Random and Blocked Practice Schedule Affect Search for New Movement Coordination Patterns Differently

Journal of Motor Learning and Development, 2025, 13, 486–505 https://doi.org/10.1123/jmld.2024-0085 ORIGINAL RESEARCH Random and Blocked Practice Schedule Affect Search for New Movement Coordination Patterns Differently Anadi Mehta, 1 Joanne Smith, 1 David Travieso, 2 and Raoul M. Bongers 1 1 Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands; 2 Facultad de Psicología, Universidad Autonoma de Madrid, Madrid, Spain Learning a novel motor task involves searching within the joint space to form new movement coordination patterns that achieve the task goal. This search process is characterized by systematicchanges in joint angle coordination over time, requiring variability in coordination patterns. Motor learning studies have often highlighted the benefits of practice variability on task performance and have primarily focused on search processes at the task level, neglecting the underlying joint level. This study aims to identify differences due to imposed task variability in search behavior within both the task space and the joint space. Participants were divided into two groups based on their practice schedule (blocked vs. random) and performed a lateral interception task using a novel body–machine interface paradigm with redundant mapping between movement signals and paddle position. The results showed that participants successfully learned the required movement coordination in both practice groups. However, random practice led to increased search behavior at both the task and joint levels. Furthermore, analysis of the search structure revealed that covariation in coordination patterns was higher with random practice. Introducing variability during practice did not affect task performance but significantly influenced the amount and structure of search behavior. Keywords:de novo motor learning, contextual interference, exploration, uncontrolled manifold analysis, lateral interception © 2025 The Authors. Published by Human Kinetics, Inc. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, CC BY 4.0, which permits unrestricted noncommercial and commercial use, distribution, and reproduction in any medium, provided the original work is properly cited, the new use includes a link to the license, and any changes are indicated. See http://creativecommons.org/licenses/by/4.0. This license does not cover any thirdparty material that may appear with permission in the article. Smith https://orcid.org/0000-0002-0126-5196 Travieso https://orcid.org/0000-0002-5804-4212 Bongers https://orcid.org/0000-0002-3518-7464 Mehta ([email protected]) is corresponding author, https://orcid.org/0000-0002-4637-5209 486 Unauthenticated | Downloaded 11/13/25 03:11 PM UTC Motor learning is the process of expanding the existing motor repertoire through practice and experience of novel action. For instance, the exceptional skill sportspersons possess for hitting or catching a ball follows from years of extensive practice. During practice, learning processes take place that involve changes in coordination between the end effector and the environment (Jacobs & Michaels, 2006;Michaels et al., 2006) along with changes in the coordination among relevant degrees of freedom (DoF) within the movement system (Newell, 1985; Ranganathan & Scheidt, 2016). It has been postulated that variations in repetitive performances during practice might aid learning processes underlying these changes in coordination (cf. Gray, 2017,2020). Starting from a dynamical systems approach to motor behavior, the current study is aimed at understanding how imposed variability during practice influences coordination patterns at the task and DoF levels when learning a virtual interception task with a body–machine interface. To understand the role of imposed variability in motor learning, we focus on the abundance of goal-directed movements from a dynamical systems approach to motor behavior. Most of the motor tasks people perform have multiple solutions and permit variability because the human movement system is abundant with more DoF (neurons, muscles, and joints) than are necessary to perform a given task (Bernstein, 1967; Latash, 2012;Turvey, 1990). The exploitation of this abundance shows up in the covariation of DoF in skilled motor behavior that characterizes task-specific coordination patterns (Scholz & Schöner, 1999;Schöner & Scholz, 2007;Tuitert et al., 2017, 2020). These coordination patterns are assumed to be attractor states emerging from nonlinear interactions between DoFs (Kelso, 2022;Profeta & Turvey, 2018;Turvey, 2007). They are stable organizations of DoF that ensure task performance and contributions