# The added value of point-light display observation combined to physical practice in learning a functional strength training movement for experienced athletes

Charly Ferrier, Arnaud Decatoire, Yves Almecija, Christel Bidet-Ildei, Yannick Blandin

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## ► To cite this version:

Charly Ferrier, Arnaud Decatoire, Yves Almecija, Christel Bidet-Ildei, Yannick Blandin. The added value of point-light display observation combined to physical practice in learning a functional strength training movement for experienced athletes. Human Movement Science, 2026, 106, pp.103452. 10.1016/j.humov.2026.103452. hal-05467321

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# The added value of point-light display observation combined to physical practice in learning a functional strength training movement for experienced athletes

Charly Ferrier<sup>a</sup>, Arnaud Decatoire<sup>c</sup>, Yves Almecija<sup>a</sup>, Christel Bidet-Ildei<sup>a,b</sup>, Yannick Blandin<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Observational learning

Coordination

Motion capture

Movement dynamics

Weightlifting

## ABSTRACT

A number of studies have shown the importance of combining observation with practice when learning an action. Although the Point-Light Display (PLD) technique is frequently used in laboratory contexts to assess the mechanisms underlying observational learning, it has only been infrequently used in ecological contexts. The present study explored whether the components of complex weightlifting movements could be learned by means of an observational learning protocol. To that end, we compared three observation conditions: action observation (Video group), Point-Light Display observation (PLD group), and no human action observation (Control group). Twenty-six athletes participated in a weekly one-hour session for 5 weeks to learn a complex weightlifting movement. During this period, the participants alternated between phases of movement observation, which varied depending on the condition to which they were assigned, and phases of movement execution. There were 12 sets consisting of 2 min of observation and 6 physical repetitions per session. Joint angles at key moments, bar trajectories, and intersegmental coordination were assessed both before (Pre-test) and after (Retention tests) the learning period. The results indicate that both observation conditions had a larger effect on learning than the control condition. Furthermore, the PLD condition was more effective than the video condition for complex intersegmental coordination. This experiment therefore suggests that PLD observation combined with physical practice can be beneficial for the acquisition of complex movements.

## 1. Introduction

The value of action observation in the acquisition of sensory-motor skills has been demonstrated in many experimental studies (Adams, 1986; Hodges, 2017; Mattar & Gribble, 2005, for reviews). In the same way, the value of action observation training has also been demonstrated in many sports, such as volleyball (Weeks & Anderson, 2000), football (Horn, Williams, & Scott, 2002), bowling (Breslin, Hodges, & Williams, 2009), golf (D'Innocenzo, Gonzalez, Williams, & Bishop, 2016), rugby (Faelli et al., 2019) and judo

<footnote><sup>a</sup> Université de Poitiers, Université de Tours, Centre National de la Recherche Scientifique, Centre de Recherches sur la Cognition et l'Apprentissage (UMR 7295), Poitiers, France</footnote>

<footnote><sup>b</sup> Institut Universitaire de France, France</footnote>

<footnote><sup>c</sup> Université de Poitiers, ISAE-ENSMA, CNRS, PPRIME, Poitiers, France</footnote>

<footnote>* Corresponding author at: Centre de recherches sur la cognition et l'apprentissage – CeRCA, CNRS UMR 7295, MSHS. Bât A5, 5, rue Théodore Lefebvre, TSA 21103, 86073 Poitiers Cedex 9, France.</footnote>

<footnote>E-mail address: yannick.blandin@univ-poitiers.fr (Y. Blandin).</footnote>
(Francisco, Decatoire, & Bidet-Ildei, 2023). However, it is essential to combine observation and physical practice in order to cause the cognitive representation of the observed action to interact with its motor realization (see Deakin & Proteau, 2000). Blandin & Proteau, 2000 developed this idea by pointing out that the combination of action observation and physical practice not only made it possible to learn a motor pattern more quickly but also to reduce its variability. More recently, Boutin, Fries, Panzer, Shea, and Blandin (2010) found that the movement times of participants who combined observational learning with physical practice of a sequence task were shorter than those of a group of participants who only observed the sequence. Interestingly, the authors also showed that the group that combined observation and physical practice required less physical practice to achieve the same performance as a practice-only group. Consequently, giving an athlete a physical training program interspersed with action observation sequences would reduce the absolute number of physical training trials, while leading to equivalent learning. Indeed, an observed technique or strategy that promises to increase performance can be implemented immediately in the following physical practice trial, thereby facilitating the learning of an error detection-correction mechanism (Blandin & Proteau, 2000). More recently, Larssen, Ho, Kraeutner, and Hodges (2021) demonstrated, using a visuomotor adaptation task, that alternating observation and physical practice across blocks within a training session yields learning outcomes comparable to physical practice alone, thereby supporting memory consolidation. However, because the after-effects were smaller following the combined practice compared to the physical practice, they suggested that the beneficial learning effects of observation were promoted by explicit processes. Furthermore, there is evidence of “micro-offline gains” during short periods of rest between physical practice trials (Bönstrup et al., 2019) that may also apply for observational practice.

