Figures
Abstract
Examining non-sport-related cognitive tasks of attention and executive control in skilled athletes may provide insight into the acquisition of highly specific skills developed in experts as well as help identify successful performance in sport. Through a cross-sectional design, this study examined performance on aspects of attention and executive control among varsity athletes playing soccer (strategic sport) or track & field (static sport) using a computerized test of attention and executive control. Ninety-seven university athletes participating in soccer (n = 50) or track and field (n = 47) were included in the study. Domains of attention and executive control were examined using the Attention Network Test-Interactions (ANT-I). Mean reaction time (RT) and intra-individual variability (IIV) were compared between groups as measures of performance speed and performance stability respectively. Soccer players demonstrated overall faster RTs (p = 0.0499; ηp2 = .04) and higher response accuracy (p = .021, d = .48) on the ANT-I compared to track and field athletes. Faster RTs were observed for soccer players when presented with an alerting tone (p = .029, d = .45), valid orienting cue (p = .019, d = .49) and incongruent flanker (p = .031, d = .45). No significant group differences were observed in IIV (p = .083, d = .36). Athletes engaging in strategic sports (i.e., soccer) demonstrated faster performance under test conditions that required higher vigilance and conflict resolution. These findings suggest that engagement in strategic sports is associated with enhanced performance on non-sport-related cognitive tasks of attention and executive control.
Citation: Rahimi A, Roberts SD, Baker JR, Wojtowicz M (2022) Attention and executive control in varsity athletes engaging in strategic and static sports. PLoS ONE 17(4): e0266933. https://doi.org/10.1371/journal.pone.0266933
Editor: Frederik J.A. Deconinck, Ghent University, BELGIUM
Received: May 5, 2021; Accepted: March 30, 2022; Published: April 22, 2022
Copyright: © 2022 Rahimi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because it is part of a baseline data set held at York University. Data are available from the York University Institutional Data Access / Ethics Committee for researchers who meet the criteria for access to confidential data. Contact: wjokhoo@yorku.ca.
Funding: The funding that we received is from the Natural Sciences and Engineering Research Council (NSERC RGPIN-2020-06452). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The beneficial effects of exercise on cognitive function across the lifespan are well-documented [1–3]. Individuals who engage in regular physical activity have been found to demonstrate more accurate and faster performance on cognitive measures that require relational memory, aspects of attention, as well as executive function [4, 5]. Executive function is a set of cognitive processes and mental skills that depend on the harmonious interaction between working memory, mental flexibility, and self-control—three types of highly interdependent brain functions that enable individuals to plan, monitor, and achieve their goals [6]. Competitive sports training, which requires high physical and motor fitness, has been associated with greater task-related neural network efficiency [7]. Elite athletes who engage in sports that require high levels of structure (i.e., abiding by a complex set of rules) and coordination (i.e., adapting to variable game situations) have been found to demonstrate superior performance on tasks requiring decision making, perception, and visual searching capacity [8–10]. The perceptual skills that distinguish sporting experts from less-skilled peers in time-constrained decision-making sports seem not only highly specific to the demands of the task but also to performers’ prior practice experiences [11]. Although this suggests a key role for practice in explaining differences between performers, the potential value of general cognitive capacities has re-emerged in discussions of skilled athletic performance (e.g., the value of multiple object tracking) [12]. While the precise role of these general cognitive capacities is not known, it is plausible that increased general attention skill underpins the acquisition of highly specific skills developed in experts and/or influence an individual’s choice of activity during early development (e.g., those with higher capacity will be drawn to sports emphasizing this quality). Examining these general skills across different types of activities would extend our understanding of ‘where’ these skills are valuable, a meaningful first step to understanding ‘why’.
In their meta-analysis of cognition and sport expertise, Voss, Kramer, Basak, Prakash and Roberts [13] used a simple approach that categorized sports into three types: static, interceptive and strategic. Static sports are self-paced, closed-skill, and require independence and consistency (e.g., track and field, swimming). Interceptive sports are externally paced, open-skill, and involve a high degree of body-object coordination (e.g., tennis, badminton). Finally, strategic sports are externally paced, open-skill, and require simultaneous information processing and effective attentional allocation (e.g., soccer, volleyball). Studies have reported differences in performance on aspects of visuomotor speed, visual attention, and executive function associated with the type of sport played [14–22]. For example, athletes who engage in strategic sports have been found to perform better on tasks requiring visual attention and motor inhibition compared to those in interceptive sports [19], as well as better inhibitory control [15, 18] and stability of performance than athletes in static sports [23]. These findings support the cognitive skill transfer hypothesis which posits that skills acquired through training in one cognitive task may improve performance on a related but untrained task [13, 24–27]. In contrast, other studies have not found significant differences in cognitive performance across sport types [28, 29] or have found evidence of superior cognitive performance—such as response inhibition—in static, self-paced sports (e.g., swimming) at a varsity level [30].