of individual DoF can vary. Hence, variability and covariation in DoF are inherent properties of coordination patterns, which were evaluated in the current paper using uncontrolled manifold (UCM) analysis (cf. Scholz & Schöner, 1999). To do this, we examined joint angles to assess the DoF level. The presence of multiple movement solutions at the task level and abundant DoF in the joint angles requires individuals to learn their preferred end-effector movement pattern and joint coordination patterns during practice (Golenia et al., 2017). The abundance at task and joint levels allow for changes and variability in these coordination patterns over practice. This variability reflects a search process for the preferred coordination pattern. These search processes can take place in the task (searching for the end-effector movement that achieves the task) and DOF spaces (searching for the coordination patterns that move the end effector along the desired trajectory). Newell et al. (1989)andPachecoetal.(2019) examined these search patterns by defining search as the process of varying within these spaces so as to settle in on a coordinative solution. Key Points •Search is the process of systematic trial-to-trial change in task space as well as joint space during practice. •De novo learning requires search within the joint space for formation of new coordination patterns to accomplish the task goal. •Random practice has increased search behavior and higher covariation in the coordination patterns compared to blocked practice. JMLD Vol. 13, No. 2, 2025 SEARCH FOR NEW MOVEMENT COORDINATION PATTERNS 487 Unauthenticated | Downloaded 11/13/25 03:11 PM UTC Ranganathan et al. (2014) demonstrate changes in search behavior during learning within the task space and that search behavior is guided by task constraints. To this end, we assessed the trial-to-trial changes in end-effector movement and task performance. Underlying the movement of the end effector are the coordination patterns in joint angles (cf. Bernstein, 1967;Kugler et al., 1980;Newell, 1985). Changes are expected over learning to find the preferred coordination pattern among joint angles (Golenia, Schoemaker, Otten, Mouton, Bongers, 2018;Golenia, Schoemaker, Otten, Tuitert, Bongers, 2018;Pacheco & Newell, 2018). Pacheco and Newell (2018,2019) showed in a series of experiments on a throwing task that participants search for different movement coordination patterns to stabilize the performance over time and that learning can be represented as a search path through DoF space. In sum, during learning, the search for preferred coordination patterns is at play both in the task space and within the joint space by exploiting their abundance. The search processes and their corresponding variability in behavior raise the question of whether imposing variability during practice aids the learning of a new task. Currently, there is no consensus within the field about the role of variability in motor learning. There is evidence to suggest that variability is necessary and helpful for learning (Dhawale et al., 2017;Sternad, 2018;Wu et al., 2014), while another set of studies testify that variability has no effect (He et al., 2016)orisdetrimentalto learning (Cardis et al., 2018;Ranganathan et al., 2021). One of the issues in the diversity of results regarding the effects of variability on learning is that studies differ in the type of variability (i.e., imposed vs. intrinsic) and the level at which it is studied (i.e., end effector vs. DoF). For instance, both Wu et al. (2014) and Singh et al. (2016) evaluate the role of variability in learning of a curved reaching trajectory. However, Wu et al. (2014) focus their study on how baseline task-relevant variability in endeffector trajectory influences learning, whereas Singh et al. (2016) correlate learning rate with covariation in the joint space. Therefore, in the current study, we simultaneously examined variability at the two levels during learning and explored the relationship between them. To do this, we examined the role of imposed variability during learning by inducing different degrees of variability and comparing the amount and structure of search behavior between task and DoF levels. We do this by employing the UCM analysis to partition the joint configuration variability into VUCM (variability not affecting end-effector performance, i.e., covariation) and VORT (variability affecting end-effector performance). Changes in VUCM and VORT across practice reveal the underlying structure in search behavior. Introducing variability during training has been widely studied as the contextual interference effect (Hall & Magill, 1995;Schmidt, 1975;Shea & Wulf, 2005) wherein the