Decomposition of a complex motor skill in learning improves experts' expertise

doi:10.21203/rs.3.rs-4148558/v1

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Decomposition of a complex motor skill in learning improves experts' expertise

https://doi.org/10.21203/rs.3.rs-4148558/v1

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Complex motor skills involve intricate sequences of movements that require precise temporal coordination across multiple body parts, posing challenges to mastery based on perceived error or reward. One approach that has been widely used is to decompose such skills into simpler, constituent movement elements during the learning process, thereby aligning the task complexity with the learners' capacity for accurate execution. Despite common belief and prevalent adoption, the effectiveness of this method remains elusive. Here we addressed this issue by decomposing a sequence of precisely timed coordination of movements across multiple fingers into individual constituent elements separately during piano practice. The results demonstrated that the decomposition training enhanced the accuracy of the original motor skill, a benefit not achieved through mere repetition of movements alone, specifically when skilled pianists received explicit visual feedback on timing error in the order of milliseconds during training. During the training, the patterns of multi-finger movements changed significantly, suggesting exploration of movements to refine the skill. By contrast, neither unskilled pianists who underwent the same training nor skilled pianists who performed the decomposition training without receiving visual feedback on the error showed improved skill through training. These findings offer novel evidences suggesting that decomposing a complex motor skill, coupled with receiving feedback on subtle movement error during training, further enhances motor expertise of skilled individuals by facilitating exploratory refinement of movements.

Biological sciences/Neuroscience/Motor control/Motor cortex

Biological sciences/Physiology/Neurophysiology

A common nature of skillful motor actions is fast and precise performance of sequential movements requiring dexterous control of multiple body parts. Mastering such skills poses challenges even for individuals who have undergone extensive training, due to the necessity for sequential and simultaneous execution of intricate movement elements. By contrast, a primary focus of motor learning research has been on simplistic motor tasks such as reaching for a target or walking, which limits unveiling learning principles underlying the mastery of complex skills such as sports and music performance. To solve this issue is increasingly significant, because unlike the prevailing belief ¹, recent studies have reported that the amount of practice only explains approximately 30% of expertise ^2–4, which requires improving ways of practicing. Furthermore, practicing over-learnt motor skills potentially poses risks of triggering movement disorders such as focal dystonia ⁵, which underscores the importance of elucidating methods for mastering complex skills efficiently.

One of the bottlenecks to learn complex motor skills lies in the intricate execution and perception of fast and accurate repetitions of complex movement elements. Motor skill learning typically relies on updating movements based on error signals and/or reinforcement cues derived from task execution ^6–11. However, this learning process encounters limitations when the task complexity surpasses the learners’ capacity for appropriate execution. One possible solution is to perform the complex motor skill at a submaximal speed so as to make the task executable and perceivable, which is effective for untrained individuals ¹². Another approach that has been widely used in motor training is to decompose intricate movement sequences into a set of simple elements, a method shown to facilitate learning of complex motor skills in untrained individuals ^13–15. It has been also documented that the nervous system represents decomposed elements of movements ¹⁶, which changes plastically through learning ^17,18. Furthermore, motor skill acquisition appears to accelerate when a newly learned skill shares common elements with previously learned motor skills ¹⁴, suggesting a possibility that learning constituent skills generalizes acquisition of the original motor skill. However, it remains unclear whether and in what manner motor skill decomposition during training aids in mastering complex motor skills, and even overcomes the ceiling effect of learning in trained individuals ¹⁹.

Here, we tested it through practicing constituent elements of intricate motor sequences requiring precisely timed coordination of multiple fingers in piano playing. We hypothesized that training involving the decomposition of an intricate skill, coupled with the provision of error information, fosters exploration of movements during learning so as to enhance performance of the original complex skill, even in a task where conventional practice methods fail to yield further improvement.

Two experiments were performed to test whether the decomposition of a complex motor skill facilitates the learning of that skill. In total, 60 skilled and 12 non-skilled pianists participated in the study.

