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# Attentional management of cognitive-motor interference in adults during walking: insights from an EEG study

Delphine Fauvel, Olivier Daniel, Lucas Struber, Estelle Palluel

► To cite this version:

Delphine Fauvel, Olivier Daniel, Lucas Struber, Estelle Palluel. Attentional management of cognitive-motor interference in adults during walking: insights from an EEG study. Neuroscience, 2024, 561, pp.144-156. 10.1016/j.neuroscience.2024.09.048. hal-04808937

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# Attentional management of cognitive-motor interference in adults during walking: insights from an EEG study

Delphine Fauvel¹, Olivier Daniel¹, Lucas Struber¹, Estelle Palluel¹

¹ Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000 Grenoble, France.

## Correspondence to:

Dr. Estelle Palluel

Laboratoire TIMC, BiomecaMot Team, UMR CNRS 5525, La Tronche, France.

Email: estelle.palluel@univ-grenoble-alpes.fr

For the purpose of Open Access, a CC-BY public copyright licence has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.
30

31 Dual-task paradigms, which involve performing cognitive and motor tasks simultaneously, are
32 commonly used to study how attentional resources are allocated and managed under varying task
demands. This study aimed to investigate cognitive-motor interferences (CMI) under different levels of
33 cognitive and motor task difficulty without instruction on task prioritization. 17 healthy young adults
34 performed an auditory oddball task with increasing cognitive and motor (walking vs. sitting) difficulty.
35 Cognitive and motor performances, along with P3 (P3a and P3b) brainwave components, were analysed.
36 Increasing cognitive difficulty resulted in more errors and increased P3a amplitude, reflecting enhanced
37 attentional demand, while P3b remained unaffected. This suggests a threshold effect on attentional
38 resources. Motor complexity lengthened P3a and P3b latencies without affecting amplitude, indicating
39 delayed attentional resource recruitment. Additionally, walking with the most difficult cognitive task
40 increased cognitive error, suggesting attentional resource limits. With increased motor and cognitive
41 complexity, CMI emerged, leading to cognitive error increase and improved gait stability without
42 amplitude changes in P3a and P3b. Two hypotheses were proposed: motor prioritization and motor
43 facilitation. Our study suggests managing attentional resources to balance cognitive and motor tasks
44 rather than linearly increasing task complexity. Viewing dual tasks as a new, integrated task is proposed,
45 supported by previous neural network integration studies. Thus, understanding how the brain organizes
46 tasks in response to constraints is crucial for comprehending complex task execution.
47

48

50 Cognitive Motor Interference; Gait; EEG; Evoked potentials; P3 component

51
## 52 Introduction

Automatic gait control is defined as the ability of the nervous system to successfully walk with minimal use of executive and attentional resources (Clark, 2015). Previous findings investigated the attentional cost of walking by using a dual task (DT) paradigm. The concept of shared attentional capacity was introduced by Kahneman in 1973 in his work "Attention and Effort" (Kahneman, 1973). He proposed that humans have a unique and limited attentional resource that can be allocated to multiple tasks simultaneously. This allocation of attention is highly flexible depending on the tasks at hand. If the required attentional resources are less than the available attention, task processing occurs efficiently both in single-task and dual-task conditions. However, if attentional demands exceed availability, there will be suboptimal processing resulting in performance decline as the total attentional capacity is exceeded (i.e., increased reaction time, increased errors) in one or more of the executed tasks. Recently, other studies reported that people have limited cognitive capacity (attentional capacity theory; Kahya et al., 2019). Consequently, a recent study confirmed that the execution of two simultaneous tasks, requiring the same cognitive resources, leads to a decrease in efficiency on one or both (dual task effect) causing a cognitive fatigue with increasing in the complexity of one or both tasks (Arpaia et al., 2024). This phenomenon is known as cognitive-motor interference, cognitive-cognitive interference, or motor-motor interference depending on whether the tasks involve cognitive and/or motor components. In this study, we focused on cognitive-motor interferences (CMI), which are directly related to the characteristics of the individual, the environment, and the tasks at hand (Huang & Mercer, 2001; Ruffieux et al., 2015). Thus, an easy or familiar task will mobilize fewer attentional resources than a complex or novel one. The individual's intentions or priorities also impact the amount of attention dedicated to the task. The higher the task's prioritization, the greater the attention allocated to it. However, an increasing number of recent studies refrain from giving instructions regarding primary and secondary tasks (Carcreff et al., 2019; Jansen et al., 2016; Schott & Klotzbier, 2018) to account for the attention management strategies implemented by the individual concerning their attentional resources and goals.