presentation schedule of the task conditions is manipulated such that certain task parameters are kept constant for one group while they are pseudorandomized for the other group. A higher degree of imposed variability in practice results in better performance, adaptability, and faster learning (Caballero et al., 2017;Gray 2017,2020). It has been shown that blocked practice improves performance (faster learning and more accurate performance) during acquisition, but random practice enhances retention and transfer (Merbah & Meulemans, 2011). In contrast, several other studies have presented contradictory findings, underscoring the absence of influence exerted by imposed variability during practice (Pacheco et al., 2023;Tuitert et al., 2017;Van Rossum, 1990). The contextual interference effect has predominantly been studied and supported by JMLD Vol. 13, No. 2, 2025 488 MEHTA ET AL. Unauthenticated | Downloaded 11/13/25 03:11 PM UTC simple motor tasks such as sequence learning; whereas, it has diminished efficacy outside of laboratory tasks and in the practice of more complex skills such as in sports (Brady, 2008). A plausible reason for this inconsistency could be that existing studies only looked at the learning outcomes, and there is limited focus on how variability influences the learning process itself. Random practice induces variability in task performance as well as in joint angles (Ranganathan & Newell, 2013). Therefore, it is pertinent to study whether imposing variability during practice impacts search behavior at both task and DoF levels. In this study, a lateral interception task with different ball trajectories utilizing a body–machine interface paradigm was developed to study the role of imposed variability in learning a novel task and understand how variability is exploited at the task and DoF levels. Inertial measurement units (IMUs) captured the movement signals, which were mapped onto a virtual paddle such that coordinated movement of the upper limb resulted in lateral movement of the paddle on the screen (Farshchiansadegh et al., 2014;Lee et al., 2018;Levac et al., 2019). The mapping required the participant to learn novel coordination patterns to successfully perform the task. The specific research questions addressed in this study are: Do participants in both practice groups (blocked and random) learn to perform the task to reduce error? Does the random group exhibit increased search behavior during acquisition? Does the change in the structure of the search differ between blocked and random practice? We hypothesize that the random practice group would show improved transfer and adaptability due to increased search behavior during acquisition, resulting in higher covariation of joint configurations and the formation of more stable coordination patterns. Methods Participants Right-handed participants were recruited for the experiment from the student population of the University of Groningen. The study was approved by the ethics committee of the University Medical Center Groningen and all participants provided written informed consent before the experiment. A nominal monetary reward was provided for participation. ThesamplesizewasestimatedinG *Power (version 3.1; Faul et al., 2009)using a priori computations. There was no appropriate data available to compute the power analysis on our effects. We therefore took a moderate effect size (f=0.5) and a correlation among repeated measures of .5 for sample size estimation. The sample size of 38 participants was arrived at by using an alpha =.05 and a power of 0.95 for an analysis of variance (ANOVA) with betweenand within-subject factors (between: practice variability groups and within: pre-, post-, and transfer tests). Apparatus The task was displayed on an 86 in. (218.44 cm) screen (Riva R2, CTOUCH Europe BV) with a 60-Hz refresh rate. The screen was at a distance of 2.5 m from where the participant was seated in a chair with no armrests (Figure 1). Participants JMLD Vol. 13, No. 2, 2025 SEARCH FOR NEW MOVEMENT COORDINATION PATTERNS 489 Unauthenticated | Downloaded 11/13/25 03:11 PM UTC were informed to sit comfortably on the chair and to only move their left arm to control the paddle while keeping their torso and upper body still against the back of the chair during the experiment. Movements of the upper limb were captured with four wireless IMUs (MTw Awinda, Movella Technologies). These were placed on the chest (sternum), upper arm (dorsal side between the biceps and triceps), lower arm (dorsal side close to the wrist), and the hand (dorsal side on the third metacarpal) of the participant as depicted in Figure 1. The movement signals were directly streamed to Unity Engine (Unity technologies) via