To date, research examining cognitive profiles of athletes participating in strategic sports suggest that these athletes may demonstrate better performance on aspects of cognitive functioning related to their training and sport demands [15, 18, 19, 23]. However, studies that have investigated performance variability in attention and executive control measures in these groups are limited [15]. Intra-individual variability (IIV) is defined as the variability of performance across occasions [31]. Those who perform well on cognitive tasks tend to show less variability and more stability than those who perform poorly [32–34]. Therefore, IIV in performance across a set of trials provides unique information about the stability of an individual’s performance, their ability to maintain focus, and may reflect neural integrity of frontal regions [35–38].
Furthermore, the presence of potential confounding factors, such as level of education and concussion history, as well as the use of mixed sport samples in the current literature, make it challenging to appropriately assess the associations between participation in strategic sports and cognitive functioning. The present study examined pre-season performance on measures of speed, accuracy, and stability on a task of attention and executive control (i.e., Attention Network Test-Interactions) [39] in a sample of healthy varsity soccer (i.e., strategic sport) and track and field (i.e., static sport) athletes, comparable on age, sex, education, and level of play. The Attention Network Test (ANT) was originally developed by Fan and colleagues [40] and later modified as the Attention Network Test-Interactions (ANT-I) by Callejas and colleagues [39] to measure attention networks postulated by Posner and Petersen [41] and their interactions in the context of a single task. Since its release, the ANT and its modifications, including the ANT-I, have been widely adopted in various studies for their convenient administration and reliable estimation of the three attentional networks: alerting, orienting, and executive control [42–47]. In this study, it was hypothesized that soccer players would (1) exhibit faster reaction time (RT) in milliseconds (ms) across all three attention networks, (2) demonstrate more accurate performance when faced with incongruent flanker information, and (3) demonstrate less variability in performance compared to track and field players.
Method
Participants
From June 2019 to January 2020, varsity athletes were recruited at pre-season baseline testing as part of a larger study that included athletes in soccer, football, hockey, basketball, volleyball, tennis, track and field, cross-country, or wrestling from York University, Toronto, Canada. All participants were varsity level players, meaning they competed at an intermediate level and possessed moderate skills and professionalism relative to their age and academic background. The current study included athletes between 17 and 24 years of age who were either playing soccer (n = 50; 50% female) or track and field (n = 50; 57% female). Participants were excluded from analyses if their mean RTs on the ANT-I were greater than three standard deviations (SDs) from the mean and if their accuracy rates were lower than 80% (ie., three track and field players were removed; ntrack and field = 47; N = 97) [48]. No participants were actively recovering from a concussion at the time of data collection. All participants provided informed written consent following procedures approved by the Human Participants Review Sub-Committee of York University’s Ethics Review Board.
Measures
Attention Network Test-Interaction (ANT-I). The ANT-I is a 10-minute computerized test measuring aspects of attention and executive control by examining RT performance across three domains: alerting (auditory tone), orienting (visual cue), and executive control (congruent and incongruent flankers) [49, 50]. Alerting refers to the ability to achieve and maintain response readiness to environmental cues and can be measured by comparing trials without or with auditory warnings before the target. Alerts are typically associated with decreased RT and increased error rate [41, 51–53]. Orienting is a function of selective attention allocation to high-priority stimuli in the environment and can be measured by comparing trials with a visuospatial cue and a target that either follows the cue or is presented opposite the cue. Performance typically deteriorates in the latter case since attention must first be disengaged then reorientated back to the target [52, 54]. Executive control involves managing conflicts and dealing with interference and can be measured by comparing trials with congruent flankers surrounding the target arrows (facing the same direction as the target) to trials with incongruent flankers (facing the opposite direction). Interference from the incongruent flankers typically deteriorates performance compared to the congruent flankers [52, 55, 56].
In our study, participants are presented with a series of five arrows located either above or below a central fixation cross and have to indicate the direction of the center arrow by pressing the "/" (right) or "z" (left) keys on a standard computer QWERTY keyboard. The test was composed of 25 practice trials and 144 test trials. The test trials contained a visual stimulus component in the form of a series of five arrows, with the center arrow pointing equally to the left or right, while the four surrounding arrows pointed in the same direction of the center arrow (congruent) in half of the trials and pointed in the opposite direction of the center arrow (incongruent) in the remaining half. The trials were presented at variable intervals, with a fixation cross appearing in the center of the screen between each trial. The intervals ranged from 400 to 1600 ms, and each trial lasted for 4450 ms. An alerting stimulus in the form of a 2000 Hz tone was played on half of the trials for 50 ms, followed by an orienting stimulus that appeared after 100 ms in the form of an asterisk on two-thirds of the trials for 50 ms. In addition, 48 trials were randomly presented with a valid spatial cue, with the asterisk located either above or below the central fixation cross, correctly signalling the upcoming position of the target stimulus. Another 48 trials were presented with an invalid spatial cue, incorrectly signalling the target stimulus’ upcoming position, and the remaining 48 trials had no cue associated with them (see Fig 1).
