Do Elite Taekwondo Athletes Invest Time for Better Choices? Analysis of Anticipatory Behavior Through a Perception

Abstract

This study examined whether elite Taekwondo athletes invest additional time to make better decisions under uncertainty by using a perception–action coupling task with temporal occlusion. Eighteen elite and 18 amateur Taekwondo athletes completed an embodied choice task in which they observed an opponent’s kicking action and executed a corresponding motor response. Three temporal occlusion levels were used to manipulate the amount of visual information available (T1, T2, and T3). Reaction time, prediction accuracy, and decision confidence were analyzed using linear mixed-effects models. Descriptive results showed that amateurs became slightly faster from T1 to T3, whereas elites were slower at T1 but fastest at T3. Elites also showed substantial improvements in prediction accuracy and decision confidence as visual information increased, whereas amateurs showed only modest changes in accuracy and a gradual decline in confidence. The linear mixed-effects models revealed significant effects of group, occlusion level, and their interaction for reaction time and prediction accuracy, and a significant Group × Occlusion interaction for decision confidence. These findings suggest that elite Taekwondo athletes do not simply react faster; rather, they strategically delay their responses under high uncertainty and respond more efficiently once decisive kinematic cues become available. The results support the applicability of the embodied choice framework to one-to-one combat sports and highlight the importance of training perceptual timing and anticipatory skill in Taekwondo.

Keywords

Taekwondo embodied choice temporal occlusion anticipation decision confidence perceptual-motor coupling

Introduction

In cognitive psychology in sports, elite athletes tend to make faster and more accurate decisions than do novices (Araújo et al., 2019; Mann et al., 2007; Silva et al., 2020; Travassos et al., 2013). In particular, open-skill sports require decision-making under constant environmental changes, time pressure, and multiple options. Therefore, response accuracy, speed, and confidence are critical factors determining performance outcomes (Chung IL Kim & Kim, 2012; Musculus et al., 2018; Raab, 2012).

To explain the complexity of such decision-making behaviors, Raab (2012) proposed simple heuristic-based rules, which later expanded into the embodied cognition perspective. This perspective posits that cognitive processes and motor execution are closely linked within a perception–action coupling structure. It suggests that measuring these processes through tasks involving actual motor responses, as in real-game situations, ensures higher ecological validity (Lepora & Pezzulo, 2015; Raab, 2017). However, previous studies have often relied on non-motor response methods such as verbal expressions (Bruce et al., 2012; Loiseau Taupin et al., 2023; van Maarseveen et al., 2018), button pressing (Bruce et al., 2012), or fine finger movements using joysticks (Savelsbergh et al., 2005).

Since these traditional methods are limited in their ability to reflect the dynamic perception–action interactions inherent in sports, they may not fully capture an athlete’s realistic decision-making capability (Huesmann et al., 2022; Travassos et al., 2013). As an alternative, the perception–action coupled choice-response paradigm allows athletes to respond via physical movement, revealing clearer differences between elite and novice performers (Hinz et al., 2022a). Recent research on handball players using defensive motor responses found that elite athletes make qualitatively superior decisions and utilize a “wait-and-see” strategy, intentionally delaying responses to ensure better quality (Aglioti et al., 2008; Ranganathan & Carlton, 2007).

However, most studies on embodied choice mechanisms have been limited to team sports like handball, basketball, or soccer (Hepler & Feltz, 2012; Musculus et al., 2018). Decision-making in team sports relies heavily on complex environmental constraints like teammate positions and ball trajectories (Williams & Jackson, 2019). This differs fundamentally from one-to-one combat sports such as Taekwondo (Araújo et al., 2019; Runswick et al., 2020). Specifically, as a one-to-one combat sport, Taekwondo requires athletes to react under extreme time pressure, relying solely on the opponent’s subtle preparatory movements and body segment kinematics (Martínez de Quel & Bennett, 2019; Ripoll et al., 1995). Supporting this, a recent study demonstrated that martial artists rely primarily on global body kinematics rather than localized cues, such as the opponent’s head or facial expressions, to anticipate kick targets (Incognito et al., 2024). In the split-second exchanges of full-body combat, the intentional response delay for information gathering—as observed in sports like handball (Hinz et al., 2022a)— could directly lead to unsuccessful defensive responses and loss of points. Therefore, it is difficult to uncritically generalize decision-making mechanisms identified in team sports to the high-stakes, one-to-one environment of combat sports (Pinder et al., 2011).