Experiment 1

The purpose of experiment 1 was to test whether the skill decomposition in practicing, together with the visualization of subtle movement errors, facilitates the learning of a complex motor skill that is extremely difficult to perform even by pianists who have undergone years of extensive piano training since childhood. Thirty-six skilled and 12 non-skilled pianists participated in the experiment, which consisted of a pretest, intervention, and posttest sessions (Fig.1A).

Experimental task

The experimental task required the alternate repetition of two movement patterns of simultaneous strikes of two keys; one the right index and ring fingers (movement pattern A) and another with simultaneous keystrokes with the right middle and little fingers (movement pattern B) (Fig.1B-a). This task is obtained from Etude Op. 25 no. 6 composed by Frédéric Chopin, being known as one of the most technically demanding pieces. Each trial consists of successful repetition of one cycle of two movement patterns 20 times. If keypresses were performed in an incorrect order (e.g., pressing the keys with the index and little fingers instead of the middle and little fingers for the movement pattern B), those keypresses were not counted in the successful 20 repetitions, and that trial was considered an error trial. We asked the participants to repeat the experimental task 10 times in the pretest and posttest sessions with maintaining a predetermined fastest tempo for each individual, which was determined for each participant as one with the probability of the error trials being approximately 6 out of 10 trials. As an index of the motor performance, we counted the number of the error trials among the 10 trials (i.e., keypress error).

Training

To confirm the ceiling effect of the conventional training on the complex motor skill, we conducted the repetition training of the experimental task. Twelve skilled pianists were instructed to play as quickly as possible while minimizing the probability of the error occurrence [9 women, 22.3 ± 2.7 years old]. The training of the repetition of the experimental task consisted of 30 trials, each of which had 20 strikes of the keys. They took a 3-minute break every 5 trials.

Another 24 skilled pianists were randomly divided into two groups with different interventions: the feedback (FB) group [skilled FB group: n = 12, 1 man, 22.9 ± 2.3 years old (mean ± SD)] and the no-FB group [n = 12, 5 men, 21.8 ± 2.1 years old]. All non-skilled pianists were allocated to the FB group [non-skilled FB group, n = 12, 2 men, 23.7 ± 5.6 years old]. All participants individually practiced two movement patterns, which were constituent elements of the experimental task (Fig.1B-b). One involved the simultaneous strikes of two piano keys leaving one white key in between with the index and ring fingers while releasing the two depressed keys adjacent to the keys to be struck with the middle and little fingers at the same time (i.e., movement pattern A). Another (movement pattern B) is the inversion of the movement pattern A (i.e. keystrokes with the middle and little fingers and key-releases with the index and ring fingers). In the training session, participants in the FB and no-FB groups were instructed to execute each movement pattern discretely, with synchronizing the timing of the movements across four fingers (Fig.1A-b). Following the execution, the participants in the FB group received visual feedback regarding the timing of each finger movement (i.e. temporal synchrony of movements between the fingers). The visual feedback information included the onset of keypress by each of the four fingers within a time window of 0-100 ms. In addition, if the difference in the timing between the earliest and latest finger movements (i.e., timing error) is lower than 10 ms or larger than 20 ms, circles corresponding to each finger displayed in the monitor turned black or blue, respectively; otherwise, it turned red. Thus, these three colors roughly indicated the amount of error. The participants were instructed to minimize the timing error based on the visual FB throughout the training session. The participants in the no-FB group were instructed to minimize the timing error without provision of the visual FB sorely based on the tactile information at the keypresses. The training of each movement pattern consisted of 10 blocks, each of which had 30 trials. The participants took a 5-minute break every 5 blocks. Half of the participants practiced the movement pattern A first and then practiced the pattern B, whereas another half practiced the movement patterns in the opposite order.

Before and after the training session (i.e., pretest and posttest sessions), all participants (i.e., participants of repetition and decomposition trainings) underwent the experimental task to assess the change in the task performance.

Performance of the experimental task

Figure 2A illustrates the mean values of the keypress error across the skilled pianists who underwent the repetition training of the experimental task in each of the pretest and posttest sessions. To confirm the repetition training effect on the motor performance, we used a generalized linear mixed effect model (GLME) with a Poisson distribution because it was a count data (fixed effect: session; random effect: participant). The GLME did not yield a significant difference in the keypress error between the pretest and posttest sessions (χ² = 3.1, P = 0.08).

