# Influence of musical background on children's handwriting: Effects of melody and rhythm

Margaux Lê, Marianne Jover, Aline Frey, Jérémy Danna

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Margaux Lê, Marianne Jover, Aline Frey, Jérémy Danna. Influence of musical background on children's handwriting: Effects of melody and rhythm. Journal of Experimental Child Psychology, 2025, 252, pp.106184. 10.1016/j.jecp.2024.106184. hal-04887882

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# Influence of musical background on children's handwriting: Effects of melody and rhythm

<figure><img src="image_4.png" /></figure>

Margaux Lê<sup>a,b,c,*</sup>, Marianne Jover<sup>b</sup>, Aline Frey<sup>a</sup>, Jérémy Danna<sup>d</sup>

## ARTICLE INFO

**Article history:**

Received 15 April 2024

Revised 6 December 2024

**Keywords:**

Music

Handwriting

Child development

Motor control

Graphomotor skills

Rhythm

## ABSTRACT

Numerous studies have reported benefits of music listening to support learning and motor rehabilitation. In the case of handwriting, previous studies suggested that musical background improves movement speed and fluency. Whether this benefit comes from the melody or is specifically related to the rhythmic cues provided by the music remains to be established. In addition, music can influence handwriting differently depending on the child's level of expertise. To disentangle these effects, we recorded graphic movement under different sound backgrounds in children of two different grades. In total, 44 s graders and 44 fifth graders needed to copy loops and isolated words under four sound conditions: silent, melodic without metronome, melodic with slow metronome (1.6 Hz), and melodic with faster metronome (2.2 Hz). The results revealed that listening to a pure melodic background reduced writing velocity, movement fluency, and loop size in both groups. In addition, the rhythmic cues influenced handwriting kinematics differently depending on grade and task. For the younger group, the two rhythms, and especially the slow rate, increased the loop copying velocity, whereas for the words the velocity and movement fluency were reduced by the fast rate. Conversely, for the older group, the two rhythmic conditions reduced writing velocity and movement fluency, and they increased the size of both the loops and the words. Finally, the effects also depend on handwriting level; poor writers seem to benefit more from the adding of rhythmic cues. These results raise

<footnote><sup>a</sup>Aix Marseille University, CNRS, CRPN 13003 Marseille, France</footnote>

<footnote><sup>b</sup>Aix Marseille University, PSYCLE 13090 Aix-en-Provence, France</footnote>

<footnote><sup>c</sup>Laboratoire de Psychologie des Cognitions, Université de Strasbourg 67000 Strasbourg, France</footnote>

<footnote><sup>d</sup>CLLE, University of Toulouse-Jean-Jaurès 31058 Toulouse, France</footnote>

<footnote>* Corresponding author at: Laboratoire de Psychologie des Cognitions, Université de Strasbourg, 67000 Strasbourg, France.
E-mail address: margaux.le@unistra.fr (M. Lè).</footnote>
interesting perspectives for learning to write and for the rehabilita-
tion of handwriting difficulties.

© 2024 The Author(s). Published by Elsevier Inc.

Introduction

Handwriting is a complex activity that takes years to master. Importantly, the ability to handwrite fluently and legibly is a strong predictor of literacy development and more generally of academic achievement from kindergarten to college (Berninger et al., 2002; Connelly et al., 2005; Downing & Caravolas, 2023; Lê et al., 2021; Pontart et al., 2013; Santangelo & Graham, 2016; Suggate et al., 2016). Therefore, it is crucial to find efficient methods that could reinforce graphomotor control and help students who struggle in learning to write. Among the possible methods, music appears to be a promising tool.

The motor system is very sensitive to music. It makes us move, modulates movement speed and smoothness, and therefore is a powerful tool to support motor learning and rehabilitation (Braun Janzen et al., 2022; Dalla Bella, 2018; Thaut et al., 2015). More specifically, the rhythmic dimension of music has been mostly studied. Indeed, this rhythmic structure induces a spontaneous synchronization of body movements with the beat (Repp, 2005; Repp & Su, 2013), and this synchronization permits stabilizing movements. Across the last decades, the benefits of metronome or rhythmic musical background to help patients with motor disorders have been reported in different clinical populations (Benoit et al., 2014; Cochen De Cock et al., 2018; Dalla Bella et al., 2017; Ghai et al., 2018; Schaefer, 2014; Thaut & Abiru, 2010). For example, patients with Parkinson's disease increase their walking speed and stability in the presence of rhythmic auditory cues, and these positive effects persist after the removal of the stimulation (Cohen De Cock et al., 2018; Dalla Bella et al., 2017; Schaefer, 2014). Importantly, the benefits of rhythmic background are not limited to motor rehabilitation but are also found on motor learning in healthy adults (Lagarrigue et al., 2021). Although the rhythmic aspect of music seems to play an important role in its influence on movement, music contributes to something more than a simple metronome; emotional aspects are conveyed with the melody, which also influences human movement. For instance, Wittwer and colleagues (2013) compared effects of rhythmic music and metronome as external cueing on gait in older adults and reported that gait speed increased with rhythmic music but not with metronome cues. Whether this benefit is specifically related to the rhythmic cues provided by the music or whether it derives from the melody, or both, remains to be established.

