Effects of visual search task on attentional bias and stress response under pressure

Abstract

BACKGROUND:

it has always been a problem for athletes that their performance is out of order due to pressure in major competitions. The change of attention pattern and the emergence of stress response (SR) caused by negative affect (NA) are the direct reasons for the greater impact on the performance of athletes. It is a hot topic to explore how to improve attention bias (AB) and SR of athletes in stressful situations.

OBJECTIVE:

the study aimed to analyze the improvement effect of visual search task (VST) training on AB and SR of athletes under pressure situations.

METHODS:

62 male basketball players with national level 2 or above of Shenyang sports institute were divided into experimental group (EG) and control group (CG). Visual search task training program was used in the EG (happy, sad, disgusted, neutral faces) and sham training program was used in the CG (all faces with neutral expression) for two months. Under the stress situation, attention behavior of all subjects before and after training was tested. Physiological coherence and autonomic balance system were used to record heart rate variability synchronously. Parallel frequency domain analysis was divided into very low frequency band (VLF), low frequency (LF), high frequency (HF) and total spectrum (TP). The normalized treatment obtained indexes such as HFnorm, LFnorm, and LF/HF. The e-prime 2.0 software was adopted to obtain the attention bias score. The Positive and Negative Affect Scale (PANAS) and the self-rating stress scale were adopted for evaluation before and after training.

RESULTS:

the self-rating pressure in the two groups was lower than that before the training, and the pressure in the experimental group was lower than that in the control group (P < 0.05). After training, the positive emotion of the experimental group was higher than that of the control group, and the EG was lower than that of the CG (P < 0.05). After training, the score of attention bias of happy and neutral faces in the EG was higher than that of theCG, while the score of attention bias of sad and disgusted faces was lower than that of the CG (P < 0.05). After training, LF/HF and LFnorm in the EG were lower than those in the CG, and HFnorm was higher than those in the CG (P < 0.05).

CONCLUSIONS:

the training of visual search task can effectively improve the athletes’ PA and AB of positive information, reduce the attention bias of negative information and psychological pressure, and relieve theSR.

Keywords

VST AB HRV SR The PANAS

1 Introduction

Stress is simply the negative emotional response of individuals when they are threatened psychologically [1, 2]. In sports competition, most of the pressure experienced by athletes is caused by the situation of competition, in which athletes are very concerned about winning or losing but are often not sure whether their ability can achieve the desired result, thus causing pressure [3]. That is to say, the pressure on athletes in sports situations is mainly caused by the imbalance between their abilities and the demands of the external environment [4]. Pressure is the biggest emotion of all the athletes nowadays, especially those who are expected to win the gold medal [5, 6]. Due to the inherent competitiveness and challenge of competitive sports, athletes are inevitably in a state of high pressure for a long time, which damages their physical and mental health [7 –9]. Relevant studies have shown that long-term stress can cause great damage to the body’s immune system and easily lead to an imbalance of yin and yang in the body. When emotions are out of control, the body’s viscera functions will be impaired, resulting in insomnia, anxiety, depression, panic disorder, and other negative emotions, so as to produce SR [10]. Therefore, how to improve athletes’ attentional bias and negative affect in stressful situations is crucial.

AB refers to an individual’s high sensitivity to a particular stimulus accompanied by selective attention, which is usually a cognitive characteristic associated with negative body image. And the training methods for AB are also the focus of many scholars [11 –13]. Machner et al. [14] proposed a new AB correction training paradigm based on eye movement tracking, and randomly assigned 75 anxious students to positive training group (PT) and sham training group (ST). The results showed that the positive AB of the PT group increased greatly, and the disengagement from NA was faster. Linke et al. [15] used attention-deviation-correction training to treat children aged 9–12 with behavioral inhibition in typical development. It was found that AB correction training greatly reduced the participants’ symptoms of separation anxiety and the activation of amygdala and insula, and greatly increased the activation of ventrolateral prefrontal cortex. AB training actually contains many paradigms, such as point detection task, VST, target pointing training, etc. [16, 17]. Among them, the point detection paradigm was used to observe the attention orientation of subjects, and to make attention choice between the target stimulus and the interference stimulus [18]. The VST required the subjects to locate a target in a number of non-target stimuli and make a specific response. Compared with the point detection paradigm, the VST would tell the subjects which was the target stimulus in advance, so that the subjects would spontaneously search and respond [19]. Therefore, this study adopted VST to train and treat athletes.

