,19) =36.838,
p=.660. As for the right hemisphere, both the Time main effect
F(1,19) =17.844, p=.000, η2
p=.484 and the Performance X Time interaction effects
were observed. The subsequent simple main effect analysis demonstrated a progressive
reduction of alpha power from T1 to T2, t(19) =5.465, p=.000, Cohen’s d=3.364,
in unsuccessful performance, whereas at T2 alpha power was significantly lower in
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 7/16
Table 1 Alpha power analysis.
PP CP HEMI TIME Ln (ALPHA POWER)
T1 1.086 ±.121
F3 T2 1.324 ±.097
T1 1.108 ±.124
F4 T2 1.212 ±.105
T1 .935 ±.123
F3 T2 1.245 ±.125
T1 .897 ±.121
F4 T2 1.282 ±.123
T1 1.341 ±.100*
F3 T2 1.027 ±.125*
T1 1.231 ±.109*
F4 T2 1.058 ±.124*
T1 1.264 ±.128*
F3 T2 .935 ±.123*
T1 1.323 ±.132*
F4 T2 .897 ±.121*
*Significant at < 0.05.
PP, prior putt performance; CP, current putt performance; Hemi, Hemisphere; S, successful putt; U, unsuccessful putt.
unsuccessful performance compared to the successful performance, t(19) =2.879,
p=.010, Cohen’s d=1.314 (See Table 1).
3. At T1, a Performance X Hemi interaction effect was observed, F(1,19) =15.369,
p=.447. The subsequent simple main effect analysis demonstrated that
alpha power was significantly lower in the right compared to the left hemisphere for
successful performance, t(19) =2.383, p=.028, Cohen’s d=1.052. As for T2, only
the Performance main effect was observed, F(1,19) =6.219, p=.022, η2
Frontal alpha asymmetry is diﬀerent between a prior successful and failed
In order to demonstrate that the frontal alpha asymmetry is unique to a prior failure,
we compared the frontal asymmetry patterns between failed and made putts. For this
intended purpose, a PP (previous performance) X CP (current performance) X Time
three-way ANOVA was performed and we focused only on any effects associated with PP.
The results showed a significant interaction effect on PP X CP X Time, F(1,19) =19.335,
p=.504. The follow up simple interaction effect analysis revealed that at T1, a
significant PP X CP interaction effect, F(1,19) =13.140, p=.002, η2
p=.409 was observed.
Subsequent simple main effect analysis demonstrated that the frontal alpha asymmetry
in current successful putts was higher than current unsuccessful putts for the previous
unsuccessful putt condition, t(19) = −3.889, p=.001, Cohen’s d=0.794 (See Fig. 2).
Similarly, at T2, the PP X CP interaction effect, F(1,19) =12.395, p=.002, η2
reached significance. Subsequent simple main effect analysis demonstrated that the frontal
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 8/16
Figure 2 Frontal alpha asymmetry for current successful and unsuccessful performance between pre-
vious successful and unsuccessful performance at T1 (-2∼-1s).
Full-size DOI: 10.7717/peerj.6777/fig-2
alpha asymmetry in current successful putts was lower than current unsuccessful putts
for the previous successful putt condition, t(19) = −3.085, p=.006, Cohen’s d=1.490
(see Fig. 3). On the contrary, the frontal alpha asymmetry in previous successful putts
was higher than previous unsuccessful putts for the current successful putt condition,
t(19) = −2.697, p=.014, Cohen’s d=0.745. These findings indicate that the frontal alpha
asymmetry is different between a prior failure and success.
Frontal alpha asymmetry patterns have been consistently linked to broad patterns of
affective style (Davidson, 2004). The current study sought to extend previous work
by examining the potential association between frontal alpha asymmetry and sport
performance with an intention to demonstrate that frontal alpha asymmetry could also be
an indicator for emotional and motivational regulation during competition. We provided
real-time psychophysiological evidence in an ecologically valid setting to show that frontal
alpha asymmetry was associated with performance in a temporally dynamic manner.
Specifically, after failed putts, skilled golfers regulated their emotion and motivation by
gradually increasing frontal alpha asymmetry, which resulted in successful performance.
On the contrary, the frontal alpha asymmetry was not increased prior to unsuccessful
performance. These findings not only provide support to the belief that emotions and
motivations influence performance and are consistent to related emotional regulation
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 9/16
Figure 3 Frontal alpha asymmetry for current successful and unsuccessful performance between pre-
vious successful and unsuccessful performance at T2 (-1∼0s).
