# Developmental changes in cognitive-motor interference management during dual-task treadmill walking in healthy children aged 7 to 12 years

Delphine Fauvel, Estelle Palluel

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Delphine Fauvel, Estelle Palluel. Developmental changes in cognitive-motor interference management during dual-task treadmill walking in healthy children aged 7 to 12 years. Neuroscience, 2025, 589, pp.240 - 247. 10.1016/j.neuroscience.2025.10.031. hal-05343040

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Distributed under a Creative Commons Attribution 4.0 International License
Research Article

Developmental changes in cognitive-motor interference management
during dual-task treadmill walking in healthy children aged 7 to 12 years

Delphine Fauvel ID, Estelle Palluel * ID

Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France

ARTICLE INFO

Keywords:

Cognitive Motor Interference
Gait
Children
Development

A B S T R A C T

This study examined cognitive-motor interference (CMI) in children and adults by manipulating both cognitive and motor task difficulty during a dual-task paradigm (DT). Participants performed an auditory oddball task with three levels of cognitive demand while either sitting or walking on a treadmill, without instructions on task prioritization. Results revealed that increasing cognitive difficulty intensified CMI across all age groups, with younger children (7–9 years) showing the greatest decline in cognitive accuracy. Older children (10–12 years) and adults demonstrated more stable performance, reflecting the maturation of attentional control. Gait analysis indicated that spatiotemporal parameters improved with age, but treadmill use may have masked subtle developmental differences. Notably, cognitive load led to reduced gait variability and increased stability, suggesting adaptive resource allocation. Children prioritized motor stability under challenging conditions, while adults balanced cognitive and motor demands more effectively. These findings support a dynamic, developmentally-sensitive model of attentional capacity rather than a fixed resource-sharing framework. The results have practical implications for education and rehabilitation, highlighting the potential of targeted dual-task interventions to enhance CMI resilience in children. Overall, the study advances understanding of how cognitive and motor systems interact across development, emphasizing the importance of tailored strategies to optimize performance during critical periods of growth.

Introduction

In a world where the interplay between cognitive and motor abilities
is increasingly studied, understanding attentional management during
complex tasks is crucial. The dual-task (DT) paradigm, which combines
cognitive and motor tasks, simulates everyday situations where children
and adults must simultaneously manage multiple demands. Cognitive-
motor interference (CMI), a phenomenon occurring when simulta-
neous cognitive and motor tasks lead to performance decrements, has
been a subject of increasing interest in neuroscience and motor control
research. Rooted in Kahneman's (Kahneman, 1973) seminal work on
limited attentional capacity, CMI underscores the inherent constraints in
how humans allocate attentional resources. The shared attentional ca-
pacity model posits that attentional resources are finite, and when the
combined demands of cognitive and motor tasks exceed this capacity,
performance in one or both tasks suffer. Building on this, Wickens'
central capacity sharing and multiple resource account provides a more
fine-grained framework for conceptualizing interference (Wickens,
2008). His model highlights that dual-task costs depend not only on

overall attentional limits but also on the degree to which tasks overlap in
processing stages (perceptual/cognitive vs. response), sensory modal-
ities (auditory vs. visual), representational codes (spatial vs. verbal), and
channels of visual information (focal vs. ambient). This perspective has
strong empirical and pragmatic appeal, explaining why certain task
combinations interfere heavily while others can be managed with min-
imal detriment. Previous studies have refined this perspective, empha-
sizing the dynamic and adaptive nature of attentional allocation in CMI
scenarios (Yogev-Seligmann et al., 2012; Plummer and Eskes, 2015).
Rather than a simple resource competition, CMI involves intricate in-
teractions shaped by factors like task complexity, individual skill level,
and available sensory information (Saxena, 2017; Ruffieux, 2015). For
instance, manipulating task difficulty can reveal threshold effects, where
performance declines sharply once cognitive or motor demands surpass
a critical point (Fauvel et al., 2024; Fauvel et al., 2025). These thresholds
may vary with age and experience, suggesting that CMI resilience is a
developmental achievement.

Developmental studies have consistently demonstrated that CMI
management improves with age (Schott and Klotzbier, 2018; Chauvel,

<footnote>* Corresponding author at: Laboratoire TIMC, BiomecaMot Team, UMR CNRS 5525, La Tronche, France.
E-mail address: estelle.palluel@univ-grenoble-alpes.fr (E. Palluel).</footnote>
2017; Palluel, 2019). Younger children typically exhibit larger dual-task costs compared to older children and adults, reflecting differences in executive function and motor automatization. Studies using DT paradigms have shown that children's gait becomes more variable and less stable when performing cognitive tasks simultaneously, indicating a competition for attentional resources (Huang et al., 2003). Furthermore, age-related changes in brain structure and function, such as increased myelination and synaptic pruning, may contribute to more efficient neural processing and greater attentional capacity in older individuals (Casey, 2005). During childhood, the neural systems supporting dual-tasking undergo significant development, chiefly involving maturation of fronto-parietal networks such as the dorsal premotor cortex, intraparietal sulcus, and frontal operculum (Engelhardt, 2019; Vinette and Bray, 2015). The prefrontal cortex matures considerably through childhood, conferring improvements in executive functions necessary for managing multiple tasks, while age-related structural changes influence processing speed and inhibitory control (Tsujimoto, 2008; Surkar, 2021). We recently employed EEG and event-related potentials to demonstrate that children, particularly those aged 7-12, exhibit increased allocation of attentional resources during dual-tasking, as indicated by larger P3a and P3b amplitudes compared to adults, reflecting immature but evolving neural processing and more pronounced resource competition between cognitive and motor tasks (Fauvel et al., 2025).