Furthermore, existing video-based studies often suffer from outcome bias by showing continuous clips of the attacker’s full movement (Farrow, 2013). While elite athletes can pick up early kinematic cues before a movement fully unfolds (Abernethy & Zawi, 2007; Gredin et al., 2023; Ripoll et al., 1995), novices can correct their predictions if the final form of the strike is exposed. Therefore, integrating a temporal occlusion technique to control the availability of kinematic cues is essential (Müller et al., 2024).

This study examined reaction time, decision confidence, and anticipation accuracy in Taekwondo athletes of different skill levels using an embodied choice task with temporal occlusion. By doing so, it addressed methodological limitations in previous research and tested the applicability of the embodied choice paradigm to Taekwondo.

Materials and Methods

Participants

Amateurs were defined as individuals with no record of athlete registration with the Korea Taekwondo Association or competition experience, no history of elite competitive sparring training, less than 5 years of Taekwondo training, and a rank of 3rd dan or lower. In Taekwondo, an athlete’s proficiency and institutional rank are formally designated by the Dan (black belt) grading system, with higher degrees reflecting advanced technical expertise and extensive training (Kukkiwon, 2020). This definition was established to clearly represent recreational practitioners who train for personal development rather than professional competition, in contrast to the elite athlete group.

Elite athletes were defined as those with a rank of 3rd dan or higher and an award record in national-level competitions within the last 5 years. All participants were selected as those without physical limitations due to neurological or musculoskeletal diseases, no history of eye surgery in the last 3 months, and static visual acuity of 1.0 or higher.

The research was conducted in accordance with the ethical principles regarding human experimentation outlined in the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board. Prior to participation, all participants were fully informed about the study’s purpose, experimental procedures, and their right to withdraw at any time, and written informed consent was obtained from all individuals.

Procedure

Experimental Design

This study applied a mixed design of 2 Groups (Elite vs. Amateur) x 3 Visual Occlusion Points (Time1, Time2, Time3). Group was a between-subjects factor, and the visual occlusion point was a within-subjects repeated measure factor. The dependent variables consisted of reaction time, decision confidence, and anticipation accuracy, with 3,240 data points collected through the entire experiment.

Stimuli and Task

To measure participants’ anticipatory ability, an embodied choice task similar to actual Taekwondo sparring was used. Participants were asked to predict two types of kicks from an opponent on a screen and react as quickly as possible. The reaction conditions for the stimuli are as follows:

Reaction Time

Temporal Occlusion Conditions: Each video clip was edited to cut off at specific points of the attacking motion. As shown in Figure 1, the points were set from Time 1 to 3 to control the amount of information provided.

• Time 1 (Initial Cues): Footage providing information from the ready stance to the moment the opponent lifts their knee to execute a kick (E1–E2).

• Time 2 (Intermediate Cues): Footage providing information from the ready stance to the moment the opponent rotates their supporting foot for the kick (E1–E3).

• Time 3 (Total Cues): Footage providing information from the ready stance to the moment the opponent has fully executed the kick (E1–E4).

Figure 1.

Illustration of the temporal occlusion points based on the opponent’s kicking kinematics

Anticipation Accuracy

For the anticipation accuracy of the perception–action coupled response test, three types of kicks with the highest frequency of use were selected as stimuli, based on a previous analysis of World Championships where the recently revised “Best of Three” rule was applied (Kim & Kang, 2024). These consisted of (1) back-leg roundhouse kick, (2) front-leg roundhouse kick, and (3) side push kick. A pre-defined corresponding response for each stimulus was considered an accurate prediction. Specifically, responding with a counter back-leg roundhouse to a back-leg roundhouse, a back kick to a front-leg roundhouse, and a front-leg side push kick to a back-leg side push kick were defined as correct responses. All other executions were treated as incorrect.

Decision Confidence

Decision confidence was a task where participants evaluated their level of certainty immediately after performing a kick response to each stimulus. This approach directly incorporates the foundational methodology established by Steel et al. (2010), which demonstrated that measuring multi-level certainty ratings immediately following a time-constrained sport response provides a critical context for evaluating how situational constraints affect an athlete’s subjective certainty alongside objective latency.