Example trial: Following a 400–1600 millisecond (ms) inter-trial period, the target stimulus is preceded by an alerting tone (second top panel) and a valid orienting cue (fourth top panel). The target (center) arrow is surrounded by incongruent flankers (second bottom panel). Possible cue, target (i.e., congruent and incongruent flankers), and alerting conditions are also displayed.
Statistical analysis
Mean RT (ms) was calculated for each of the twelve combinations of independent variables and IIV was measured by calculating individual standard deviations (ISD) across all trials for each participant [52, 57, 58]. In order to examine performance across all conditions, twelve condition scores were calculated based on every possible combination of alerting (tone vs. no tone), orienting (valid vs. invalid vs. no cue) and executive control (congruent vs. incongruent flankers) factors. To examine the effect of sport type on athletes’ performance on the three attention networks, a 2 (alerting) x 3 (orienting) x 2 (executive) x 2 (sport type) repeated measures ANOVA was conducted, and findings were followed up with independent-samples t-tests. Pairwise comparisons were conducted with adjusted p-values using the Bonferroni correction. A series of independent-samples t-tests were also conducted to compare inter-group response accuracy rates as well as IIV across all trials. A Mann-Whitney U test was used to examine between-group differences in lifetime concussion history. Two-tailed alpha was set at .05 for all procedures, and data were analyzed using Statistical Package for the Social Sciences (SPSS) version 26 (IBM corp. Released, 2019). Suggested standard effect size magnitude for ηp2 are: small = 0.01; medium = 0.06; large = 0.14; and for Cohen’s d are: small = 0.20; medium = 0.50; large = 0.80 [59].
Results
There was no significant difference in self-reported lifetime concussion history for soccer players (M = 0.62, Mdn = 0.00; SD = 1.00) and track and field players (M = 0.38, Mdn = 0.00, SD = 0.23; U = 1028.50, Z = -1.31, p = 0.190). The results of the 2x3x2x2 ANOVA indicated a significant main effect of alerting (F(1,95) = 25.68, p < .001) with a large effect size (ηp2 = .21), a main effect of orienting (F(2,190) = 144.21, p < .001), with a large effect size (ηp2 = .60), as well as a main effect of executive control (F(1,95) = 995.67, p < .001), with a large effect size (ηp2 = .91). Pairwise comparisons for the main effect of each network condition indicated faster performance in the presence of a tone, compared to no tone (p < .001; 95% CIs[-14.53, -6.35]), valid cue compared to an invalid cue (p < .001; 95% CIs[-54.65, -34.28]), or no cue (p < .001; 95% CIs[-70.06, -54.23]); and invalid cue compared to no cue (p < .001; 95% CIs[-27.01, -8.34]) across both groups. In addition, performance was faster when the flankers were congruent compared to incongruent (p < .001; 95% CIs[-109.37, -96.43]) across both groups. There was also a main effect of sport type (F(1,95) = 3.94, p = .049), with a small effect size (ηp2 = .04), with overall faster performance among soccer players (M = 597.37; SD = 65.25), compared to track and field players (M = 624.15; SD = 67.54; p = .049; d = .40), with a small-to-medium effect size.
No significant interactions were observed between sport type and the alerting (F(1, 95) = 3.43, p = .067, ηp2 = .04), orienting (F(2,190) = 1.47, p = .233, ηp2 = .02), or executive control networks (F(1,95) = 3.80, p = .054, ηp2 = .04). For exploratory purposes, post-hoc analyses were conducted. Compared to track and field players, faster RTs (ms) were observed for soccer players under alerting no tone (t(95) = -2.22, p = .029, d = .45), orienting valid cue (t(95) = -2.39, p = .019, d = .49), and executive incongruent flanker conditions (t(95) = -2.19, p = .031, d = .45; see Table 1), all with small-to-medium effect sizes. The results also indicated a small-to-medium significant difference in overall response accuracy rates between the two sports (t(95) = 2.35, p = .021, d = .48), with soccer players responding somewhat more accurately to test conditions in comparison to track and field players (97.7% versus 96.2%). Lastly, there was no statistically significant between-group difference in IIV, (t(95) = -1.75, p = .083, d = .36).