Although several observation techniques exist (direct observation of a person, video-based or static images), one of the most widely studied is the Point-Light Display (PLD) technique, which represents an action solely in terms of the movement of an actor's main joints (Johansson, 1973). This technique is an interesting method for observing and optimizing motor learning because it only provides kinematic information. Despite the sparseness of the stimuli presented, it has been shown that observed actions are recognized quickly and accurately (e.g., Bidet-Ildei et al., 2022) and can be learned when associated with physical practice (Ghorbani & Bund, 2016; Horn, Williams, Scott, & Hodges, 2005). However, except for in the recent study by Francisco et al. (2023), the beneficial effects of PLD observation combined with physical practice have mainly been reported for discrete and mono-segmental actions in beginner participants.

In the present experiment, we assessed the combined effects of observation and physical practice on the learning of functional training movements often used in the physical preparation of experienced athletes in order to directly increase vertical power (Berton, Lixandrão, Silva C.M., & Tricoli, 2018; Bolger, Lyons, Harrison, & Kenny, 2016; Hackett, Davies, Soomro, & Halaki, 2016; Tricoli, Lamas, Carnevale, & Ugrinowitsch, 2005). Although these experienced athletes have undeniable motor skills in their respective specialties, they often have no or little experience of performing this type of movement. It is therefore necessary to devote a significant amount of time to the technical acquisition of these movements before they can lead to the optimal development of bar path and vertical power. By evaluating changes in kinematic variables and the trajectory of the bar in the sagittal plane during the movement, Haug, Drinkwater, and Chapman (2015) showed that it took international gymnasts 169 days of training to learn the power clean. In the present experiment, we chose a movement that combines weightlifting with functional training: the cluster. This combines two popular functional fitness movements, the clean and the thruster, to create one full-body exercise. The cluster thus consists of a power clean followed by a push-press movement (see Fig. 1). We chose this movement because it has the advantage of developing the explosiveness of the posterior and anterior chains, both in the lower and upper limbs, while requiring specific motor coordination. The pulling phase can be considered as a discrete skill, with a clear beginning and end, and a complex motor skill because it involves many

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Illustration of the different phases of the cluster, the movement to be learned.</figcaption></figure>
degrees of freedom (Kelso, Del Colle, & Schöner, 1990). To better understand how such coordination patterns emerge and evolve during learning, we relied on the dynamic approach to motor learning. Although originally developed for continuous and cyclic movements, this framework is also relevant for discrete skills and has therefore been used to investigate coordination changes during the learning of several discrete skills (e.g., soccer kicking, Chow, Davids, Button, & Rein, 2008; volleyball serve, Temprado, Della-Grasta, Farrell, & Laurent, 1997). With this in mind, Scholz (1993) demonstrated that relative phase analysis could be used to characterize inter-segmental organization during a squat lift. In weightlifting, Kim, Lee, Lam, and Sun (2019) supplemented this approach by calculating the angular couplings between the lower limb joints in order to account for inter-segmental influences during a snatch. Regarding this same movement, Ahn and Kipp (2024) highlighted the emergence of an antiphase pattern during the transition between the first and second pulls in expert weightlifters: the knee moves from extension to flexion while the trunk continues its extension. Conversely, weightlifting beginners often adopt less developed motor coordination, with no knee flexion during the transition phase and early pulling of the arms. The evaluation of the relative phase is therefore a particularly useful tool for analyzing the acquisition and development of motor coordination related to the performance of a cluster in athletes unfamiliar with weight-lifting. Beginners to weightlifting generally adopt hip extension and early flexion of the upper limbs in the first phase of the movement (Santos et al., 2021) when performing a power clean (Steps 1, 2 and 3). The second part of the movement requires simpler motor coordination, combining leg thrust on the push-press call with brachial extension (Steps 4 and 5). Then, at the end of the push-press, a quick scapular fixation is necessary to stabilize the bar when the arms are extended (Step 6). In a motor learning context combining observation and physical practice, it therefore seems interesting to evaluate the effect of a PLD on complex coordination, such as the opposition-phase pulling movement, as well as on simpler coordination, such as the in-phase pushing movement.

Three observation conditions were contrasted: Participants observed the model's actions in PLD or Video modes (the same model's action was used in both cases), while the participants in a Control group watched irrelevant movies (see Method section for details). Several measures were used during the pre-test (P0, unloaded bar) as well as following the acquisition phase, namely during retention test 1 (R1, unloaded bar) and retention test 2 (R2, loaded bar). The purpose of these was to assess the effect of these two observation conditions on the learning of the pulling and pushing phases of the cluster. Joint angulations and the displacement of the bar relative to the body were measured (see Kipp, Redden, Sabick, & Harris, 2012; Cao, Dan, Yan, Li, & Liang, 2022; for similar procedures). We also chose to assess relative phase, which characterizes coordination in terms of either phase (φrel ≈ 0°), or antiphase (φrel ≈ 180°), or in terms of the phase shift (the other values) between two oscillators. Studying the phase shifts of two oscillators thus makes it possible to highlight attractor pools characteristic of more evolved motor coordination in the context of motor learning (Kelso et al., 1990).