However, the relationship between cognitive and motor performance during CMI is not always straightforward (Al-Yahya, 2011; Cinar et al., 2021). While Schaefer et al. (2015) observed improved gait regularity in 7-9-year-olds during simple cognitive tasks, Hocking et al. (2020) reported reduced gait variability in 6-11-year-olds during demanding working memory tasks, with prioritization of motor stability over cognitive accuracy. Moreover, in this study, adolescents (12-16 years old) exhibited greater gait variability under similar conditions. Our recent study reinforce this age-dependent resource allocation: children aged 10-12 years demonstrate more adult-like CMI management, marked by efficient attentional resource utilization despite heightened neural activity (e.g., larger P3a/P3b amplitudes during DT walking), while younger children (7-9 years) show greater cognitive-motor trade-offs (Fauvel et al., 2025). These different findings may be due to increased arousal, strategic prioritization, or the use of cognitive strategies to compensate for motor instability (Hausdorff, 2005). For example, children may focus more intently on maintaining balance when distracted by an easy cognitive task, leading to improved gait stability.

Task domain further modulates outcomes. In children aged 6-13 years, motor-motor DTs (e.g., button manipulation) disrupt gait more severely than cognitive-motor dual tasks (e.g., digit recall) (Hagmann-von Arx, 2016). This pattern is similarly observed in adolescents with low balance performance (Weedon, 2024). Notably, DT complexity influences walking performances in multiple ways. Schott and Klotzbier (2018) revealed cognitive performance benefits during higher-level executive tasks on straight walking but cognitive costs on change-of-direction pathways, indicating increased cognitive demands in 10-14-year-olds. Collectively, the literature underscores that CMI patterns are shaped by task domain (motor vs. cognitive), age-specific attentional strategies, and complexity. This phenomenon also challenges the traditional view of CMI as purely detrimental and opens new avenues for understanding how the brain organizes tasks in response to varying constraints.

Our study aims to understand the evolution of CMI during walking with changing attentional demands. Using an auditory oddball cognitive task at three levels of difficulty, combined with two levels of motor difficulty (sitting and walking), we seek to elucidate the attentional management strategies employed by individuals based on their resources and goals. The auditory oddball paradigm, a widely used cognitive task in EEG studies, provides a valuable tool for investigating attentional allocation during CMI. By requiring participants to detect

rare target stimuli among frequent distractors, the oddball paradigm refers to a cognitive attentional task that initially captures involuntary attention toward a target stimulus, followed by voluntary attention directed toward the response. Moreover, this auditory oddball paradigm has the advantage of not relying on the processing of visual information, which is also used for gait control in order to avoid structural interference (Boisgontier, 2013). Fauvel et al. (2024, 2025) found that increased P3a amplitude, a neural marker of attentional orienting, was associated with higher cognitive load during walking, suggesting that individuals adaptively allocate attentional resources in response to changing task demands.

While some studies classify cognitive tasks performed in a seated position as single tasks (Beurskens, 2016; Lajoie, 1993; Patel et al., 2014), our work argues that an auditory oddball task executed while seated constitutes a dual task. This distinction arises from two key considerations. First, seated postural control – particularly on a stool, as in our experimental setup – demands active motor stabilization, which competes for attentional resources alongside cognitive processing. Second, the definition of a dual task requires precise operational clarity. McIsaac et al. (2015) propose that dual tasks involve distinct, measurable goals for each component; for example, walking while performing serial subtractions is evaluated through both gait parameters (locomotor goal) and calculation accuracy (cognitive goal). By this framework, even seemingly simple seated postures are not passive: sitting can occur independently of cognitive tasks (e.g., “doing nothing”), much like walking without additional demands. Applying this operationalization, our paradigm qualifies as dual-task because: 1) Sitting, especially on a stool, requires active postural control. The participant must maintain balance and stability without back support, demanding continuous motor engagement. This postural demand is dissociable and independently measurable from the cognitive performance, and 2) The cognitive task has its own distinct goal. Participants respond to specific stimuli or perform cognitive operations, with performance metrics that do not overlap with postural control. Thus, when combined with a cognitive task (e.g., oddball), seated posture introduces a secondary motor goal (postural maintenance) alongside the primary cognitive objective. Methodologically, we therefore classify seated cognitive tasks as a cognitivo-motor dual tasks, albeit less resource-intensive than those performed during walking, challenging traditional assumptions that seated contexts are inherently single-task environments.

By integrating gait kinematics and cognitive error rates, this study seeks to provide a more comprehensive understanding of how the development of executive function and motor automatization shape CMI resilience. The findings of this study have potential implications for education, rehabilitation, and the design of environments that support optimal cognitive-motor performance across the lifespan. We hypothesize that as children develop from age 7 into adulthood, their motor and cognitive performance improves, resulting in a reduced need for attentional resources under similar conditions. Younger children will exhibit greater CMI than older children and adults due to immature attentional networks. Higher cognitive difficulty will exacerbate CMI, particularly during DT walking conditions. We also predict that increasing the motor complexity of the dual task will lead to a decrease in cognitive performance in children, especially younger ones. Gait stability metrics such as harmonic ratio might be more sensitive to cognitive load than traditional spatiotemporal parameters (Leban, 2020; Bisi and Stagni, 2016; Bisi and Stagni, 2016). Under high cognitive load conditions, younger children will prioritize motor stability over cognitive accuracy, resulting in greater cognitive errors but more stable gait patterns, whereas older children and adults will demonstrate a more balanced allocation of attentional resources, maintaining both cognitive performance and gait stability.
<table><caption>**Table 1**
Characteristics of the population.</caption><thead><tr><th></th><th>7-9 years<br>(N = 14)</th><th>10-12 years<br>(N = 17)</th><th>Adults<br>(N = 24)</th></tr></thead><tbody><tr><td>Mean age (years)</td><td>8.6 (0.6)</td><td>10.5 (0.8)</td><td>26.3 (3.4)</td></tr><tr><td>Height (cm)</td><td>134.5 (8.3)</td><td>147.3 (10.3)</td><td>172.4 (9.9)</td></tr><tr><td>Weight (kg)</td><td>28.5 (4.7)</td><td>37.8 (7.6)</td><td>66.1 (9.5)</td></tr><tr><td>Gender</td><td>7 M; 7F</td><td>11 M; 6F</td><td>17 M; 7F</td></tr><tr><td>BMI</td><td>15.7 (1.5)</td><td>17.2 (2.8)</td><td>22.2 (2)</td></tr></tbody><tfoot><tr><td colspan="4">Mean (standard deviation). M: male, F: female, BMI: body mass index.</td></tr></tfoot></table>

