Sensorimotor Synchronization Abilities for Polyrhythmic Tasks in Artistic Swimming: A Comparison Between Elite,Novice,and Non-Artistic Swimmers

Abstract

We investigated sensorimotor synchronization abilities across swimmers of different artistic expertise levels. Elite, novice, and non-artistic female swimmer participants completed finger and foot tapping tasks involving single and polyrhythmic patterns that were intended to simulate the rhythmic coordination required in artistic swimming. Although no significant group differences were found in basic sensorimotor synchronization skills, elite athletes exhibited superior performance on polyrhythmic tasks. This observed superior performance could be partly attributable to a pre-existing predisposition for such motor skills and/or the effects of rigorous training. These findings highlight the critical impact of sport-specific demands on temporal control skills and suggest important implications for training methodologies in artistic swimmers.

Keywords

polyrhythmic motor skills elite and novice athletes motor training sport-specific motor skills artistry dance

Introduction

Artistic swimming demands an intricate coordination of movements and synchronizing limbs when performing routines to music. This sport presents a unique challenge in which the upper and lower body must operate in harmony to follow different rhythmic patterns. For instance, when executing leg movements above the water, the legs must move in synchronization with the music, while the arms and upper body beneath the water must work to stabilize the lower body and maintain the highest possible leg position. Conversely, when the arms and upper body perform movements synchronized with music, the legs must adjust to support these actions, often leading to different rhythm patterns for upper and lower limbs. This complex coordinationis known as polyrhythmic movement (Sanders & Levitin, 2020).

Central to achieving these complex movements is sensorimotor synchronization, a process in which sensory inputs are integrated with motor actions to enable precise timing and coordination. This coordinated synchronization is particularly critical in artistic swimming, for which athletes must synchronize their movements with external auditory cues (music) while simultaneously managing different rhythmic patterns across different muscle groups (Repp & Su, 2013). The control of rhythmic and polyrhythmic movements is especially demanding, as it requires complex neural mechanisms to plan, execute, and modify with feedback the coordination of multiple body parts in distinct rhythmic patterns (Karpati et al., 2016; Krause et al., 2010). Previous investigators have shown that expertise in rhythmic activities, such as music or dance enhances sensorimotor synchronization abilities, that are crucial for successfully executing polyrhythmic patterns (Jin et al., 2019; Karpati et al., 2016; Repp & Su, 2013). These findings have suggested that the observed differences in performance between elite, novice, and non-artistic swimmers may stem from their varying levels of experience and specialized motor training.

The coordination of the arms and legs in creating polyrhythmic patterns requires integration of both limb coordination and sensorimotor synchronization (Repp & Su, 2013). Research has shown that professional musicians, dancers, and athletes exhibit superior limb coordination compared to non-performers. For instance, Martins et al. (2018) found enhanced bimanual coordination in children trained in music, while Mo and Chow (2018) observed more consistent lower-limb coordination in experienced runners. Seifert et al. (2010) demonstrated that skilled swimmers exhibited better upper-lower limb coordination when swimming at different speeds, highlighting how skill level influences inter-limb coordination. Additionally, Seifert et al. (2011) further explored the variability in upper-lower limb coordination in swimmers, finding that skilled swimmers maintained better coordination despite inter-individual differences. These investigators underscored that superior limb coordination can be measured in both sport-specific and general tasks, depending on the population evaluated.

Individuals with high experience in music and dance have been shown to possess better sensorimotor synchronization compared to those with little or no experience (Jin et al., 2019; Karpati et al., 2016; Repp, 2010; Sommer et al., 2018). Yet, there is a relative scarcity of research that has examined these abilities in athletes, even though many sports require precise synchronization of movements with external rhythms (Buhmann et al., 2018; Maes et al., 2018; van de Rijt, 2018). Understanding how sensorimotor synchronization develops in athletes could provide valuable insights for training methodologies to enhance performance in rhythmically demanding sports.

Human coordination of the arms and legs can be classified into three distinct phase patterns: in-phase, anti-phase, and out-of-phase (Kelso, 1984). In in-phase coordination, limbs on the same side move together in the same direction. In anti-phase coordination, limbs on the same side move in opposite directions by 180°. Out-of-phase coordination occurs when limbs are neither fully in-phase nor fully anti-phase, with movement angles between zero and 180°. Limb movements can also be synchronized in different ratios, such as in a 1:1 ratio in which limbs move together at the same rate, or harmonic ratios like 1:2 or 1:3 with one limb moving faster than the other. Polyrhythms (e.g., 2:3, 3:4) are more complex, involving limbs moving at different rates that are not simple multiples of each other. While all polyrhythms are out-of-phase, not all out-of-phase movements are polyrhythmic. For example, in a 1:1 pattern limbs may move out of sync. These complexities make achieving polyrhythmic patterns in arm and leg movement highly challenging (Shih et al., 2019).