In summary, currently, the AB correction training paradigm is also a hot topic to relieve the stress of individuals, but there are few studies on the stress of athletes. Based on this, 62 male basketball players of national level 2 level or above from Shenyang sports institute were selected as research objects, and divided into two groups to receive treatment with different training programs. PA, NA, self-rating stress degree, AB score, and HRV level before and after training were compared, to comprehensively evaluate the effect of VST training on AB and SR of athletes in stressful situations.

2 Methodology

2.1 Research objects and grouping

As shown in Table 1, 62 male basketball players of national level 2 or above from Shenyang sports institute were selected as research objects, with an average age of 23.61±2.07 years (20–25), all of whom were right-handed. All the subjects were divided into EG and CG by blind selection, with 31 people in each group. All subjects and their families were informed and volunteered to participate in the study. Table 1 showed the comparison on the average age, height, and weight of the research objects from the two groups. It was found that there was no marked difference in the basic characteristics of the athletes from the two groups (P > 0.05).

Table 1
Basic characteristics of all research objects

Indexes EG CG X² P

Age 23.16±1.74 23.16±1.74 12.538 0.075

Height 186.63±5.39 185.91±4.82 11.372 0.146

Weight 71.43±5.21 72.16±6.49 8.428 0.128

Indexes	EG	CG	X²	P
Age	23.16±1.74	23.16±1.74	12.538	0.075
Height	186.63±5.39	185.91±4.82	11.372	0.146
Weight	71.43±5.21	72.16±6.49	8.428	0.128

2.2 The training process

Both groups received AB training for two months. The training materials of the EG were from the Chinese facial affective picture system (CFAPS) [20], including 1 happy face, 7 sad faces, and 8 disgusted faces. The 16 face materials were placed in a 5 × 5 grid and displayed on a computer screen at a resolution of 200px × 200px. The subjects were then asked to quickly identify happy faces and click ok with the mouse. The control subjects were trained with 16 neutral faces, including 15 neutral faces and one neutral face with a black birthmark. The 16 face materials were placed in a 5 × 5 grid and displayed on a computer screen at a resolution of 200px × 200px. The subjects were then asked to quickly identify happy faces and click ok with the mouse. Subjects in both groups were trained 100 times each time. Training materials were edited and presented by E-Prime 2.0 Software developed by PST (Psychology Software Tools).

2.3 Pressure scenario simulation

Before the experiment, all the subjects were told that their scores on this test would be used as an important basis for selection in the future. Each group of two was ranked according to the total score of each group, that was, each person’s performance would be affected by the performance of his teammates. And the first three subjects of the total score would be rewarded with 5000, 3000, and 1000 yuan. The entire test process would be filmed, and experts and other students were required to watch the whole process.

2.4 Attention behavior test and index evaluation before and after training

The test materials were selected from the three-dimensional facial expression database (BU-3DFE) [21]. A total of 180 Asian faces were selected, including 33 happy faces, 33 sad faces, 33 disgusted faces, and 66 neutral faces. There were four groups of faces pair: happy - neutral, sad - neutral, disgusted - neutral, and neutral - neutral, each with 33 pictures. Each pair of faces should be made up of the same person. E-Prime 2.0 software was used for image editing, so that the size of the pictures was all 180×236 mm, the format was PNG, and the position of the pictures was balanced from left to right.

Prior to the start of the test, subjects would be given an explanatory test procedure. The stimulus materials were displayed on a computer screen, with a white plus sign in the center of the screen for 1 second, to remind the subjects to pay attention. Then the image pair was presented for 0.5 s, and when the image pair disappeared, the arrows appeared on or off the random side of the screen. Subjects were asked to press up or down depending on the direction of the arrow, pressing up when it was up and down when it was down. The subjects responded to the button and continued with the new test.