# Materials and Methods

## Population

This study was initially conducted on 35 healthy children and 24 healthy young adults (11 males and 6 females; mean age: 26 years ± 3 years, age range: 21–30). The adult population was derived from the study published in Fauvel et al. (2024), with the addition of a few more participants. The population of children differed from the one in Fauvel et al. (2025). Some children data were lost or excluded from this study due to locomotion recording problems. After these exclusions, the final usable and complete database consisted of 14 children aged 7–9 years adults (7 males and 7 females; mean age: 8.6 years ± 0.6 years) and 17 children aged 10–12 years (8 males and 6 females; mean age: 10.5 years ± 0.8 years; Table 1). All participants had normal or corrected to normal vision, no auditory deficits, and had no history of neurological or psychiatric disorders. Parents gave written informed consent for their children’s participation in this study. Due to participation was voluntary and uncompensated. This study was approved by the Ethics Committee (IRB00012476-2020-13-05-59).

## Task and procedures

This study was conducted as a complementary investigation to the work of Fauvel et al. (2024, 2025). The cognitive and motor tasks adhered to the same methodology as described in Fauvel et al. (2024), with participants performing three levels of cognitive difficulty under each of two levels of motor complexity. The detailed methodology has been described previously (Fauvel et al., 2024). Here, we provide a brief overview of the key steps in our approach.

### Auditory oddball cognitive tasks

The cognitive task involved an auditory oddball task manipulated with three levels of cognitive difficulty. The frequent stimuli constituted 70 % of the presented stimuli and did not require any response. How-

$$ \% \text{cognitive error (CE)} = \frac{|\text{Stimuli counted by the participant} - \text{Stimuli to count}|}{\text{Stimuli to count}} * 100 $$

ever, the target stimuli constituted 30 % of the presented stimuli and necessitated a response from the participant. The auditory stimuli corresponded to animal noises (dog, cat, crow, frog, goat, seagull). For each difficulty level, 150 stimuli were presented, with 45 target stimuli distributed across four one-minute sessions. In each session, four different animal sounds out of the six possible were presented. Each sound lasted for 500 ms and appeared in a semi-randomized order. The inter-stimulus interval (ISI) ranged from 1250 ms to 2000 ms and was semi-randomly defined to avoid rhythm prediction effects (Borchard et al., 2015).

The three levels of cognitive task difficulty depended on the number

of target stimuli to be discriminated and counted independently: 1, 2, or 3 target sounds out of 4. The target sounds were announced to the participant at the beginning of each session so they could count only the target stimuli. Participants were asked to count them silently. The number of stimuli counted by the participant for each sound (1, 2, or 3 sounds) was registered by the experimenter at the end of each session.

## Motor tasks

The protocol included two levels of motor difficulty. Participants were either seated or walking on a treadmill. During the seated task, participants sat on a stool without backrest or armrest, placed on the treadmill so that the distance to the speakers and stimuli of the auditory oddball task was consistent across all motor conditions. The walking motor task was performed at preferred walking speed on a treadmill. The walking speed was determined at the beginning of the experiment following the protocol outlined by Dal et al. (2010). The familiarization period on the treadmill to identify the preferred speed ranged from 1 to 3 min depending on the participants. The initial tested speed was 1.0 km/h and was gradually increased in increments of 0.2 km/h to find the speed that best matched the participant’s preferred walking pace. The chosen speed was used for all conditions involving walking during the experiment.

## Experimental conditions

In total, participants completed seven experimental conditions, which were executed in a random order. The different cognitive tasks (levels 1, 2, and 3) were thus performed as dual tasks based on motor difficulty: while seated (DTS1, DTS2, DTS3) and while walking (DTW1, DTW2, DTW3). The walking task (W) was also performed without stimulus.

## Data acquisition and analysis

The E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA) was used to design the script for the auditory oddball tasks. All auditory stimuli were generated pseudo-randomly by the E-Prime software and delivered through the computer speakers placed one meter behind the treadmill. The number of stimuli presented per session was recorded by the E-Prime software, and the number of stimuli counted by the participants was manually recorded by the experimenter at the end of each session. The cognitive score was calculated as the average cognitive error across sessions for conditions involving an auditory oddball paradigm. It was measured as the percentage of cognitive errors using the formula below:

The gait parameters were recorded using the S-Move device (TIMC, Grenoble, France). This system consists of six MPU-9520 inertial measurement units (IMUs) (TDK – Invensense, San Jose, USA), each containing 3 accelerometers, 3 magnetometers, and 3 gyroscopes (Struber, 2021). They were placed on the sacrum (pelvis sensor), the back (thoracic sensor) and bilaterally on the lateral thigh and shank. Data was recorded at 100 Hz. Angular kinematic data was segmented into individual strides using a dynamic time warping algorithm. All identified strides were normalized to span from 0 % to 100 % of the gait cycle. Gait measurements included stride time, normalized stride length, cadence,
coefficients of variation for the stride time and stride length (CV), and harmonic ratio. These measures were categorized into three domains: spatiotemporal parameters, variability, and stability. The stride length was normalized by the lower limb length of each participant using Winter (2009) anthropometric tables for adults and Fredriks (2005) tables for children. Stride length and time coefficient of variation represent gait variability, while harmonic ratio provides an indication of gait stability and smoothness. For the harmonic ratio (HR), calculated as the ratio between the sum of even and odd harmonic amplitudes on the anteroposterior (AP), vertical (V), and mediolateral axes, discrete Fourier transform was initially computed from the 3D-acceleration signal of each stride. HR is assessed based on strides as the unit of measurement. A stable gait pattern is characterized by acceleration patterns that repeat in multiples of two within any given stride. Higher ratios indicate a more stable walking pattern (Menz et al., 2003).

## Statistics

Statistics were conducted using Statistica 10 software. The significance threshold was set at $p < 0.05$. ANOVAs were performed on all gait parameters and cognitive score. Whenever necessary, post hoc tests (Duncan) were also conducted to analyse differences between factors. Effect sizes were measured for each factor of the ANOVAs using partial eta-squared ($\eta^2$ partial) index.

For the cognitive score, 3 x 2 x 3 ANOVAs were conducted with 3 age groups (7–9 years; 10–12 years; Adults) x 2 motor complexities (DTS: seated, DTW: walking) x 3 levels of cognitive difficulty (level 1, 2 and 3) in dual-task conditions, with repeated measures on the last two factors.

For gait parameters, 3 x 4 ANOVAs were conducted with 3 age groups (7–9 years; 10–12 years; Adults) x 4 levels of cognitive difficulty (W: single walking task; DTW1: dual-task walking with cognitive task level 1; DTW2: level 2; DTW3: level 3), with repeated measures on each factor.

## Results

### Cognitive performances

The ANOVA revealed a main effect of age ($F_{2,52} = 34.60, p < 0.001,$

$\eta_p^2 = 0.571$) and cognitive difficulty ($F_{3,156} = 44.61, p < 0.001, \eta_p^2 = 0.462$). The percentage of cognitive errors for children aged 7–9 years was higher than for children aged 10–12 years ($p = 0.009$) and adults ($p < 0.001$). It was also higher for children aged 10–12 years than adults ($p < 0.001$). Post hoc tests showed that the percentage of cognitive errors for the highest difficulty (level 3) was higher than for lower difficulties (levels 1 and 2, $p_s < 0.001$). The error percentage was also higher for level 2 compared to level 1 ($p = 0.005$). A marginal effect of motor complexity ($p = 0.064$) as well as a marginal interaction between age and cognitive difficulty level ($p = 0.064$) were observed. Cognitive error tended to increase with motor complexity. Additionally, the percentage of cognitive errors in children, particularly in younger ones, seemed to increase for each level of cognitive difficulty, whereas in adults, it appeared to increase only for the level 3 (Fig. 1).

### Motor performances

The preferred walking speed on the treadmill was 2.94 km/h ($\pm 0.5$) for children aged 7–9 years, 3.16 km/h ($\pm 0.4$) for those aged 10–12 years, and 3.46 km/h ($\pm 0.5$) for adults.

A main effect of age was observed for all spatiotemporal parameters. Cadence decreased significantly with age ($F_{2,52} = 10.53, p < 0.001, \eta_p^2 = 0.288$). Post hoc tests revealed that cadence was significantly lower in children aged 10–12 years compared to children aged 7–9 years ($p = 0.043$), and lower in adults compared to both 10–12-year-olds ($p = 0.020$) and 7–9-year-olds ($p < 0.001$). Stride length was greater ($F_{2,52} = 14.40, p < 0.001, \eta_p^2 = 0.356$) and stride duration was shorter ($F_{2,52} = 9.72, p < 0.001, \eta_p^2 = 0.272$) in children aged 7–9 years and 10–12 years compared to adults ($p < 0.001$ and $p = 0.020$, respectively). However, these parameters were identical between the two groups of children. An effect of cognitive difficulty was observed on stride length ($F_{3,156} = 3.179, p = 0.026, \eta_p^2 = 0.058$), only. It was significantly greater in DTW3 compared to W ($p = 0.039$, DTW1 ($p = 0.044$) and DTW2 ($p = 0.012$).

A main effect of age was observed for both coefficients of variation for the stride length ($F_{2,52} = 19.77, p < 0.001, \eta_p^2 = 0.432$) and stride time ($F_{2,52} = 8.22, p < 0.001, \eta_p^2 = 0.240$). Post hoc tests revealed that these coefficients were significantly higher in children aged 7–9 years and 10–12 years compared to adults ($p < 0.001$ and $p = 0.024$, respectively). No significant differences were observed between the two

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Means and standard deviations of the percentage of cognitive errors as a function of age group (7–9 years, 10–12 years, adults), motor complexity (DTS: seated, DTW: walking), and cognitive difficulty level (level 1, 2 and 3) of the dual task. Please refer to the text for the significant differences.</figcaption></figure>
groups of children. An effect of cognitive difficulty was also observed for the coefficients of variation for the stride length ($F_{3,156} = 6.22$, $p < 0.001$, $\eta_p^2 = 0.107$) and stride time ($F_{3,156} = 2.83$, $p = 0.040$, $\eta_p^2 = 0.056$). Post hoc tests showed that both decreased in DTW2 ($p = 0.004$ and $p = 0.035$, respectively) and DTW3 ($p < 0.001$ and $p = 0.006$, respectively) compared to W. Additionally, the coefficient of variation for the stride length decreased in DTW3 compared to DTW1 ($p = 0.028$), while the coefficient of variation for the stride duration decreased in DTW1 compared to W ($p = 0.038$).