Discussion
The current study examined speed and stability of performance on a measure of attention and executive control among strategic (i.e., soccer) and static sport varsity athletes (i.e., track and field). It was hypothesized soccer players would (1) exhibit faster RT across all three attention networks, (2) demonstrate better performance when faced with incongruent flanker information, and (3) demonstrate less variability in performance compared to track and field players. Consistent with prior studies examining ANT-I performance, both strategic and static players displayed faster performance in conditions where there was a tone compared to no tone, a valid cue compared to an invalid cue or no cue, and where the flankers were congruent compared to incongruent [49, 50]. Consistent with our first hypothesis, a small difference in overall RT between groups was observed, with strategic athletes demonstrating faster overall performance compared to static athletes. Previous research examining cognitive performance between athletes in strategic and non-strategic sports (i.e., static or interceptive sports) has revealed mixed results depending on the aspects of cognition that were measured, how that domain was measured (i.e., via computerized tests of cognition or paper and pencil neuropsychological measures), as well as which groups were examined (i.e., elite athletes, disabled athletes, single sport, or mixed sports athletes) [14, 15, 18, 19, 22, 23].
To date, there is some evidence of superior performance (i.e., faster RTs and higher accuracy) on tasks of motor speed, visual attention, and inhibition among athletes participating in strategic sports [15, 18, 19, 23]. However, others have reported that elite athletes participating in endurance (i.e., marathon runners) or motorically complex (i.e., Wushu) sports did not demonstrate significantly different performance on neuropsychological tests of inhibition, cognitive flexibility, and planning (i.e., Stroop; Wisconsin Card Sorting Test; WCST, Tower of London) [28]. Similarly, no differences were observed between externally-paced and self-paced athletes on similar measures of inhibition and cognitive flexibility (i.e., Stroop; D-KEFS Tower Test) [30]. In the present study, using athletes who were comparable in age, years of education, and level of play (i.e., varsity), we observed evidence of overall faster RTs in strategic sport athletes. While this difference was relatively small (i.e., in the order of 26 ms), the small effects are meaningful and significant [60]. It is possible that a difference of a few milliseconds in competitive sports can not only protect athletes from severe injuries [61, 62] but also place them at an advantage when processing multiple external stimuli, engaging in coordinated actions and executing complex motor functions [63, 64].
Further examination of performance across differing conditions revealed that soccer players were less affected under more difficult alerting (i.e., faster RTs in the absence of a tone) and executive control conditions (i.e., faster RTs with incongruent flankers), which was consistent with our second hypothesis. This suggests these athletes were able to maintain their speed despite the need for increased vigilance, error detection, and conflict resolution compared to track and field players. Performance variability across trials for the two groups was examined and a statistically significant difference was not observed, which was inconsistent with our third hypothesis. However, it is possible for an effect to be observed with a larger and more diverse sample. Playing strategic sports often necessitates the use of enhanced inhibitory functions in order to tune out irrelevant and distracting environmental stimuli and to attend to informative cues regarding each player’s position [65, 66]. Therefore, it is possible the specific cognitive demands of playing a strategic sport, such as soccer, may train athletes to perform better on domain-general tasks of attention and executive control. Recent findings of a study using the Cambridge Brain Sciences (CBS) test battery support this claim by indicating that young athletes playing team sports (i.e., soccer, basketball, volleyball, korfball, or hockey) display enhanced executive function in contrast to athletes playing self-paced sports (i.e., cycling, swimming, or athletics) and sedentary controls [67]. This would align with the broad cognitive skill transfer hypothesis, which suggests that training in one cognitive task enhances performance on another related, yet untrained cognitive task [13, 25–27]. However, given the cross-sectional nature of these data, it is difficult to determine whether the observed differences are due to self-selection (i.e., whether those with superior attention/executive control choose to engage in strategic sports) as opposed to the effects of specific training. This remains a key question for future research to determine the role of domain-general capacities in highly skilled performance.
It was also observed that soccer players responded more accurately on the ANT-I compared to track and field players, although this difference was relatively small (97.74% versus 96.23%, respectively). Nevertheless, a 1.5% difference in accuracy could have a significant effect on performance outcomes for an athlete, particularly in groups at this level of skill [60]. Prior research has reported that athletes in open-skilled sports (i.e., volleyball) perform more accurately than athletes from closed-skilled sport and non-sport controls on measures of selective information processing [23]. Therefore, it is possible that athletes playing strategic sports may have an advantage in information processing efficiency, both in terms of speed and accuracy, which may translate to maximizing top-down processes under time pressure in anticipation of the opponents’ tactical approaches, as well as the ball’s movements across the field [13, 21, 23, 68, 69]. Future research is needed to further understand this relationship. These characteristics are also referred to as "game intelligence", which allow strategic athletes to make fast and effective decisions based on their prior training [8, 70]. On the one hand, the observed findings of increased speed and accuracy may reflect cognitive adaptability that soccer players develop over time as they learn to adjust to their opponents’ advances in highly variable circumstances. On the other, the findings may reflect selection by athletes (i.e., self-selection) or coaches (i.e., through talent selection initiatives) of those who have predispositions to success in these activities.