In this study, measurement items were adapted from recent embodied choice research (Hinz et al., 2022b), and after each trial, the question “How accurate do you think your choice was?” was presented, and participants responded on a 6-point Likert scale (1 = absolutely ambiguous, 2 = ambiguous, 3 = indecisive, 4 = tendentious, 5 = unambiguous, 6 = absolutely unambiguous).

Experimental Procedure

The experiment was conducted individually in a laboratory where sound and lighting could be controlled. Participants were asked to place both feet on ground sensors installed 2 m in front of the screen and assume a sparring ready stance. Once pressure was detected on the sensors, the “Ready for Measurement” signal was confirmed in the program, and final readiness was assumed upon the measurer’s verbal signal “Ready.”

Each trial began after the participant was ready. Considering the possibility of a main testing effect where external variables are not controlled if subjects perform tasks repeatedly, 30 videos (10 each for T1, T2, T3) were presented in random order. Participants were asked to predict the attack type and perform the corresponding kick response. Reaction time and accuracy were measured immediately when the foot defending against the attack left the ground. Following the response, the participant verbally reported their confidence on a 6-point scale. The entire session took approximately 15 min.

Data Analysis

Reaction time (ms)

This was measured from the start of the video (after a random delay) until the moment the participant’s foot left the ground. A trimmed mean was calculated by excluding the maximum and minimum values from the 10 observations per Time interval.

R T_{{i, T}} = \frac{1}{8} \sum_{t = 1}^{8} Reaction {Time}_{{i, t}}

(1)

Anticipation Accuracy (%)

Each correct response was given 10 points. Accuracy was calculated as a percentage (%) of the total points earned per Time interval relative to the maximum possible points (100).

A c c u r a c y_{i, t} (%) = \frac{C o r r e c t_{i, t}}{10} \times 100

(2)

Decision Confidence (point)

The 1–6 point Likert scale values were averaged (trimmed mean) per Time interval for final analysis.

T M_{C o n f_{i, t}} = \frac{1}{8} \sum_{k = 2}^{9} C_{i, t, (k)}

(3)

Statistical Analysis

The independent variables of this study are the Group (2 levels) and Temporal Occlusion Level (3 levels). The dependent variables were reaction time (ms), decision confidence (points), and anticipation accuracy (%). Sample size was determined based on previous studies (Bruce et al., 2012; Hinz et al., 2022a; Raab & Laborde, 2011). These studies used MANOVA for group comparisons and reported medium-to-large effect sizes (f = 0.48). Additionally, the final effect size was calculated based on the mean and standard deviation (independent t-test results) between elite and Amateurs in a Taekwondo sparring situation provided by a previous study (Chung IL Kim & Kim, 2012) similar to this study.

Based on previous results, with settings of Test family = t-tests, Statistical test = Means: Difference between two independent means (two groups), Type of power analysis = A priori, Tail(s) = Two, Effect size d = 1.342, α = .05, Power (1−β) = 0.95, and Allocation ratio N2/N1 = 1, the minimum required sample size was 16 per group (total 32). Considering a 10% dropout rate, 36 participants were recruited.

To verify differences in the perception–action coupled-task performance between elite and amateur Taekwondo athletes, a 2 (Group: Elite, Amateur) × 3 (Occlusion Level: T1, T2, T3) mixed design was applied. Data preprocessing involved outlier testing using the Median Absolute Deviation (MAD) (Leys et al., 2013). Observations exceeding the median ± 2.5 × MAD were defined as outliers and excluded.

Statistical analysis was performed using a Linear Mixed Model (LMM). Fixed effects included Group, Occlusion Level, and their interaction, tested using Type III Sum of Squares. The repeated measure factor, Occlusion Level, was designated as SUBJECT, and a First-Order Autoregressive (AR (1)) covariance structure was applied. Parameters were estimated using Restricted Maximum Likelihood (REML), with degrees of freedom calculated via the Satterthwaite approximation.

The significance of fixed effects was tested via F-statistics. Significant main effects or interactions were followed by pairwise comparisons using Estimated Marginal Means (EMMEANS) with Bonferroni correction. All analyses were conducted using SPSS 31, with a significance level of α = .05.