At the trunk level, a main effect of age was observed for $HR_V$ ($F_{2,52} = 12.95$, $p < 0.001$, $\eta_p^2 = 0.332$), $HR_{ML}$ ($F_{2,52} = 4.13$, $p = 0.022$, $\eta_p^2 = 0.137$) and $HR_{AP}$ ($F_{2,52} = 4.77$, $p = 0.013$, $\eta_p^2 = 0.155$). Post hoc tests revealed that $HR_V$, $HR_{ML}$ and $HR_{AP}$ were significantly lower in children aged 7-9 years ($p < 0.001$, $p = 0.040$, $p = 0.020$, respectively) and 10-12 years ($p < 0.001$, $p = 0.023$, $p = 0.018$, respectively) compared to adults. A main effect of cognitive difficulty was also observed, but only for the $HR_V$ ($F_{3,156} = 2.70$, $p = 0.048$, $\eta_p^2 = 0.049$). Post hoc tests showed that the $HR_V$ was significantly higher in DTW3 compared to W ($p = 0.005$) and DTW1 ($p = 0.026$).

At the pelvis level, the ANOVAs revealed an interaction between age and cognitive difficulty on the $HR_{ML}$. For both groups of children (7-9 years and 10-12 years), the $HR_{ML}$ was significantly higher in DTW3 ($p_s < 0.001$), DTW2 ($p = 0.011$ and $p = 0.003$, respectively), and DTW1 ($p_s < 0.001$) compared to W. The $HR_{ML}$ of adults remained constant regardless of the cognitive difficulty level associated with walking. A main effect of age was observed only on $HR_V$ and $HR_{AP}$. Post hoc tests showed that $HR_V$ and $HR_{AP}$ evolved in the same manner as at the trunk level: they increased between aged 7-9 years ($p = 0.007$ and $p < 0.001$, respectively) and 10-12 years ($p = 0.008$ and $p < 0.001$, respectively) compared to adults. These ratios were identical between the two groups of children. Furthermore, a main effect of cognitive difficulty was

observed on $HR_V$. Post hoc tests revealed that it was significantly higher DTW3 compared to W ($p < 0.001$), DTW1 ($p < 0.001$) and DTW2 ($p = 0.002$) (Table 2).

## Discussion

This study aimed to understand the evolution of cognitive-motor interferences (CMI) during locomotion across development. An auditory oddball task was performed with three levels of cognitive difficulty and two levels of motor complexity. No instruction regarding prioritization of either task was given. The findings of this study, combined with previous research (Fauvel et al., 2024; Fauvel et al., 2025), reveal how CMI progressively changes under dual-task (DT) conditions, offering insights into developmental shifts in attentional resource allocation, with implications for both theoretical models and practical applications.

## Cognitive Performance and Developmental Trajectories

As expected, increasing cognitive difficulty intensified cognitive errors across all age groups. Consistent with our hypotheses and previous research (Fauvel et al., 2024; Fauvel et al., 2025; Verrel, 2009; Berger et al., 2018; Mohring, 2021), cognitive error rates significantly decreased with age, indicating the maturation of attentional control systems. Under dual-task conditions – regardless of the level of associated motor difficulty – children aged 7-9 years exhibited significantly higher error rates than those of children aged 10-12 years and adults. This indicates that younger children experience stronger CMI, struggling more to maintain accuracy while walking and processing auditory stimuli. This finding is consistent with the idea that younger children have limited attentional resources and are more susceptible to cognitive overload. For example, under the most demanding condition (DTW3),

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

Mean and SD of the spatiotemporal parameters, variability and stability of walking during the walking (W) and Dual Task Walking at cognitive level 1, 2 and 3 (DTW1, DTW2 and DTW3). Values in bold indicate significant results.