Although coordinating out-of-phase or polyrhythmic patterns can be challenging, certain high-level physical activities such as playing musical instruments, dancing, or playing sports require these complex movements to enhance efficiency in exerting force (Sanders & Levitin, 2020). Therefore, the ability to generate polyrhythmic patterns or move arms and legs in different phases is crucial for quality movement (Poudrier & Repp, 2013). Studies on polyrhythmic movements have found that musicians are better at tapping into polyrhythmic patterns compared to non-musicians (Deutsch, 1996; Kurtz & Lee, 2003; Peper & Beek, 1998; Shaffer, 1981; Summers et al., 1993). Among musicians, drummers, who typically undergo coordinated training of arms and legs by tapping different rhythms with both hands and feet, are more adept at generating polyrhythmic patterns than are other types of musicians (Fujii et al., 2011; Krause et al., 2010). Additionally, athletes who perform synchronously with music, such as artistic swimmers, have shown abilities to tap polyrhythmic patterns that are similar to drummers (Vathagavorakul et al., 2021).

While research on athletes’ polyrhythmic movements remains limited, findings to date indicate that artistic swimmers can generate polyrhythmic patterns effectively. For instance, when asked to tap 3:2 and 2:3 polyrhythmic patterns with hands and feet, artistic swimmers performed as well as drummers and better than water polo athletes (Vathagavorakul et al., 2021). Furthermore, performing movements in water, artistic swimmers were able to adjust arm rotations while maintaining consistent leg movements (Vathagavorakul et al., 2020). These findings suggest that experience in practicing and aligning body movements with rhythm contributes to the athlete’s ability to perform polyrhythmic patterns.

However, it is not yet clear whether more experienced artistic swimmers have better polyrhythmic abilities than less experienced artistic swimmers. By comparing the sensorimotor synchronization abilities of elite, novice, and non-artistic swimmers, we sought to examine the association between experience and performance on general tests of polyrhythmic coordination. Understanding these group differences may be critical, not only for refining training methodologies, but also for contributing to a broader understanding of how complex motor skills develop. We aimed to inform the development of targeted training interventions to help novice athletes advance their polyrhythmic skills more effectively. Before engaging in complex and costly intervention studies, it is important to first establish whether expertise-level differences in polyrhythmic skills exist. If such differences are found, they justify further intervention studies. Conversely, if no differences are observed, it may suggest that training interventions are unlikely to be effective.

Method

Ethical Considerations

Our procedures in this study received advance approval from our university ethical review board, and each participant signed an informed consent document.

Participants

Our participants were three groups of swimmers - novice artistic swimmers, elite artistic swimmers, and non-artistic swimmers - aged between 20-29 years. The novice group (eight females; Mage = 20.5, SD = 0.93) averaged 2.56 (SD = 1.68) years of experience in artistic swimming and had never competed nationally or internationally. The elite group (eight females; Mage = 23.1 (SD = 2.9 years) averaged 13.25 (SD = 2.5) years of experience in artistic swimming and had competed nationally and/or internationally. The data for the elite artistic swimmers in this study were previously published in Vathagavorakul et al. (2021) and were reused here to compare with the novice and non-artistic swimmer groups for this study’s specific objectives. The non-artistic group (eight female; Mage = 23.63 (SD = 3.16) had no prior experience in artistic swimming and no formal swimming experience beyond participation in physical education (PE) classes. Additionally, no participants in this group had formal training or a background in music, dance, or any other sport requiring rhythmic coordination. This selection criterion was applied to minimize potentially confounding effects of these other variables on sensorimotor synchronization abilities.

Participants were recruited primarily through local artistic swimming clubs and universities. Approximately 50 individuals were approached to participate in the study through email invitations and flyers posted at the clubs and university bulletin boards. Participants were selected based on their availability, willingness to participate, and their fit within the study’s specific inclusion criteria (e.g., years of experience in artistic swimming for elite and novice groups, and lack of related experience for non-artistic swimmers). No compensation was offered for participation, and all participants volunteered freely.

We conducted an a priori power analysis using G*Power (version 3.1.9.7) software. We assumed a large effect size (Cohen’s f = 0.40) based on prior studies involving sensorimotor synchronization tasks in rhythmic sports (Karpati et al., 2016; Miura et al., 2011; Miyata & Kudo, 2014; Vathagavorakul et al., 2020, 2021), and following Cohen’s guidelines for effect size classification (Cohen, 1988). The analysis indicated that a total sample size of 18 participants (6 per group) would be sufficient to detect significant differences with a power of 0.80 and an alpha level of 0.05. However, to further ensure the study’s ability to detect smaller effects and increase the precision of our findings, we opted to include 24 participants (8 per group). This choice slightly strengthened the generalizability of our results and reduced the likelihood of Type II errors, particularly in detecting more subtle differences across the three rhythm tasks.