To date, the benefits of music for handwriting have seldom been studied. However, several results suggest that adding sounds while writing could be a useful way of helping children to stabilize their writing movement because the auditory modality is available and relevant to perceive supplementary temporal or dynamic information without interfering with visual and sensorimotor control (Danna et al., 2014, 2015; Danna & Velay, 2015, 2017; Véron-Delor et al., 2020). For example, handwriting sonification, which consists of transforming writing kinematics into sounds to provide real-time additional feedback, improves handwriting in children and adults with difficulties (Danna et al., 2014; Véron-Delor et al., 2020). Interestingly, sonification is considered more satisfying when musical stimuli are used, confirming the positive effect of music on motivation during handwriting tasks (Andersen & Zhai, 2010). Beyond delivering feedback, the auditory modality can be used to provide auditory temporal cues to help stabilize handwriting movement. Indeed, handwriting is a rhythmic activity, and numerous studies have reported links between rhythm and handwriting skills (Ben-Pazi et al., 2007; Frey et al., 2022; Nutt, 1917; Pagliarini et al., 2017). Because of its rhythmicity, handwriting may synchronize easily to the beat. In line with this idea, it has been shown that children aged 7 to 9 years are able to synchronize the tracing of loops with a metronome that beats at 1, 2, or 3 Hz (Bosga-Stork et al., 2011, 2013, 2016). In these studies, the metronome frequency modulated the size of the loops in the older group of children; loop amplitude was lower with higher frequencies and vice versa (Bosga-Stork et al., 2011). Unfortunately, movement fluency that corresponded to the smoothness of handwriting was not assessed in these studies, which
prevents us from confirming the potential benefits of rhythmic listening for graphic movement. Finally, a few studies have reported that graphomotor training with musical background enhances handwriting performance (Véron-Delor et al., 2017, 2020). A case study of a 6-year-old child with writing difficulties showed that handwriting velocity and movement fluency were improved after rehabilitation sessions with musical background compared with silent rehabilitation sessions (Véron-Delor et al., 2017). Note that a variety of music was played during the sessions, so the specific effect of rhythmic cues could not be tested. Importantly, the music was chosen by the child, enhancing the potential motivation effect. It is necessary to clarify whether the reported effects were specifically related to the music rhythm or whether other mechanisms, such as the induced emotions that may foster motivation (Wittwer et al., 2013), explain a part of the benefits of music for handwriting. Specifically, the effect of music on arousal could enhance the attention to the task. Indeed, Large and Jones (1999) suggested that rhythmic perception is intimately linked to attention and proposed a theory of attention applied to rhythmic events unfolding over time, the so-called *dynamic attending theory*. Fluctuations in attention correspond to internal oscillations, which the authors referred to as “attentional rhythms.” These attentional rhythms generate expectations that enable the anticipation of future events (top-down process) but also that an external rhythmic event drives attentional rhythms through the salience of its temporal structure (bottom-up process). This entrainment mechanism is the result of a tendency for the rhythms to synchronize, triggering neuronal resonance and resulting in a phase and/or period coupling of the internal oscillation (Snyder & Large, 2005). Therefore, some studies suggest that listening to background music improves performance in various attention tasks (Mendes et al., 2021), particularly when the music has a regular rhythm that entrains attentional oscillations. However, the results are not consensual, and some studies suggest that background music may have a distracting effect on certain tasks, particularly those involving language. Importantly, all the above-mentioned studies used background music during pre-graphic exercises (e.g., loop copying) but not during writing tasks (Bosga-Stork et al., 2011, 2013; Véron-Delor et al., 2017). Although the benefits of graphomotor rehabilitation generalize to handwriting (Véron-Delor et al., 2017), it is essential to study the impact of background music on writing tasks, which differ from pre-graphic tasks in two main aspects. The first aspect concerns the involvement of cognitive-linguistic processes in handwriting. The proximity between language and music is well-known, even if their overlap intrigues scientists interested in the nature of these skills, their evolution, and their instantiation in the brain (e.g., Besson & Schön, 2001; McMullen & Saffran, 2004). This raises the question of the benefits or interference of background music for the cognitive-linguistic processes involved in handwriting but not in other graphomotor tasks. The second aspect relates more specifically to the task-related rhythmic component, which is simpler in loop tracing tasks given that the rhythmic structure is regular and consistent, whereas it is more complex in handwriting.

Importantly, individual characteristics seem to modulate the influence of rhythmic listening on movement (Cochen De Cock et al., 2018), and different effects of rhythmic background can be expected depending on the level of handwriting expertise. It has been shown that an intervention based on rhythmic stimulation is more efficient for individuals with poor motor control (Cochen De Cock et al., 2018). Therefore, the benefits of rhythm cues could be larger in young children who do not have automated handwriting yet and still have slow and dysfluent writing (e.g., novices or children with handwriting difficulties or dysgraphia). Interestingly, an opposite developmental hypothesis can also be put forward because the influence of rhythm background also depends on rhythm skills that evolve with age. Indeed, when children grow older, they become more able to synchronize their movements, including their handwriting, to the beat (Bonacina et al., 2019; Bosga-Stork et al., 2013; Carrer et al., 2023; Monier & Droit-Volet, 2019). Rhythmic skills, and especially the ability to synchronize precisely, begin to stabilize from 9 years of age (Carrer et al., 2023). Hence, graphic movement may be more influenced by rhythmic listening from this age onward given that children would synchronize more easily to the rhythm. Finally, a third and more specific hypothesis suggests that the influence of rhythmic background on handwriting depends on both expertise level and rhythmic cues' parameters. Indeed, when the presented metronome frequency is too far from the preferred cadence, rhythmic listening deteriorates the movement rather than stabilizing it (Cochen De Cock et al., 2018). In the case of graphic movement, the writing frequency evolves rapidly with handwriting automation; it moves from 1 Hz at the beginning of Grade 1 to 2.5 Hz at the end of Grade 4 (Sattler & Marquardt, 2010) and reaches 5 Hz in expert writers (Stetter et al., 2023). Therefore, young children who still
write slowly should benefit from slow rates (e.g., 1–1.5 Hz) but would be affected by higher rates. On the contrary, metronome frequencies lower than 2 Hz would be too slow for older students, but higher rates would help them to stabilize their movements. To sum up, school level can modulate the effects of rhythmic background on handwriting in different ways, and it is necessary to confront these different hypotheses to clarify which children should benefit most from a rhythmic intervention to improve their graphic movement.

The current study aimed to investigate how musical background, and more specifically melodic and rhythmic auditory cues, influences graphic movement and handwriting. To ensure that the influence of music listening was not limited to graphomotor tasks but could be generalized to handwriting, two different tasks were used: a loop copying task and a word copying task. First, we investigated whether graphic movement was affected by listening to a melodic background without a salient beat compared with a silent condition. Second, the influence of rhythmic cues was tested by adding a slow and fast metronome on the melodic background. We hypothesized that rhythmic cues would improve graphic movement more than the melody alone. In addition, the rhythm tempo would differently affect handwriting according to automation level. To test this hypothesis, we studied second graders, who are still learning to write and depend on external feedback to control their trace, and fifth graders, who have started to automate their writing gesture (for a review, see Palmis et al., 2017). This would permit clarifying the influence of music (melody and rhythm) on children with contrasted levels of handwriting expertise. Finally, children performed a standardized test to assess their handwriting level, permitting conducting exploratory analyses to determine whether poor writers were affected differently by the musical backgrounds compared with their peers who did not have handwriting difficulties.

# Method

## Participants

In total, 47 s graders and 44 fifth graders were recruited in two schools of Marseille (France) for the experiment. Four second-grade classes with 12, 12, 17, and 6 students, respectively, and four fifth-grade classes with 4, 5, 17, and 18 students, respectively, participated in the study. Three second graders were excluded because of attention difficulties that prevented them from performing the task correctly. Hence, the analyses were conducted on 44 s graders (mean age = 7 years 8 months ± 4 months; 28 girls) and 44 fifth graders (mean age = 10 years 5 months ± 3 months; 24 girls). The protocol was approved by the local ethical committee of Aix-Marseille University and was in accordance with the Declaration of Helsinki. Informed consent was obtained from all parents.

## Tasks

### Loop and word copying tasks

Participants performed two tasks in which they were asked to copy loops and words on a sheet attached to a graphic tablet (Wacom Intuos Pro L or Wacom Intuos 860 L) to record the writing kinetics. First, children copied lines of counterclockwise loops in blank rectangles of 11 × 3 cm. They were asked to respect the loops' size shown on the model and to avoid lifting the pen. Three items were copied three times each and presented in a fixed order: small loops of 0.7 cm, big loops of 1.4 cm, and alternating big and small loops. Second, the children copied three isolated words with their usual handwriting style (cursive or manuscript) in blank rectangles of 4.5 × 2.5 cm. For each word, a cursive model was presented to the children throughout the exercise. These words were presented in a fixed order with an increase of the graphomotor constraints. The first word, "elle" (she), was short and composed of simple letters; the second word, "cellule" (cell), had a longer length; and the third word, "buffle" (beefalo), involved more complex letters such as b and f (see Danna et al., 2022). Each word was copied six times. The two tasks (loop and word copying) were performed in a fixed order during all the sessions.