<table><thead><tr><th rowspan="2">Parameters</th><th rowspan="2"></th><th colspan="4">Conditions</th><th rowspan="2">F</th><th rowspan="2">p</th><th rowspan="2">η<sup>2</sup><sub>p</sub></th></tr><tr><th>W</th><th>DTW1</th><th>DTW2</th><th>DTW3</th></tr></thead><tbody><tr><td rowspan="3">Cadence (step/min)</td><td>7-9 years</td><td>109.5 (7.5)</td><td>108.2 (9.1)</td><td>108.7 (11.4)</td><td>106.9 (9.6)</td><td><strong>Age group</strong></td><td><strong>10.53</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.288</strong></td></tr><tr><td>10-12 years</td><td>103.3 (9.1)</td><td>103.1 (10.7)</td><td>102.6 (9.8)</td><td>101.6 (10.3)</td><td>COG</td><td>1.530</td><td>0.209</td><td>0.029</td></tr><tr><td>Adults</td><td>95.2 (7.2)</td><td>97.0 (7.1)</td><td>96.1 (6.8)</td><td>95.9 (6.4)</td><td>Age group x COG</td><td>0.980</td><td>0.441</td><td>0.036</td></tr><tr><td rowspan="3">Normalized stride length (m)</td><td>7-9 years</td><td>0.44 (0.04)</td><td>0.45 (0.04)</td><td>0.45 (0.04)</td><td>0.45 (0.05)</td><td><strong>Age group</strong></td><td><strong>14.403</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.356</strong></td></tr><tr><td>10-12 years</td><td>0.44 (0.03)</td><td>0.45 (0.03)</td><td>0.44 (0.03)</td><td>0.45 (0.02)</td><td>COG</td><td>3.179</td><td>0.026</td><td>0.058</td></tr><tr><td>Adults</td><td>0.40 (0.3)</td><td>0.39 (0.3)</td><td>0.39 (0.3)</td><td>0.40 (0.3)</td><td>Age group x COG</td><td>1.528</td><td>0.172</td><td>0.056</td></tr><tr><td rowspan="3">Stride time (s)</td><td>7-9 years</td><td>1.11 (0.09)</td><td>1.12 (0.09)</td><td>1.2 (0.12)</td><td>1.14 (0.10)</td><td><strong>Age group</strong></td><td><strong>9.723</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.272</strong></td></tr><tr><td>10-12 years</td><td>1.17 (0.10)</td><td>1.19 (0.13)</td><td>1.19 (0.12)</td><td>1.20 (0.13)</td><td>COG</td><td>1.145</td><td>0.333</td><td>0.022</td></tr><tr><td>Adults</td><td>1.28 (0.10)</td><td>1.26 (0.10)</td><td>1.26 (0.09)</td><td>1.26 (0.09)</td><td>Age group x COG</td><td>1.773</td><td>0.108</td><td>0.069</td></tr><tr><td rowspan="3">CV stride length (%)</td><td>7-9 years</td><td>4.4 (0.7)</td><td>4.40 (0.9)</td><td>4.15 (0.9)</td><td>3.84 (0.75)</td><td><strong>Age group</strong></td><td><strong>19.77</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.432</strong></td></tr><tr><td>10-12 years</td><td>0.44 (0.03)</td><td>0.45 (0.03)</td><td>0.44 (0.03)</td><td>0.45 (0.02)</td><td>COG</td><td>6.222</td><td>&lt;.001</td><td>0.107</td></tr><tr><td>Adults</td><td>0.40 (0.3)</td><td>0.39 (0.3)</td><td>0.39 (0.3)</td><td>0.40 (0.3)</td><td>Age group x COG</td><td>0.630</td><td>0.706</td><td>0.024</td></tr><tr><td rowspan="3">CV stride time (%)</td><td>7-9 years</td><td>5.88 (2.7)</td><td>5.25 (1.7)</td><td>5.08 (1.8)</td><td>4.97 (1.2)</td><td><strong>Age group</strong></td><td><strong>8.215</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.240</strong></td></tr><tr><td>10-12 years</td><td>4.65 (2.1)</td><td>4.17 (1.9)</td><td>4.44 (2.0)</td><td>4.12 (1.62)</td><td>COG</td><td>2.828</td><td>0.040</td><td>0.056</td></tr><tr><td>Adults</td><td>3.58 (3.4)</td><td>2.89 (2.7)</td><td>2.71 (1.83)</td><td>2.53 (1.8)</td><td>Age group x COG</td><td>0.206</td><td>0.975</td><td>0.008</td></tr><tr><td rowspan="3">Trunk HR V</td><td>7-9 years</td><td>2.09 (0.3)</td><td>2.06 (0.4)</td><td>2.08 (0.4)</td><td>2.12 (0.3)</td><td><strong>Age group</strong></td><td><strong>12.95</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.332</strong></td></tr><tr><td>10-12 years</td><td>1.88 (0.3)</td><td>1.94 (0.3)</td><td>1.99 (0.4)</td><td>1.99 (0.3)</td><td>COG</td><td>2.70</td><td>0.048</td><td>0.049</td></tr><tr><td>Adults</td><td>2.72 (0.71)</td><td>2.74 (0.7)</td><td>2.77 (0.8)</td><td>2.84 (0.8)</td><td>Age group x COG</td><td>0.49</td><td>0.815</td><td>0.019</td></tr><tr><td rowspan="3">Trunk HR ML</td><td>7-9 years</td><td>1.82 (0.2)</td><td>1.78 (0.3)</td><td>1.83 (0.2)</td><td>1.83 (0.3)</td><td><strong>Age group</strong></td><td><strong>4.132</strong></td><td><strong>0.022</strong></td><td><strong>0.137</strong></td></tr><tr><td>10-12 years</td><td>1.77 (0.4)</td><td>1.77 (0.4)</td><td>1.80 (0.4)</td><td>1.79 (0.4)</td><td>COG</td><td>1.876</td><td>0.136</td><td>0.035</td></tr><tr><td>Adults</td><td>2.06 (0.5)</td><td>2.13 (0.6)</td><td>2.13 (0.5)</td><td>2.14 (0.5)</td><td>Age group x COG</td><td>0.555</td><td>0.766</td><td>0.021</td></tr><tr><td rowspan="3">Trunk HR AP</td><td>7-9 years</td><td>1.63 (0.4)</td><td>1.72 (0.3)</td><td>1.7 (0.3)</td><td>1.67 (0.3)</td><td><strong>Age group</strong></td><td><strong>4.769</strong></td><td><strong>0.013</strong></td><td><strong>0.155</strong></td></tr><tr><td>10-12 years</td><td>1.59 (0.3)</td><td>1.71 (0.3)</td><td>1.71 (0.3)</td><td>1.74 (0.2)</td><td>COG</td><td>1.958</td><td>0.123</td><td>0.036</td></tr><tr><td>Adults</td><td>2.07 (0.6)</td><td>2.05 (0.6)</td><td>2.05 (0.6)</td><td>2.04 (0.6)</td><td>Age group x COG</td><td>1.704</td><td>0.123</td><td>0.061</td></tr><tr><td rowspan="3">Pelvis HR V</td><td>7-9 years</td><td>1.97 (0.4)</td><td>2.02 (0.4)</td><td>2.06 (0.4)</td><td>2.09 (0.4)</td><td><strong>Age group</strong></td><td><strong>6.23</strong></td><td><strong>0.004</strong></td><td><strong>0.193</strong></td></tr><tr><td>10-12 years</td><td>1.94 (0.3)</td><td>2.08 (0.3)</td><td>2.11 (0.3)</td><td>2.15 (0.3)</td><td>COG</td><td>11.04</td><td>&lt;.001</td><td>0.175</td></tr><tr><td>Adults</td><td>2.51 (0.7)</td><td>2.55 (0.7)</td><td>2.63 (0.7)</td><td>2.65 (0.8)</td><td>Age group x COG</td><td>0.550</td><td>0.769</td><td>0.021</td></tr><tr><td rowspan="3">Pelvis HR ML</td><td>7-9 years</td><td>1.63 (0.2)</td><td>1.77 (0.3)</td><td>1.74 (0.2)</td><td>1.77 (0.2)</td><td>Age group</td><td>0.115</td><td>0.892</td><td>0.004</td></tr><tr><td>10-12 years</td><td>1.60 (0.3)</td><td>1.72 (0.3)</td><td>1.72 (0.3)</td><td>1.74 (0.3)</td><td>COG</td><td>8.791</td><td>&lt;.001</td><td>0.145</td></tr><tr><td>Adults</td><td>1.68 (0.3)</td><td>1.67 (0.3)</td><td>1.68 (0.3)</td><td>1.70 (0.4)</td><td>Age group x COG</td><td>2.361</td><td>0.003</td><td>0.083</td></tr><tr><td rowspan="3">Pelvis HR AP</td><td>7-9 years</td><td>2.28 (0.5)</td><td>2.37 (0.4)</td><td>2.24 (0.3)</td><td>2.29 (0.3)</td><td><strong>Age group</strong></td><td><strong>22.77</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.467</strong></td></tr><tr><td>10-12 years</td><td>2.32 (0.4)</td><td>2.36 (0.4)</td><td>2.39 (0.5)</td><td>2.47 (0.5)</td><td>COG</td><td>0.120</td><td>0.947</td><td>0.002</td></tr><tr><td>Adults</td><td>3.27 (0.5)</td><td>3.17 (0.6)</td><td>3.24 (0.6)</td><td>3.18 (0.6)</td><td>Age group x COG</td><td>1.94</td><td>0.078</td><td>0.069</td></tr></tbody></table>
7-9-year-olds made ~ 20.5 % errors compared to ~ 15 % for 10-12-year-olds and ~ 7 % for adults, confirming a trajectory of reduced interference with age.