Experimental Tasks

Participants were instructed to complete tapping tasks with their finger and foot in time with metronome beats. We identified the dominant hand and foot for each participant through self-report, asking them to indicate which hand they preferred for tasks like writing and which foot for activities like kicking. Participants with different dominant sides for hand and foot used the dominant hand, and the corresponding foot to maintain consistency on the task. These tasks comprised three variations: one involved tapping with a single rhythm (every 750 ms with both finger and foot), while the other two tasks required polyrhythmic tapping (tapping every 500 ms with the finger and every 750 ms with the foot, or vice versa). Tapping was performed on percussion pads (SPD ONE Percussion, Roland Corporation, Shizuoka, Japan), and participants were isolated from auditory feedback, hearing only the sound of their finger or foot hitting the pad. The metronome was set to tick every 750 ms in all trials. In the single-rhythm task (750:750), participants synchronized both their finger and foot tapping with the metronome beat. In the 750:500 polyrhythm task, participants tapped their finger every 750 ms with the metronome while tapping their foot every 500 ms (or three times for every two metronome ticks). Conversely, in the 500:750 polyrhythm task, participants tapped their finger every 500 ms while tapping their foot every 750 ms. Table 1 summarizes these three task descriptions.

Table 1.

Descriptions of Participant Tasks.

	Metronome (ms)	Finger (ms)	Foot (ms)	Types of rhythm
Task 1	750	750	750	Single rhythm
Task 2	750	750	500	Polyrhythm
Task 3	750	500	750	Polyrhythm

Design and Procedure

The participants completed a total of six trials, with two trials assigned to each of the three tasks. Upon arrival, participants received instructions regarding the tasks. They were instructed to sit on a chair while wearing headphones, with the percussion pads positioned in front of them. Each task was repeated twice, and we used the average of the two trials for further analyses. We provided a 5-min practice session for each task before the commencement of trials, leading to a total practice time of 15 minutes. The order of trials was fully randomized. Participants were instructed to initiate their first tap on the fifth beat and to continue tapping for 20 seconds (20,000 ms). They were required to maintain continuous tapping until the end of the trial, with a short break allowed after each trial Table 2.

Table 2.

Mean Cycle Durations (in Milliseconds) in the Tapping Tasks.

Tapping tasks	Elite swimmers	Novice swimmers	Non-swimmers
750:750 single rhythm
Finger (750 ms)	749.4	747.9	750.9
Foot (750 ms)	748.4	750	749.4
750:500 polyrhythm
Finger (750 ms)	745.8	878.6	1121.5
Foot (500 ms)	512.0	736.6	831.8
500:750 polyrhythm
Finger (500 ms)	509.4	720.5	798.8
Foot (750 ms)	775.1	878.6	1039.9

Data Collection and Analysis

We recorded all tasks using Logic Pro X (Apple Inc, Cupertino, California, USA) at a sampling frequency of 44,100 Hz, and we then converted to digital audio files (.wav format) with the same sampling frequency. Subsequently, we employed MATLAB (R2023b, MathWorks, Inc, Natick, MA, USA) for signal analysis. The audio data were smoothed via a bidirectional second-order low-pass filter with a cut-off frequency of 150 Hz. Following filtering, the onsets of tapping and metronome beats were detected. For each participant trial, we analyzed 24 response cycles in the single-rhythm task and 12 response cycles in the polyrhythmic tasks. We calculated the percentage of correct response cycles (PCRC) based on the ratio between the number of correct response cycles and the total number of cycles analyzed, as per the methodology outlined by Summers and Kennedy (1992); Summers et al. (1993). A response cycle was considered correct if (i) all taps and metronome beats adhered to the same pattern, and (ii) taps intended to synchronize with metronome beats occurred within 20 ms of the associated metronome beat. Additionally, we computed the inter-tap interval (ITI) to provide an absolute timing measurement, although it did not elucidate how participants executed their tapping relative to the given metronome beats. Consequently, we calculated the coefficient of variation of ITI (CV_ITI) as the ratio between the mean and standard deviation of ITI.

Statistical Analysis

We used a two-way mixed-design analysis of variance (ANOVA) to examine differences in PCRC and CV_ITI among the groups, with the type of rhythm (750:750 single rhythm, 750:500, and 500:750 polyrhythms) as a within-subjects factor and group (elite, novice, or non-artistic swimmers) as a between-subjects factor. We applied the Greenhouse-Geisser correction in cases where Mauchly’s test of sphericity was significant. For factors with more than two levels (group and rhythm), we utilized a one-way ANOVA as a post hoc analysis if statistical significance was observed. Statistical significance was set at p < .05, with Bonferroni corrections applied to the post hoc comparisons using Bonferroni-corrected t-tests. In addition to p-values, we calculated effect sizes using partial eta squared (η²p) to evaluate the magnitude of differences between groups. Effect sizes were interpreted based on guidelines commonly used in behavioral research (Lakens, 2013), where η²p = .01 represents a small effect, η²p = .06 represents a moderate effect, and η²p = .14 represents a large effect. All statistical analyses were conducted using IBM SPSS Statistics version 29 (IBM, NY, USA).