Figure 2B illustrates the group means of the keypress error in each of the pretest and posttest sessions for the skilled FB, no-FB, and non-skilled FB groups. The results confirmed that the keypress error in the pretest session did not differ across all groups. This is because the movement tempo was personalized so that the probability of error trial could become 60% for each participant. To assess the decomposition training effect on the motor performance, we used a GLME with a Poisson distribution (fixed effects: group, session, and their interaction; random effect: participant). The GLME identified a significant interaction effect between the factors (χ² = 6.8, P = 0.03). A simple effect test unveiled a significant reduction in the keypress error specifically in the skilled FB group (z ratio = -2.7, P < 0.01). Moreover, we calculated a mean value of the interval between repeated keystrokes of the two movement patterns in each trial of the experimental task, in order to verify whether the movement tempo changes between the pretest and posttest sessions. We used a GLME with a logarithm link function and a gamma distribution (fixed and random effects were same as the keypress error) due to non-negative values and the distribution skewing towards 0. The GLME showed no significant difference in the movement tempo between the sessions (χ² = 1.5, P = 0.21).

In summary, the skilled FB group showed an improvement in probability of the error occurrence while maintaining the movement tempo. On the contrary, the repetition training of the experimental task and the decomposition training in each of the skilled pianists without error feedback and in the non-skilled pianists did not show such improvement.

Timing error

Figures 2C (a) and (b) illustrate the time course of the timing error derived from the training session for the movement patterns A and B in the skilled FB, no-FB, and non-skilled FB groups. To test whether the timing error was changed through training based on the difference in the timing error at the block 1, we used a GLME with a logarithm link function and a gamma distribution (fixed effects: group, block, movement pattern, and their interactions; covariate: timing error at block 1; random effects: participant, participant×block, participant×movement pattern). A GLME yielded main effects of group (χ² = 10.2, P < 0.01) and timing error at the block 1 (χ² = 137.9, P < 0.01), and an interaction effect between group and block factors (χ2 = 35.5, P < 0.01) on the timing error. A post hoc test revealed significant group differences in the timing error at the block 5 (skilled FB < non-skilled FB: z ratio = -3.2, P < 0.01), at the block 6 (skilled FB < non-skilled FB: z ratio = -2.6, P = 0.02), at the block 7 (skilled FB < no-FB: z ratio = -3.0, P < 0.01; skilled FB < non-skilled FB: z ratio = -3.4, P < 0.01), at the block 9 (skilled FB < no-FB: z ratio = -3.1, P < 0.01; skilled FB < non-skilled FB: z ratio = -3.8, P < 0.01), and at the block 10 (skilled FB < no-FB: z ratio = -3.3, P < 0.01; skilled FB < non-skilled FB: z ratio = -3.9, P < 0.01). This indicates the timing error was reduced through the training of both the movement patterns specifically in the skilled FB group.

Figures 2D (a) and (b) illustrate the group means of the timing error derived from the pretest and posttest sessions for the movement patterns A and B in the skilled FB, no-FB, and non-skilled FB groups. To confirm the decomposition training effect on the timing error during the experimental task, we used a GLME with a logarithm link function and a gamma distribution (fixed effects: group, session, movement pattern, and their interactions; random effects: participant, participant × session, participant × movement pattern). The GLME yielded a significant main effect of the movement pattern (χ² = 6.0, P = 0.01).

In summary, for the skilled FB group, the result of the training showed improvement of the timing error in both movement patterns A and B, whereas the training effect on the timing error did not reach it during the experimental task.