Results

Descriptive Statistics

Table 1 presents the descriptive statistics for reaction time, prediction accuracy, and decision confidence by group and occlusion level. Overall, amateurs showed only small changes across occlusion levels, whereas elites showed clearer changes as more visual information became available. In particular, elites demonstrated marked improvements in prediction accuracy and decision confidence from T1 to T3, while amateurs remained relatively stable in prediction accuracy and showed a gradual decline in decision confidence. For reaction time, amateurs became slightly faster over time, whereas elites were slower at T1 but showed the fastest responses at T3.

Table 1.

Descriptive Statistics (Mean ± SD) for Reaction Time, Prediction Accuracy, and Decision Confidence by Group and Time

Group/Variable	T1	T2	T3
Reaction time (s) - amateur	0.813 ± 0.025	0.802 ± 0.018	0.793 ± 0.015
Reaction time (s) - elite	0.833 ± 0.017	0.825 ± 0.011	0.748 ± 0.018
Accuracy (%) – amateur	29.44 ± 11.43	30.00 ± 14.15	33.33 ± 7.86
Accuracy (%) – elite	47.78 ± 5.97	65.56 ± 12.51	78.89 ± 8.61
Confidence - amateur	3.33 ± 0.18	3.17 ± 0.17	3.04 ± 0.14
Confidence - elite	3.54 ± 0.29	3.53 ± 0.22	3.88 ± 0.18

Reaction Time

The linear mixed-effects model results for reaction time are presented in Table 2. Significant effects of group, occlusion level, and their interaction were observed. These findings indicate that the pattern of change in reaction time across occlusion levels differed between groups. Descriptively, amateurs showed a slight reduction in reaction time from T1 to T3, whereas elites responded more slowly at T1 but showed the fastest responses at T3.

Table 2.

Linear Mixed-Effects Model Results for Reaction Time

Predictors	Estimates	95% CI	p
(Intercept)	0.748	.736 –.759	<.001
Group (elite)	0.046	.030 –.062	<.001
Time T2	0.085	.069 –.101	<.001
Time T3	0.077	.060 –.094	<.001
Group × T2	−0.066	−.088 –-.043	<.001
Group × T3	−0.068	−.093 –-.044	<.001
Random effects
σ²	0
τ00 (ID)	0
Observations	60
Marginal R²/conditional R²	0.548/0.548

Anticipation Accuracy

Table 3 presents the linear mixed-effects model results for prediction accuracy. Significant effects of group, occlusion level, and their interaction were found. Overall, elites showed a substantial increase in prediction accuracy as more visual information became available, whereas amateurs showed only modest changes across occlusion levels. This pattern indicates that additional visual information contributed more strongly to accuracy improvements in elites than in amateurs.

Table 3.

Linear Mixed-Effects Model Results for Prediction Accuracy

Predictors	Estimates	95% CI	p
(Intercept)	78.889	72.254 – 85.523	<.001
Group (elite)	33.148	27.103 – 39.193	<.001
Time T2	−31.111	−40.414 – -21.808	<.001
Time T3	−13.333	−22.201 – -4.466	.004
Group × T2	27.222	14.065 – 40.379	<.001
Group × T3	10	−2.541–22.541	.115
Random effects
σ²	109.423
τ00 (ID)	32.556
Observations	60
Marginal R²/conditional R²	0.308/0.412

Decision Confidence

The linear mixed-effects model results for decision confidence are shown in Table 4. A significant interaction between group and occlusion level was observed. Across occlusion levels, decision confidence increased in elites but gradually decreased in amateurs, indicating opposite confidence trajectories between the two groups. This result suggests that additional visual information strengthened subjective certainty in elites, whereas it did not produce the same effect in amateurs.

Table 4.

Linear Mixed-Effects Model Results for Decision Confidence

Predictors	Estimates	95% CI	p
(Intercept)	3.878	3.750 – 4.006	<.001
Group (elite)	0.469	.347 –.591	<.001
Time T2	−0.34	−.517 – -.162	<.001
Time T3	−0.352	−.518 – -.187	<.001
Group × T2	0.631	.379 –.882	<.001
Group × T3	0.483	.248 –.717	<.001
Random effects
σ²	0.041
τ00 (ID)	0.018
Observations	60
Marginal R²/conditional R²	0.442/0.516

Discussion

This study examined changes in reaction time, accuracy, and confidence across visual occlusion levels in elite and amateur. The results showed that elite athletes responded significantly slower than did amateurs at T1, where information was extremely limited, but recorded the fastest response at T3, showing different timing patterns across occlusion levels. Furthermore, elite accuracy and confidence rose dramatically as information increased, while amateurs showed stagnated accuracy and reduced confidence. Unlike previous studies based on single time points, this study examined how decision-making changed as visual information accumulated.