HRV: During the entire test, subjects were required to connect to the self-generate Physiological Coherence System produced by Beijing Haofeng Technology Development Co., LTD. The subjects’ HRV was recorded synchronously. The heart rate spectrum of normal people ranged from 0 to 0.4 Hz. In this study, frequency domain analysis of HRV signals was conducted [22], which was divided into three types: 0–0.04 Hz belonged to the VLF, 0.04–0.15 Hz belonged to the LF, and 0.15–0.4 Hz belonged to the HF. The HF and LF were normalized, so as to calculate LF/HF, LFnorm (LFnorm = 100×LF/(TP-VLF)), and HFnorm (HFnorm = 100×HF/(TP-VLF)).AB score: AB score can accurately assess the individual’s selective attention to various stimuli. Compared with neutral stimuli, individuals generally preferred to perceive positive and negative stimuli. The higher the score of which type of stimulus, the more biased stimulus. The AB score was calculated when the target stimulus response near the face of the stimulus material was collected by E-Prime 2.0 software. The calculation equation of the AB score was as follows. AB score = the reaction time of the research object with the detection point in the matched neutral expression position - the reaction time of the research object with the detection point in the happy, disgusted, or sad expression position. The PANAS and the self - rating stress scale were adopted to evaluate the PA, NA, and stress level of the research objects before and after training.

2.5 The scales used

2.2.5.1 I. The PANAS

This scale was revised according to the PANAS [23] revised by Watson et al. In 1988. The words that were not suitable for athletes in the original scale were deleted, and some new adjectives were added. The subjects were then assessed before and after the training. As shown in Table 2, the scale included two dimensions of positive affect (PA) and negative affect (NA). The PA dimension included 10 adjectives describing PA and the NA dimension included 10 adjectives describing negative affect. Likert rating system was adopted, a score of 1 to 5 meant almost nothing, relatively little, moderate, relatively much, and extremely much, respectively. The higher the PA score was, the more active and energetic the individual was; the higher the NA score was, the more depressed and unhappy the individual was. Through the reliability analysis, it can be found that the internal consistency of the two dimensions of the scale, namely, PA and NA were 0.91 and 0.87, respectively.

Table 2
The PANAS

Dimension Adjectives

Positive affect Interested

Energetic

Enthusiastic

Passionate

Proud

Encouraged

Youthful

Determined

Focused

Confident

Negative affect Afraid

Restless

Nervous

Bad-tempered

Scared

Obsessed

Alert

Timid

Weak-willed

Anxious

Dimension	Adjectives
Positive affect	Interested
	Energetic
	Enthusiastic
	Passionate
	Proud
	Encouraged
	Youthful
	Determined
	Focused
	Confident
Negative affect	Afraid
	Restless
	Nervous
	Bad-tempered
	Scared
	Obsessed
	Alert
	Timid
	Weak-willed
	Anxious

2.2.5.2 II. The self-rating stress scale

According to the self-rating scale designed by predecessors [24], some problems that did not conform to basketball players were modified, so as to ensure the accuracy of the scoring. As shown in Table 3, the scale provided 18 items, including the 8 reverse scoring questions and the 10 forward scoring questions. The 4-level scoring system was adopted, 0–4 points meant never, occasionally, often, very frequent, almost always. The higher the score was, the higher the stress level was.

Table 3
The self-rating stress scale

Dimensions Measurement items

Pressure I feel very enthusiastic about finishing the training

Having enough sleep makes me feel energetic

I feel a great sense of accomplishment in fulfilling my responsibilities

When faced with difficulties, I choose to calmly solve them

When there are demands on my time and energy, I will face them

I’m better than average at making decisions

I think I’m more efficient

My resistance to disease has increased

I am indifferent to the problems and needs of others

To train me to eat differently, I drank too much coffee

My interest in recreational activities has diminished

My communication with the coach and teammates is tense

I have trouble concentrating

I felt dissatisfied, lost, out of place

I’m forgetful

I felt physically and mentally exhausted

I get emotional and irritable when faced with minor problems

I feel negative, unproductive, or depressed about what I am doing

Dimensions	Measurement items
Pressure	I feel very enthusiastic about finishing the training
	Having enough sleep makes me feel energetic
	I feel a great sense of accomplishment in fulfilling my responsibilities
	When faced with difficulties, I choose to calmly solve them
	When there are demands on my time and energy, I will face them
	I’m better than average at making decisions
	I think I’m more efficient
	My resistance to disease has increased
	I am indifferent to the problems and needs of others
	To train me to eat differently, I drank too much coffee
	My interest in recreational activities has diminished
	My communication with the coach and teammates is tense
	I have trouble concentrating
	I felt dissatisfied, lost, out of place
	I’m forgetful
	I felt physically and mentally exhausted
	I get emotional and irritable when faced with minor problems
	I feel negative, unproductive, or depressed about what I am doing