It is also important to address the issue of defining concepts such as single task, complex single task, dual task, or "more than dual task." To accurately and precisely define the concept of dual task, it is necessary to consider the definition of what constitutes a task. For example, the task of walking while holding a glass without spilling water can be considered either a single complex task or a dual motor task involving the combined maintenance of locomotor performance and effectively holding a glass of water (McIsaac et al., 2015). These authors proposed defining a dual task when each single and isolated task was evaluated with different measures, and each task could be attributed a specific goal. According to this definition, the previous example would thus correspond to a dual task, with performances in each task being evaluated based on walking parameters and the amount of remaining water in the glass, and the goals assigned to each task respectively: moving and stabilizing a glass of water. In this sense, many
studies have considered a cognitive task to be performed as a single task when executed in a seated position (Beurskens et al., 2016; Lajoie et al., 1993; Luder et al., 2018; Patel et al., 2014). However, just like walking, sitting can be done alone (doing nothing), separate from any cognitive task execution that could otherwise be performed lying down. Methodologically, we will consider in this study that a cognitive task performed in a seated position can be assimilated to a dual task and less than when performed while walking.

In the model of shared attentional capacity, the serial processing of information explained by the bottleneck model would be one adaptation strategy among others (Miller et al., 2009). This idea of selecting a strategy is consistent with the integrated task prioritization model proposed by Yogev-Seligman et al. (2012) or the U-shaped model (Huxhold et al., 2006). According to Plummer et al. (2013), there are nine possible scenarios of CMI, with some interferences being more likely to occur than others. In cases where interference is present, there may be deterioration and/or improvement in the performance of one or both tasks. Numerous studies have demonstrated this CMI on locomotor tasks at all ages (Al-Yahya et al., 2011; Chauvel et al., 2017; Hocking et al., 2020; Palluel et al., 2019) and in numerous pathologies (Carcreff et al., 2019; Palluel et al., 2019; Plummer et al., 2013). This interference is generally calculated using an index of the effect of dual tasking (DTE; Plummer & Eskes, 2015). Conventionally, a negative DTE implies performance deterioration in dual-task conditions, whereas a positive sign indicates improvement.

From a neurophysiological point of view, attention relies on the activation of numerous brain structures (Corbetta et al., 2008; Dosenbach et al., 2008; Petersen & Posner, 2012; Raz, 2004). Automatic or controlled allocation of attention particularly relies on frontal and parietal regions functioning in networks (Dosenbach et al., 2008). This allocation of attentional resources can occur through two fronto-parietal systems: the ventral fronto-parietal network (VFP) and the dorsal fronto-parietal network (DFP). The VFP connects the ventrolateral prefrontal cortex, the inferior frontal gyrus, and the medial frontal gyrus to the temporoparietal junction (TPJ) and is activated when relevant objects are presented, particularly during infrequent (target) or unexpected (distractor) events (Corbetta & Shulman, 2002; Vossel et al., 2014). This network is notably associated with the vigilance function of attention. It is primarily associated with a stimulus-driven control of attention to engage attentional resources more automatically. Therefore, the VFP network is described as playing a key role in the involuntary allocation of attention when a salient stimulus appears (distractor or target). The DFP network connects the dorsolateral prefrontal cortex to the superior parietal cortex via the superior and inferior parietal lobules as well as the intraparietal sulci. The DFP network plays a major role in maintaining goal-relevant information in memory and intervenes when the task requires the allocation of attentional resources (Fagioli & Macaluso, 2016; Santangelo, 2018). A meta-analysis conducted by Kim et al. (2014) on the results of 75 studies notably highlighted the involvement of these networks in
126 cognitive tasks involving visual or auditory oddballs. The VFP network appears strongly associated with
127 oddball paradigm effects due to its involvement in salient element detection. Specifically, the
128 involvement of the parietal component of the VFP network (i.e. temporoparietal junction) underscores
129 the need for a stimulus-oriented "alert" system (stimulus-driven). Conversely, according to this meta-
130 analysis, the DFP network appears to be less associated with oddball task effects except for its anterior
131 component (i.e. inferior frontal junction). Activation of the DFP network thus supports the involvement
132 of top-down central control in detecting target stimuli not specific to an oddball task but more generally
133 to tasks requiring a response to a specific goal. The networks specifically underlying the management
134 of attentional resources in cognitive-motor dual tasks remain unclear to date. However, the study by
135 Cole et al. (2013) demonstrated the importance of the fronto-parietal network in motor control
136 adaptation and flexibility across different tasks through its connectivity with all brain networks. Some
137 studies showed that the fronto-parietal network is involved in attentional management of cognitive-
138 motor dual tasks (Bayot et al., 2018; Leone et al., 2017; Wu et al., 2013).
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140