Experiment 2

Experiment 1 demonstrated that the decomposition of complex motor skill with provision of the visual FB on the error information in training improved the timing error for both movement patterns during training session and the keypress error of the complex skill in the skilled pianists. By contrast, the result of the experimental task showed no improvement in the timing error for both movement patterns. The lack of improvement may be attributed to the number of trials, although this aspect remains unclear. Moreover, we found the reduction of the keypress error by the decomposition training, whereas the underlying mechanism remains unclear. To clarify these, in Experiment 2, we asked participants to intensively practice movement pattern B, which turned out to be more challenging than movement pattern A (because the timing error of the experimental task was larger in the movement pattern B than in the movement pattern A) and examined the effects of training on the kinematics of multi-finger movements. Twenty-four skilled pianists participated in Experiment 2, where they were trained solely on movement pattern B while recording the angle of the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints of 4 fingers using a data glove implementing sensors recording the joint angles (Fig. 1A).

Experimental task

Some participants in the skilled FB group in Experiment 1 showed no error in the task performance after the training. To prevent this floor effect, the participants in Experiment 2 performed the experimental task with a movement tempo that was confirmed to elicit approximately 8 error trials out of 10 trials at preliminary investigation done prior to the pretest session, indicating an increased task difficulty Experiment 2 compared to Experiment 1.

Training

Participants were randomly divided into two groups with different interventions: the FB group [n = 12, 4 men, 26.6 ± 5.3 years old] and the no-FB group [n = 12, 1 man, 22.9 ± 4.2 years old]. Based on the results that facilitation of the exploration of the movement pattern B correlated with improvement in the motor performance, the participants in both the FB and no-FB groups were instructed to perform the movement pattern B with and without the visual FB regarding the timing of the finger movements in the same manner as Experiment 1. Both the FB and no-FB groups underwent a training session consisting of 600 trials (30 trials × 20 blocks) and took a 5-minute break every 5 blocks. In addition to the measurement of the piano keystrokes, we also measured the MCP and PIP joint angles of the 4 fingers using the data glove during the pretest, posttest, and training sessions.

Performance of the experimental task.

Figure 3A illustrates the group means of the keypress error in the pretest and posttest sessions for both groups. This value in the pretest session was higher in Experiment 2 than Experiment 1, reflecting the increased task difficulty in Experiment 2. A GLME with a Poisson distribution for the keypress error (fixed effects: group, session, and their interaction; random effect: participant) yielded significant main (group: χ² = 6.1, P = 0.01, session: χ² = 10.4, P < 0.01) and interaction (group×session: χ² = 6.0, P = 0.01) effects. Post hoc tests identified significant improvement of the keypress error through the training specifically in the FB group (z ratio = -3.9, P < 0.01) but not in the no-FB group (z ratio = -1.0, P = 0.35).

Figures 3B and C illustrate the group means of the timing error for the movement patterns A and B in the pretest and posttest sessions for both groups. A GLME with a logarithm link function and a gamma distribution (fixed effects: group, session, movement pattern, and their interactions; random effects: participant, participant×session, participant×movement pattern) revealed a significant interaction effect between session and movement pattern factors (χ² = 10.6, P < 0.01). Post hoc tests found a significant session-wise difference in the timing error for the movement pattern B (z ratio= -2.3, P = 0.02), but not the movement pattern A (z ratio = -0.4, P = 0.72).

Changes in the finger joint movements

Because the decomposition training simplifies intricate movements so that they can be executed and perceived, it is postulated that the exploration of the finger movements is facilitated during the learning process. To test this hypothesis, we assessed changes in the finger joint movements throughout the training. Figures 3D and E illustrate averaged time-varying finger joint angles across 600 trials in the training session at one representative pianist of each group. Positive and negative values indicate extension and flexion of the finger joints, respectively.A shaded area of the time course of the finger joint angles, which represents the variance across trials, was larger for a pianist of the FB group than for one of the no-FB group.

To quantify the amount of the exploration during the training session, we first computed the angular velocities (Figs. 3F and G: same pianist as in Figs. 3D and E, respectively) and then computed the amount of change in the angular velocities between two consecutive training blocks. Specifically, we first calculated the average peak angular velocity of each finger joint across trials for each training block. Thus, one vector consisting of angular velocity values of the eight finger joints was computed for each block (i.e., 20 vectors in total). Then, we computed the Euclidean distances in the angular velocity vector between all possible pairs of two adjacent training blocks (i.e., 19 pairs). This reflects the time course of the change in finger joint movements between adjacent blocks. We refer this value as a variability index (VI). A high VI value indicates different patterns of the finger joint movements between the two blocks (i.e., large exploration), whereas a low VI value indicates performing similar finger joint movements between the two blocks (i.e., low exploration).