The most notable finding is the delay in elite response at T1. This contradicts the traditional view that superior athletes consistently make faster decisions (Araújo et al., 2019; Mann et al., 2007; Travassos et al., 2013). This finding supports the view of Müller et al. (2024) that temporal occlusion is useful for capturing decision processes that are not visible in full-action displays.

These results are clearly explained by the “embodied choice” paradigm (Lepora & Pezzulo, 2015; Raab, 2017). Hinz et al. (2022b) reported that elite handball players intentionally invest time in information gathering to ensure higher-quality decisions. Elite athletes likely delayed their responses to reduce errors caused by feints and to select more appropriate defensive actions (Ratcliff & Starns, 2013). This suggests that elite athletes adjust the timing of their responses according to the level of uncertainty.

Whereas Hinz et al. (2022b) focused predominantly on team sports, the present study extends this paradigm to the one-to-one combat context of Taekwondo. In combat sports, this strategic delay may reflect highly focused attention directed toward subtle kinematic cues from the opponent’s body and lower limbs. Perceptual expertise in visual anticipation hinges upon the capacity to accurately extract kinematic cues transitioning from proximal to distal body segments (Morris-Binelli & Müller, 2017; Ripoll et al., 1995). This argument is strongly contextualized by Incognito et al. (2024), who demonstrated that spatial occlusion of the opponent’s head and facial expressions did not significantly degrade performance in martial artists, indicating that action anticipation in combat environments relies on global body kinematics rather than localized facial or head cues. Within this framework, the intentional delay observed in experts at T1 can be interpreted not as a deficit in execution velocity, but as a calculated strategic investment to accumulate macro-level kinematic motion cues from the trunk and limbs, which are critical for deciphering true tactical intent.

Thus, the delayed responses observed at T1 likely signify systematic efforts to detect early kinematic motion patterns that betray the opponent’s true tactical intentions (Morris-Binelli & Müller, 2017; Ripoll et al., 1995). As richer visual information became available at T2 and T3, elite athletes demonstrated a superior capacity to convert these cues into appropriate defensive motor actions compared to amateurs. This finding is consistent with previous literature establishing that elite athletes possess enhanced efficiency in coupling relevant perceptual cues to motor responses (Loiseau Taupin et al., 2023; Silva et al., 2020). The ability of elite athletes to rapidly process unfolding visual cues in such time-constrained environments aligns with the foundational premise of Steel et al. (2010) that subjective certainty is dynamically modulated by situational familiarity and the availability of task-specific kinematic information. Furthermore, their capacity to process these unfolding cues echoes findings that experienced performers can accurately extract familiar kinematic motion patterns within milliseconds to guide subsequent judgments, underscoring the efficiency of expert perceptual-motor coupling under severe time pressure. Conversely, amateurs exhibited a marked reduction in executing motor responses even as information was added, reconfirming a significant decline in accuracy commonly observed in less-skilled individuals during ecological tasks (Bruce et al., 2012; Huesmann et al., 2022; van Maarseveen et al., 2018).

Confidence changed in opposite directions across groups: it increased in elites but decreased in amateurs. Confidence in sports is a key variable predicting the quality and success of decision-making (Hepler & Feltz, 2012; Musculus et al., 2018). As more information became available, elites may have become more confident because later cues confirmed their initial judgments. After making blind guesses under uncertainty, amateurs likely realized their errors through visual feedback as the actual motion unfolded, causing confidence to drop. These findings suggest that decision-making is a dynamic process in which visual information, motor execution, and confidence are continuously updated in real time.