2.6 Statistical analysis

The data were processed using SPSS19.0 version statistical software, and the mean±standard deviation (x±s) was adopted to express measurement data. The independent sample t test was adopted to compare the self-rated stress level before and after the training of the EG and the CG, the frequency domain analysis of heart rate variability (TP, VLF, LF, HF, LF/HF, HFnorm, LFnorm), the AB score of different expressions, and the score data of PANAS. The score of AB of different facial expressions between groups and within groups was analyzed by variance. The figures were drawn with Origin8.0.

3 Results

3.1 The stress levels before and after the training

As shown in Fig. 1 and Table 4, there were no statistically obvious differences in the stress level before the training (P > 0.05). After the training, the stress level of subjects was greatly lower than that before the training, and there were statistically obvious differences (P < 0.05). After training, the stress level of the EG was greatly lower than that of the CG and there were statistically obvious differences (P < 0.05).

Fig. 1

The stress levels before and after the training. Note: ^*indicated that there were statistically obvious differences between the EG and the CG (P < 0.05); # indicated that there were statistically obvious differences (P < 0.05).

Table 4

Self-rating stress levels before and after training

Group	EG	CG
Before training	7.27±1.21	7.09±1.42
After training	4.96±0.73	6.26±0.96

3.2 The positive and negative affect levels before and after the training

As shown in Fig. 2A and Table 5 below, there were no statistically obvious differences in the scores of PA and NA before the training (P > 0.05). The PA scores were greatly higher after the training than before, and there were statistically obvious differences (P < 0.05). After training, the PA score of the EG was greatly higher than that of the CG, and there were statistically obvious differences (P < 0.05). As shown in Fig. 2B and Table 5, the NA scores were greatly lower after the training than before, and there were statistically obvious differences (P < 0.05). After training, the NA score of the EG was greatly lower than that of the CG, and there were statistically obvious differences (P < 0.05).

Fig. 2

The positive and negative affects levels before and after training. ^*Indicated that compared with the EG, there were statistically obvious differences (P < 0.05). # Indicated that there were statistically obvious differences compared with that before training (P < 0.05)

Table 5

The positive and negative affect levels before and after training

Group	EG		CG
	Positive affect	Negative affect	Positive affect	Negative affect
Before training	25.33±5.71	34.57±6.71	24.54±4.39	36.01±5.06
After training	36.38±6.21	24.01±4.23	28.31±4.64	29.51±3.40

3.3 Comparison of HRV before and after training

As shown in Fig. 3 and Table 6 below, there were no statistically obvious differences in the levels of TP, VLF, LF, and HF in the frequency domain analysis of HRV before and after training (P > 0.05).

Fig. 3

Frequency domain analysis of HRV before and after training. Note: A was the level of TP segment before and after training; B was the level of VIF segment before and after training; C was the level of LF segment before and after training; D was the level of HF segment before and after training.

Table 6

Comparison of TP, VIF, LF, and HF in frequency domain analysis of HRV before and after training

Group		Before training	After training
EG	TP	481.53±210.17	411.72±173.22
	VIF	252.11±147.53	210.55±160.72
	LF	301.35±197.46	276.18±213.55
	HF	48.38±37.18	51.55±33.66
CG	TP	474.81±205.29	447.18±190.61
	VIF	239.08±153.73	227.19±183.42
	LF	325.77±180.61	283.72±164.67
	HF	47.93±40.21	49.31±37.57

As shown in Fig. 4 and Table 7, there were no statistically obvious differences in LF/HF, HFnorm, and LFnorm before the training (P > 0.05). After training, LF/HF, HFnorm, and LFnorm of subjects in the CG showed there were no statistically obvious differences compared with those before training (P > 0.05). After the training, LF/HF and LFnorm in the EG were greatly lower than before the training, and there were statistically obvious differences (P < 0.05). After the training, HFnorm in the EG was greatly higher than before the training, and there were statistically obvious differences (P < 0.05). After training, LF/HF and LFnorm of subjects in the EG were greatly lower than those in the CG, and HFnorm was greatly higher than those in the CG, and there were statistically obvious differences (P < 0.05).