Figure 3H illustrates the group mean of the VI in the FB and no-FB groups. A linear mixed effects model (LME: fixed effects: group, block, and their interactions; random effects: participant) revealed significant main (group: χ²(1) = 4.0, P = 0.04; block: χ²(18) = 69.29, P < 0.01) and interaction effects between the factors (χ²(18) = 59.1, P < 0.04). Post hoc tests yielded group differences in the VI at each of the block 1 (t(344) = 3.4, P < 0.01), at the block 2 (t(338) = 3.0, P < 0.01), at the block 7 (t(338) = 3.4, P < 0.01), at the block 8 (t(338) = 2.0, P = 0.03), at the block 9 (t(338) = 3.0, P < 0.01), and at the block 10 (t(338) = 3.0, P < 0.01).

A relation between finger joint coordination pattern and motor performance

To identify whether the amount of the exploration of the finger joint movements is associated with the improvement of the performance of the experimental task by the training, we computed Pearson’s correlation between the VI and the change in the keypress error between the pretest and posttest sessions. Overall, the VI was larger in the first few blocks than the subsequent blocks in the FB group, and was kept constant in the last few blocks in both groups. We thus used the difference in the VI between the blocks 1-2 and 19-20 for this analysis. Figure 3I illustrates the scatterplots of the differential value of the VI relative to the differential value of the keypress error in each of the FB and no-FB groups. We found a significant positive relationship between these variables specifically in the FB group (R=0.66, P=0.02) but not in the no-FB group (R=0.25, P=0.44).

The present study tested whether decomposition of a complex motor skill into simple constituent skills in training enhanced the complex skills in skilled and nonskilled pianists. First, we found that a mere repetition a complex motor skill in practicing the piano failed to enhance the performance of this task in the skilled pianists, confirming the learning plateau. By contrast, practicing each of two simple constituent movement patterns separately, which were obtained by decomposing the target complex motor skill, yielded reduction of the error occurrence in skilled pianists, specifically when the visual feedback of the subtle error information was provided during the training. This indicates that the decomposition training with visual feedback of movement errors is an effective way to enhance complex motor skills that cannot be improved through the ordinal training. However, this training effect was not observed in the non-skilled pianists. Further analyses based on the finger joint movements provided evidence of increases in the amount of movement exploration during training of one of the constituent movement patterns only in the FB group of the skilled pianists. This facilitation of movement exploration during training was correlated positively with the performance improvement. Our findings indicate that training with the skill decomposition and error augmentation facilitates the performance of complex motor skills specifically in the skilled individuals. Furthermore, the skill decomposition training uniquely enabled learners to explore the finger movements so as to reduce error in performing complex motor skill while maintaining constant movement tempo, which may reflect improvement in the speed-accuracy tradeoff of that skill.

Motor skill learning typically accompanies exploration to optimize the movements that can achieve a task goal. Previous studies have demonstrated that movement exploration emerges early in motor skill learning ^20–23 and is associated with the learning rate ^7,24–26. However, movement exploration in learning is largely constrained when the task complexity is high, since learners cannot successfully execute the task in a movement space with high dimensionality. Thus, a lack of motor performance improvement when skilled pianists merely repeated the complex motor skill in practicing may be because the high complexity of the present motor task limited movement exploration. By contrast, movement exploration and performance were facilitated when skilled pianists practiced the simple movement elements derived from the decomposition of the complex skill. This suggests that breaking down complex skills into executable elements drives exploration of the movement in learning. This effect, however, was only evident when the performance of decomposed elements was visually fed back to the learners. Here, the feedback information to be provided included both precise timing error and abstract reinforcement signals that can be associated with reward. The reward signals has been demonstrated to facilitate the reinforcement learning that involves movement exploration^26,27. It is therefore possible that the present pianists sought for discovering an optimal movement based on the error-related feedback. We also found that movement exploration occurred in the beginning of training, whereas the movement variability decreased as the training progressed. Thus, once a movement coordination pattern that can facilitate the performance is discovered in the early training phase, learners may switch from exploration to the strategy of further sophisticating movement ^20,28,29. Although the learning process that leverages movement variability has been also demonstrated in the bird song learning^30,31, a common challenge among trained individuals is that reduced movement variability at the late stage of learning can be the bottleneck of movement exploration for further sophisticating the skill¹⁹. The present finding thus provides novel insights that the skill decomposition with augmented error feedback in learning overcomes this limit.