Overall, this study contributes to the growing body of literature examining associations between sport type and performance on domain-general cognitive tasks in athletes. It highlights the potential bidirectional relationship between cognitive benefits and engaging in strategic, open-skill, invasion-style activities in young adulthood. Nevertheless, there are several limitations associated with the interpretations that require addressing. First, this study did not include objective measures of cardiovascular fitness or any anthropometric variables such as weight, height, and body mass index, which may have contributed to individual differences. Future studies would benefit from incorporating heartrate monitors and other anthropometric and physiological indices—such as percentage of body fat and muscle mass—to address whether a superior fitness status or long-term engagement in athletic training is associated with differences in cognitive performance. A further limitation of this study is that it did not account for behavioural and socioemotional functions (e.g., mood, motivation, sleep habits), as well as overall neuromuscular fitness (e.g., postural control, muscle strength, agility, muscle reflex activity, etc.), which may have been associated with improved performance on the cognitive test. Future studies would benefit from including these factors in their analyses for a more thorough characterization of the processes by which sports participation influences cognitive performance. Lastly, characterizing athletes’ years of training and specialization of training (e.g., single sport versus multi-sport participation) would help elucidate the association between sport engagement and cognitive functioning.
Conclusions
This study found that, in a sample of varsity athletes of comparable education, sex, and level of play, those engaging in strategic sports demonstrated increased speed, accuracy, and conflict resolution compared to static athletes on a non-sport-specific cognitive test. Findings from this study align with prior research suggesting that sport type may be associated with differential cognitive profiles [14–22] and suggests that the deliberate practice of strategic sports may play a role in enhancing performance on non-sport-specific cognitive tasks. However, it is notable that the observed differences were relatively small (effect sizes less than 0.50), suggesting that differences in cognitive performance may be subtle when using relatively a well-matched sample of highly fit individuals, or that differences may be explained by a disproportionate representation of athletes who are predisposed to succeed in these tasks as a result of selection bias. Future research should explore the possible influence of different levels and specialization of training on attention and executive control performance. In addition, further research examining relationships between domain-specific athletic training and cognitive performance on a variety of sport-and non-sport-related test batteries is warranted.
Acknowledgments
The authors would like to acknowledge Alanna Pierias, Diana Gorbet, and Melissa Miljanovski for their significant support and contribution to data collection, as well as the York University Sports Injury Clinic for their assistance with recruitment.
References
- 1. Chu C-H, Alderman BL, Wei G-X, Chang Y-K. Effects of acute aerobic exercise on motor response inhibition: An ERP study using the stop-signal task. J Sport Health Sci. 2015 Mar 1;4(1):73–81.
- 2. Kramer AF, Colcombe S. Fitness Effects on the Cognitive Function of Older Adults: A Meta-Analytic Study-Revisited. Perspect Psychol Sci J Assoc Psychol Sci. 2018 Mar;13(2):213–7. pmid:29592650
- 3. Kramer AF, Erickson KI. Capitalizing on cortical plasticity: influence of physical activity on cognition and brain function. Trends Cogn Sci. 2007 Aug;11(8):342–8. pmid:17629545
- 4. Chaddock L, Hillman CH, Buck SM, Cohen NJ. Aerobic fitness and executive control of relational memory in preadolescent children. Med Sci Sports Exerc. 2011 Feb;43(2):344–9. pmid:20508533
- 5. Netz Y, Dwolatzky T, Zinker Y, Argov E, Agmon R. Aerobic fitness and multidomain cognitive function in advanced age. Int Psychogeriatr. 2011 Feb;23(1):114–24. pmid:20566000
- 6. Stuss DT, Alexander MP. Executive functions and the frontal lobes: a conceptual view. Psychol Res. 2000 Aug 1;63(3):289–98. pmid:11004882
- 7. Guo Z, Li A, Yu L. “Neural Efficiency” of Athletes’ Brain during Visuo-Spatial Task: An fMRI Study on Table Tennis Players. Front Behav Neurosci. 2017;11:72. pmid:28491026
- 8. Barreiros A, Abreu A. Sports expertise: Is nature or nurture to blame? No, it’s the brain! undefined [Internet]. 2017 [cited 2022 Jan 22]; Available from: https://www.semanticscholar.org/paper/SPORTS-EXPERTISE%3A-IS-NATURE-OR-NURTURE-TO-BLAME-NO%2C-Barreiros-Abreu/7c7f48d5f93ff80afe8e1a14307fa1aa1b945b88
- 9. Loffing F, Hagemann N, Schorer J, Baker J. Skilled players’ and novices’ difficulty anticipating left- vs. right-handed opponents’ action intentions varies across different points in time. Hum Mov Sci. 2015 Apr;40:410–21. pmid:25689236
- 10. Mann DTY, Williams AM, Ward P, Janelle CM. Perceptual-cognitive expertise in sport: a meta-analysis. J Sport Exerc Psychol. 2007 Aug;29(4):457–78. pmid:17968048
- 11. Loffing F, Schorer J, Hagemann N, Baker J. On the advantage of being left-handed in volleyball: further evidence of the specificity of skilled visual perception. Atten Percept Psychophys. 2012 Feb;74(2):446–53. pmid:22147534
- 12. Romeas T, Guldner A, Faubert J. 3D-Multiple Object Tracking training task improves passing decision-making accuracy in soccer players. Psychol Sport Exerc. 2015 Jun 16;19.