Reliability Analysis

To assess test-retest reliability of the tasks, we calculated intraclass correlation coefficients (ICCs) for both the Percentage of Correct Response Cycles (PCRC) and the Coefficient of Variation of the Intertap Interval (CV_ITI). ICCs were computed separately for each group (elite artistic swimmers, novice artistic swimmers, and non-artistic swimmers) and for each task (single rhythm and polyrhythms). The ICCs were calculated using a two-way mixed-effects model, which is appropriate for evaluating the reliability of the average of two trials per task in test-retest reliability.

Results

Percentage of Correct Response Cycles

PCRC results are illustrated in Figure 1, showing the interrelationships with rhythm type. We found a significant main effect of group, F(2,21) = 9.282, p < .001, η²p = .469, indicating that elite artistic swimmers had significantly higher PCRC compared to both novice and non-artistic swimmers. Post hoc Bonferroni-corrected t-tests revealed that elite artistic swimmers had significantly higher PCRC compared to both novice artistic swimmers (p = .002) and non-artistic swimmers (p = .002). However, we found no statistically significant difference between novice artistic swimmers and non-artistic swimmers (p > .99). Additionally, we observed a significant main effect of rhythm, F(2,42) = 41.728, p < .001, η²p = .665, Post hoc Bonferroni-corrected t-tests revealed that participants performed significantly better in the single-rhythm task compared to both the 750:500 polyrhythmic task (p < .001) and the 500:750 polyrhythmic task (p < .001), with no significant difference found between the two polyrhythmic tasks (p > .99).

Figure 1.

Percentage of Correct Response Cycles for Three Rhythm Tasks by Levels of Expertise.

We observed a significant interaction effect between group and rhythm tasks, F(4,42) = 5.696, p < .001, η²p = .352. Post hoc comparisons using the Bonferroni test showed no significant differences between the groups in the single-rhythm task, with no statistically significant differences found between elite and novice artistic swimmers (p > .99), elite and non-artistic swimmers (p = .705), and novice and non-artistic swimmers (p = .788). On the 750:500 polyrhythmic task, PCRC for elite artistic swimmers was significantly higher than the novice group (p = .008) and the non-artistic swimmers (p = .002). Similarly, on the 500:750 polyrhythmic task, elite artistic swimmers outperformed both novice (p = .007) and non-artistic swimmers (p = .01).

Coefficients of Variation of the Inter-Tap Interval

Figure 2 shows the mean CV_ITI across different rhythm types, while Table 3 presents the mean Inter-tap Intervals (ITI) for each group, limb, and rhythm task. We observed a significant main effect of group, F(2,45) = 10.631, p < .001, η²p = .321, indicating that elite artistic swimmers had a lower CV_ITI compared to both novice artistic swimmers (p = .007) and non-artistic swimmers (p < .001). However, there was no statistically significant difference between novice and non-artistic swimmers (p = .669). We also observed a significant main effect of rhythm, F(2,90) = 45.668, p < .001, η²p = .504, showing that the CV_ITI in the single-rhythm task was lower than in the two polyrhythmic tasks, with no significant difference in CV_ITI between the polyrhythmic tasks.

Figure 2.

Mean Coefficient of Variation of the Inter-Tapping Interval for Finger and Foot by Levels of Expertise.

Table 3.

Intraclass Correlation Coefficients (ICCs) for PCRC and CVITI Across Tasks and Groups.

Group	Task	PCRC ICC	CVITI ICC
Elite artistic swimmers	Single rhythm (750:750)	1	0.716
	Polyrhythm (750:500)	0.994	0.788
	Polyrhythm (500:750)	0.972	0.978
Novice artistic swimmers	Single rhythm (750:750)	1	0.751
	Polyrhythm (750:500)	0.996	0.952
	Polyrhythm (500:750)	0.993	0.793
Non-artistic swimmers	Single rhythm (750:750)	1	0.983
	Polyrhythm (750:500)	0.999	0.794
	Polyrhythm (500:750)	0.986	0.855

We found a significant interaction effect between group and rhythm tasks, F(4,90) = 3.832, p = .006, η²p = .146. Post hoc analysis revealed that in the single-rhythm task, there was no statistically significant difference in CV_ITI between the groups. However, in the 750:500 polyrhythmic task, elite artistic swimmers exhibited a lower CV_ITI compared to novice artistic swimmers (p = .047) and non-artistic swimmers (p < .001). Similarly, in the 500:750 polyrhythmic task, elite artistic swimmers had a lower CV_ITI compared to novice artistic swimmers (p = .034) and non-artistic swimmers (p = .004).