We hypothesized that all three groups would improve from the pre-test to the two retention tests, with the PLD and Video groups improving the most compared to the Control group. Finally, we expected the PLD group to progress more than the participants in the Video condition. Of our three variables, we expected the most robust results to come from the dynamic analysis of relative phase, which reflects the evolution of motor coordination in beginners better than the calculation of RMSE and joint angulations, which are commonly used with experienced subjects. Given that the appearance of pools of attractors as revealed by the analysis of the relative phase provides information about the adoption of a new motor coordination in beginners, we hypothesized that the PLD group would have a larger phase shift than the other groups in R1 and R2. Finally, insofar as the load added to the bar in R2 constitutes an important constraint, we thought it likely that the results would be even more favorable to the PLD group in R2 than in R1 with an unloaded bar.

## 2. Method

### 2.1. Participants

34 beginner undergraduate students taking part in the Kinesiology program at Université de Poitiers volunteered for the study (sex: 9 females, 25 males; age $19 \pm 2$ years; weight $70 \pm 15.3$ kg; height $175 \pm 10.2$ cm; see Supplementary Material for anthropometric details). These undergraduate athletes practice one of various sport activities (soccer, basketball-ball, hand-ball, track and field, gymnastic, tennis, cycling) since about 4 years, three to four times per week. We checked that the participants had sufficient ankle, hip and shoulder mobility to enable them to perform the movement. All subjects also had a medical certificate stating that they had no contraindications to practicing sport. All signed an informed consent form prior to the experiment. Additionally, all participants had normal or corrected-to-normal vision and were unfamiliar with the movement proposed. They all also completed a short questionnaire, which indicated that none of them were taking any medications or antidepressants or subject to significant dietary restrictions. The protocol was approved by a local ethics committee for research on human participant (No. 2021-02-05). Each participant was assigned to one of three groups with a specific video viewing modality. Due to personal situations, only 26 participants completed the experiment with a PLD Group (11 participants), a Video Group (10 participants) and a Control Group, (5 participants).

All participants performed a maximum load extrapolation test during a push-press. The aim was to individually assess the load to be lifted using the cluster movement during sretention test 2 (the pre-test and retention test 1 were performed with an unloaded Olympic bar of 15 kg for females and 20 kg for males). The push-press is a movement often used by athletes, and all the participants had already practiced it as part of their physical preparation. The push-press consists of lifting an Olympic bar with the arms from a clean and jerk with the help of a slight leg extension. It was important not to reveal to the participants all or part of the movement they were about to learn in order to avoid biasing the results. Participants were therefore instructed to use their arms and legs to lift the bar above their heads. We evaluated each participant's maximum load (RM: Repetition Maximum) during a push-press by means of theoretical extrapolation (Reiss, Prévost, & Cazorla, 2013):
$$1RM = \frac{1}{2} * \left( \frac{W}{0.522 + 0.419e^{-0.055R}} + 0.033RW + W \right)$$

where W is the weight of the loaded bar lifted by the subject and R is the maximum number of repetitions that the subject was able to perform with this load.

To estimate maximum load, all participants were asked, before the retention test 1, to repeat a push-press between 10 and 20 times up to the point of failure. The extrapolation formula enabled us to identify the maximum load for each subject. We then calculated the 80% RM of the push-press in order to establish an individualized load to be lifted at the end of the learning sequence during the second retention test (R2). This percentage corresponds to the level of solicitation commonly used to develop vertical power in athletes (Haug et al., 2015).

## 2.2. Procedure used to construct the observed material

One experienced weightlifter (male; 32 years old; 1.73 cm; 80 kg; college level in weightlifting) was used as the model to create video media for the weightlifting movement to be observed. The model was equipped with body-hugging clothing to which we attached 52 passive reflective markers. The trajectory of the bar required us to move some markers compared with the plug-in-gait marker placement scheme (Davis, Öunpuu, Tyburski, & Gage, 1991). The markers at the iliac spine and acromion were moved laterally, the tibia marker was placed at calf level, and the sternum marker was moved slightly upwards. Two additional markers were placed on the left and right end of the lifting bar. We created simultaneously two movement sequences: one in the video modality and one in the PLD modality. This ensures that the video group and the PLD group will observe exactly the same movement.

Functional movements were also recorded to calculate joint centers for the neck, shoulders and hips (Ehrig, Taylor, Duda, & Heller, 2007). We calculated elbows, knees and ankles joint centers as the midpoints of the internal and external joint markers. During a familiarization phase, we first recorded 3 repetitions of all weightlifting derivatives (high pull clean, power clean, squat clean, push-press, etc....) with an Olympic weightlifting bar (20 or 15 kg) which was placed on steps at the same height as a loaded bar. We then recorded 3 repetitions of the cluster with an unloaded bar. Finally, the subject performed an ascent of load and we recorded three repetitions of the cluster with a bar loaded to 80% RM (1 Repetition Maximum) of a push-press. Although a cluster is performed by combining a push-press with a power clean, we decided to consider only the load lifted by the participants during the push-press, which is the most restrictive part of the cluster. Indeed, the push-press mainly mobilizes small muscles in the arms and back to lift the load overhead, unlike the power clean, which mainly involve strong muscles in the lower limbs. This load level also has the advantage of being that used for coordination training in weightlifting as well as for the development of maximum power as sought by elite athletes in their physical preparation.