In this study, our aim was not only to enhance exploration during training but also to guide the learning via providing error feedback. It has been widely recognized that providing feedback on movement errors through various sensory modalities facilitates motor learning ³¹, in which visual feedback has been commonly used. In our training that visually presents the timing of movements of the individual fingers after executing simple movement elements consisting of the complex motor skill, the skilled FB group showed a decrease of the timing error during training. Conversely, in Experiment 1, the timing error of motor performance in the experimental task was not changed following the entire training. This may be due firstly to the higher complexity of the experimental task compared to the decomposed elements during learning, and secondly to limited effects of the present decomposed training on the timing error of movements in performing the original experimental task. However, in Experiment 2 where the movement pattern B was specifically trained and the amount of the training was twice as much as one in Experiment 1, the timing error of the experimental task following the training was reduced specifically for the trained movement pattern B. These results suggest potential impacts of prolonged training of the decomposition elements with visual feedback on the original motor task.

The present training indicated expertise-dependent training effects on the exploration and motor performance. One reason why these changes did not manifest in the non-skilled group could be their lower finger dexterity. Our previous studies showed that skilled pianists are able to perform coordinated finger movements through adaptation in the biomechanical and neuromuscular features of the hand ^32,33. In contrast, the non-skilled participants did not undergo such adaptations, which implies that even the decomposed movement elements were complex and difficult to be executed. Since the present constituent movement patterns require opposing movements of adjacent fingers simultaneously, low independence of movement between the fingers, such as the middle and ring fingers, would make the task performance difficult for the non-skilled individuals ^34–37. In fact, the timing errors during the training session were larger for the non-skilled FB group compared to the skilled FB group. The uncontrollably large movement variability in the unskilled individuals may interfere volitional exploration of movements and/or exploitation of error information to update motor actions.

In conclusion, the present study provides novel evidence that the decomposition of the complex motor skill in training with providing feedback of the timing error of synchronous multi-finger movements facilitates exploration of the finger movement pattern and thereby further improves the speed-accuracy tradeoff specifically for skilled pianists who had years of instrumental training. The lack of these improvements in the non-skilled pianists who underwent the skill decomposition training with error feedback, as well as in the skilled pianists who underwent each of the decomposition training without error feedback and conventional repetition training of the original complex task highlights that expertise reflects abilities of taking advantage of learning from the decomposition training with subtle error information for further sophisticating the plateaued motor skill.

Participants

Thirty-six right-handed skilled pianists (27 female) and 12 right-handed age-matched non-skilled pianists (10 female) participated in Experiment 1, and 24 right-handed skilled pianists (19 female) participated in Experiment 2. Skilled pianists in the Experiments 1 and 2 were randomly assigned to 2 groups (12 per group) and to 2 groups (12 per group), respectively. These groups differed in ways of training, as described in the "Experiment 1" and "Experiment 2” sections. All pianists majored in piano performance at a musical conservatory and/or had extensive and continuous private piano training under the supervision of a professional pianist and/or a piano professor. In particular, the skilled pianists won prizes at international piano competitions and/or underwent at least 10 years of piano training, whereas the non-skilled pianists had neither of these experiences. Exclusion criteria were a history of neurosurgery, movement and neuropsychiatric disorders such as focal dystonia, or metal or electronic implants inside the body. Informed consent was obtained from all participants prior to participation in the experiment. All experimental procedure was conducted by following the Declaration of Helsinki and was approved by the ethics committee of Sony Corporation. To eliminate any potential effects of auditory and visual FB on motor performance and training, the digital piano was muted, and the participants were asked not to observe their fingers during the experiments, but to keep watching a screen in front of them.