a USB connection and sampled at 50 Hz. Calibration Before the start of the experiment, two calibration procedures were performed: one for aligning the IMU axis to joint segments and the other to capture the range of motion. The calibration procedure for IMU alignment to joint segments was performed Figure 1 —Experimental setup with wireless IMU sensors. The ball (green, solid circle) and paddle (red, solid rectangle) along with the possible ball departure positions (dashed circles on top) and ball arrival positions (dashed circles at the bottom) are displayed on the screen. The arrow on the paddle depicts the movement along the interception axis. IMU = inertial measurement unit. JMLD Vol. 13, No. 2, 2025 490 MEHTA ET AL. Unauthenticated | Downloaded 11/13/25 03:11 PM UTC standing upright (except for wrist ulnar–radial deviation) and consisted of a static pose (neutral position) followed by elbow flexion–extension, elbow pronation–supination, wrist flexion–extension, and wrist ulnar–radial deviation. During the calibration of wrist ulnar–radial deviation, the participant was seated on the chair with the lower arm and hand supported on a flat surface. Participants performed each movement thrice at a steady pace, and the IMU calibration data was recorded and processed in MATLAB. The maxima in movement signals of respective IMUs were used to identify the joint movement axes and compute joint angles (Bonfiglio, Farella, et al., 2024;Bonfiglio, Tacconi, et al., 2024). This was followed by the range of motion calibration, which included the following movements—elbow flexion and extension, elbow pronation and supination (at 90° elbow flexion); wrist flexion and extension (at 90° elbow flexion in complete pronation and 180° elbow flexion in neutral position); wrist ulnar and radial deviations (at 90° elbow flexion in complete pronation and 180° elbow flexion in neutral position); shoulder abduction and adduction; and shoulder flexion and extension. Each movement was performed to their extreme ranges once, and the range of motion was computed within the Unity engine. Mapping A virtual lateral interception task was performed on the screen (total screen size: 42 (width) ×22 (height) Unity units) wherein a downward moving virtual ball (one Unity unit diameter, 1.03° visual angle) was intercepted by a virtual paddle (1.5 (width) × 0.5 (height) Unity units) that was controlled by the movements of the left upper limb. The ball always started from the top of the screen, and the paddle moved only laterally at the bottom of the screen along the interception axis (the shortest distance between the ball’s starting position and the interception axis was 10 Unity units). An abstract redundant mapping was introduced between the joint angles (ScX: shoulder abduction–adduction, ElY: elbow pronation–supination, ElZ: elbow flexion–extension, and WrY: wrist ulnar–radial deviation) and the paddle position (P; Equation 1). Each joint angle value was normalized after calibration, and the mapping ensured that they could only contribute a maximum of 8 units (+/−4 units from the center) of horizontal movement on the interception axis while the total end-to-end distance to be covered was 32 units (+/−16 units from the center). Therefore, a combination of joint angles was required to intercept the ball at all locations. Note that abundance gradually reduces when reaching the more extreme interception locations, which is also the case in regular reaching to a target that is at the edge of the workspace. P=½8888−16  2 6 6 6 6 4 ScX ElY ElZ WrY 1 3 7 7 7 7 5 (1) Procedure Before the start of the learning task, the participants were instructed to move their left arm (shoulder, elbow, and wrist) to control the paddle and intercept the descending ball. Participants were also informed that the task involved motor JMLD Vol. 13, No. 2, 2025 SEARCH FOR NEW MOVEMENT COORDINATION PATTERNS 491 Unauthenticated | Downloaded 11/13/25 03:11 PM UTC learning, emphasizing that successful ball interception might not be achieved in the initial trials, and accurate control of the paddle should be learned through practice. They were also shown the joint configuration that brings the paddle to the start position (elbow adjoining the hip and in 90° flexion with the wrist in neutral position). They were asked to maintain this configuration at the commencement of each trial and start moving only once the ball began its descent. At the commencement of each trial, the paddle had to be brought to the center of the screen. Once the paddle was in position, the ball was displayed immediately and began to move downwards after a certain delay. The delay across trials was randomized between 700 and 1200 ms. The ball took 2 s to