Test-Retest Reliability

To assess the test-retest reliability of the sensorimotor synchronization tasks, we calculated Intraclass Correlation Coefficients (ICC) values for both the PCRC and the CV_ITI across all three groups: elite artistic swimmers, novice artistic swimmers, and non-artistic swimmers. These ICC values are summarized in Table 3. For PCRC, the ICC values were exceptionally high across all groups and tasks, ranging from 0.972 to 1.000, indicating excellent reliability. For CV_ITI, the ICC values showed moderate to excellent reliability, with elite artistic swimmers showing ICCs ranging from 0.716 to 0.978, novice artistic swimmers from 0.751 to 0.952, and non-artistic swimmers from 0.794 to 0.983. The consistently high ICC values across all groups and tasks for PCRC suggest that the participants performed the tasks with a high degree of consistency across the two trials. The ICC values for CV_ITI, while slightly lower, still indicate good to excellent reliability, particularly for the polyrhythmic tasks, underscoring the robustness of the measurements.

Discussion

Our study delved into the intricate realm of rhythmic synchronization abilities across individuals with varying levels of expertise in artistic swimming. Surprisingly, we found no discernible differences in the proficiency of coordinating finger and foot tapping with external rhythms during the single rhythm task on measures such as PCRC and CV_ITI when comparing elite artistic swimmers, novice artistic swimmers, and non-artistic swimmers. This finding indicates that basic sensorimotor synchronization abilities were not associated with expertise in artistic swimming. However, this result should be interpreted in the context of the specific tasks used in this study.

Traditionally, single-rhythm tapping tasks are often used as a measure of basic sensorimotor synchronization abilities, where individuals are required to tap in synchrony with a metronome or rhythmic cue (Repp, 2005; Repp & Doggett, 2007). Such tasks may primarily engage fundamental sensorimotor processes and may overlook the nuanced motor skills cultivated through extensive training in artistic swimming, which often involve complex, polyrhythmic coordination (Keller et al., 2010; Studenka et al., 2012). Findings from Vathagavorakul et al. (2021), revealing no significant differences in single-rhythm tasks between artistic swimmers and water polo players, further support this notion, suggesting that basic synchronization tasks may not fully capture the specialized motor control developed in elite artistic swimmers.

The distinctive performance patterns we observed on polyrhythmic tasks provide insights into the specialized motor abilities of elite artistic swimmers. These tasks, which require the coordination of finger and foot tapping with multiple overlapping rhythms, are more representative of the complex movement demands faced by artistic swimmers. While our results highlight the superior performance of elite artistic swimmers on both PCRC and CV_ITI during polyrhythmic tasks, further research is required to determine whether targeted training interventions specifically enhance these abilities.

The high intraclass correlation coefficients (ICCs) for PCRC, ranging from .972 to 1.000, further support the reliability of these measures across all groups and tasks, highlighting the consistency of participants' performance in the trials. Similarly, the ICCs for CV_ITI, which ranged from moderate to excellent reliability, reinforce the robustness of the temporal control abilities assessed in this study. The notably lower CV_ITI scores among elite swimmers, compared to novice and non-artistic swimmers, reflect the precision and consistency of their temporal control abilities, which are essential for successfully executing complex, polyrhythmic movements (Miyake et al., 2004; Repp & Doggett, 2007; Studenka et al., 2012).

Limitations and Directions for Further Research

While these findings underscore the intricate motor skills honed through elite artistic swimming training, there are several limitations to this research. It is crucial to acknowledge that this study’s cross-sectional design limits interpretations of causal relationships between training and sensorimotor synchronization abilities. While cross-sectional studies can identify differences between groups at a single point in time, they do not provide direct evidence of how these abilities develop over time as, for example, as a result of training (Levin, 2006; Spector, 2019).

It is also possible that the superior performance observed in elite artistic swimmers may be influenced by factors beyond long-term training alone. Differences may have emerged during the 5-min practice sessions provided before each task. For example, elite athletes may have used this short practice period more effectively by executing more trials, focusing more intensely, or employing better strategies compared to novice and non-artistic swimmers (Ericsson et al., 1993; Mallett & Hanrahan, 2004). Moreover, individuals who excel in artistic swimming may inherently possess superior sensorimotor coordination, providing them with a natural advantage in these tasks (Baker & Horton, 2004; Gucciardi & Gordon, 2009).

Stronger evidence would come from research designs investigating training effects longitudinally by tracking changes in sensorimotor synchronization abilities over time (Guralnik et al., 1995; Molenaar & Campbell, 2009; Twisk, 2004). Such work would investigation of the separate effects of specific training interventions and improvements attributable to pre-existing abilities. For example, a longitudinal study could investigate how targeted polyrhythmic training influences synchronization skills in novice artistic swimmers and whether these skills approach the levels observed in elite athletes. It is also important to consider the potential influence of selection bias. Individuals who naturally possess higher polyrhythmic abilities may be more likely to continue training at an elite level, while those with lower innate abilities might be more inclined to drop out during the earlier stages of training. This phenomenon has been documented in various domains, including sports and music (Baker & Horton, 2004; Ericsson et al., 1993).