Measurement of piano movements

We used a digital piano with wooden keys in which MIDI sensors were implemented (VPC1 KAWAI Co.) in the two experiments. The movement timing and velocity of keypresses and key-releases were sampled at 1000 Hz using a custom-made program (LabVIEW, National Instruments Inc.).

Measurement of finger movements

To assess kinematics of the finger movements, we recorded the time course of the finger joint angles by using a data glove with sensors surrounding the individual finger joints (CyberGlove III, CyberGlove Systems Inc.) in Experiment 2. We recorded the motions at 8 degrees of freedom with angular resolution < 0.5°, at 8.3 msec intervals (i.e., sampling frequency = 120 Hz). The measured angles were the MCP and PIP joint angles of the four fingers.

Experiment 1

Thirty-six skilled pianists and 12 non-skilled pianists participated in Experiment 1. Experiment 1 consisted of a succession of pretest, training, and posttest sessions.

In the pretest and posttest sessions, the experimental task required the alternate repetition of two movement patterns that involve simultaneous strikes of the two keys 20 times, using the right index and ring fingers for one movement pattern (movement pattern A) and the right middle and little fingers for another movement pattern (movement pattern B) (Fig. 1A). When the keypresses were performed in an incorrect order/fingering (e.g., pressing the keys with the index and little fingers instead of the middle and little fingers for the movement pattern B), those keypresses were not counted as the successful 20 repetitions, and that trial was considered as an error trial. Prior to the pretest session, the participants repeated the experimental task 20 times to determine a movement tempo at which the number of error trials was approximately 6 out of 10 trials, and they were instructed to maintain the movement tempo throughout the experiment. Each of the pretest and posttest sessions consisted of 10 trials. To confirm the ceiling effect of the repetition training of the complex motor skill, the 12 skilled pianists repeated the experimental task 30 times (i.e., 20 simultaneous strikes × 30 times = 600 times) without any visual FB and took a 3-minute break every 5 times.

In the training session, 12 skilled and 12 non-skilled pianists were allocated to the FB group (skilled and non-skilled FB groups). The participants in the FB group performed each movement pattern individually (i.e., either movement patterns A or B) but not their alternate repetition. In the training of the movement pattern A, the participants were asked to keep two keys with the middle and little fingers depressed prior to performing the task. After an auditory cue was provided (2000 Hz, 200 ms), they were asked to press two keys with the index and ring fingers together with releasing two keys with the middle and little fingers simultaneously (i.e., movement pattern A). After the combination of the keypresses and key-releases, participants received visual FB on the timing of these four finger motions. The FB displayed the onset of these key-press and key-release events with the four fingers within a time window of 0-100 ms. If the difference in the timing between the earliest and latest events of the key motions (i.e., timing error) was lower than 10 ms or larger than 20 ms, the circle corresponding to each finger on the display turned black or blue, respectively; otherwise, it turned red. The participants were instructed to minimize the timing error based on the two types of the visual FB. The training of the movement pattern B was conducted in the same manner as the training for the movement pattern A. In addition, 12 skilled pianists underwent training of each movement pattern separately without providing any visual FB about the timing error of movement synchrony between the fingers in order to identify the learning effect of training with the visual FB of the subtle timing error information on experts' expertise (no-FB group). The participants in the no-FB group were instructed to minimize the timing error of the finger movements based only on the somatosensory information derived from the motion and touches. The training sessions consisted of 600 trials (30 trials × 10 blocks × 2 movement patterns) in the FB and no-FB groups. The participants took a 5-minute break every 5 blocks. A half of them practiced the movement pattern A first and then practiced the pattern B. The other participants practiced the movement patterns in the reverse order.

Experiment 2

Twenty-four skilled pianists participated in the Experiment 2 and were randomly assigned to the FB group (n = 12) and the no-FB group (n = 12). Experiment 2 also consisted of a succession of pretest, training, and posttest sessions, although a paradigm of Experiment 2 was modified based on the results of Experiment 1.