Both tasks were performed under four different sound background conditions: silent, melodic, melodic with slow metronome, and melodic with rapid metronome (audio files are in the online
supplementary material). The melodic background was relaxing music without a salient beat. The same melody was used in the two rhythmic backgrounds, but a metronome was added. The rate was fixed at 1.6 Hz in the slow rate (SR) condition and was fixed at 2.2 Hz in the rapid rate (RR) condition. Before the task, the children were informed that there would be a musical background but were not asked to pay attention to the music or to synchronize their writing with the beat.

For the two copying tasks, different measures were extracted from the data recorded with the digital tablet recording. Because the background music was expected to affect the graphic movement rather than the writing product, we calculated measures assessing the writing process. The period (i.e., the duration of a loop) was used because it could be compared with the metronome period for the rhythmic conditions. Because period depends on both writing velocity and the size of the product, these two additional measures were also extracted. Finally, the movement dysfluency measure provided information on movement smoothness. Data processing is described in greater detail in a subsequent section.

## Handwriting assessment

To assess the children's level of handwriting expertise, a standardized assessment of handwriting, the BHK test (Hamstra-Bletz, De Bie, & Den Brinker, 1987); French adaptation by Charles et al., 2003), was administered. It consisted of copying a text during 5 min. Two variables were calculated, assessing handwriting quality and handwriting speed, respectively. The handwriting quality score corresponds to the sum of 13 items assessing tracing quality according to the BHK test (e.g., handwriting size, letter shape distortion, letter correction; Charles et al., 2003). Given that the French adaptation of the BHK test was designed to assess the legibility of cursive handwriting, we used the BHK Ado criteria (Albaret & Sopelsa, 2012) for some children who had personalized their writing style and written in manuscript. The handwriting speed corresponded to the number of characters written in 5 min. Note that both measures were then expressed as z scores (with negative values corresponding to poor performances) to simplify the interpretations of the results.

## Procedure

Each participant performed the loop and word copying tasks under the four background conditions during four individual sessions (one per condition). Each session lasted about 20 min. The four sessions were separated by at least 2 days to avoid the effect of training transfer between conditions. The condition order was counterbalanced between participants in each group using a Latin square design. In addition, at the end of each session with music backgrounds, feedback from the children was collected. The results are not included in the current article. The handwriting assessment (BHK test) was provided at the end of the silent condition.

## Data processing for the loop and word copying tasks

Concerning the loop copying task, lines of loops were segmented to perform a loop-by-loop analysis (with boundaries at each minimum on the y-axis). Only seven loops per line were kept to homogenize the data for each condition and participant. For each loop, the different variables were calculated using GNU Octave: *period* (i.e., duration between the two bounds delimiting each loop), *mean velocity*, *movement dysfluency* assessed by the signal-to-noise velocity peaks difference (SNVpd), and *loop size error* assessed as the size difference (in millimeters) between the produced loop and the model. This last variable was chosen rather than the loop size to consider the two sizes of the loop (large = 1.4 cm or small = 0.7 cm). The SNVpd used to assess movement dysfluency corresponds to the difference between number of velocity peaks after filtering tangential velocity with a frequency cutoff of 10 Hz and number of velocity peaks after filtering tangential velocity with a cutoff of 5 Hz (Danna et al., 2014). When a pen lift within a loop was detected, the loop was not considered for the analysis. In total, 6.90% of the loops were discarded for second graders and 11.80% were discarded for fifth graders.

Concerning the word copying task, we first checked for word copying errors and removed mis-spelled words for the analyses (6% of the data). Self-corrected words (e.g., strikethrough characters, addition of an omitted letter at the end of a word) were also removed (1.6% of the data). Then, *mean*
period, mean velocity, movement dysfluency (SNVpd), and word mean size were calculated for each word. Note that here the mean period corresponded to the mean duration to trace one loop. The number of loops in each word depended on the letters produced; in cursive, the letters c, e, and l contained only one loop, whereas complex letters like b, u, and f contained two loops. Note that depending on the writing style, the letter f contained one or two loops. Hence, the words “elle” and “cellule” contained 4 and 8 loops, respectively, in both cursive and manuscript and the word “buffle” contained 8 to 10 loops.

## Statistical analyses

Before the statistical analyses, outliers were removed; they corresponded to data points deviating from the median by 3 mean absolute deviations. Normality assumption was checked using histograms and QQ plot examination (Gel et al., 2005). Because the assumption was not checked for the movement dysfluency variable, we used the Yeo-Johnson transformation from the “bestNormalize” package of R. The data were then analyzed using mixed models via the “lmerTest” package of R. First, the influence of melodic background was tested by comparing the melodic condition with the silent condition. Second, the influence of rhythmic cues was investigated by comparing the three musical conditions: melodic, SR, and RR. For both analyses, the same formula was applied, including two fixed effects: condition (silent vs. melodic or melodic vs. SR vs. RR) and grade (second vs. fifth). Three random effects were controlled in the model: participants nested in class and trial (for the loops, the loop number was also included and nested in trial). The effect of items (i.e., type of loop or word) and session (1, 2, 3, or 4) were also controlled. The formulas were as follows:

$$ \textit{Loopanalyses:DependentVariable} \sim \textit{Condition} * \textit{Grade} + \textit{Item} + \textit{Session} \\ + (1|\textit{Class/Participant}) + (1|\textit{Trial/Loop}) $$

$$ \textit{Wordanalyses:DependentVariable} \sim \textit{Condition} * \textit{Grade} + \textit{Item} + \textit{Session} \\ + (1|\textit{Class/Participant}) + (1|\textit{Trial}) $$

Post hoc analyses were performed using the “emmeans” package of R. For interaction effects, we only compared the different conditions within each group using Sidak adjustment.

Finally, to go a step further, we conducted two exploratory analyses. First, we investigated the effect of item's complexity by testing the interaction between rhythmic background condition and item. The results are presented in the supplementary material. In addition, we tested whether handwriting difficulties interacted with the musical background effects by integrating the BHK score and speed in the models as continuous variables. We used the following formulas:

$$ \textit{Loopanalyses:DependentVariable} \sim \textit{Condition} * \textit{Grade} * \textit{BHK} + \textit{Item} + \textit{Session} \\ + (1|\textit{Class/Participant}) + (1|\textit{Trial/Loop}) $$

$$ \textit{Wordanalyses:DependentVariable} \sim \textit{Condition} * \textit{Grade} * \textit{BHK} + \textit{Item} + \textit{Session} \\ + (1|\textit{Class/Participant}) + (1|\textit{Trial}) $$

## Results

### Descriptive statistics

Descriptive statistics for each condition and for each grade are reported in Table 1. Correlations between the different variables during the silent session are presented in Table 2. Most of the variables collected within a same task were highly correlated (rs ranged from.14 to.95 for the loop copying task
<caption>**Table 1**</caption>

Mean (and standard deviation) performance for the two grades and the different sound background conditions.