The marginal interaction between age and cognitive difficulty sug-
gests a possible trend that children may experience greater challenges as
task complexity increases. While adults maintained relatively stable
performance across the first two levels, younger children showed a
decline in accuracy beginning at level 2, reflecting early thresholds of
CMI. This pattern is consistent with findings from Schott and Klotzbier
(2018), who identified potential thresholds where increasing cognitive
or motor demands can exceed attentional capacity, resulting in perfor-
mance decrements. However, given the marginal nature of the interac-
tion, these observations should be interpreted cautiously.

Motor Performance: Variability, stability and trade-offs

Spatiotemporal parameters such as cadence, stride length and stride
duration improved with age, reflecting biomechanical maturation. A
closer analysis of the results for children aged 7–9 and 10–12 years re-
veals that their locomotor parameters are quite similar and continue to
improve into adulthood. This finding partially supports the idea that
locomotor abilities develop progressively throughout childhood and are
not fully mature by age 12 (Chauvel, 2017; Sutherland, 1980; Hausdorff,
1999). However, the absence of significant differences in locomotor
parameters between the 7–9 and 10–12 age groups may be partly
explained by the use of a treadmill. Previous studies have indicated that
treadmill walking can reduce the variability of spatiotemporal param-
eters by promoting a more uniform stride, which may mask potential
differences between groups (Hollman, 2016).

Cognitive difficulty selectively impacted normalized stride length, CV of the normalized stride length and stride duration. However, contrary to our initial hypothesis and our recent article (Fauvel et al., 2025), these findings do not provide conclusive evidence that children systematically prioritized motor efficiency over cognitive accuracy. However, overall walking variability and normalized stride length decreased with increased cognitive difficulty, suggesting an improvement and probably a prioritization of walking under cognitive-motor DT. Without explicit instructions to prioritize either task in our protocol, the observed locomotor evolution is likely at least partly attributable to the constraints imposed by the treadmill's mechanical operation.

As already observed in adults (Fauvel et al., 2024), this study
demonstrated enhanced gait stability under dual-task conditions, as
indicated by increased harmonic ratios at level 3, even in younger
children. Notably, the only significant interaction between age and
cognitive task difficulty was found for the pelvic harmonic ratio along
the mediolateral axis, a metric closely linked to balance control. These
finding highlights that under DT condition, children show more regular
and stable mediolateral weight transfer at the pelvis, reflecting devel-
opmental adaptations in postural control mechanisms critical for
maintaining lateral balance. Such mediolateral control is especially
important during locomotion, as broader and more stable bases of
support help prevent falls during complex tasks. The fact that children
increased the regularity of mediolateral weight transfer potentially re-
flects adaptive balance strategies rather than more globally “adult-like”
motor control.

Improvements were also observed in harmonic ratios along the
vertical axis at both pelvis and trunk levels in all populations, aligning
with previous work (Fauvel et al., 2025). However, despite these trends,
other gait stability outcome measures did not exhibit significant in-
teractions between age and cognitive load. This might result from the
relatively subtle nature of their modulation, the different developmental
trajectories of these parameters, or the limitations of our gait analysis

system (S-Move), which lacks direct and precise step width measure-
ment and relies on mediolateral oscillations as a proxy (Hurt, 2010).
Given that step width influences mediolateral movement and base of
support (Iosa, 2013), future studies incorporating other direct gait sta-
bility and variability measurements (e.g., Lyaponov coefficient, en-
tropy) may better elucidate these aspects (Bisi et al., 2014).