Additionally, we assumed a large effect size (Cohen’s f = 0.40) in our a priori power analysis to determine the sample size for this study. This assumption was based on previous research in motor coordination tasks among athletes (Karpati et al., 2016; Miura et al., 2011; Miyata & Kudo, 2014; Vathagavorakul et al., 2020, 2021) and follows Cohen’s guidelines (Cohen, 1988). While we had sufficient statistical power to detect large effects, we cannot rule out the possibility of a Type II error, where smaller but practically significant differences between groups may have gone undetected. For instance, in the single rhythm task, subtle effects may have been missed due to this assumption of a large effect size. Related to this concern, we only included female participants, potentially overlooking gender-based differences in rhythmic coordination. Aoki et al. (2005) and Au et al. (2015) found no gender influence on tapping ability, and we doubt that our exclusive focus on female participants seriously limited generalizability of these results. Another limitation relates to task specificity, with our tasks limited to tapping with finger and foot synchronized to metronome beats. Although these tasks simulate aspects of rhythmic coordination observed in artistic swimming, they may not fully capture the complexity and variability of movements involved in actual artistic swimming routines. Artistic swimming routines often integrate multiple body parts and movements simultaneously, such as arm gestures, leg kicks, and body rotation, all synchronized to music. Consequently, future investigators might strengthen this research by using larger and more diverse participant samples and employing polyrhythmic tasks of greater complexity.

Conclusion

In this study, we provided nuanced insight into the sensorimotor synchronization abilities of individuals across varying levels of expertise in artistic swimming. While we found no significant disparities in basic sensorimotor synchronization skills among elite, novice, and non-artistic swimmers, our analysis of polyrhythmic tasks revealed superior performance among elite athletes, suggesting specialized motor expertise developed through training. These findings underscore the impact of sport-specific demands on temporal control skills, offering valuable implications for training methodologies and advancing our understanding of expertise development in rhythmic domains.

Our findings suggest an association between performance on polyrhythmic tasks and sporting expertise. While these results offer valuable insights, further research is necessary to determine whether enhancing temporal control and sensorimotor synchronization through specific training interventions could lead to improved performance in rhythmic sports. Training programs could potentially incorporate more advanced polyrhythmic exercises tailored to refine these skills, but the efficacy of such interventions needs to be further explored before they are widely implemented (Karpati et al., 2016). For talent identification specialists, assessing sensorimotor synchronization abilities early in an athlete’s development could help identify individuals with a natural aptitude for rhythmic sports, allowing for more personalized and effective training programs. For researchers, these results provide a foundation for further exploration into the neural and cognitive mechanisms underlying advanced coordination and timing skills in athletes. Investigating how these abilities evolve with extended training durations, particularly through longitudinal studies, could offer deeper insights into motor learning and adaptation processes.

Footnotes

Acknowledgements

We thank all participants who participated in this study.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research project is supported by grants for development of new faculty staff, Ratchadaphiseksomphot Fund, Chulalongkorn University (DNS 67_012_27_002_2).

ORCID iD

Ravisara Trongjitpituk

Author Biographies

Ravisara Trongjitpituk, PhD, is a lecturer at Chulalongkorn University, Thailand, specializing in motorcoordination and sensorimotor synchronization. Her research focuses on artistic swimming and how individualsmanage complex movement patterns such as polyrhythmic coordination. Dr. Trongjitpituk’s studies provide insightsinto effective training methodologies, advancing the understanding of synchronization abilities and motor coordination,and their impact on performance, particularly in artistic swimmers.

Miwako Homma, PhD, is a professor at the University of Tsukuba, Japan, specializing in biomechanics and coachingmethodology in aquatic sports, with a focus on artistic swimming. Her research examines sculling techniques andhydrodynamic forces in artistic swimming. Dr. Homma’s work has advanced understanding of movement mechanics inelite swimmers, providing insights that inform both scientific research and coaching practices.

References

Aoki

Furuya

Kinoshita

(2005). Finger-tapping ability in male and female pianists and nonmusician controls. Motor Control, 9(1), 23–39. https://doi.org/10.1123/mcj.9.1.23

W.-L.

Seah

Tan

(2015). Effects of age and gender on hand motion tasks. Parkinson's Disease, 2015(1), 1–5. https://doi.org/10.1155/2015/862427

Baker

Horton

(2004). A review of primary and secondary influences on sport expertise. High Ability Studies, 15(2), 211–228. https://doi.org/10.1080/1359813042000314781

Buhmann

Moens

Van Dyck

Dotov

Leman

(2018). Optimizing beat synchronized running to music. PLoS One, 13(12), Aticle e0208702. https://doi.org/10.1371/journal.pone.0208702

Cohen

(1988). Statistical power for the behavioural sciences (58(1), pp. 7–19). Lawrence Erlbaum.