<table><thead><tr><th rowspan="2"></th><th colspan="4">Second graders</th><th colspan="4">Fifth graders</th></tr><tr><th>Silent</th><th>Melodic only</th><th>Melodic + slow rate</th><th>Melodic + rapid rate</th><th>Silent</th><th>Melodic only</th><th>Melodic + slow rate</th><th>Melodic + nbsp;rapid rate</th></tr></thead><tbody><tr><td><strong>Loops</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Period (ms)</td><td>1007.67<br>(371.59)</td><td>1042.61<br>(377.10)</td><td>1012.29<br>(347.23)</td><td>1050.53<br>(363.95)</td><td>865.35<br>(357.81)</td><td>880.46<br>(373.75)</td><td>906.34<br>(375.98)</td><td>896.75<br>(373.57)</td></tr><tr><td>Mean velocity (mm/s)</td><td>24.26<br>(9.27)</td><td>22.01<br>(9.79)</td><td>23.00<br>(9.47)</td><td>22.70<br>(9.48)</td><td>27.32<br>(9.45)</td><td>25.75<br>(9.80)</td><td>25.10<br>(9.73)</td><td>25.16<br>(9.15)</td></tr><tr><td>Movement dysfluency (SNVpd)</td><td>3.01<br>(2.04)</td><td>3.14<br>(2.16)</td><td>3.04<br>(2.01)</td><td>3.14<br>(2.05)</td><td>2.44<br>(2.00)</td><td>2.66<br>(2.17)</td><td>2.79<br>(2.22)</td><td>2.78<br>(2.16)</td></tr><tr><td>Loop size error (mm)</td><td>-1.16<br>(2.51)</td><td>-1.36<br>(2.64)</td><td>-1.31<br>(2.57)</td><td>-1.48<br>(2.58)</td><td>-1.15<br>(2.46)</td><td>-1.60<br>(2.44)</td><td>-1.40<br>(2.58)</td><td>-1.43<br>(2.45)</td></tr><tr><td><strong>Words</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Period (ms)</td><td>900.78<br>(252.12)</td><td>874.86<br>(265.38)</td><td>903.12<br>(246.28)</td><td>910.02<br>(239.73)</td><td>565.38<br>(204.03)</td><td>579.79<br>(231.43)</td><td>600.32<br>(228.11)</td><td>577.82<br>(217.77)</td></tr><tr><td>Mean velocity (mm/s)</td><td>20.86<br>(7.97)</td><td>20.06<br>(8.06)</td><td>19.94<br>(7.31)</td><td>19.33<br>(7.29)</td><td>28.88<br>(8.16)</td><td>29.15<br>(8.44)</td><td>27.68<br>(8.70)</td><td>28.93<br>(8.24)</td></tr><tr><td>Movement dysfluency (SNVpd)</td><td>21.96<br>(9.73)</td><td>21.14<br>(9.86)</td><td>22.37<br>(9.83)</td><td>22.86<br>(9.71)</td><td>13.20<br>(7.00)</td><td>13.29<br>(7.27)</td><td>13.96<br>(8.11)</td><td>13.20<br>(6.92)</td></tr><tr><td>Mean word size (mm)</td><td>11.78<br>(3.49)</td><td>11.80<br>(3.36)</td><td>11.50<br>(3.53)</td><td>11.06<br>(3.45)</td><td>9.69<br>(3.53)</td><td>9.34<br>(3.44)</td><td>9.38<br>(3.53)</td><td>9.77<br>(3.74)</td></tr></tbody></table>

Note. SNVpd, signal-to-noise velocity peaks difference.

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

Correlations between the different handwriting variables collected during the silent session.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Handwriting test</th><th colspan="4">Loop copying</th><th colspan="4">Word copying</th></tr><tr><th>BHK score</th><th>BHK speed</th><th>Period</th><th>Mean velocity</th><th>Movement dysfluency</th><th>Loop size error</th><th>Period</th><th>Mean velocity</th><th>Movement dysfluency</th><th>Mean word size</th></tr></thead><tbody><tr><td rowspan="2">Handwriting test</td><td>BHK score</td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>BHK speed</td><td>.28<sup>**</sup></td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Loop copying</td><td>Period</td><td>.20</td><td>-.39<sup>**</sup></td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean velocity</td><td>-.29<sup>**</sup></td><td>.31<sup>**</sup></td><td>-.73<sup>**</sup></td><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Loop size error</td><td>Movement dysfluency</td><td>.18</td><td>-.30<sup>**</sup></td><td>.95<sup>***</sup></td><td>-.74<sup>***</sup></td><td>-</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Loop size</td><td>-.20</td><td>-.15</td><td>.22<sup>*</sup></td><td>.39<sup>***</sup></td><td>.14</td><td>-</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">Word copying</td><td>Period</td><td>.021</td><td>-.76<sup>***</sup></td><td>.70<sup>***</sup></td><td>-.54<sup>***</sup></td><td>.62<sup>***</sup></td><td>.16</td><td>-</td><td></td><td></td><td></td></tr><tr><td>Mean velocity</td><td>-.13</td><td>.62<sup>***</sup></td><td>-.58<sup>***</sup></td><td>.68<sup>***</sup></td><td>-.50<sup>***</sup></td><td>.19</td><td>-.83<sup>***</sup></td><td>-</td><td></td><td></td></tr><tr><td rowspan="2">Movement dysfluency</td><td>Movement dysfluency</td><td>-.03</td><td>-.71<sup>***</sup></td><td>.55<sup>***</sup></td><td>-.52<sup>***</sup></td><td>.47<sup>***</sup></td><td>.06</td><td>.87<sup>***</sup></td><td>-.79<sup>***</sup></td><td>-</td><td></td></tr><tr><td>Mean word size</td><td>-.03</td><td>-.25<sup>*</sup></td><td>.11</td><td>.08</td><td>.03</td><td>.42<sup>***</sup></td><td>.26<sup>*</sup></td><td>.04</td><td>.43<sup>***</sup></td><td>-</td></tr></tbody></table>

<footnote>* p < .05.</footnote>

<footnote>** p < .01.</footnote>

<footnote>*** p < .001.</footnote>
and from.04 to.87 for the word copying task). The kinematics collected from the loop and word copying tasks were also correlated (rs ranged from.42 to.70). Finally, although the handwriting speed, assessed with the BHK test, was moderately to highly correlated with the other collected measures, the handwriting quality was only correlated with loop copying velocity (r = -.29, p < .01).

## Influence of melodic background on handwriting

The results of the models investigating the effect of the melodic background on handwriting kinematics are represented in Fig. 1 for the loop copying task and in Fig. 2 for the word copying task.