- 13. Voss MW, Kramer AF, Basak C, Prakash RS, Roberts B. Are expert athletes ‘expert’ in the cognitive laboratory? A meta-analytic review of cognition and sport expertise. Appl Cogn Psychol. 2010;24(6):812–26.
- 14. Burris K, Liu S, Appelbaum L. Visual-motor expertise in athletes: Insights from semiparametric modelling of 2317 athletes tested on the Nike SPARQ Sensory Station. J Sports Sci. 2020 Feb;38(3):320–9. pmid:31782684
- 15. Di Russo F, Bultrini A, Brunelli S, Delussu AS, Polidori L, Taddei F, et al. Benefits of sports participation for executive function in disabled athletes. J Neurotrauma. 2010 Dec;27(12):2309–19. pmid:20925480
- 16. Gu Q, Zou L, Loprinzi PD, Quan M, Huang T. Effects of Open Versus Closed Skill Exercise on Cognitive Function: A Systematic Review. Front Psychol. 2019;10:1707. pmid:31507472
- 17. Heinen T. Do static-sport athletes and dynamic-sport athletes differ in their visual focused attention? Sport J [Internet]. 2011 [cited 2022 Jan 22]; Available from: https://thesportjournal.org/
- 18. Krenn B, Finkenzeller T, Würth S, Amesberger G. Sport type determines differences in executive functions in elite athletes. Psychol Sport Exerc. 2018 Sep 1;38:72–9.
- 19. Meng F-W, Yao Z-F, Chang EC, Chen Y-L. Team sport expertise shows superior stimulus-driven visual attention and motor inhibition. PloS One. 2019;14(5):e0217056. pmid:31091297
- 20. Moreau D, Clerc J, Mansy-Dannay A, Guerrien A. Enhancing spatial ability through sport practice: Evidence for an effect of motor training on mental rotation performance. J Individ Differ. 2012;33(2):83–8.
- 21. Yu M, Liu YB, Yang G. Differences of attentional networks function in athletes from open-skill sports: an functional near-infrared spectroscopy study. Neuroreport. 2019 Dec 18;30(18):1239–45. pmid:31568205
- 22. Yu M, Liu Y. Differences in executive function of the attention network between athletes from interceptive and strategic sports. J Mot Behav. 2021;53(4):419–30. pmid:32654658
- 23. Chiu CN, Chen C-Y, Muggleton NG. Sport, time pressure, and cognitive performance. Prog Brain Res. 2017;234:85–99. pmid:29031474
- 24. Formenti D, Trecroci A, Duca M, Cavaggioni L, D’Angelo F, Passi A, et al. Differences in inhibitory control and motor fitness in children practicing open and closed skill sports. Sci Rep. 2021 Feb 17;11(1):4033. pmid:33597630
- 25. Furley P, Memmert D. Differences in spatial working memory as a function of team sports expertise: the Corsi Block-tapping task in sport psychological assessment. Percept Mot Skills. 2010 Jun;110(3 Pt 1):801–8. pmid:20681333
- 26. Taatgen NA. The nature and transfer of cognitive skills. Psychol Rev. 2013 Jul;120(3):439–71. pmid:23750831
- 27. Wang C-H, Chang C-C, Liang Y-M, Shih C-M, Chiu W-S, Tseng P, et al. Open vs. closed skill sports and the modulation of inhibitory control. PloS One. 2013;8(2):e55773. pmid:23418458
- 28. Chang EC-H, Chu C-H, Karageorghis CI, Wang C-C, Tsai JH-C, Wang Y-S, et al. Relationship between mode of sport training and general cognitive performance. J Sport Health Sci. 2017 Mar;6(1):89–95. pmid:30356524
- 29. Chueh T-Y, Huang C-J, Hsieh S-S, Chen K-F, Chang Y-K, Hung T-M. Sports training enhances visuo-spatial cognition regardless of open-closed typology. PeerJ. 2017;5:e3336. pmid:28560098
- 30. Jacobson J, Matthaeus L. Athletics and executive functioning: How athletic participation and sport type correlate with cognitive performance. Psychol Sport Exerc. 2014 Sep 1;15(5):521–7.