To produce the PLD video media (see Fig. 2), we recorded all weightlifting movement using the Qualisys MacReflex motion capture system (Qualisys; Göteborg, Sweden) consisting of 16 Oqus 7 + cameras at a sampling rate of 100 Hz. The obtained recordings were then processed with Qualisys Track Manager (QTM). The camera positions after calibration of the 3D acquisition volume are provided in Supplementary material (Fig. A2). The average residue per camera does not exceed 0.74 mm. The standard deviation (0.29 mm) over the length of the wand (602.3 mm) used to calibrate the cameras represents a deviation of 0.05%. We used Matlab to recalculate the model's joint centers from the positions of the markers in order to finalize the video that displayed the movement in PLD mode (19 points). The Qualisys system was synchronized with the Kinect cameras and Final Cut was used to generate the video that displayed the

<figure><img src="image_6.png" /><figcaption>Fig. 2. Synchronization between the Qualisys System and the Kinect cameras of the side (top) and front (bottom) videos observed by the Video condition group (on the left) and the PLD videos observed by the PLD group (on the right).</figcaption></figure>
movement in the Video condition. To ensure that the two videos were synchronized, a passive reflective marker was thrown into the scene recorded by the two systems. The first frame when the marker hit the ground was used as the synchronization event.

Finally, a third video not showing human motricity was used (https://osf.io/qpxfc/files/osfstorage/673c60d1ccf43ddf02943217 with the sound turned off) for the Control group. Whatever the observation group, the video clips used during the learning session were of the same length and were installed on ipads pro 3 so that participants could combine observation of a video clip of the cluster to be learned with physical practice during the learning session (detailed below).

## 2.3. Procedure and experimental phases

The procedure consisted of 4 phases: a pre-test (P0), a practice session and two retention tests (R1 and R2) spread over 6 weeks. Before each phase, each participant completed a standardized 15 min warm-up. This consisted of joint mobility exercises targeting the ankle, hip, and shoulder, in order to prepare the joints most involved in the movement under study. These exercises were interspersed with bodyweight functional strength exercises engaging the whole body (e.g., squats, lunges, push-ups, burpees), with the aim of gradually increasing body temperature and activating the main muscle groups required for the execution of the cluster. Each participant was then fitted by the same person with a close-fitting garment on which the markers were positioned using double-sided adhesive tape.

**Pre-test.** At the beginning of the pre-test, all participants were familiarized with the cluster to be produced in a video presented on a 50' screen (see Fig. 3A). They saw video played in real-time presenting 3 repetitions of the movement from the front point of view (24 s. duration) immediately (2 s.) followed by 3 repetitions from the left side point of view (24 s. duration) performed by the experienced weightlifter who had been used to create the observation material. They then repeated the action three times (Fig. 3A, B, C). The bar was placed on steps whose height corresponded to the official starting position in competition (at a height of 22.5 cm above the ground). Participants then performed the movement with an approved Olympic bar of 20 kg for men and 15 kg for women (International Weightlifting Federation standard). Their lifts were recorded for subsequent analysis in the same way as described above for the Qualisys system. No feedback was provided regarding their movement during the pre-test. A recovery period of 90 s. took place after each session of observing action combined with physical exercise.

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Verbal instructions were given to ensure that all participants used the same starting position. A: Participants had to observe the cluster to be produced in a video presented on a 50' screen. B: Ankles up against the bar with feet hip-width apart. Bar gripped on the steps with hands pronated shoulder-width apart. Look forward then down. C: Clean the bar. D: Push bar overhead.</figcaption></figure>
**Practice sessions.** They began the day after the pre-test and the 26 participants were trained for 5 consecutive days over a period of 5 consecutive weeks. Following a warm up described previously, each day of training followed the same structure: 1) the participants in the PLD and Video groups saw a short real-time video clip showing 3 repetitions of derivative movements or the global movement for the PLD and Video (the Control group watched an irrelevant movie for the same length of time). Participants of the three groups received the same instruction: "Watch the video"; 2) the participants repeated the same movement six times (Table 1). This combination of observation and physical practice was repeated 12 times within each practice session, resulting in a combination of approximately 20 min of observation and 30 min of physical practice for each participant. At the end of each practice session, each participant had to repeat the cluster 3 times. During the 5th session, the load was gradually increased to 60% of the maximum push-press load identified for each participant. No feedback was provided regarding their movement during the practice sessions.

**Retention tests.** The retention tests were carried out in the same way as the pre-test and took place one week after the end of the practice sessions. Following a warm-up phase, the participants watched the same videos from the expert as those seen before the pre-test. They then repeated the cluster 3 times themselves with an unloaded bar (R1). A second retention test with a loaded bar (R2) immediately followed the first retention test. We asked participants to watch the video of the same expert (3 front and 3 side views) with a loaded bar. Each participant then performed the lift 3 times with a bar loaded to 80% RM of an individual push-press. No feedback was provided regarding their movement during the retention tests.

## 2.4. Data processing

We used MATLAB (version r2020b; Mathworks, Natick, MA) to process the motion capture data. Position data were low-pass filtered with a dual-pass, no-lag Butterworth filter (cutoff frequency: 5 Hz; order: 5). These data were used to identify the pushing and the pulling phases (see Fig. 4) and to compute three dependent variables in each of these phases.

For the pulling phase, the three dependent variables were: 1) the coxofemoral angle (°) at the beginning of the pulling of the arms (see Fig. 5); 2) the root mean-square error corresponding to the area (mm²) between the anteroposterior axis passing through the participant's ankles and the bar trajectory (Fig. 5); 3) the relative phase (°) calculated based on the vertical displacement and speed of the hip and the bar (Fig. 6).