Full-size DOI: 10.7717/peerj.6777/fig-3
literature (Totterdell, 2000;Woodman et al., 2009) but also demonstrate that state frontal
alpha asymmetry could explain emotional and motivational regulation during emotional
challenges (Goodman et al., 2013).
The progressive increase of frontal alpha asymmetry prior to successful performance
reflects an increasing activation of the left frontal area. Frontal alpha asymmetry has
been associated with emotional states, with increased frontal alpha asymmetry relating to
positive emotion whereas reduced frontal alpha asymmetry is linked to negative emotion
(Quaedflieg et al., 2016). This finding suggests that when facing a previous putting failure,
skilled golfers regulate emotion and motivation by increasing left frontal activity during
the last two seconds before putting execution, which results in successful performance.
This interpretation was consistent with the alpha power analysis in our control analysis
(i.e., control analysis 4). Specifically, on successful performance condition, alpha power
progressively reduced from T1 to T2 in the left frontal area, indicating an increase of
positive emotional and motivational regulation, which led to successful performance. On
the contrary, alpha power progressively decreased from T1 to T2 in the right frontal area on
unsuccessful performance condition, indicating an increase of negative emotion. In other
words, golfers could use strategies to start, maintain, modify, or display positive emotion
(Gross & Thompson, 2007) to cope with the possible negative emotion induced by the failed
putt, and perform successfully. There is sufficient evidence to show that athletes can use
strategies to create a more appropriate emotional state during competition (Jones, 2012).
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 10/16
Negative emotional reactions can exhaust an athlete’s cognitive resources and adversely
impact performance if poorly-managed (Tonnaer et al., 2017). On the contrary, if they have
better emotional regulation, they can reinforce positive thoughts and feelings (Weytens et
al., 2014) and have greater opportunities to win, even in difficult and challenging situations.
In contrast, skilled golfers were not able to reverse from previous performance failure when
their frontal asymmetry was not increasing during the last two seconds. These findings not
only corroborate previous reports of positive emotions enhancing performance (Totterdell,
2000;Uphill, Groom & Jones, 2014;Woodman et al., 2009), but also signify the importance
of examining the temporal dynamic of emotional regulation during sport performance,
which happens to be the strength of EEG, a high temporal resolution psychophysiological
The significantly higher frontal alpha asymmetry during the first epoch of motor
preparation for failed (following previously failed putts), compared to the successful putts,
was unexpected. It implied that emotion at the beginning of the preparatory period in
unsuccessful putts situation was relatively positive. According to the viewpoint where
frontal alpha asymmetry reflects approach motivation (Harmon-Jones & Allen, 1998),
negative emotions such as anger could lead to higher frontal alpha asymmetry. Given
that the present study didn’t take any subjective measures of emotion, future studies
are encouraged to include subjective emotional measures to precisely pinpoint the exact
emotions experienced during the regulatory process preceding successful and unsuccessful
The relevance of frontal alpha asymmetry to sport performance is strengthened by our
control analyses. First of all, in order to demonstrate that the frontal alpha asymmetry is
unique to a prior failure, we compared the frontal asymmetry patterns between failed and
made putts. The results showed that the frontal alpha asymmetry is different between a
prior failure and success, which demonstrated the unique requirement for emotional and
motivational regulation in the present study. Our control analysis also provided evidence
for the regional specificity of frontal alpha asymmetry. It is only in the frontal region, not
all other regions including central, temporal, parietal, and occipital regions, that alpha
asymmetry was related to putting performance. Similarly, a frequency specificity of frontal
alpha asymmetry was supported by showing that only the alpha frequency band, not the
neighboring frequency bands in frontal region, was related to putting performance. These
two results support the emotional and motivational regulation interpretation of the present
study, given the context of facing putting failure during task performance for the skilled
golfers. Furthermore, our baseline analysis showed that the golfers were in an emotionally
neutral state before the putting task began, which suggested that emotions during putting
performance were specifically induced by experimental manipulation.
According to the MAP model, performers regulate their levels of competitive anxiety
and pleasant/unpleasant emotions to achieve individualized optimal states for outstanding
performance (Robazza, Pellizzari & Hanin, 2004). Bortoli et al. (2012) further specified
that Type 2 is hypothesized to be a functional-unpleasant and effortful (nervous, angry)
performance state. Our results support this hypothesis by showing that following failure, left
frontal cortex was more active from T1 to T2 prior to subsequent successful performance.