reach the ball arrival position (BAP) from the ball departure position (BDP), that is, the time for interception was kept constant for all trials. The interception of the ball required horizontal motion of the virtual paddle on the interception axis through arm movements. Feedback was provided on successful interceptions wherein the paddle would change color from white to green for 20 ms after which the paddle disappeared. On completion of a trial, either when a ball was intercepted or reached the BAP, the participants had to return the paddle to the starting position, at which point it would reappear. A short pause (maximum: 5 min) was provided after every 100 trials for the participants to rest their arms, if needed. The participants let the experimenter know when they were ready to resume. Task Design There were four possible BDPs and four BAPs allowing for 16 different trajectories of the ball. During the pretest, practice, and posttest, BDP and BAP on the horizontal axis were ±14 and ±7, respectively; while for the transfer test, the BAP was changed to ±16 and ±9 (Figure 1). In Figure 1, the dashed rectangle (red) at the center is the starting position of the paddle in each trial. The dashed circles (green) on the top of the screen are the possible BDPs, and those at the bottom (green and yellow) are the possible BAPs. The dashed circles (yellow) at the bottom not corresponding to the same horizontal location as displayed on the top depict BAPs during the transfer test. The experiment had four sessions presented in succession without any interruption—a pretest followed by a practice session, a posttest, and a transfer test. The test (pre, post, and transfer) conditions consisted of 16 trials (each ball trajectory presented once), with half the trials presented in blocked condition and the other half in random condition. The presentation order for the test conditions was counterbalanced among participants. In the practice session, 512 trials (four phases of 128 trials) were presented with trajectories blocked on BAP for the blocked practice group and pseudorandomized for the random practice group such that eight repetitions of each trajectory were presented in each practice phase. The BDP was also pseudorandomized for both groups. Typically, studies include retention and transfer tasks to evaluate learning. Implementation of a retention task in our study might introduce a confound of data because recalling participants on a different day would require recalibration based on the new placement of sensors, resulting in altered values of joint angles. This would make a comparison of the experimental data with retention data difficult, as delineating the difference in performance because of learning or sensor placement would not be possible. Hence, posttest and transfer tests were conducted on the same day after the practice session. JMLD Vol. 13, No. 2, 2025 492 MEHTA ET AL. Unauthenticated | Downloaded 11/13/25 03:11 PM UTC Data Analysis The complete time series signal for each participant was segmented into trial-wise data using the ball positions. For each trial, the start and end were identified as the first frame where the position of the ball on the vertical axis was less than 10 and greater than −10, respectively. If the ball was intercepted, the end of the trial was taken as the first frame in which the interception was recorded. The trial wise time series of the ball, paddle, and joint angles were filtered using a second-order recursive Butterworth filter with a cutoff frequency of 2.5 Hz. All trials were time normalized to 100 timestamps. The trials for each BAP within the practice session were divided into four phases of 32 trials each to estimate changes across the learning process. Absolute error was used to gauge learning and was determined as the absolute distance between the center of the paddle and the center of the ball at the end of the trial. The unit of distance was arbitrary as it was a virtual task within the Unity environment. The search was computed both in the joint and task spaces. Search in the task space was computed as the sum of the Euclidean distances between the paddle position of successive trials at each time step with the same BAP. This quantifies the extent of variation in trajectories over subsequent trials. Similarly, search in the joint space was operationalized as the sum of the Euclidean distances in the joint space. As an alternative, we could have computed search as a difference in total pathlength in the joint space of successive trials, but this compresses the withintrial temporal aspect of the trajectory and is not sensitive to variations in the shape of successive trajectories; and therefore, the current computation