Developmental Dynamics of Cognitive-Motor Interference

Consistent with previous studies, children aged 7–12 displayed
higher error rates, greater gait variability, and lower stability compared
to adults. Younger children (7–9 years) appear less capable of distrib-
uting limited attentional resources across tasks, which leads them to
stabilize gait under cognitive loads at the expense of accuracy. This re-
flects motor prioritization strategies in childhood, though not neces-
sarily a generalized resource reallocation across all gait parameters.
Older children (10–12 years) showed reduced error rates relative to
younger children, suggesting gradual improvement in attentional allo-
cation. However, the absence of clear age × motor or cognitive difficulty
interactions and the persistence of reduced stability in harmonic ratios
indicate that their resource allocation cannot yet be described as fully
“adult-like”. Neurophysiological findings suggest that executive func-
tion maturation contributes to these developmental gains, but within the
current dataset, the conclusions must remain limited (Fauvel et al.,
2025).

Moreover, contrary to the hypotheses proposed in the previous study (Fauvel et al., 2025), which included only the first two levels of cognitive difficulty, the inclusion of a third level did not reveal a U-shaped developmental trajectory. This result can be primarily attributed to the use of the treadmill. Indeed, because the treadmill imposes a constant mechanical pacing, gait may have been implicitly prioritized, as children were unable to slow down, possibly out of fear of falling. Although treadmill walking does not represent an ecologically valid form of locomotion, our results still offer valuable insights into attentional resource management strategies under constrained environmental conditions, such as those commonly encountered in real-world contexts (e. g., walking on a narrow sidewalk or through a crowd, etc.). This points to an important nuance: developmental evolution of CMI may depend on ecological context, with constrained environments amplifying motor prioritization in children.

These results highlight the complexity of assessing cognitive-motor
system development and defining its maturity based on behavioral pa-
rameters and age. Some measures, such as cognitive performance and
walking cadence, show steady progress over time, whereas others, like
walking stability coefficients, tend to reach plateau phases. Yet, even
during these plateaus, our findings indicate that children refine their
efficiency by improving how they allocate attentional resources. Thus,
cognitive-motor development cannot be fully understood by examining
parameters in isolation, but rather as the result of a finely adjusted
interplay between multiple factors adapting to task demands and
available resources. Thus, when parameters are viewed in isolation,
cognitive-motor development appears highly variable across ages and
individuals. In contrast, when cognitive-motor and attentional processes
are considered as a unified and interdependent system, their develop-
ment seems to follow a more continuous trajectory without major
interruptions.

Theoretical and practical implications

The findings support a dynamic rather than fixed model of CMI.
Reductions in gait variability and increases in stability under higher
loads likely reflect adaptive resource allocation, but these adaptations
differ by age. Younger children appear to stabilize gait when challenged, whereas adults manage to sustain both accuracy and stability. Therefore, this study shows that examining parameters independently may obscure the broader picture, as cognitive-motor development reflects a dynamic and finely regulated balance among interacting processes that adjust according to both task demands and individual resources. From this perspective, our methodological approach – combining seated and walking dual-task – gains particular relevance, as it conceptualizes each experimental condition as an integrated component of a broader system rather than as a mere contrast between single and dual tasks. The developmental insights gained from this study have practical implications for education and rehabilitation. For younger children, interventions aimed at improving motor automatization through progressively challenging DT exercises could enhance their ability to manage CMI effectively. Similarly, educational strategies that minimize cognitive load during physical activities—such as walking or sports—could help optimize performance during critical developmental stages.

## Limitations of the study and perspectives

Several limitations should be noted when interpreting the study's findings. The relatively small sample size may reduce the generalizability of the results, as a larger participant pool could yield stronger statistical outcomes, particularly for complex analytical designs such as those used in our study. Additionally, relying on treadmill-based walking as a substitute for natural ambulation might not fully capture real-world gait dynamics, potentially limiting the ecological validity of the findings. Because treadmill walking can constrain natural gait variability and postural adjustments, it may in turn influence the observed expression of cognitive-motor interference. An avenue for future research involves examining specific executive functions (e.g., inhibition vs. working memory) within DT paradigms to determine how different cognitive processes influence CMI resilience. For instance, tasks requiring inhibitory control may exert distinct effects on gait stability compared to tasks involving serial subtraction or digit recall.

In conclusion, this study contributes to a growing body of literature on CMI during treadmill walking by elucidating how attentional resource allocation evolves with age and task complexity. By integrating behavioral measures with theoretical models of attentional dynamics, it offers a comprehensive framework for understanding how individuals adapt to dual-task challenges across the lifespan. The findings of this study underscore the dynamic nature of attentional capacity and its development during childhood. By demonstrating that children adaptively allocate cognitive and motor resources based on task demands and developmental stage, the results challenge traditional fixed resource-sharing models. Children tend to prioritize motor stability under cognitive load, reflecting immature executive functions and motor automatization, while adults exhibit more balanced resource distribution, indicative of neural maturation and greater efficiency in attentional resource management. These insights have significant practical implications, suggesting that targeted interventions and educational strategies can enhance motor automatization and cognitive-motor integration during critical developmental periods. Ultimately, understanding the evolving interplay between cognition and motor control can inform approaches to optimize learning, rehabilitation, and everyday functioning across the lifespan.

## CRediT authorship contribution statement

**Delphine Fauvel:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Estelle Palluel:** Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

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