Some participants from the skilled FB groups in Experiment 1 showed no error trial after the training, displaying the floor effect. Moreover, Experiment 1 was designed to train the movement patterns A and B, whereas the movement exploration was changed specifically in the movement pattern B. To further enhance the motor performance, we adjusted the experimental task and training in the Experiment 2. First, participants were instructed to explore a movement tempo that resulted in approximately 8 error trials out of 10 trials prior to the pretest session, indicating the increased difficulty in the experimental task for Experiment 2 compared to Experiment 1. Second, Experiment 2 was designed to train only the movement pattern B. Thus, both the FB and no-FB groups underwent a training session consisting of 600 trials (30 trials × 20 blocks), with participants taking a 5-minute break every 5 blocks. Third, in the Experiment 1, we evaluated changes in the movement exploration using information on pressing and releasing velocities of the piano keys. In Experiment 2, to further assess these changes from actual finger joint movements, we used a data glove and assessed the movement exploration using the angular velocity.

Data analysis

We calculated the number of the error trials in 10 trials as the index of the probability of the error occurrence in both the pretest and posttest sessions (keypress error).

In the Experiment 2, we calculated the rotational velocity of each finger joint to examine whether the exploration of finger joint movements occurred. First, we resampled the finger joint angles from each trial of the training session to 1,000 Hz, which were then preprocessed with low-pass filtering (the second-order Butterworth filter with a cutoff of 10 Hz). Second, we extracted data from 100 ms before the first key-motion to 100 ms after the last key-motion. Third, we resampled these finger joint angles to 1,000 samples to match the length across trials of the training session. Fourth, we calculated an absolute value of the angular velocity at each finger joint, and then calculated the mean absolute value between 8 finger joints and assessed the timing of the peak velocity. Finally, at the timing of the peak velocity, we extracted the signed angular velocity of each of the 8 finger joints (i.e. 8 values for each trial, in which the positive and negative value indicates the extension and flexion, respectively). To evaluate the amount of exploration of the finger joint movements during the training session, we calculated average values of extracted angular velocity of each of eight finger joints for each block. This average value is a vector that contains 8 elements for each block in the training session (i.e., 20 vectors). Then, we calculated a Euclidean distance in these vectors between all possible pairs of the two successive blocks (i.e., 19 Euclidean distances).

Statistics

We used a linear mixed effects model (LME) or generalized LME (GLME) implemented in the lmerTest package in R (https://www.r-project.org). Our model included the factors appropriate for the data among the group, session (or block), and movement pattern as fixed effects along with their interactions. Individual variability was assessed by including participants and interactions between participants and within-factors (when the number of within factors is 2 or more) as random factors. For the timing error, the value of this variable at the block 1 was included as a covariate to account for the difference between groups. To determine the significance of the fixed effects, Type II Wald chi-squared tests were performed using the Anova function in R with the car package. Multiple comparisons were performed using the emmeans package in R. Degrees of freedom of such comparisons were calculated using the Kenward-Roge method, while P-values were adjusted using the Holm method.

Competing interests

All authors (YK, MH, SF) have no conflicts of financial and/or non-financial interest to declare.

Data and code availability

The data that support the findings of this study are available on request from the

corresponding author [Y.K.]. The data are not publicly available because they contain information that could compromise research participant privacy. Codes will be provided upon request.

Acknowledgements

This study was supported by JST CREST (JPMJCR20D4) and JST Moonshot R&D (Grant Number 527 JPMJMS2012).

Author Contributions

Y.K. participated in the conception, organization, and design of the study, collected and analyzed the data, designed and ran the statistical analyses, and wrote the first draft and revised the manuscript. M.H. and S.F. helped to conceptualize and design the study, interpreted the data, and revised the manuscript. All authors reviewed and critiqued the draft and approved the final manuscript.

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Decomposition of a complex motor skill in learning improves experts' expertise

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INTRODUCTION

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DISCUSSION

MATERIALS AND METHODS

Participants

Measurement of piano movements

Measurement of finger movements

Experiment 1

Experiment 2

Data analysis

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