was implemented wherein the trial-to-trial difference is assessed at each time step. The UCM analysis was performed with the four joint angles as the elemental variables and the paddle position as the performance variable using the covariance matrix method (Verrel, 2010). The VUCM and VORT variance were computed similar to Tuitert et al. (2017) except that the Jacobian was not computed from the trials but defined by the mapping between the joint angles and the paddle position. VUCM and VORT were computed for each BAP in different practice phases separately, that is, ∼32 trials were taken at a time to compute the variance. The variance was averaged across BAPs for each participant. Statistical Analysis Mixed ANOVAs were performed to test the effects of the independent variables across sessions or learning phases using the JASP (version 0.17.0) software. Greenhouse–Geisser corrections were used whenever the sphericity assumption was violated. Bonferroni corrections were used for post hoc analyses. Generalized eta-squared (Bakeman, 2005) was used to calculate effect sizes and interpreted according to Cohen’s recommendation of 0.02 for a small effect, 0.13 for a medium effect, and 0.26 for a large effect (Cohen, 1988). Normality was checked with visual inspection of Q–Q plots and the Shapiro– Wilk test. Only the error data in certain groups was found to have deviations from normal distribution while the data for the search was normally distributed, and the variance in UCM analysis was log-transformed. Although it has been shown that repeated-measures ANOVA is robust enough to analyze nonnormally distributed JMLD Vol. 13, No. 2, 2025 SEARCH FOR NEW MOVEMENT COORDINATION PATTERNS 493 Unauthenticated | Downloaded 11/13/25 03:11 PM UTC data if sphericity corrections are carried out (Blanca et al., 2023), we performed nonparametric Friedman ANOVA when any group showed nonnormality to corroborate the ANOVA and post hoc comparisons. The significance level was set at p=.05 for all tests. The first analysis performed was to establish whether learning occurred. A mixed ANOVA was performed on an absolute error with test (pre, post, and transfer) and test condition (blocked and random) as repeated measures and practice schedule (blocked and random) as a between-group factor. Additionally, to explore the difference in performance between acquisition and posttest, another mixed ANOVA was conducted on absolute error with the session (Phase 4, posttest–blocked and posttest–random) as repeated measures and practice schedule (blocked and random) as a between-group factor. To evaluate how changes in error during acquisition are affected by different practice schedules, a mixed ANOVA was performed on absolute error with practice phases (Phases 1–4) as repeated measures and practice schedule (blocked and random) as between-group factors. The amount of search behavior was analyzed by performing separate mixed ANOVAs on pathlength difference in the task space and the joint space with practice phases (Phases 1–4) as repeated measures and practice schedule (blocked and random) as between-group factor. Lastly, the structure of the search was analyzed using the UCM analysis wherein the log-transformed VUCM and VORT at the end of the trial were compared across different phases of learning. A three-way (Practice Phase × Variance ×Practice Schedule) mixed ANOVA was performed on the variance. Results Thirty-eight right-handed participants (age: 22.35 ± 2.5 years; 17 males and 20 females) performed the task. One participant did not learn the task at all and was excluded from the analysis. Task Performance Trials with deviation in paddle position at the start of a trial greater than +/−2 units from the center were removed as outliers (1.5% of all trials across participants). The percentage of successful interceptions across participants in the pretest was 16.47 ± 2.68%, whereas it increased in the posttest to 63.67 ± 2.80% and in the transfer test to 57.59 ± 2.26%. Reduction in absolute error was observed throughout the experiment, as is shown in Figure 2. The three-way mixed ANOVA on an absolute error with the test, test condition, and the group as factors revealed only a main effect of test, F(1.21, 70) =133.56, p<.001, η2 G=.60. The post hoc test showed significant differences between preand posttests (p<.001) as well as between preand transfer tests (p<0.001) but not between postand transfer tests (p=1). These results indicated an effect of practice and that the learned movement transferred to different BAPs. This analysis did not reveal any significance for either the main or interaction effect between the test condition and group. 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