### Loops

*Period.* The period was longer in the melodic condition than in the silent condition, $F(1, 8716.5) = 47.41, p < .001$, partial $\eta^2 (\eta_p^2) = .005$. The grade effect was not significant. The analyses also revealed a significant Grade \* Condition interaction, $F(1, 8720.2) = 10.28, p = .001$, $\eta_p^2 = .001$. Post hoc analysis indicated that the effect of melody on the period was larger for second graders, $t(8722.2) = 7.02, p < .001$, $d = 0.16$, than for fifth graders, $t(8714.1) = 2.64, p = .02$, $d = 0.05$.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Effect of silent (Sil) and melodic (Melo) conditions on period, mean velocity, movement dysfluency, and size errors during the loop copying task in second graders and fifth graders. Error bars correspond to 95% confidence intervals. Note that the movement dysfluency measure was normalized using a Yeo-Johnson transformation. SNVpd, signal-to-noise velocity peaks difference. \*p < .05; \*\*p < .01; \*\*\*p < .001.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Effect of silent (Sil) and melodic (Melo) conditions on period, mean velocity, movement dysfluency, and size errors during the word copying task in second graders and fifth graders. Error bars correspond to 95% confidence intervals. The condition and interaction effects were not significant. SNVpd, signal-to-noise velocity peaks difference. \*p < .05; \*\*p < .01; \*\*\*p < .001.</figcaption></figure>
**Mean velocity.** Participants were slower in the melodic condition than in the silent condition, $F(1, 8529.4) = 199.91, p < .001, \eta_p^2 = .02$. The grade effect was also significant; second graders wrote more slowly than fifth graders, $F(1, 85.3) = 9.52, p = .002, \eta_p^2 = .10$. Finally, the Grade * Condition interaction revealed that although both groups decreased their mean velocity in the melodic condition, second graders were more affected, $F(1, 8528.0) = 10.80, p = .001, \eta_p^2 = .001$ [second graders: $t(8530.0) = 12.58, p < .001, d = 0.29$; fifth graders: $t(8527.6) = 7.53, p < .001, d = 0.18$].

**Movement dysfluency.** Participants were more dysfluent (i.e., produced more atypical velocity) in the melodic condition than in the silent condition, $F(1, 9433.1) = 14.37, p < .001, \eta_p^2 = .001$. The main effect of grade and the Grade * Condition interaction were not significant.

**Loop size error.** The condition effect was significant: in both conditions; children produced smaller loops than those of the template, and these differences from the model were larger in the melodic condition than in the silent condition, $F(1, 8622.3) = 83.01, p < .001, \eta_p^2 = .01$. The grade effect and the Grade * Condition interaction were not significant.

## Words

**Mean period.** The analyses indicated that the period was larger in second graders than in fifth graders, $F(1, 5.1) = 28.70, p = .003, \eta_p^2 = .85$. The condition effect was not significant. Finally, although the interaction effect was significant, $F(1, 2314.8) = 5.05, p = .02, \eta_p^2 = .003$, the post hoc analyses did not reveal significant differences between the background conditions in each group.

**Mean velocity.** Mean velocity was slower in second graders than in fifth graders, $F(1, 4.6) = 12.93, p = .02, \eta_p^2 = .74$. The condition and interaction effects were not significant.

**Movement dysfluency.** The analyses revealed that second graders were more dysfluent than fifth graders, $F(1, 4.7) = 33.57, p = .003, \eta_p^2 = .88$. The condition effect and Grade * Condition interaction were not significant.

**Word mean size.** The condition effect was not significant. The grade effect revealed that fifth graders wrote smaller words than second graders, $F(1, 84.9) = 20.86, p < .001, \eta_p^2 = .20$. Finally, the Grade * Condition interaction was also significant, $F(1, 2480.4) = 9.57, p = .002, \eta_p^2 = .004$. The fifth graders were influenced by the sound background and decreased the size of the produced words in the melodic condition, $t(2474) = 2.91, p = .007, d = 0.12$, whereas no significant difference was found for the second graders, $t(2486) = 1.53, p = .24$.

## Influence of melodic background on mean velocity depending on grade and handwriting skills

To go a step further, we tested how children's handwriting level modulated the sound background effect. To this aim, we integrated the BHK quality and speed *z* scores as continuous variables in the mixed models and tested the interactions between these variables. To avoid multiple analyses, we chose to conduct the models on only one variable. The mean velocity was chosen because this measure was comparable in the two writing tasks (loop and word copying) and was the most influenced by the musical background conditions. The effects of these variables are reported below. Note that the addition of these new variables did not modify the significance of the condition effect described earlier. Hence, only the main effects and interactions involving handwriting quality and speed are reported below (see supplementary material for the detailed models).

## Models integrating handwriting quality

**Loops (Fig. 3A).** A significant interaction between condition and handwriting quality indicated that poor writers were more affected by the condition than good writers. They copied loops faster than good writers in the silent condition but not in the melodic condition, $F(1, 8259.1) = 49.28, p < .001, \eta_p^2 = .006$. This interaction was significant only for second graders but not for fifth graders [Condition * Grade * Handwriting Quality interaction: $F(1, 8218.6) = 43.16, p < .001, \eta_p^2 = .005$].
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** (A) Mean velocity (mm/s) during loop copying depending on BHK quality score, grade, and sound background conditions. (B) Mean velocity (mm/s) during word copying depending on BHK quality score, grade, and sound background conditions. (C) Mean velocity (mm/s) during loop copying depending on BHK speed score, grade, and sound background conditions. (D) Mean velocity (mm/s) during word copying depending on BHK speed score, grade, and sound background conditions. Melodic background is presented in black, and silence is presented in gray. The effect of the BHK was reported for each condition separately. ns, not significant; \*p < .05; \*\*p < .01.</figcaption></figure>

Words (Fig. 3B). Although the main effect of handwriting quality was not significant, this variable interacted significantly with grade, $F(1, 79.2) = 4.22, p = .04, \eta_p^2 = .07$; writing velocity depended on handwriting quality in fifth graders but not in second graders. The Grade * Condition * Handwriting Quality interaction was also significant, $F(1, 2375.7) = 7.69, p = .006, \eta_p^2 = .003$, and the post hoc analyses indicated that in fifth graders the difference between poor and good writers was attenuated in the melodic condition compared with the silent condition. The difference was not significant in second graders.

## Models integrating handwriting speed

Loops (Fig. 3C). Results indicated that a slow handwriting speed in the BHK test was associated with a lower velocity in the loop copying task, $F(1, 78.8) = 9.06, p = .004, \eta_p^2 = .10$. In addition, the analysis indicated that in both grades slower writers were more affected by listening to the melodic background than their peers [Condition * Handwriting Speed effect: $F(1, 8374.8) = 21.78, p < .004, \eta_p^2 = .003$].

Words (Fig. 3D). The main effect of handwriting speed was significant; children who wrote slower in the BHK test were also slower in the word copying task, $F(1, 67.5) = 15.21, p < .001, \eta_p^2 = .18$. In addition, handwriting speed interacted with condition, $F(1, 2452.4) = 8.32, p = .004, \eta_p^2 = .004$; slow writers wrote more slowly in the melodic condition than in the silent condition, whereas the opposite pattern was observed for fast writers.
## Influence of rhythmic backgrounds on handwriting

The effects of the rhythmic cues and tempo on handwriting kinematics are represented in Fig. 4 for the loop copying task and in Fig. 5 for the word copying task.