- 31. Ram N, Rabbitt P, Stollery B, Nesselroade JR. Cognitive performance inconsistency: intraindividual change and variability. Psychol Aging. 2005 Dec;20(4):623–33. pmid:16420137
- 32. Li S, Aggen SH, Nesselroade JR, Baltes PB. Short-term fluctuations in elderly people’s sensorimotor functioning predict text and spatial memory performance: The Macarthur Successful Aging Studies. Gerontology. 2001 Apr;47(2):100–16. pmid:11287736
- 33. Rabbitt P, Osman P, Moore B, Stollery B. There are stable individual differences in performance variability, both from moment to moment and from day to day. Q J Exp Psychol A. 2001 Nov;54(4):981–1003. pmid:11765745
- 34. MacDonald SWS, Nyberg L, Bäckman L. Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends Neurosci. 2006 Aug;29(8):474–80. pmid:16820224
- 35. Garrett DD, Samanez-Larkin GR, MacDonald SWS, Lindenberger U, McIntosh AR, Grady CL. Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci Biobehav Rev. 2013 May;37(4):610–24. pmid:23458776
- 36. MacDonald SWS, Hultsch DF, Dixon RA. Performance variability is related to change in cognition: evidence from the Victoria Longitudinal Study. Psychol Aging. 2003 Sep;18(3):510–23. pmid:14518812
- 37. Macdonald SWS, Hultsch DF, Dixon RA. Predicting impending death: inconsistency in speed is a selective and early marker. Psychol Aging. 2008 Sep;23(3):595–607. pmid:18808249
- 38. Perri RL, Di Russo F. Executive Functions and Performance Variability Measured by Event-Related Potentials to Understand the Neural Bases of Perceptual Decision-Making. Front Hum Neurosci. 2017;11:556. pmid:29187818
- 39. Callejas A, Lupiàñez J, Funes MJ, Tudela P. Modulations among the alerting, orienting and executive control networks. Exp Brain Res. 2005 Nov;167(1):27–37. pmid:16021429
- 40. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. J Cogn Neurosci. 2002 Apr 1;14(3):340–7. pmid:11970796
- 41. Posner MI, Petersen SE. The attention system of the human brain. Annu Rev Neurosci. 1990;13:25–42. pmid:2183676
- 42. Fan J, McCandliss BD, Fossella J, Flombaum JI, Posner MI. The activation of attentional networks. NeuroImage. 2005 Jun;26(2):471–9. pmid:15907304
- 43. Mannarelli D, Pauletti C, Currà A, Marinelli L, Corrado A, Delle Chiaie R, et al. The Cerebellum Modulates Attention Network Functioning: Evidence from a Cerebellar Transcranial Direct Current Stimulation and Attention Network Test Study. Cerebellum Lond Engl. 2019 Jun;18(3):457–68. pmid:30798474
- 44. Pauletti C, Mannarelli D, Locuratolo N, Pollini L, Currà A, Marinelli L, et al. Attention in Parkinson’s disease with fatigue: evidence from the attention network test. J Neural Transm Vienna Austria 1996. 2017 Mar;124(3):335–45. pmid:27783210
- 45. Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, et al. Development of attentional networks in childhood. Neuropsychologia. 2004;42(8):1029–40. pmid:15093142
- 46. Williams RS, Biel AL, Wegier P, Lapp LK, Dyson BJ, Spaniol J. Age differences in the Attention Network Test: Evidence from behavior and event-related potentials. Brain Cogn. 2016 Feb;102:65–79. pmid:26760449
- 47. Yang T, Xiang L. Executive control dysfunction in subclinical depressive undergraduates: Evidence from the Attention Network Test. J Affect Disord. 2019 Feb 15;245:130–9. pmid:30388555
- 48. Garrett DD, MacDonald SWS, Craik FIM. Intraindividual reaction time variability is malleable: Feedback-and education-related reductions in variability with age. Front Hum Neurosci. 2012;6. pmid:22375109
- 49. Callejas A, Lupiáñez J, Tudela P. The three attentional networks: on their independence and interactions. Brain Cogn. 2004 Apr;54(3):225–7. pmid:15050779
- 50. Ishigami Y, Eskes G, Tyndall A, Longman R, Drogos L, Poulin M. The Attention Network Test-Interaction (ANT-I): reliability and validity in healthy older adults. Exp Brain Res. 2016 Mar 1;234. pmid:26645310
- 51. Coull JT, Frith CD, Frackowiak RS, Grasby PM. A fronto-parietal network for rapid visual information processing: a PET study of sustained attention and working memory. Neuropsychologia. 1996 Nov;34(11):1085–95. pmid:8904746
- 52. Ishigami Y, Klein RM. Repeated measurement of the components of attention using two versions of the Attention Network Test (ANT): stability, isolability, robustness, and reliability. J Neurosci Methods. 2010 Jun 30;190(1):117–28. pmid:20435062
- 53.