Fig. 4

Comparison of LF/HF, HFnorm, and LFnorm before and after training. Note: A referred LF/HF before and after the training; B was LFnorm for the two groups before and after the training; C was HFnorm for the two groups before and after training. ^* Indicated that compared with the EG, there were statistically obvious differences (P < 0.05). ^* Indicated that there were statistically obvious differences compared with that before training (P < 0.05).

Table 7

Comparison of LF/HF, HFnorm, and LFnorm in the frequency range of HRV before and after training

Group		Before training	After training
EG	LF/HF	6.23±5.12	4.76±3.72
	HFnorm	7.15±4.76	13.2±6.84
	LFnorm	67.35±11.15	56.52±10.41
CG	LF/HF	6.80±5.33	5.77±4.81
	HFnorm	7.58±5.42	8.15±4.38
	LFnorm	68.05±13.63	67.12±12.84

3.4 Comparison of AB score before and after training

Figure 5 showed the comparison of AB scores of different faces before and after training. Before the training, there were no statistically obvious differences in the score of the AB of happy, sad, disgusted, and neutral faces (P > 0.05). After training, the AB scores of happy and neutral faces were greatly higher than before, and there were statistically obvious differences (P < 0.05). After the training, the scores of AB of sad and disgusted faces were greatly lower than before the training, and there were statistically obvious differences (P < 0.05). After training, the scores of the AB of happy and neutral expression faces in the EG were greatly higher than those in the CG, and there were statistically obvious differences (P < 0.05). After training, the scores of the AB of the sad and aversive faces in the EG were greatly lower than those in the CG, and there were statistically obvious differences (P < 0.05).

Fig. 5

The AB score of different faces before and after training. Note: A was the score of AB of happy expression before and after training; B was the score of AB of sad expression before and after training; C was the score of AB of aversive expression before and after training; D was the score of AB of neutral expression before and after training. ^* Indicated that compared with the EG, there were statistically obvious differences (P < 0.05). ^#Indicated that there were statistically obvious differences compared with that before training (P < 0.05).

Table 8 showed the repeated measurement analysis of variance of AB score of different faces of subjects. The interaction between different faces and test time in the group was obvious (F = 13.281, P = 0.036), while the main effects of different faces (F = 0.627, P = 0.062) and test time (F = 3.011, P = 0.051) were not obvious. The main effect between groups was obvious (F = 22.521, P = 0.017), the main effect of test time was obvious (F = 11.682, P = 0.028), and the interaction between groups and test time was obvious (F = 15.691, P = 0.033).

Table 8

Analysis of variance of attentional bias of different faces

Indexes	Within groups			Between groups
	Different faces	Different faces×test time	Test time	Group	Test time	Group×test time
Sum of squares of deviations	3864.733	48168.617	6175.397	1582.661	3711.734	26708.317
Degree of freedom	4	39	112	5	16	173
Mean square	1356.310	6356.721	976.572	2417.08	456.211	8321.822
F test value	0.627	13.281	3.011	22.521	11.682	15.691
P	0.062	0.036	0.051	0.017	0.028	0.033

4 Discussion

In today’s society, the pressure is everywhere. Everyone has stress. Primary school students have the pressure of homework, middle school students have the pressure of going to school, college students have the pressure of employment, adults have the pressure of being laid off, and enterprises have the pressure of survival and development [25]. Because of the external expectations, the tension of the big competition, and the desire for gold, the psychological pressure of the athletes is particularly prominent. The specific performance is the situation that some competent athletes can’t respond decisively, thus affecting their normal performance [26]. Therefore, the VST was used to train and treat the subjects of athletes, to help them relieve stress and reduce SR. Firstly, it was found that the self-rating stress level was greatly lower than that before the training, while the stress level of the EG was greatly lower than that of the CG (P < 0.05). This was consistent with the research results of Ramey et al. [27], indicating that the EG trained with VST had more obvious effect on relieving psychological pressure. The PA score of the EG was greatly higher than that of the CG, and the NA score was greatly lower than that of the CG (P < 0.05). This was similar to the analysis results of A. Waters and D. Panchuk [28] on track and field athletes, indicating that VST can effectively reduce the NA of athletes and improve the level of PA.