### Loops

*Period.* Results indicated that the period was shorter in the slow rate condition compared with the other two conditions (rapid rate and melodic), $F(2, 12814.3) = 3.70, p = .02, \eta_p^2 = .006$. However, this effect depended on the grade [interaction effect: $F(2, 12814.0) = 16.73, p < .001, \eta_p^2 = .003$]. The period decreased in the slow rate condition for second graders only [SR vs. melodic: $t(12819.98) = 5.33, p < .001, d = 0.12$; SR vs RR: $t(12816.8) = 4.38, p < .001, d = 0.10$] but not for fifth graders [SR vs. melodic: $t(12802.2) = 2.58, p = .14$; SR vs RR: $t(12810.3) = 0.95, p = 1.00$]. The grade effect was not significant.

*Mean velocity.* The analysis did not reveal any significant effects of condition and grade. The Grade * Condition interaction was significant, $F(2, 12900.5) = 15.40, p < .001, \eta_p^2 = .002$. On the one hand, sec-

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Effect of melodic (Melo), slow rate (SR), and rapid rate (RR) conditions on period, mean velocity, movement dysfluency, and size errors during the loop copying task in second graders and fifth graders. Error bars correspond to 95% confidence intervals. Note that the movement dysfluency measure was normalized using a Yeo-Johnson transformation. SNVpd, signal-to-noise velocity peaks difference. \*p < .05; \*\*p < .01; \*\*\*p < .001.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Effects of melodic (Melo), slow rate (SR), and rapid rate (RR) conditions on period, mean velocity, movement dysfluency, and size errors during the word copying task in second graders and fifth graders. Error bars correspond to 95% confidence intervals. SNVpd, signal-to-noise velocity peaks difference. \*p < .05; \*\*p < .01; \*\*\*p < .001.</figcaption></figure>
ond graders increased their mean velocity in the rhythmic conditions compared with the melodic condition [SR vs. melodic: t(12908.3) = 4.10, *p* < .001, *d* = 0.10; RR vs. melodic: t(12908.9) = 3.87, *p* = .001, *d* = 0.09]. On the other hand, fifth graders decreased their mean velocity in the slow rate condition compared with the melodic condition [SR vs. melodic: t(12889.9) = 3.40, *p* = .004, *d* = 0.08].

*Movement dysfluency.* The condition and grade effects were not significant. The Grade * Condition interaction revealed that dysfluency depended on the grade and condition, F(2, 13983) = 6.36, *p* = .001, *η*<sup>2</sup><sub>p</sub> = .001. Post hoc analysis indicated that fifth graders were more dysfluent with the rhythmic conditions compared with the melodic condition [SR vs. melodic: t(13965.8) = 2.67, *p* = .05, *d* = 0.05; RR vs. melodic: t(13976.14) = 2.71, *p* = .04, *d* = 0.05].

*Loop size error (difference from the loop model).* The main effects of condition and grade were not sig-
nificant, but the Grade * Condition interaction was significant, F(2, 12806.2) = 7.22, *p* < .001, *η*<sup>2</sup><sub>p</sub> = .001.
Although the background did not influence loop size in second graders, fifth graders produced smaller
loops in the melodic condition compared with the two rhythmic conditions [SR vs. melodic: *t*
(12791.8) = 3.30, *p* = .005, *d* = 0.06; RR vs. melodic: *t*(12800.6) = 3.77, *p* < .001, *d* = 0.07].

Words

*Mean period.* Results indicated that the period was larger in second graders than in fifth graders, F(1, 4.4) = 32.07, *p* = .004, *η*<sup>2</sup><sub>p</sub> = .88. In addition, the interaction was significant, F(2, 3541.3) = 3.54, *p* = .03, *η*<sup>2</sup><sub>p</sub> = .002, but the post hoc analyses did not report significant differences between the conditions for either second graders or fifth graders. The condition effect was not significant.

*Mean velocity.* The grade effect was significant: The second graders wrote more slowly than the fifth graders, F(1, 4.0) = 14.17, *p* = .02, *η*<sup>2</sup><sub>p</sub> = .78. In addition, the mean velocity was slower in the slow rate condition compared with the melodic condition, F(2, 3761.1) = 5.22, *p* = .005, *η*<sup>2</sup><sub>p</sub> = .003 [SR vs. melodic: *t*(3760) = 3.18, *p* = .004, *d* = 0.10]. However, the interaction effect revealed that the condition effect depended on the grade, F(2, 3760.1) = 13.95, *p* < .001, *η*<sup>2</sup><sub>p</sub> = .003. Indeed, whereas second graders' writing slowed down in the rapid rate condition [RR vs. melodic: *t*(3766.5) = 2.91, *p* = .02, *d* = 0.09; RR vs SR: *t*(3761.9) = 2.88, *p* = .02, *d* = 0.09], fifth graders' writing slowed down in the slow rate condition [RR vs. melodic: *t*(3751.4) = 4.61, *p* < .001, *d* = 0.15; SR vs. melodic: *t*(3759.4) = 4.55, *p* < .001, *d* = 0.15].

*Movement dysfluency.* The movement dysfluency was greater in second graders than in fifth graders, F(1, 4.7) = 34.44, *p* = .002, *η*<sup>2</sup><sub>p</sub> = .88. The condition effect was also significant, F(2, 3456.4) = 3.08, *p* = .05, *η*<sup>2</sup><sub>p</sub> = .002; writing was more dysfluent in the rapid rate condition than in the melodic condition [RR vs. melodic: *t*(3459) = 2.40, *p* = .04, *d* = 0.08]. The significant Grade * Condition interaction, F(2, 3456.4) = 5.14, *p* = .006, *η*<sup>2</sup><sub>p</sub> = .003, completed by post hoc tests, revealed that this condition effect was significant only in second graders [RR vs. melodic: *t*(3467.8) = 3.09, *p* = .01, *d* = 0.10], whereas fifth graders' movement dysfluency did not significantly differ depending on the condition.

*Words mean size.* Fifth graders wrote smaller words than second graders, F(1, 84.9) = 16.55, *p* < .001, *η*<sup>2</sup><sub>p</sub> = .16. The condition effect was not significant. The Grade * Condition interaction was significant, F(2, 3794.1) = 21.24, *p* < .001, *η*<sup>2</sup><sub>p</sub> = .01. Post hoc analysis revealed that the rapid rate decreased the writing size in second graders [RR vs. melodic: *t*(3802.9) = 4.56, *p* < .001, *d* = 0.15; RR vs. SR: *t*(3795.7) = 3.07, *p* = .01, *d* = 0.10], whereas it increased the writing size in fifth graders [RR vs. melodic: *t*(3787.7) = 4.25, *p* < .001, *d* = 0.14; RR vs. SR: *t*(3788.3) = 3.69, *p* = .001, *d* = 0.12].

Influence of rhythmic background on mean velocity depending on grade and handwriting skills

In a final model, the BHK z scores were integrated as continuous variables to test how children's handwriting quality and speed modulated the rhythm background effect on mean velocity. The results are presented below, and details on complete models can be found in the supplementary material.
## Models integrating handwriting quality

Loops (Fig. 6A). The Grade * Condition * Handwriting Quality interaction was significant, $F(2, 12262.7) = 19.50, p < .001$, $\eta_p^2 = .003$. Post hoc analysis indicated that for second graders the difference between poor and good writers (i.e., poor writers wrote faster) increased in the two rhythmic conditions compared with the melodic only condition. For fifth graders, the differences between poor and good writers decreased in the rapid rate condition compared with the other two conditions.