The dependent variables for the pushing phase were: 1) the Head-Shoulder-Elbow angle (°) when the bar was stabilized high above the head with the arms outstretched (Fig. 5); 2) the root mean-square error corresponding to the area (mm²) between the anteroposterior axis passing through the participant's ankles and the bar trajectory (Fig. 5); 3) the relative phase (°) calculated from the horizontal displacement and speed of the shoulder in relation to the head (Fig. 6) were the dependent variables.

$$RMSE = \frac{1}{Height_{Subject}} \int_{t_i}^{t_f} Z_{bar}(t) \cdot d(X_{bar}(t) - X_{ankle}(t))$$

The relative phase was calculated by measuring the time lag between the movements of these two oscillators (Haken, Kelso, & Bunz, 1985). For the pulling phase, the two oscillators were the vertical displacements of hips and the bar while for the pushing phase, the horizontal displacement of the head and the elbows were selected as oscillators.

We used JASP (0.19.1) to run mixed analyses of variance (ANOVA). The level of significance was set at 0.05, and the effect size is reported for all significant effects (partial eta-squared, ηp²). A homogeneity test (Levene) was performed and the Greenhouse-Geisser correction applied when necessary. Post hoc tests (Bonferroni) or planned comparisons were also performed as appropriate for the various hypotheses.

# 3. Results

The results section is organized as follows: we first present the results for the pulling phase, with the coxofemoral angle, the RMSE of the bar trajectory and then the hip-bar coordination. For the pulling phase, the Head-Shoulder-Elbow angle, the RMSE of the bar trajectory and the head-bar coordination are presented.

## 3.1. Pulling phase

**Coxofemoral angle.** Angle data were submitted to a mixed Anova with Group (PLD, Video, Control) as a between-subjects factor and Phase (P0, R1, R2) as a repeated-measures factor. The analysis revealed a Phase effect, F(2, 46) = 5.2, *p* = .009, ηp² = 0.19. No Group effect or Group x Phase interaction were found, *ps* > .05 (Fig. 7). The planned comparison on the mean between Phase indicated a significant angle increase from P0 to R1 for both the PLD and Video groups, *t*(23) = 2.7, *p* = .01, *d* = .69, *t*(23) = 2.6, *p* = .01, *d* = .68,

**Table 1**

Description of the movement to be observed and practiced over the 5 weeks of practice sessions.

<table><thead><tr><th colspan="5">Practice sessions</th></tr><tr><th>Week 1</th><th>Week 2</th><th>Week 3</th><th>Week 4</th><th>Week 5</th></tr></thead><tbody><tr><td>High pull and Power clean with unloaded barbell</td><td>Power clean and Squat with unloaded barbell</td><td>Push press with unloaded barbell</td><td>Cluster with unloaded barbell</td><td>Cluster with loaded barbell</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** The 3 phases of the movement illustrated based on the vertical movement of the bar. Four events were manually identified based on the vertical position of the bar: the start of the movement (the last image in which the bar is in its initial position), the end of the pulling phase (the first local maximum height reached by the bar), the end of the squat (the first local minimum height reached by the bar after the end of the pulling phase) and the end of the movement (the first local maximum height reached by the bar after the end of the squat). We analyzed the kinematic data of the movement with regard to 2 phases: the pulling phase between the first and second events (power clean) and the pushing phase between the third and fourth events (push-press). An additional event during the power clean phase was identified by the same person corresponding to the beginning of the pulling of the arms (elbow flexion).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Assessment of joint angulation and RMSE (colored areas) for the pulling and pushing phases. The angle formed by the shoulders, hips and knees was recorded during the first brachial flexion. To estimate the red-colored area (RMSE), we performed the following calculation and then normalizing the RMSE by the height of each participant, where $t_i$= starting time of the pulling/pushing phase, $t_f$= the ending time of the pulling/pushing phase: $X_{bar}(t)$ and $Z_{bar}(t)$ = the time history of the antero-posterior and vertical positions of the bar respectively, $X_{ankle}(t)$ = the time history of the antero-posterior positions of the ankles. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

respectively. No such increase was found for the Control group, $t(23) = 0.4, p > .05$. No significant difference was found between R1 and R2 for either group (ps > .05).

**RMSE.** RMSE data were submitted to a mixed Anova with Group (PLD, Video, Control) as a between-subjects factor and Phase (P0,
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Illustration of hip-bar coordination during the pulling phase (left) and shoulder-head coordination during the pushing phase (right) for the reference participant (i.e., the model). 3 events were manually located for each participant based on the relative phase computation. Two straight line segments were created between A and B on the one hand, and B and C on the other. For the pulling phase, the green zone corresponds to the upward movement of the hips on the vertical axis as the bar rises. The red zone corresponds to the downward movement of the hips as the bar rises. For the pushing phase, the green zone corresponds to the backwards movement of the head on the horizontal axis while the elbows remain fixed. The red zone corresponds to scapular fixation during stabilization of the bar, i.e. a forward movement of the head while the elbows move backwards. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Coxofemoral angle for the three groups and phases. Data for the model participant (REF) are also presented. Error bars denote the standard error.</figcaption></figure>

R1, R2) as a repeated-measures factor. The analysis only revealed a Phase effect, F(2, 46) = 16.72, *p* < .01, ηp² = 0.42 (Fig. 8). The planned comparison on the mean between Phase indicated a significant RMSE decrease from P0 to R1, t(46) = 2.67, *p* = .02, *d* = .63, and from R1 to R2, t(46) = 3.14, *p* = .001, *d* = .75.