In order to more accurately analyze the changes of AB of athletes, E-Prime 2.0 software was specially used to collect the responses of target stimuli near the faces of stimulus materials and calculate the AB score [29]. The results showed that after training, the scores of the AB of happy and neutral faces in the EG were greatly higher than those in the CG, while the scores of the AB of sad and aversive faces were greatly lower than those in the CG (P < 0.05). This was the same as the research results of Sani et al. (2019) [30]. This showed that after two months of training on VST, athletes paid less attention to negative information and paid more attention to positive and neutral information in stressful situations. The repeated measures ANOVA showed that the interaction between different faces and test time was obvious (F = 13.281, P = 0.036). The interaction between groups and test time was obvious (F = 15.691, P = 0.033). This result further proved the effect of VST on AB of athletes. Relevant studies have shown that when people were unfamiliar with an individual, they can obtain some positive and negative information of the individual at the same time. However, people tended to focus on the negative information of the individual and neglected the positive characteristics of the individual in the final evaluation [31]. However, this trait was particularly prominent in athletes, as a group with too much expectation from the outside world and too much competition, they would tend to receive more negative information from the outside world than ordinary people. In the case of asymmetry of positive and negative information, athletes would show more negative affect bias and ignore positive stimuli [32]. The analysis in this study also proved this point.

HRV referred to the small difference or small fluctuation phenomenon between successive heartbeat RR interval (instantaneous heart rate), which can quantitatively assess the tension of sympathetic nerve and vagus nerve activity of the heart, so as to determine the SR of the subjects. The results after training, LF/HF and LFnorm in the EG were greatly lower than those in the CG (P < 0.05). This was similar to the research results of Katayama et al. [33], which showed that after training, the sympathetic nerve activity of athletes in the EG was greatly reduced and the baroreceptor reflex activity was weakened. The specific performance was the athlete’s excitability decreased, and the heart rate decreased. In addition, HFnorm in the EG was greatly higher than that in the CG after training (P < 0.05), which indicated that after training of VST, the vagal nerve activity of the EG of athletes was greatly enhanced. Specifically, the blood pressure of athletes was reduced [34]. In addition, there were no statistically obvious differences in the frequency domain analysis of HRV before and after training (P > 0.05). It may be that the value of HRV was too small, and it was difficult to analyze the difference without data normalization. In fact, in addition to the frequency-domain analysis, there was a more common time-domain analysis. However, according to Lynn et al. [35], the analysis time domain index was an index to measure the total activity of autonomic nerve. The frequency domain index can better reflect the activity and equilibrium state of vagus nerve and sympathetic nerve, which was selected in the study.

In addition, the pressure scenario was set by referring to the way of Voogd et al. [36]. Group competition, test reward, audience, and camera recording during the test were all external environmental factors that may affect the performance of athletes. Therefore, the test was conducted in a group of two to tie their own performance to others and increase the nervousness of the subjects. Inviting experts and other students to watch can be used as a situational variable to increase the tightness of the test scene. A cash reward for the top three on the test can enhance the perception of the importance of the situation. The camera, on the other hand, played a similar role as the audience, enhancing the formality of the test scene. Therefore, a variety of control methods were used to simulate the pressure scenarios to ensure the reliability of the VST training efficacy analysis.

5 Conclusion

With the help of AB score and HRV, the improvement effect of VST training on AB and emergency response of athletes under pressure situations were analysed. It was found that the training of VST can effectively improve the athletes’ PA and AB of positive information, reduce the AB of negative information and psychological pressure, and relieve the SR. However, only the AB score, pressure, and HRV of athletes before and after training were evaluated in real time, and there was no long time follow up for all the athletes, which made the study lack the discussion on the continuous effect of training. The follow-up plan is to expand the sample size and introduce female athletes for follow-up investigation. In a word, the study provided a reference for athletes to regulate their attention before competition in the future.