Although this study provides meaningful insights into sports decision-making research, it possesses several limitations. First, as measurements were conducted in a controlled laboratory environment using 2D screens, a limit exists to fully reflect the psychological pressure of an actual arena or three-dimensional spatial perception factors (Kredel et al., 2017). Furthermore, while the process of information acquisition was inferred, the lack of eye-tracking equipment meant that the study could not directly identify on which specific body parts of the opponent the athletes’ gaze was focused (Mann et al., 2007). Future research needs to combine Virtual Reality (VR)-based 3D environments with eye-tracking technology to more precisely analyze the causal relationship between kinematic cue acquisition and motor responses in environments that closely resemble actual competition settings.

In conclusion, Elite Taekwondo athletes do not simply react faster. Instead, they delay their responses under uncertainty and act quickly once decisive cues become available. These results suggest a paradigm shift in Taekwondo coaching—away from merely maximizing reaction time toward situational awareness and timing control training. Simulation training that combines temporal occlusion with actual kicking may help develop anticipatory skill in less experienced athletes.

Footnotes

Acknowledgments

The authors thank all participants for their valuable contribution to this study.

ORCID iDs

Hyesoo Cho

Hong-Suk Kim

Min-woo Jeon

Ji-Yong Park

Ethical Considerations

This study received approval from the ethics committee and complied with the Declaration of Helsinki (HYUIRB-202509-029).

Author Contribution

Hyesoo Cho and Ji-Yong Park conceived and designed the study. Hyesoo Cho collected the data and drafted the manuscript. Ji-Yong Park analyzed the data, supervised the study, and critically revised the manuscript. Hong-Suk Kim contributed to interpretation of the findings and revision of the manuscript. Min-woo Jeon contributed to revision of the manuscript. All authors participated in drafting or revising the manuscript, approved the final version, and agreed with the content of the manuscript and the order of authorship.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.*

Author Biographies

Hyesoo Cho, Ph.D., is an Assistant Professor in the Department of Sports Science at Hanyang University ERICA, whose research focuses on sports measurement, big data, and AI-based performance analysis. Specializing in data analytics to improve the performance of national and elite athletes, her current work particularly emphasizes the real-time field application of sports AI technologies and practical evaluation systems.

Hong-Suk Kim, Ph.D., received his Ph.D. in Physical Education from the Department of Sport Science at Hanyang University, Republic of Korea. His area of specialization is Measurement and Evaluation in Physical Education. His research interests include sports performance analysis, sports data analytics, athletic performance assessment, and sports statistics.

Min-Woo Jeon, Ph.D., is an Assistant Professor in the Department of Taekwondo at Kyung Hee University. His primary research interests include sport psychology, physical education pedagogy, taekwondo coaching and performance psychology, and athletic performance. His research has focused particularly on routine design and psychological mechanisms across taekwondo disciplines, including kyorugi, poomsae, and taekwondo breaking, as well as phenomenological investigations of expertise and fairness in sport. He has published extensively in leading domestic and international journals in the fields of sport psychology and taekwondo performance.

Ji-Yong Park is a doctoral researcher in sport science at Hanyang University, Republic of Korea. His research interests include the measurement and evaluation of physical fitness, the standardization of fitness indicators, and sports performance analysis.

References

Abernethy

Zawi

(2007). Pickup of essential kinematics underpins expert perception of movement patterns. Journal of Motor Behavior, 39(5), 353–367. https://doi.org/10.3200/jmbr.39.5.353-368

Aglioti

S. M.

Cesari

Romani

Urgesi

(2008). Action anticipation and motor resonance in elite basketball players. Nature Neuroscience, 11(9), 1109–1116. https://doi.org/10.1038/nn.2182

Araújo

Hristovski

Seifert

Carvalho

Davids

(2019). Ecological cognition: Expert decision-making behaviour in sport. International Review of Sport and Exercise Psychology, 12(1), 1–25. https://doi.org/10.1080/1750984x.2017.1349826

Bruce

Farrow

Raynor

Mann

(2012). But I can’t pass that far! the influence of motor skill on decision making. Psychology of Sport and Exercise, 13(2), 152–161. https://doi.org/10.1016/j.psychsport.2011.10.005

Chung

I. L.

Kim

(2012). Visual search strategies and anticipation ability between expert and novice taekwondo players. Korean Journal of Sport Psychology, 23(3), 61–70.