Marrocco RT, Davidson MC. Neurochemistry of attention. In: The attentive brain. Cambridge, MA, US: The MIT Press; 1998. p. 35–50.
- 54. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002 Mar;3(3):201–15. pmid:11994752
- 55. Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci. 2000 Jun;4(6):215–22. pmid:10827444
- 56. MacDonald AW, Cohen JD, Stenger VA, Carter CS. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science. 2000 Jun 9;288(5472):1835–8. pmid:10846167
- 57. Mazerolle EL, Wojtowicz MA, Omisade A, Fisk JD. Intra-individual variability in information processing speed reflects white matter microstructure in multiple sclerosis. NeuroImage Clin. 2013;2:894–902. pmid:24179840
- 58. Wojtowicz MA, Ishigami Y, Mazerolle EL, Fisk JD. Stability of intraindividual variability as a marker of neurologic dysfunction in relapsing remitting multiple sclerosis. J Clin Exp Neuropsychol. 2014;36(5):455–63. pmid:24742170
- 59.
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge; 1988. 567 p.
- 60.
Baker J, Young BW, Tedesqui RAB, McCardle L. New Perspectives on Deliberate Practice and the Development of Sport Expertise. In: darby [Internet]. John Wiley & Sons, Ltd; 2020 [cited 2022 Jan 22]. p. 556–77. Available from: http://onlinelibrary.wiley.com/doi/abs/10.1002/9781119568124.ch26
- 61. Eckner JT, Lipps DB, Kim H, Richardson JK, Ashton-Miller JA. Can a Clinical Test of Reaction Time Predict a Functional Head-Protective Response? Med Sci Sports Exerc. 2011 Mar;43(3):382–7. pmid:20689458
- 62. Wilkerson G. Neurocognitive Reaction Time Predicts Lower Extremity Sprains and Strains. Int J Athl Ther Train. 2012 Nov 1;17:4–9.
- 63. Darby D, Moriarity J, Pietrzak R, Kutcher J, McAward K, McCrory P. Prediction of winning amateur boxers using pretournament reaction times. J Sports Med Phys Fitness. 2014 Jun;54(3):340–6. pmid:24739297
- 64. Hülsdünker T, Ostermann M, Mierau A. Standardised computer-based reaction tests predict the sport-specific visuomotor speed and performance of young elite table tennis athletes. Int J Perform Anal Sport. 2019 Nov 2;19(6):953–70.
- 65. Ballester R, Huertas F, Pablos-Abella C, Llorens F, Pesce C. Chronic participation in externally paced, but not self-paced sports is associated with the modulation of domain-general cognition. Eur J Sport Sci. 2019 Sep;19(8):1110–9. pmid:30786834
- 66. Singer RN. Performance and human factors: considerations about cognition and attention for self-paced and externally-paced events. Ergonomics. 2000 Oct;43(10):1661–80. pmid:11083145
- 67. De Waelle S, Laureys F, Lenoir M, Bennett SJ, Deconinck FJA. Children Involved in Team Sports Show Superior Executive Function Compared to Their Peers Involved in Self-Paced Sports. Child Basel Switz. 2021 Mar 30;8(4):264. pmid:33808250
- 68. Kida N, Oda S, Matsumura M. Intensive baseball practice improves the Go/Nogo reaction time, but not the simple reaction time. Brain Res Cogn Brain Res. 2005 Feb;22(2):257–64. pmid:15653298
- 69. Nakamoto H, Mori S. Effects of stimulus-response compatibility in mediating expert performance in baseball players. Brain Res. 2008 Jan 16;1189:179–88. pmid:18048011
- 70. Vestberg T, Gustafson R, Maurex L, Ingvar M, Petrovic P. Executive functions predict the success of top-soccer players. PloS One. 2012;7(4):e34731. pmid:22496850