Footnotes

Acknowledgments

This work was supported by the Major Program of the National Social Science Fund of China, “Research on the Problems and Countermeasures of Major Institutional Mechanism to Revitalize the Old Industrial Base in Northeast China” (Grant No.17ZDA060); Funding achievement of the world top discipline construction of applied economics.

Conflict of interest

None to report.

References

Van Bockstaele

, Lamens

, Salemink

, et al. Reliability and validity of measures of attentional bias towards threat in unselected student samples: Seek, but will you find?Cognition and Emotion. 2020;34(2):217–28.

Pluhar

, McCracken

, Griffith

, et al. Meehan WP 3rd, Team Sport Athletes May Be Less Likely To Suffer Anxiety or Depression than Individual Sport Athletes. J Sports Sci Med. 2019;18(3):490–6. PMID: 31427871; PMCID: PMC6683619.

Gustafsson

, Sagar

, Stenling

. Fear of failure, psychological stress, and burnout among adolescent athletes competing in high level sport. Scand J Med Sci Sports. 2017;27(12):2091–102.

Mehrsafar

, Strahler

, Gazerani

, et al. The effects of mindfulness training on competition-induced anxiety and salivary stress markers in elite Wushu athletes: A pilot study. Physiol Behav. 2019;210:112655.

Pluhar

, McCracken

, Griffith

, et al. Team Sport Athletes May Be Less Likely To Suffer Anxiety or Depression than Individual Sport Athletes. J Sports Sci Med. 2019;18(3):490–6. PMID: 31427871; PMCID: PMC6683619.

Chang

, Putukian

, Aerni

, et al. Mental health issues and psychological factors in athletes: detection, management, effect on performance and prevention: American Medical Society for Sports Medicine Position Statement-Executive Summary. Br J Sports Med. 2020;54(4):216–20.

Born

, Lomax

, Romann

. Variation in competition performance, number of races, and age: Long-term athlete development in elite female swimmers. PLoS One. 2020;15(11):e0242442.

Forsdyke

, Smith

, Jones

, Gledhill

. Psychosocial factors associated with outcomes of sports injury rehabilitation in competitive athletes: a mixed studies systematic review. Br J Sports Med. 2016;50(9):537–44.

Kitaguchi

, Tanaka

, Takeshita

, et al. Importance of functional performance and psychological readiness for return to preinjury level of sports 1 year after ACL reconstruction in competitive athletes. Knee Surg Sports Traumatol Arthrosc. 2020;28(7):2203–12.

10.

Courage Scale for American English and Validation of the Factor Structure with Student-Athletes at Clemson University. Int J Environ Res Public Health. 2020;17(13):4834.

11.

Möbius

, Ferrari

GRA

, van den Bergh

, et al. Eye-Tracking Based Attention Bias Modification (ET-ABM) Facilitates Disengagement from Negative Stimuli in Dysphoric Individuals. Cogn Ther Res. 2018;42(4):408–20.

12.

Liu

, Taberthomas

, Fu

. Biobehavioral Markers of Attention Bias Modification in Temperamental Risk for Anxiety: A Randomized Control Trial. Journal of the American Academy of Child & Adolescent Psychiatry. 2018;57(2):103–10.

13.

Masataka

, Koda

, Atsumi

, et al. Preferential attentional engagement drives attentional bias to snakes in Japanese macaques (Macaca fuscata) and humans (Homo sapiens). Scientific Reports. 2018;8(1):1–9.

14.

Machner

, Könemund

, von der Gablentz

, et al. The ipsilesional attention bias in right-hemisphere stroke patients as revealed by a realistic visual search task: Neuroanatomical correlates and functional relevance. Neuropsychology. 2018;32(7):850.

15.

Linke

, Jones

, Pagliaccio

, et al. Efficacy and mechanisms underlying a gamified attention bias modification training in anxious youth: protocol for a randomized controlled trial. BMC Psychiatry. 2019;19(1):246.

16.

Hilchey

, Antinucci

, Lamy

, et al. Is attention really biased toward the last target location in visual search? Attention, response rules, distractors, and eye movements. Psychonomic Bulletin & Review. 2019;26(2):506–14.