140 In the current EEG study, event-related potentials (ERP) were used to explore the evolution of
141 brain markers during an oddball task while walking or sitting. The P3 component under investigation in
142 this study is a characteristic component of the ERP produced by this paradigm. It reflects the level of
143 cognitive and attentional resources involved in stimulus processing (Key et al., 2005; Polich, 2007). The
144 characteristics of P3 (i.e. latency, amplitude, topography) vary depending on experimental conditions
145 such as task complexity, stimulus interval, probability of stimulus occurrence, and its physical
146 characteristics (Polich, 2004). P3 latency is correlated with the speed of cognitive processes involved in
147 information processing and increases with task complexity or during dual-task situations (Cortney
148 Bradford et al., 2019; De Sanctis et al., 2014; Segalowitz et al., 2001). P3 amplitude serves as an
149 indicator of the number of neurons activated during information processing (Corbetta et al., 2008). An
150 increase in amplitude suggests a greater recruitment of attentional resources allocated to task execution.
151 However, the relationship between P3 amplitude and task complexity remains unclear due to
152 heterogeneous results across protocols. Nonetheless, in certain situations, especially in tasks requiring
153 resource sharing between two tasks, P3 amplitude may be inversely correlated with attentional resources
154 allocated to information processing due to the need to divide attention (Polich, 2007).
155

156

156 During an oddball paradigm, and in response to a target stimulus, the P3 wave is often divided
157 into two components: the anterior, fronto-central P3a, and the posterior, parietal P3b, based on the
158 location of their generators and associated cognitive processes (Polich, 2007; Wronka et al., 2012). The
159 anterior component, P3a, is generated in the fronto-central region earlier in response to initial,
160 involuntary attention allocation to unexpected, infrequent, or non-target events. It indicates the amount
161 of attentional resources involved in rapidly detecting stimulus congruence or incongruence. The later
162 component, P3b, is generated in the centro-parietal region following the appearance of infrequent but
expected target stimuli. Its precise role is still debated, but it appears to be involved in the voluntary mobilization of attentional resources (Blundon & Ward, 2019; Corbetta et al., 2008; Polich, 2007). Both P3a and P3b are primarily thought to originate from parietal and frontal regions, reflecting automatic and voluntary attention networks, respectively.

The objective of this study was to understand the evolution of CMI during walking with changing attentional demands and to determine cerebral markers of this interference. An auditory oddball cognitive task, varying in three levels of difficulty, was performed under two levels of motor difficulty (sitting and walking). The gait parameters were evaluated using Inertial Measurements Unit (IMU). Regarding the assessment of gait and motor tasks the 3D gait analysis is considered the gold standard for the evaluation of spatial temporal and whole-body kinematics parameters (De Blasiis et al., 2022). Moreover, the IMU are also used to evaluate spatial-temporal, and kinematic parameters because these technologies are reliable, extremely portable, and affordable according to a previous study (Watkins et al., 2020).

The hypotheses were: 1) cognitive performance should deteriorate with increasing cognitive and motor difficulty, 2) motor performance may improve in dual-task situations due to external attentional focus from cognitive tasks, and 3) these changes in cognitive and motor performance should be reflected in alterations in the latency and amplitude of P3a and P3b components. The latency and amplitude of P3a should primarily increase with cognitive task difficulty. Additionally, an increase in P3b latency should be observed with increasing motor complexity (De Sanctis et al., 2014; Maidan et al., 2019).

## Materials and Methods

### Population

This study was conducted on 17 healthy young adults (11 males and 6 females; mean age: 26 years ± 4 years; see Table 1). Participation was voluntary and uncompensated. This study was approved by the Ethics Committee (IRB00012476-2020-13-05-59).