Deutsch

(1996). Chapter 8 the perception of auditory patterns. In Prinz

Bridgeman

(Eds.), Handbook of perception and action (1, pp. 253–296). Academic Press. https://doi.org/10.1016/s1874-5822(96)80011-9

Ericsson

K. A.

Krampe

R. T.

Tesch-Römer

(1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406. https://doi.org/10.1037/0033-295x.100.3.363

Fujii

Hirashima

Kudo

Ohtsuki

Nakamura

Oda

(2011). Synchronization error of drum kit playing with a metronome at different tempi by professional drummers. Music Perception: An Interdisciplinary Journal, 28(5), 491–503. https://doi.org/10.1525/mp.2011.28.5.491

Gucciardi

D. F.

Gordon

(2009). Development and preliminary validation of the cricket mental toughness inventory (cmti). Journal of Sports Sciences, 27(12), 1293–1310. https://doi.org/10.1080/02640410903242306

10.

Guralnik

J. M.

Ferrucci

Simonsick

E. M.

Salive

M. E.

Wallace

R. B.

(1995). Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. New England Journal of Medicine, 332(9), 556–561. https://doi.org/10.1056/NEJM199503023320902

11.

Jin

Biye

Chen

Zhou

(2019). Does dance training influence beat sensorimotor synchronization? Differences in finger-tapping sensorimotor synchronization between competitive ballroom dancers and nondancers. Experimental Brain Research, 237(4), 5. https://doi.org/10.1007/s00221-018-5410-4

12.

Karpati

F. J.

Giacosa

Foster

N. E.

Penhune

V. B.

Hyde

K. L.

(2016). Sensorimotor integration is enhanced in dancers and musicians. Experimental Brain Research, 234(3), 893–903. https://doi.org/10.1007/s00221-015-4524-1

13.

Keller

P. E.

Dalla Bella

Koch

(2010). Auditory imagery shapes movement timing and kinematics: Evidence from a musical task. Journal of Experimental Psychology: Human Perception and Performance, 36(2), 508–513. https://doi.org/10.1037/a0017604

14.

Kelso

(1984). Phase transitions and critical behavior in human bimanual coordination. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology, 246(6 Pt 2), R1000–R1004. https://doi.org/10.1152/ajpregu.1984.246.6.R1000

15.

Krause

Pollok

Schnitzler

(2010). Perception in action: The impact of sensory information on sensorimotor synchronization in musicians and non-musicians. Acta Psychologica, 133(1), 28–37. https://doi.org/10.1016/j.actpsy.2009.08.003

16.

Kurtz

Lee

T. D.

(2003). Part and whole perceptual-motor practice of a polyrhythm. Neuroscience Letters, 338(3), 205–208. https://doi.org/10.1016/s0304-3940(02)01394-0

17.

Lakens

(2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and anovas. Frontiers in Psychology, 4(8), 863. https://doi.org/10.3389/fpsyg.2013.00863

18.

Levin

K. A.

(2006). Study design iii: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24–25. https://doi.org/10.1038/sj.ebd.6400375

19.

Maes

P.-J.

Lorenzoni

Six

(2018). The soundbike: Musical sonification strategies to enhance cyclists’ spontaneous synchronization to external music. Journal on Multimodal User Interfaces, 13(3), 155–166. https://doi.org/10.1007/s12193-018-0279-x

20.

Mallett

C. J.

Hanrahan

S. J.

(2004). Elite athletes: Why does the ‘fire’burn so brightly? Psychology of Sport and Exercise, 5(2), 183–200. https://doi.org/10.1016/s1469-0292(02)00043-2

21.

Martins

Neves

Rodrigues

Vasconcelos

Castro

S. L.

(2018). Orff-based music training enhances children’s manual dexterity and bimanual coordination. Frontiers in Psychology, 9(3), 2616. https://doi.org/10.3389/fpsyg.2018.02616

22.

Miura

Kudo

Ohtsuki

Kanehisa

(2011). Coordination modes in sensorimotor synchronization of whole-body movement: A study of street dancers and non-dancers. Human Movement Science, 30(6), 1260–1271. https://doi.org/10.1016/j.humov.2010.08.006

23.

Miyake

Onishi

Pöppel

(2004). Two types of anticipation in synchronization tapping. Acta Neurobiologiae Experimentalis, 64(3), 415–426. https://doi.org/10.55782/ane-2004-1524

24.

Miyata

Kudo

(2014). Mutual stabilization of rhythmic vocalization and whole-body movement. PLoS One, 9(12), Article e115495. https://doi.org/10.1371/journal.pone.0115495

25.

Chow

(2018). Differences in lower-limb coordination and coordination variability between novice and experienced runners during a prolonged treadmill run at anaerobic threshold speed. Journal of Sports Sciences, 37(9), 1021–1028. https://doi.org/10.1080/02640414.2018.1539294

26.