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 11/16
On the contrary, progressive higher activation from T1 to T2 in the right frontal cortex
results in unsuccessful performance. This was consistent with previous studies and offered
important information regarding how successful performance in Type 2 was regulated
by left frontal activation that accompany with positive emotion and approach related
motivation, whereas unsuccessful performance in Type 3 was regulated by right frontal
activation that is associated with negative emotion and withdrawal related motivation.
In contrast with the automatic processing of Type 1 (functional-pleasant) and Type 4
(dysfunctional-pleasant), Type 2 and 3 are more dependent on the controlled processing
as formulated in the MAP model (Bortoli et al., 2012), which could be induced by the prior
performance failure used in the present study. Our findings not only provide evidence
to support the MAP model, but also go one step further to discriminate Type 2 from
Type 3, by showing that the effective and effortful process of Type 2 was characterized by
positive-going, emotional and motivational regulation with a very short temporal dynamic
nature. The delineation of this refined and dynamic process furthers our understanding of
successful regulation. Nevertheless, manipulation of the frontal alpha asymmetry preceding
motor performance is needed to establish a causal relationship between emotional and
motivational regulation, and subsequent performance.
There are some methodological limitations that should be addressed in future studies.
First, intensity and duration are two central characteristics of an emotional response (Brans
& Verduyn, 2014). Larger frontal alpha asymmetry index reflects stronger intensity. The
assessments were only based on the two seconds prior to putting execution. Giving the
advantage of high temporal resolution for EEG measurement, frontal alpha asymmetry
measured outside of the two second period could also be important for understanding
emotional regulation in skilled golfers. Second, emotions tend to be evoked by certain
events, mostly by a cognitive antecedent that determines which emotion is triggered
(Schutz & Davis, 2000). Cognitive appraisal plays a key role in determining which emotion
was induced (Sakakibara & Endo, 2016). Therefore, subjective measurements of emotion
would be useful to clarify exactly what negative or positive emotions are regulated.
Third, how attention plays a role in regulating emotion during sport performance warrants
further investigation. Gross & Thompson (2007) proposed the modal model of emotion and
inferred that the emotion generation process occurs in a particular sequence from situation,
attention, and appraisal to response. Allocation of attention involves directing one’s
attention towards or away from an emotional situation. Therefore, measures of attention
could provide critical information for the mechanism in emotional and motivational
regulation processes. And lastly, given that the differences in frontal alpha asymmetry
between successful and unsuccessful putts following previously successful putts could also
provide insight on the emotional and motivational regulation of the skilled golfers, future
studies looking into this aspect are warranted.
In conclusion, this study showed that skilled golfers successfully regulate their emotion and
motivation by increasing relatively more left frontal activation during the last two seconds
Chen et al. (2019), PeerJ , DOI 10.7717/peerj.6777 12/16
prior to putting execution when facing a performance failure, an emotionally provocative
event commonly encountered by athletes. The study demonstrated the practical utility of
the frontal alpha asymmetry for understanding the temporal dynamic of emotional and
motivational regulation during sport performance.
ADDITIONAL INFORMATION AND DECLARATIONS
This work was financially supported by the Higher Education Sprout Project by the Ministry
of Education (MOE) in Taiwan and a grant from the Ministry of Science and Technology
(Taiwan) under grant MOST 103-2410-H-003-113-MY3. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
The following grant information was disclosed by the authors:
Ministry of Education (MOE).
Ministry of Science and Technology (Taiwan): MOST 103-2410-H-003-113-MY3.
Tsung-Min Hung is an Academic Editor for PeerJ.
•Tai-Ting Chen conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, approved the final draft.
•Kuo-Pin Wang and Ming-Yang Cheng performed the experiments, analyzed the data,
authored or reviewed drafts of the paper, approved the final draft.
•Yi-Ting Chang conceived and designed the experiments, performed the experiments,
authored or reviewed drafts of the paper, approved the final draft.
•Chung-Ju Huang and Tsung-Min Hung conceived and designed the experiments,
contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper,
approved the final draft.
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
National Taiwan Normal University granted Ethical approval to carry out the study
within its facilities (Ethical Application Ref: 201312ES055).
The following information was supplied regarding data availability:
The raw data are provided in the Supplemental Files.
Chen et al. (2019), PeerJ, DOI 10.7717/peerj.6777 13/16
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
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