**Hip-Bar coordination.** A change in coordination was characterized by the number of peaks and troughs in the relative phase profile (Table 2) and the area covered. A descriptive analysis indicated that the number of peaks and troughs increased from P0 to R1

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** RMSE for the three groups and phases. Data for the model participant (REF) are also presented. Error bars denote the standard error.</figcaption></figure>
and stabilized from R1 to R2, with no clear difference between groups.

The area covered by the first and the second trough, as well as the cumulative area, is probably a more important index for characterizing coordination change. These 3 areas were analyzed with 3 separated mixed Anovas, with Group (PLD, Video, Control) as a between-subjects factor and Phase (P0, R1, R2) as a repeated-measures factor. For the first trough area, the analysis only revealed a Phase effect, F(2, 46) = 4.4, p = .02, ηp² = 0.17. Bonferroni post-hoc comparisons indicated that the area increased from P0 to R1 and R2; it then remained stable from R1 to R2 (see Fig. 9a). For the second trough area, the analysis revealed a Phase effect, F(2, 46) = 6.3, p = .004, ηp² = 0.22; and a tendency toward a Group effect, F(2, 23) = 2.6, p = .098, ηp² = 0.18. Bonferroni post-hoc comparisons for the Phase indicated that the area increased from P0 to R1 and R2; it then remained stable from R1 to R2 (see Fig. 9b). Bonferroni post-hoc comparisons for the Group indicated that the area tended to be larger for the PLD group than for the Control group (p = .07); other comparisons were not significant. When the two areas were added together, the analysis revealed a Phase effect, F(2, 46) = 7.1, p = .002, ηp² = 0.24; and a Group effect, F(2, 23) = 3.4, p = .05, ηp² = 0.23. Bonferroni post-hoc comparisons for the Phase indicated that the area increased from P0 to R1 and R2 and then remained stable from R1 to R2. Bonferroni post-hoc comparisons for the Group indicated that the area was larger for the PLD group than for the Control group. Furthermore, the area tended to be larger for the PLD group than for the Video group (p = .07); the areas for the Video and Control group did not differ (see Fig. 9c).

## 3.2. Pushing phase

**Head-Shoulder-Elbow angle.** Angle data were submitted to a mixed Anova, with Group (PLD, Video, Control) as a between-subjects factor and Phase (P0, R1, R2) as a repeated-measures factor. The analysis revealed a Group x Phase interaction, F(4, 46) = 2.6, p = .05, ηp² = 0.19, as well as a Phase effect, F(2, 46) = 4.2, p = .02, ηp² = 0.16. The Group main effect was not significant, p > .05. Data are illustrated in Fig. 10. The planned comparison indicated a significant angle increase from P0 to R1 for both the PLD and Video groups, t(23) = 2.6, p = .01, d = 1.04, t(23) = 2.1, p = .04, d = 0.88, respectively. No such increase was found for the Control group, t(23) = 1.1, p = .30. Furthermore, the planned comparison from R1 to R2 indicated a significant angle increase for the PLD group, t(23) = 2.1, p = .04, d = 0.48. No such increase was found for the Video and Control groups, t(23) = 0.5, p = .64 and, t(23) = 1.4, p = .18, respectively.

**RMSE.** RMSE data were submitted to a mixed Anova, with Group (PLD, Video, Control) as a between-subjects factor and Phase (P0, R1, R2) as a repeated-measures factor. The analysis revealed a Phase effect, F(2, 46) = 9.4, p = .001, ηp² = 0.29 (Fig. 11). The planned comparison on the mean between Phase indicated a significant decrease in RMSE from P0 to R2, t(46) = 2.8, p = .02, d = 0.68, and from R1 to R2, t(46) = 4.5, p = .01, d = 1.1. The analysis also indicated a trend toward a Group x Phase interaction, F(4, 46) = 2.2, p = .08, ηp² = 0.16.

**Shoulder-Head coordination.** A change in coordination was characterized by the number of peaks and troughs in the relative phase profile (Table 3) and the area covered. A descriptive analysis indicated no clear difference between groups.

The area covered by the first and the second troughs, as well as the cumulative area, is probably a more important index for characterizing coordination change. These 3 areas were analyzed with 3 different mixed Anovas, with Group (PLD, Video, Control) as a between-subjects factor and Phase (P0, R1, R2) as a repeated-measures factor. For the first trough area, the analysis did not reveal any Phase, or Group effect, or any Group * Phase interaction, ps > .05 (see Fig. 12a). For the second trough area, the analysis revealed a Phase effect, F(2, 46) = 3.5, p = .04, ηp² = 0.14 (Fig. 12b). Bonferroni post-hoc comparisons only indicated that the area for P0 was smaller than for R2 (p = .04). When the two areas were added together, the analysis revealed a Phase effect, F(2, 46) = 6.4, p = .004, ηp² = 0.22. Bonferroni post-hoc comparisons indicated that the area remained stable from P0 to R1 (p = .13) and increased from P0 to R2 as well as from R1 to R2 (p = .02 and .04, respectively; Fig. 12c).