17.

Foerster

, Schneider

. Involuntary top-down control by search-irrelevant features: Visual working memory biases attention in an object-based manner. Cognition. 2018;172, 37–45.

18.

Wieser

, Hambach

, Weymar

. Neurophysiological correlates of attentional bias for emotional faces in socially anxious individuals - Evidence from a visual search task and N2pc. Biological Psychology. 2018;132:192–201.

19.

Trapp

, Kalzendorf

, Baum

, et al. Attentional biases in patients suffering from unipolar depression: results of a dot probe task investigation. Psychiatry Res. 2018;261:325–31.

20.

Harvold

, Macleod

, Vaegter

. Attentional Avoidance is Associated With Increased Pain Sensitivity in Patients With Chronic Posttraumatic Pain and Comorbid Posttraumatic Stress. Clinical Journal of Pain. 2018;34(1):22–9.

21.

Wang

, Lai

. Reconstructing 3D Face Model with Associated Expression Deformation from a Single Face Image via Constructing a Low-Dimensional Expression Deformation Manifold. IEEE Trans Pattern Anal Mach Intell. 2011;33(10):2115–21.

22.

Kret

, Muramatsu

, Matsuzawa

. Emotion Processing Across and Within Species: A Comparison Between Humans (Homo sapiens) and Chimpanzees (Pan troglodytes). Journal of Comparative Psychology. 2018;132(4):395–409.

23.

Littman-Ovadia

, Russo-Netzer

. Prioritizing positivity across the adult lifespan: initial evidence for differential associations with positive and negative emotions. Qual Life Res. 2019;28(2):411–20.

24.

Yang

, Tang

, Zhou

. Effect of Mindfulness-Based Stress Reduction Therapy on Work Stress and Mental Health of Psychiatric Nurses. Psychiatr Danub. 2018;30(2):189–96.

25.

Zhang

, Ying

, Song

, et al. Mobile Phone Cognitive Bias Modification Research Platform for Substance Use Disorders: Protocol for a Feasibility Study. Jmir Research Protocols. 2018;7(6):e153.

26.

Robert

, Peter

, Catherine

, et al. 18.3 disturbed and intact attentional processes during working memory encoding in schizophrenia: converging behavioral and imaging genetics evidence. Schizophrenia Bulletin(suppl_1). 2018;44(Suppl 1):30.

27.

Ramey

, Yonelinas

, Henderson

. Conscious and unconscious memory differentially impact attention: Eye movements, visual search, and recognition processes. Cognition. 2019;185, 71–82.

28.

Waters

, Panchuk

, Phillips

, et al. Experiential Knowledge Affects the Visual Search Behaviors of Sprint Coaches and Sport Biomechanists. Front Sports Act Living. 2020;2:95.

29.

Nozadi

, Spinrad

, Johnson

, et al. Relations of Emotion-Related Temperamental Characteristics to Attentional Biases and Social Functioning. Emotion. 2017;18(4):481–92.

30.

Sani

SHZ

, Sadeghi-Bazargani

, Fathirezaie

, et al. Higher symptoms of attention-deficit/hyperactivity disorders (ADHD) and younger age were associated with faster visual perception, but not with lower traffic violations. Transportation Research Part F: Traffic Psychology and Behaviour. 2019;66:419–29.

31.

Caudek

, Ceccarini

, Sica

. Facial expression movement enhances the measurement of temporal dynamics of attentional bias in the dot-probe task. Behaviour Research & Therapy. 2017;95:58–70.

32.

Fullagar

, Skorski

, Duffield

, et al. Sleep and athletic performance: the effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Med. 2015;45(2):161–86.

33.

Katayama

, Saito

. Muscle sympathetic nerve activity during exercise. J Physiol Sci. 2019;69(4):589–98.

34.

Cavanaugh

, Barbot

, Carrasco

, et al. Attentional cues potentiate full recovery of fine motion discrimination in cortical blindness. Journal of Vision. 2017;17(7):57.

35.

Chaabène

, Hachana

, Franchini

, et al. Physical and physiological profile of elite karate athletes. Sports Med. 2012;42(10):829–43.

36.

De Voogd

, Wiers