Farrow

(2013). Practice-enhancing technology: A review of perceptual training applications in sport. Sports Technology, 6(4), 170–176. https://doi.org/10.1080/19346182.2013.875031

Gredin

N. V.

Bishop

D. T.

Williams

A. M.

Broadbent

D. P.

(2023). The use of contextual priors and kinematic information during anticipation in sport: Toward a Bayesian integration framework. International Review of Sport and Exercise Psychology, 16(1), 286–310. https://doi.org/10.1080/1750984x.2020.1855667

Hepler

T. J.

Feltz

D. L.

(2012). Take the first heuristic, self-efficacy, and decision-making in sport. Journal of Experimental Psychology: Applied, 18(2), 154–161. https://doi.org/10.1037/a0027807

Hinz

Lehmann

Aye

Melcher

Tolentino-Castro

J. W.

Wagner

Taubert

(2022a). Differences in decision-making behavior between elite and amateur team-handball players in a near-game test situation. Frontiers in Psychology, 13, 854208. https://doi.org/10.3389/fpsyg.2022.854208

10.

Hinz

Lehmann

Musculus

(2022b). Elite players invest additional time for making better embodied choices. Frontiers in Psychology, 13, 873474. https://doi.org/10.3389/fpsyg.2022.873474

11.

Huesmann

Loffing

Büsch

Schorer

Hagemann

(2022). Varying degrees of perception-action coupling and anticipation in handball goalkeeping. Journal of Motor Behavior, 54(4), 391–400. https://doi.org/10.1080/00222895.2021.1984868

12.

Incognito

Watson

Weidemann

Steel

K. A.

(2024). The role of the opponent’s head in perception of kick target location in martial arts. Frontiers in Sports and Active Living, 6, 1468209. https://doi.org/10.3389/fspor.2024.1468209

13.

Kim

T.-H.

Kang

S.-K.

(2024). Analysis of competition contents before and after the revision of taekwondo competition rules in 2022. Korea Institute of Sport Science, 35(3), 412–426. https://doi.org/10.24985/kjss.2024.35.3.412

14.

Kredel

Vater

Klostermann

Hossner

E.-J.

(2017). Eye-tracking technology and the dynamics of natural gaze behavior in sports: A systematic review of 40 years of research. Frontiers in Psychology, 8, 1845. https://doi.org/10.3389/fpsyg.2017.01845

15.

Kukkiwon . (2020). Taekwondo textbook. Kukkiwon (World Taekwondo Headquarters).

16.

Lepora

N. F.

Pezzulo

(2015). Embodied choice: How action influences perceptual decision making. PLoS Computational Biology, 11(4), e1004110. https://doi.org/10.1371/journal.pcbi.1004110

17.

Leys

Ley

Klein

Bernard

Licata

(2013). Detecting outliers: Do not use standard deviation around the mean; use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013

18.

Loiseau Taupin

Romeas

Juste

Labbé

D. R.

(2023). Exploring the effects of 3D-360° VR and 2D viewing modes on gaze behavior, head excursion, and workload during a boxing-specific anticipation task. Frontiers in Psychology, 14, 1235984. https://doi.org/10.3389/fpsyg.2023.1235984

19.

Mann

D. T.

Williams

A. M.

Ward

Janelle

C. M.

(2007). Perceptual-cognitive expertise in sport: A meta-analysis. Journal of Sport & Exercise Psychology, 29(4), 457–478. https://doi.org/10.1123/jsep.29.4.457

20.

Martínez de Quel

Ó.

Bennett

S. J.

(2019). Perceptual-cognitive expertise in combat sports: A narrative review and a model of perception–action. RICYDE: Revista Internacional de Ciencias del Deporte, 15(58), 323–338.

21.

Morris-Binelli

Müller

(2017). Advancements to the understanding of expert visual anticipation skill in striking sports. Canadian Journal of Behavioural Science, 49(4), 262–268. https://doi.org/10.1037/cbs0000079

22.

Müller

Morris-Binelli

Hambrick

D. Z.

Macnamara

B. N.

(2024). Accelerating visual anticipation in sport through temporal occlusion training: A meta-analysis. Sports Medicine, 54(10), 2597–2606. https://doi.org/10.1007/s40279-024-02073-6

23.

Musculus

Raab

Belling

Lobinger

(2018). Linking self-efficacy and decision-making processes in developing soccer players. Psychology of Sport and Exercise, 39, 72–80. https://doi.org/10.1016/j.psychsport.2018.07.008

24.