## 4. Discussion

The aim of this study was to evaluate the impact of combining observation with physical practice on the learning of a weightlifting movement. A Point-Light Display (PLD) condition, which only displayed the movement of the joints to the observer, was contrasted with a Video condition (produced using the same model as the one used for the PLD condition) and a Control condition (not showing human motricity).

Our results, whether concerning joint position variables, bar displacement, or coordination measures, reveal learning effects across all three groups between the pre-test and retention 1, both of which were performed with an unloaded bar. These effects were maintained or even amplified at retention 2 (loaded bar). Although learning occurred in all three groups, the two groups that benefited from observation of the movement improved more than the Control group. Therefore, while the improvements observed in the Control group can mainly be attributed to physical practice (we cannot dismiss that the visual information provided to the Control group had

<caption>**Table 2**</caption>

Mean number (0, 1 or 2 value per participant) and standard deviation of peaks and troughs during the pulling phase for the three groups and three tests.

<table><thead><tr><th>Groups</th><th>P0</th><th>R1</th><th>R2</th></tr></thead><tbody><tr><td>PLD</td><td>0.73 ± 0.9</td><td>1.3 ± 0.7</td><td>1.4 ± 0.7</td></tr><tr><td>Video</td><td>0.9 ± 0.7</td><td>1.1 ± 0.7</td><td>1.2 ± 0.6</td></tr><tr><td>Control</td><td>0.8 ± 0.8</td><td>1.0 ± 0.9</td><td>1.6 ± 0.5</td></tr></tbody></table>
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Area (mm²) according to group and phase for the first (A: green area of the pulling phase of Fig. 6), second (B: red area of the pulling phase of Fig. 6) and sum of troughs (C). Data for the model participant (REF) are also presented. Error bars denote the standard error. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Head-Shoulder-Elbow angle for the three groups and phases. Data for the model participant (REF) are also presented. Error bars denote the standard error.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 11.** RMSE for the three groups and phases. Data for the model participant (REF) are also presented. Error bars denote the standard error.</figcaption></figure>

an effect), the additional learning observed in the video and PLD groups can be attributed to the combination of observation and physical practice. These findings confirm the beneficial effects of action observation and, more specifically, the positive impact of combining observation with physical practice (Larssen et al., 2021), suggesting that observation can serve as a substitute for physical practice when delivered intermittently and is also more beneficial than simply resting. Unlike Larssen et al. (2021), who demonstrated with a visuomotor adaptation task that the beneficial effects of observation combined with practice were primarily driven by explicit processes, we cannot precisely delineate the nature of the processes underlying our learning effects. However, as demonstrated by
<caption>**Table 3**</caption>

Mean number (0, 1 or 2 value per participant) and standard deviation of peaks and troughs during the pushing phase for the three groups and three tests.

<table><thead><tr><th>Groups</th><th>P0</th><th>R1</th><th>R2</th></tr></thead><tbody><tr><td>PLD</td><td>1.91 ± 0.3</td><td>1.91 ± 0.3</td><td>1.91 ± 0.3</td></tr><tr><td>Video</td><td>2.0 ± 0.0</td><td>2.0 ± 0.0</td><td>1.9 ± 0.3</td></tr><tr><td>Control</td><td>1.9 ± 0.4</td><td>1.8 ± 0.4</td><td>2 ± 0.0</td></tr></tbody></table>

<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Area (mm²) according to group and phase for the first (A: green area of the pushing phase of Fig. 6), second (B: red area of the pushing phase of Fig. 6) and sum of troughs (C). Data for the model participant (REF) are also presented. Error bars denote the standard error. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

Shea, Wright, Wulf, and Whitacre (2000) combining observation with physical practice permits unique opportunities for learning beyond those available with either practice form alone. Although not tested directly, our findings suggest that these unique opportunities are the immediate implementation of observed strategies and the development of an error detection-correction mechanism (Blandin & Proteau, 2000).

The beneficial effects of PLD observation reported here contrast with those reported in the literature, which typically describes limited effects. Indeed, numerous studies have looked at the influence of PLD on motor learning. It has been shown that combining PLD observation with physical practice does not improve learning in beginners when compared to normal video observation in a dart aiming task (Al-Abood, Davids, Bennett, Ashford, & Marin, 2001), in learning a football kick (Horn et al., 2005) and in learning a baseball throw (Ghorbani & Bund, 2016). However, combining PLD observation with physical practice significantly contributed to expert participants' learning of a complex judo technique (Francisco et al., 2023). In our study, the beneficial effects of PLD observation varied considerably depending on the variables analyzed. Specifically, the positive effects of action observation (both PLD and Video) were relatively modest in terms of bar displacement (RMSE). This result is likely due to the fact that the goal of the task (the “what to do”) was relatively simple—bringing the bar to shoulder level and then overhead—and already familiar to the athletes. Another possible explanation relates to the loads used. In the literature (see Cao et al., 2022; Kipp et al., 2012), improvements in bar trajectory, reflected by a reduction in RMSE, have typically been observed in expert weightlifters using loads close to their maximum capacity. It is therefore possible that, in our study, the task constraints associated with the load were not sufficiently demanding to reveal more pronounced differences in bar trajectory.