<table><tr><td>N</td><td>17</td></tr><tr><td>Age (years)</td><td>26.4 (3.7)</td></tr><tr><td>Height (cm)</td><td>171.2 (10.3)</td></tr><tr><td>Weight (kg)</td><td>36.5 (5.5)</td></tr><tr><td>BMI</td><td>18.7 (1.9)</td></tr></table>

Mean (standard deviation). BMI: Body Mass Index

<caption>Table 1: Characteristics of the population</caption>
192 *Task and procedures*

193 Auditive oddball cognitive tasks

194 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. However, 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).

203 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
204 announced to the participant at the beginning of each session so they could count only the target stimuli.
205 Participants were asked to count them silently. The number of stimuli counted by the participant for
206 each sound (1, 2, or 3 sounds) was registered by the experimenter at the end of each session. These three
207 levels of increasing difficulty, 1, 2, and 3, are represented in Figure 1.
208
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209
<figure><img src="image_3.png" /><figcaption>Figure 1: Sequence of the auditory oddball paradigm for the level 1 (easy), level 2 (intermediate) and level 3 (difficult) cognitive task.</figcaption></figure>

Motor tasks
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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 (WM) was performed at preferred walking speed on a treadmill (Figure 2). 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 minutes 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.

<figure><img src="image_4.png" /><figcaption>Figure 2: setup for the walking task.</figcaption></figure>

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## Experimental conditions

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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 (Table 3).

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## Data acquisition and analysis

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The E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA) was used to design the script for the auditory oddball tasks and to synchronize behavioral and brain data. 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:

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

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 et al., 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. 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).

Electroencephalography (EEG): recording and pre-processing

Continuous EEG was recorded with a sampling frequency of 2048 Hz from 64 active scalp electrodes (BioSemi, Netherlands) arranged in accordance with the 10–20 system and referenced to the common mode sense-driven right leg ground (CMS-DRL). The wet electrodes included conventional midline sites Fz, Cz, Pz, Oz and sites over the left and right hemispheres. Electrooculogram was recorded to control for artifacts related to eye movements, using a bipolar montage with electrodes positioned on the left and right side of the eyes (for horizontal eye movements), as well as above and below the right eye (for vertical eye movements). Participants were instructed to avoid moving their eyes. A metallic arm also enabled the projection of cables above the participant's head to mitigate non-cerebral signals generated by walking. This system also had the advantage of sparing participants from wearing the EEG device directly on them to avoid affecting their gait (Figure 2). The open-source MNE (Version 0.22.dev 0) and Spyder (Python version 3.6) were used to process and analyse all EEG data.

During the pre-processing, noisy electrodes were removed using visual analysis of the temporal evolution and power spectral density (PSD) of each electrode. The PSD corresponds to the frequency
distribution of the power of the EEG signal. It is obtained using a Fast Fourier Transform (FFT). It allows for the identification and removal of electrodes with outlier values when their frequency distribution differs from the rest of the electrodes. This phenomenon is particularly noticeable in the low frequencies (0 to 10 Hz). Two finite impulse response (FIR) filters of the Firwin type were then applied to select frequencies of interest: a high-pass filter at 1 Hz and a low-pass filter at 80 Hz. It aims to reduce the presence of amplitude drift (slow drift) and DC offset drift in low frequencies (< 1 Hz). Additionally, this bandpass filter limits muscular artefacts present in high frequencies (> 80 Hz). Furthermore, a stop-band filter at 50 Hz was added to remove noise related to electronic devices (European standards). The data were then resampled to a frequency of 512 Hz. Following this pre-processing step, the temporal evolution of all electrodes was once again manually analysed. This allowed for the removal, when necessary, of electrodes still exhibiting noise as well as signal segments containing characteristic muscular artefacts (such as coughing, jaw movements, neck movements, walking movements, etc.).

Once the signal-to-noise ratio was increased through pre-processing, Independent Component Analysis (ICA) was performed. We used the maximization of the information flow method to extract the first 50 principal components. Each component was characterized by its topography, its temporal evolution across different signal segments, and its distribution (ICLabel; Chaumon et al., 2015; Hipp & Siegel, 2013). Based on these elements, components could be semi-automatically removed. Once the components associated with noise (i.e. eye movements, muscular artefacts, locomotor artefacts) were identified, they were extracted from the EEG signal. On average, 8 components (± 2) were extracted per experimental condition. The EEG signal was then reconstructed from the remaining components.