Pinder

R. A.

Davids

Renshaw

Araújo

(2011). Representative learning design and functionality of research and practice in sport. Journal of Sport & Exercise Psychology, 33(1), 146–155. https://doi.org/10.1123/jsep.33.1.146

25.

Raab

(2012). Simple heuristics in sports. International Review of Sport and Exercise Psychology, 5(2), 104–120. https://doi.org/10.1080/1750984x.2012.654810

26.

Raab

(2017). Motor heuristics and embodied choices: How to choose and act. Current Opinion in Psychology, 16, 34–37. https://doi.org/10.1016/j.copsyc.2017.02.029

27.

Raab

Laborde

(2011). When to blink and when to think: Preference for intuitive decisions results in faster and better tactical choices. Research Quarterly for Exercise & Sport, 82(1), 89–98. https://doi.org/10.1080/02701367.2011.10599725

28.

Ranganathan

Carlton

L. G.

(2007). Perception–action coupling and anticipatory performance in baseball batting. Journal of Motor Behavior, 39(5), 369–380. https://doi.org/10.3200/JMBR.39.5.369-380

29.

Ratcliff

Starns

J. J.

(2013). Modeling confidence judgments, response times, and multiple choices in decision making: Recognition memory and motion discrimination. Psychological Review, 120(3), 697–719. https://doi.org/10.1037/a0033152

30.

Ripoll

Kerlirzin

Stein

J.-F.

Reine

(1995). Analysis of information processing, decision making, and visual strategies in complex problem-solving sport situations. Human Movement Science, 14(3), 325–349. https://doi.org/10.1016/0167-9457(95)00019-o

31.

Runswick

O. R.

Roca

Williams

A. M.

North

J. S.

(2020). A model of information use during anticipation in striking sports (MIDASS). Journal of Expertise, 3(4), 197–211.

32.

Savelsbergh

G. J. P.

Van der Kamp

Williams

A. M.

Ward

(2005). Anticipation and visual search behaviour in expert soccer goalkeepers. Ergonomics, 48(11–14), 1686–1697. https://doi.org/10.1080/00140130500101346

33.

Silva

A. F.

Conte

Clemente

F. M.

(2020). Decision-making in youth team-sports players: A systematic review. International Journal of Environmental Research and Public Health, 17(11), 3803. https://doi.org/10.3390/ijerph17113803

34.

Steel

K. A.

Adams

R. D.

Canning

C. G.

Eisenhuth

(2010). The team-mate identification (TM-ID) test: Effect of participant and situation familiarity on response accuracy and latency. International Journal of Sports Science & Coaching, 5(2), 321–336. https://doi.org/10.1260/1747-9541.5.2.321

35.

Travassos

Araújo

Davids

O'Hara

Leitão

Cortinhas

(2013). Expertise effects on decision-making in sport are constrained by requisite response behaviours: A meta-analysis. Psychology of Sport and Exercise, 14(2), 211–219. https://doi.org/10.1016/j.psychsport.2012.11.002

36.

van Maarseveen

M. J. J.

Savelsbergh

G. J. P.

Oudejans

R. R. D.

(2018). In situ examination of decision-making skills and gaze behaviour of basketball players. Human Movement Science, 57, 205–216. https://doi.org/10.1016/j.humov.2017.12.006

37.

Williams

A. M.

Jackson

R. C.

(2019). Anticipation in sport: Fifty years on, what have we learned and what research still needs to be undertaken? Psychology of Sport and Exercise, 42, 16–24. https://doi.org/10.1016/j.psychsport.2018.11.014

Do Elite Taekwondo Athletes Invest Time for Better Choices? Analysis of Anticipatory Behavior Through a Perception–Action Coupling Task

Abstract

Keywords

Introduction

Materials and Methods

Participants

Procedure

Experimental Design

Stimuli and Task

Reaction Time

Anticipation Accuracy

Decision Confidence

Experimental Procedure

Data Analysis

Reaction time (ms)

Anticipation Accuracy (%)

Decision Confidence (point)

Statistical Analysis

Results

Descriptive Statistics

Reaction Time

Anticipation Accuracy

Decision Confidence

Discussion

Footnotes

Acknowledgments

ORCID iDs

Ethical Considerations

Author Contribution

Funding

Declaration of Conflicting Interests

Data Availability Statement

Author Biographies

References