In contrast, the positive effects of combining observation (PLD and Video) with physical practice were notably greater for the joint position variables, and especially for intersegmental coordination. These variables primarily account for the “how to do” aspect. Indeed, whether with regard to the Hip angle during the pulling phase or the Head-Shoulder-Elbow angle during the pushing phase, the values were higher (indicating better learning) for the PLD and Video groups than for the Control group. Interestingly, the PLD condition was found to be more advantageous than the Video condition for the Head-Shoulder-Elbow angle (pushing phase) in retention 2, as well as for Hip-Bar coordination (A and B surfaces) during the pulling phase. This result suggests that action observation is an effective means of learning intersegmental coordination when combined with physical practice (Maslovat, Hodges, Krigolson, & Handy, 2010). Consequently, in addition to inferring action goals, a key function of the human mirror neuron system may be to anticipate the trajectories and dynamics of observed actions, which is a prerequisite for any timely interaction (Hesse, Sparing, & Fink, 2009).
The dynamic analysis of the motor coordination of each group revealed greater motor learning in the PLD group during the pulling phase. The evaluation of the relative phase of the participants in this group showed the greatest increases, particularly in terms of the combined surfaces (A and B) during R2. This means that the PLD group acquired new motor coordination abilities that allowed them to optimize the pull of the bar when it was loaded. This suggests that the restricted information conveyed by the PLD allowed participants to focus on salient points (see Fehd & Seiffert, 2008; Horn et al., 2005; Williams & Davids, 1998) of the motor behavior to be adopted and therefore facilitated the planning of the complex motor coordination to be executed when the bar was loaded.

In consequence, our study demonstrates the effects of observation in an ecological setting. The combined training of physical practice and point-light video observation as part of physical preparation allowed participants to improve their motor coordination. These results suggest that training centers can now use PLD for weightlifting, in particular to optimize athletes' technical learning. In the specific case of powerlifting, it is preferable not to have beginners train for extended periods with an unloaded bar or initiation bar when acquiring a technical or semi-technical weightlifting movement. Instead, it is recommended that they quickly switch to a moderate load identified as a percentage of the Repetition Maximum (RM). In addition, it seems judicious to incorporate observational learning in the PLD modality, combined with physical practice, to reduce athletes' motor engagement time and consequently optimize the training load in achieving sports performance.

However, several limitations of the study need to be acknowledged. Firstly, the small number of participants and the unequal sample sizes limit our conclusion. Secondly, we were unable to assess progress during practice. Evaluating coordination during practice would permit individual progress tracking and consequently a more precise adjustment of the individual workload. However, this type of monitoring requires a complex methodology involving the attachment of markers and the recording of movements using a motion capture system, which can be quite time-consuming. This method should be considered as part of long-term performance monitoring, particularly in training centers or for athletes who engage in demanding physical preparation. This comment concerning the methodological complexity of evaluating coordination during practice also applies to the creation of PLD, which forms the basis of the observation program. However, tools such as Smart-Detector, a new artificial intelligence (AI)-based tool for automatic PLD creation from RGB videos (Bidet-Ildei et al., 2024), could be used both to assess coordination and create the PLD to be observed. Finally, if we consider the studies conducted by Richter, Gilbert, and Baldis (2012), it is highly likely that athletes visualizing a weightlifting movement use a rhythmic memorization strategy to determine faster or slower execution phases in order to optimize the trajectory of the bar. This rhythmic memorization strategy is facilitated by the PLD modality, as it makes the kinematics of the observed action easily accessible. Thus, in the light of the striatal beat-frequency model (Matell & Meck, 2004), which highlights cerebral involvement in the identification of the rhythm of a movement by the observer, it would be interesting to sonify a PLD with different pitches that reflect the vertical speed of the bar in order to study the added value of multi-sensory integration in the context of motor learning. In this context, it is possible that the learning of weightlifting techniques could be further optimized by complementing the PLDs with a sound signature that provides information on the rhythm of the movement observed.

# 5. Conclusion

This study highlights the value of integrating observational learning with physical practice for the acquisition of complex motor skills in ecological contexts. Moreover, by demonstrating that PLD observation not only enhances performance but also facilitates superior intersegmental coordination compared to classical video observation, our findings support the unique effectiveness of minimalistic motion cues in motor learning. These results underscore the potential of PLD as a powerful tool in sports training and rehabilitation settings, offering a promising avenue for improving the efficiency of movement learning protocols. Future research should explore the broader applicability of PLD-based training across various skill domains and populations.

# CRediT authorship contribution statement

**Charly Ferrier:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation. **Arnaud Decatoire:** Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Yves Almecija:** Writing – review & editing, Methodology. **Christel Bidet-Ildei:** Writing – review & editing, Methodology. **Yannick Blandin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

# Disclosure statement

The authors report that there are no conflict interests to declare

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.humov.2026.103452.
## Data availability

All data and videos are available at https://osf.io/qpxfc/files/osfstorage/673c60d1ccf43ddf02943217

All PLD are also available in the platform PLAViMoP: https://plavimop.prd.fr/en/motions

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