Molenaar

P. C.

Campbell

C. G.

(2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x

27.

Peper

Beek

P. J.

(1998). Distinguishing between the effects of frequency and amplitude on interlimb coupling in tapping a 2: 3 polyrhythm. Experimental Brain Research, 118(1), 78–92. https://doi.org/10.1007/s002210050257

28.

Repp

B. H.

(2005). Sensorimotor synchronization: A review of the tapping literature. Psychonomic Bulletin & Review, 12(6), 969–992. https://doi.org/10.3758/BF03206433

29.

Repp

B. H.

(2010). Sensorimotor synchronization and perception of timing: Effects of music training and task experience. Human Movement Science, 29(2), 200–213. https://doi.org/10.1016/j.humov.2009.08.002

30.

Repp

B. H.

Doggett

(2007). Tapping to a very slow beat: A comparison of musicians and nonmusicians. Music Perception, 24(4), 367–376. https://doi.org/10.1525/mp.2007.24.4.367

31.

Repp

B. H.

Y.-H.

(2013). Sensorimotor synchronization: A review of recent research (2006–2012). Psychonomic Bulletin & Review, 20(3), 403–452. https://doi.org/10.3758/s13423-012-0371-2

32.

Sanders

R. H.

Levitin

D. J.

(2020). Towards an understanding of control of complex rhythmical “wavelike” coordination in humans. Brain Sciences, 10(4), 215. https://doi.org/10.3390/brainsci10040215

33.

Seifert

Leblanc

Chollet

Delignières

(2010). Inter-limb coordination in swimming: Effect of speed and skill level. Human Movement Science, 29(1), 103–113. https://doi.org/10.1016/j.humov.2009.05.003

34.

Seifert

Leblanc

Herault

Komar

Button

Chollet

(2011). Inter-individual variability in the upper–lower limb breaststroke coordination. Human Movement Science, 30(3), 550–565. https://doi.org/10.1016/j.humov.2010.12.003

35.

Shaffer

L. H.

(1981). Performances of chopin, bach, and bartok - studies in motor programming. Cognitive Psychology, 13(3), 326–376. https://doi.org/10.1016/0010-0285(81)90013-x

36.

Shih

P.-C.

Steele

C. J.

Nikulin

Villringer

Sehm

(2019). Kinematic profiles suggest differential control processes involved in bilateral in-phase and anti-phase movements. Scientific Reports, 9(1), 3273. https://doi.org/10.1038/s41598-019-40295-1

37.

Sommer

Häger

C. K.

Boraxbekk

C. J.

Rönnqvist

(2018). Timing training in female soccer players: Effects on skilled movement performance and brain responses. Frontiers in Human Neuroscience, 12(3), 311. https://doi.org/10.3389/fnhum.2018.00311

38.

Spector

P. E.

(2019). Do not cross me: Optimizing the use of cross-sectional designs. Journal of Business and Psychology, 34(2), 125–137. https://doi.org/10.1007/s10869-018-09613-8

39.

Studenka

B. E.

Zelaznik

H. N.

Balasubramaniam

(2012). The distinction between tapping and circle drawing with and without tactile feedback: An examination of the sources of timing variance. Quarterly Journal of Experimental Psychology, 65(6), 1086–1100. https://doi.org/10.1080/17470218.2011.640404

40.

Summers

J. J.

Kennedy

T. M.

(1992). Strategies in the production of a 5:3 polyrhythm. Human Movement Science, 11(1-2), 101–112. https://doi.org/10.1016/0167-9457(92)90053-E

41.

Summers

J. J.

Rosenbaum

D. A.

Burns

B. D.

Ford

S. K.

(1993). Production of polyrhythms. Journal of Experimental Psychology: Human Perception and Performance, 19(2), 416–428. https://doi.org/10.1037//0096-1523.19.2.416

42.

Twisk

J. W.

(2004). Longitudinal data analysis. A comparison between generalized estimating equations and random coefficient analysis. European Journal of Epidemiology, 19(8), 769–776. https://doi.org/10.1023/b:ejep.0000036572.00663.f2

43.

van de Rijt

(2018). All-sense-all networks are suboptimal for sensorimotor synchronization. PLoS One, 13(8), Article e0202056. https://doi.org/10.1371/journal.pone.0202056

44.

Vathagavorakul

Gonjo

Homma

(2020). The effect of experience in movement coordination with music on polyrhythmic production: Comparison between artistic swimmers and water polo players during eggbeater kick performance. PLoS One, 15(8), Article e0238197. https://doi.org/10.1371/journal.pone.0238197

45.

Vathagavorakul

Gonjo

Homma

(2021). Differences in limb coordination in polyrhythmic production among water polo players, artistic swimmers and drummers. Journal of Motor Behavior, 53(2), 191–199. https://doi.org/10.1080/00222895.2020.1748860