Words (Fig. 6B). There was a significant Grade * Handwriting Quality interaction, $F(1, 78.1) = 5.91, p = .02$, $\eta_p^2 = .07$, revealing that the effect of handwriting quality was stronger for fifth graders. Poor writers wrote the words faster than good writers in Grade 5 but not in Grade 2. In addition, there was a Grade * Condition * Handwriting Quality interaction, $F(2, 3634.4) = 6.32, p = .002$, $\eta_p^2 = .003$. Post hoc analysis revealed that in fifth graders the slow pace increased the difference between poor and good writers compared with the melodic background.

## Models integrating handwriting speed

Loops (Fig. 6C). First, the model indicated that the mean velocity during the loop task was lower for slow writers on the BHK test, $F(1, 79.8) = 4.87, p = .03$, $\eta_p^2 = .06$. In addition, there was a Condition * Handwriting Speed interaction indicating that the velocity difference between slow and good writers decreased in the rhythmic conditions; slow writers tended to be quicker and good writers tended to be slower with the beat. This attenuation was more pronounced in the rapid rate condition than in the slow rate condition, $F(1, 12457.6) = 33.64, p < .001$, $\eta_p^2 = .003$.

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** (A) Mean velocity (mm/s) during loop copying depending on BHK quality score, grade, and sound background conditions. (B) Mean velocity (mm/s) during word copying depending on BHK quality score, grade, and sound background conditions. (C) Mean velocity (mm/s) during loop copying depending on BHK speed, grade, and sound background conditions. (D) Mean velocity (mm/s) during word copying depending on BHK speed, grade, and sound background conditions. Melodic background is presented in solid line, and rhythmic backgrounds are presented in dotted lines with slow rate in gray and rapid rate in black. The effect of the BHK was reported for each condition separately. \*p < .05; \*\*p < .01; \*\*\*p < .001.</figcaption></figure>
Words (Fig. 6D). Results indicated that slower writers on the BHK test were also slower on the word writing task, $F(1, 73.7) = 13.64, p < .001, η_p^2 = .15$. Interestingly, this difference was reduced by the two rhythmic backgrounds [Condition * Handwriting Speed interaction: $F(2, 3709.6) = 10.83, p < .001, η_p^2 = .007$].

Discussion

This study investigated the influence of music listening on the motor control of graphic gesture in loop and word copying. We aimed to disentangle the influence of melodic and rhythmic information by comparing different musical backgrounds with and without rhythmic cues. It was expected that listening to music would improve handwriting kinematics, especially when rhythmic cues were provided. Importantly, these effects were tested in two different groups of children (second graders and fifth graders) because the handwriting expertise may modulate the effect of music on handwriting. The analyses were divided into two steps. First, the melodic background was compared with silence in the two groups. Second, we investigated the specific effect of rhythmic cues by comparing the three musical backgrounds (melodic only, melodic with slow rate metronome, and melodic with rapid rate metronome). Finally, exploratory analyses were performed to investigate how handwriting quality and speed modulate the influence of music background in each grade.

*Melodic background slows down loop copying but not word copying*

First, we tested the influence of melodic background on handwriting kinematics. The analyses indicated that in the melodic condition both groups of children decreased their writing velocity and size and increased their period and movement dysfluency for the loop copying task. The effect was not present in the word copying task. Therefore, our results were not consistent with those of Véron-Delor et al. (2017), who showed a positive effect of music listening on writing velocity and movement dysfluency. However, in this previous study the music presented had a strong rhythmic component, and it is possible that background with a low rhythmic component, as in our study, does not have such a positive effect.

Importantly, the literature has reported divergent results; whereas some studies reported benefits of music for attention (Mendes et al., 2021), others suggested that music listening sometimes interferes with cognitive tasks (Cheah et al., 2022; de la Mora Velasco & Hirumi, 2020). This interference effect depends on participants' working memory capacity (Lehmann & Seufert, 2017). Therefore, younger children are more affected by noise or music background, especially when the task is complex (Caviola et al., 2021). In line with this idea, our results indicated that young children (second graders) and slow writers were more affected by the melodic background than children who have automated their writing. Hence, it seems that in children for whom writing is still difficult, listening to music increases the demands of the task. Because of the close relationship between music and language processing, music interference has mainly been reported in tasks involving language such as text comprehension (Cheah et al., 2022) and text composition (Ransdell & Gilroy, 2001). In our study, the effect was found only on loop copying but not on word copying, although this task involves language processing and therefore should be more sensitive to music listening (Canette et al., 2020; Cheah et al., 2022). Therefore, the distractor effect hypothesis is not sufficient to explain the results.

Another explanation could be that music influences movement and cognition by modifying the arousal of the listener (i.e., arousal inducer hypothesis) (Day et al., 2009). Indeed, background music has different effects depending on its arousal; listening to a relaxing background sound decreases motor speed (e.g., walking cadence), whereas stimulating sounds have an opposite effect (Franěk et al., 2014; Kämpfe et al., 2011). In our study, we chose peaceful music without a salient beat in order to dissociate the influence of melody and rhythm. Therefore, it is possible that children slowed down their movement because of the low arousal of the stimuli. These changes in velocity would affect the size of the loops, which would decrease to preserve writing duration, as predicted by the isochrony principle (Bosga-Stork et al., 2013; Viviani & Terzuolo, 1982). Interestingly, the slowdown was found only on the loop copying task but not on the word copying task. A similar finding was reported in a
previous study reporting an effect of arousal (manipulated through music tempo) on the number of fixations in easy texts but not in difficult texts (Su et al., 2023). Therefore, these results suggest that music arousal differently influences performances depending on task complexity. According to Kiss and Linnell (2023), a simple task that tends to be under-stimulating would benefit from listening to stimulating music that would increase vigilance, whereas relaxing music would have a detrimental effect. This is compatible with our results showing that the less demanding task (i.e., copying loops) is more affected by the relaxing music. On the contrary, when the task is more demanding, stimulating music would increase the arousal to a supra-optimal level, resulting in a distractive effect, whereas relaxing music would permit focusing on the task (Kiss & Linnell, 2023).

To sum up, our study clearly indicated that a relaxing musical background slows down loops trac-
ing movement but not handwriting. We assume that the arousal inducer hypothesis most likely
explains our results, but further studies are needed to better address it.

Music with *rhythmic cues* influences handwriting in different ways depending on children's grade

A second aim of the study was to test the influence of rhythmic cues on writing. We hypothesized that listening to a rhythmic background would permit stabilizing the graphic movement through spontaneous synchronization (Dalla Bella, 2018). The results partly confirmed this hypothesis and revealed that the rhythm effect mainly depends on children's grade and the rhythm rate.

On the one hand, second graders' writing was influenced by the adjunction of rhythm, but differ-
ently for rapid and slow rates. Indeed, for the loops copying task, the graphomotor process was accel-
erated by the slow rate (increase of the writing velocity resulting in a shorter period) without
modifying the product (the size of the loops). Conversely, the rapid rate induced a decrease of velocity,
fluidity, and word size during the word copying task. Hence, young children, who have not yet auto-
mated handwriting, seem to benefit from additional rhythmic cues, but only when the metronome fre-
quency is close to their own writing frequency. Conversely, a metronome that beats too fast (2.2 Hz)
affects their writing process. This is consistent with previous work emphasizing the importance of
using a metronome frequency based on the participants' spontaneous cadence to avoid impairing
movement (Cochen De Cock et al., 2018). Importantly, rhythmic background influences loop and word
copying differently in second graders. Indeed, although the rhythm, and especially the slow rate,
increased graphic movement velocity for the loop copying task, this effect was not observed on the
word copying task. Conversely, the detrimental effect of rapid rate on the writing process was found
only during word copying. Several explanations can be put forward to explain these different effects.
First, the two tasks differ in term of rhythmicity; copying lines of loops involves repeated and more
continuous movement, whereas isolated word writing induces shorter, more irregular, and less con-
tinuous movement that can be less attracted by the auditory stimuli. Consequently, it would be easier
to synchronize loops to the beat, resulting in larger benefits of rhythm listening for this graphomotor
task. This result confirms that the temporal control of movements depends on both the nature of the
motor task and the parameters of the extrinsic auditory rhythm (Loràs et al., 2012). Second, as devel-
oped in a previous section, word copying is a more demanding task because it involves orthographic
processing in addition to graphomotor processing. Consequently, a rhythm too different from the indi-
vidual writing frequency should have a more distracting effect on this task.

Concerning fifth graders, the two rhythmic backgrounds deteriorated graphic movement during both loop and word copying. Indeed, slow and rapid rates decreased mean velocity and increased movement dysfluency during the loop copying task. The size of the loop was also larger. For the word copying task, the slow rate decreased the mean velocity, whereas the rapid rate affected the size of the word, written larger in this condition. Hence, it seems that for children who automated their writing, listening to rhythmic cues had a detrimental effect on both the handwriting process (decrease of velocity and increase of movement dysfluency) and the product (increase of the size). It is possible that these rhythmic backgrounds interfere with children's own writing rhythm that would emerge with the handwriting automation (Sakai et al., 2004; Sattler & Marquardt, 2010).

To sum up, this study suggests that different parameters, such as individual characteristics (i.e., handwriting expertise) and music tempo, modulate the influence of rhythmic cues on handwriting. In addition, the effects of rhythm were also different depending on the task, suggesting that different
mechanisms may explain its influence on pure graphomotor processes and on handwriting. Further
studies are needed to confirm these interpretations.

*The influence of musical background differs according to the quality and speed of children's handwriting*

Handwriting expertise depends on grade but can also be examined through the children's scores on the BHK test. We conducted additional analyses to investigate whether the effect of music listening on writing velocity depends on children's handwriting quality and speed—assessed with this test. Note that the BHK quality and speed scores were not related to writing velocity in the same way. Although poor handwriting quality was associated with high mean velocity during loop/word copying tasks, a low handwriting speed in the BHK test was associated with a slow mean velocity during the two graphomotor tasks. Similar results were found in previous studies (e.g., Jolly et al., 2024).

Concerning the influence of musical backgrounds, it was not clear whether or not they were mod-
ulated by children's handwriting quality. However, children's handwriting speed changed the effects
of the different musical backgrounds on writing velocity. Slower writers were more affected by the
melody and decreased their writing velocity more in this condition. Conversely, the rhythmic back-
grounds increased their writing velocity more in both the loop and word copying tasks. These results
are consistent with those obtained with the grade comparison; children who automated their writing
to a lesser degree (younger and/or slower children) were more sensitive to the musical background.
This opens up an interesting perspective for improving graphomotor learning at school through the
use of rhythmic cues. In addition, this tool could be efficient for the rehabilitation of handwriting dif-
ficulties. For this last point, our results emphasize the need to distinguish children with handwriting
slowness or impaired quality because they may respond differently to musical background. Further
studies are needed to investigate this issue in children with dysgraphia.

Limitations and perspectives

Despite interesting advances, our study presents several limitations. First, we constructed rhythmic stimuli combining melodic and rhythmic cues in order to compare the three musical conditions. Because of that, we cannot confirm, with a “rhythm only” condition, that rhythmic cues improve writing compared with a silent baseline, and further studies are needed to address this specific question. A second limitation is that we did not couple the music and handwriting recordings. Therefore, we were unable to check whether the children were writing in synchrony with the beat of rhythmic cues in the musical background. Note that previous studies have reported that participants who benefited from rhythmic auditory stimulation (RAS) did not systematically synchronize their steps to the beat. This led the authors to suggest that other mechanisms, such as arousal induction driven by the music, can explain the positive effects of RAS (Cochen De Cock et al., 2018). This limitation highlights the need for a more detailed study of the mechanisms underlying the music's influence on handwriting considering, for example, musical arousal and handwriting synchronization. Another limitation is that the tasks were presented in a fixed order with the loop copying task first and then the word copying task. This could explain why the background sometimes influenced loop copying more than word copying kinematics; children might have paid more attention to the music at the beginning of the experiment and paid less attention at the end. Finally, the current study included only one measure of the product (the size), but the quality of the production was not assessed. Further work should include these measures in addition to kinematics recording to confirm the beneficial or detrimental effect of music on handwriting.

Conclusion

This study confirms that musical background listening influences both the handwriting process (period, velocity, and movement dysfluency) and the product (size) in children. Importantly, the parameters of the musical background play a key role because the addition of rhythmic cues modifies music's influence on handwriting kinematics. This highlights the necessity to consider the various
components of music while testing the influence of musical background on movement. In addition, individual characteristics, such as handwriting expertise, also modulate this influence; children who have not automated handwriting yet were more affected by listening to the relaxing music but benefited from rhythmic cues, especially in the slow rate condition. On the contrary, older children were more impaired when they listened to the rhythm, especially in the slow rate condition. The differential effect of tempo highlights the necessity to adjust the metronome on children's writing frequency in further studies. Globally, the results offer interesting avenues for music-based interventions to address handwriting difficulties.

### CRediT authorship contribution statement

**Margaux Lê:** Writing – original draft, Methodology, Data curation, Conceptualization. **Marianne Jover:** Writing – review & editing, Methodology, Conceptualization. **Aline Frey:** Writing – review & editing, Methodology, Data curation, Conceptualization. **Jérémy Danna:** Writing – review & editing, Methodology, Conceptualization.

### Fundings

This work, carried out within the Institute of Language, Communication and the Brain (ANR-16-CONV-0002), has benefited from support from the French government (France 2030), managed by the French National Agency for Research (ANR) and the Excellence Initiative of Aix-Marseille University (A*MIDEX).

### Data availability

Data will be made available on request.

### Acknowledgement

We are grateful to Fanny Gardette for her help with data collection. We would also like to thank the children and teachers who participated in the study.

### Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jecp.2024.106184.

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