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## EEG processing and analysis

The signal was segmented into epochs of 1400 ms to analyse the evoked potentials (EPs) elicited by auditory stimuli. These epochs were defined between 200 ms before the onset of the auditory stimulus and 1200 ms post-stimulus for target stimuli. Each segment was visually and semi-automatically inspected (threshold at +25 µV and -25 µV) to reject epochs containing aberrant values. Following this, the epochs were averaged for each electrode, each condition, and each participant to obtain the EPs elicited by target stimuli. A baseline correction was applied to each epoch between -200 ms and 0 ms relative to the onset of the stimulus. A moving average was applied to smooth the signal by eliminating less significant fluctuations. It also enables continuous averaging of different subsets of points to avoid signal time shifting.

The EPs were averaged over the electrodes of the two clusters of interest: the frontal cluster Fz and the parietal cluster Pz. The amplitude of the P3a wave was identified by the maximum positive value within the time interval between 150 and 450 ms on the frontal cluster Fz, including electrodes around electrode Fz (C12, C13, C20, C21, C22, C25, C26). The amplitude of the wave was identified by the maximum positive value within the interval from 250 to 800 ms on the parietal cluster Pz, including
<figure><img src="image_5.png" /></figure>

electrodes around Pz (A4, A5, A18, A19, A20, A31, A32). The latency of P3a and P3b was determined by the delay between the onset of the stimulus (t = 0 s) and the time at which the observed amplitude was maximal.

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

## Statistics

<figure><img src="image_8.png" /></figure>

Statistics were conducted using Statistica 10 software. The significance threshold was set at p < .05. ANOVAs were performed on all gait parameters, cognitive score, and on the latency and amplitude components of the frontal P3a and parietal P3b. 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 (η² partial) index.

<figure><img src="image_9.png" /></figure>

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<figure><img src="image_17.png" /></figure>

For the cognitive score and cerebral indicators, two-way ANOVAs with repeated measures on each of the factors were conducted, with the levels being motor complexity (DTS: sitting, DTW: walking) and cognitive difficulty (level 1, 2, and 3). For gait parameters, one-way ANOVAs with repeated measures on the cognitive difficulty factor associated with walking (W: walking task alone; DTW1: dual-task walking with cognitive task of level 1; DTW2: level 2; DTW3: level 3) were conducted. For cognitive and motor DTEs, one-way ANOVAs with repeated measures on the cognitive difficulty factor (level 1, 2, and 3) were conducted.

<figure><img src="image_18.png" /></figure>

## Results

### Cognitive performances

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

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<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

As predicted, the results show a higher cognitive error when the difficulty of the cognitive and motor tasks increase (Figure 3). The ANOVA revealed a significant interaction between motor complexity and cognitive difficulty ($F_{2,32} = 3.81, p = 0.033, \eta^2_p = 0.192$). Post hoc indicated that participants made more mistakes in DTW3 than DTS3 (p=0.017). The difference was not significant at and between level 1 and 2. We also found a main effect of cognitive difficulty ($F_{2,32} = 20.29, p<0.001, \eta^2_p = 0.559$).
<figure><img src="image_26.png" /><figcaption>Figure 3: Mean and standard deviation of the cognitive error during the Dual Task Sitting (DST) and Walking (DTW) at level 1, 2 and 3. \*p<0.05</figcaption></figure>

Motor performances

The preferred speed of the participants was 3.4 km/h (± 0.5). Analysis of gait parameters revealed moderate effects of the cognitive difficulty on spatiotemporal parameters, variability, and stability. The ANOVA showed a main effect of cognitive difficulty on the stride time ($F_{3,48} = 3.44, p=0.024, \eta^2_p = 0.117$) and the HR$_V$ of the trunk ($F_{3,48} = 4.41, p=0.008, \eta^2_p = 0.216$). The stride time was lower in DTW1, DTW2 and DTW3 as compared to W ($p = .025; p = .014; p = .012$, respectively). The HR$_V$ of the trunk was significantly higher for DTW3 than W and DTW1 ($p = 0.005$ and $p = 0.006$, respectively). Coefficients of variation for the stride time and stride length as well as the HR of the pelvis and the HR$_AP$ and HR$_ML$ of the trunk were not modified with the difficulty of the cognitive task (Table 2).

<table><caption>Table 2: 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.</caption><thead><tr><th rowspan="2">Parameters</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>Cadence (step/min)</td><td>94.5 (7.7)</td><td>96.0 (8.2)</td><td>95.3 (7.6)</td><td>95.2 (6.9)</td><td>1.328</td><td>.278</td><td>.008</td></tr><tr><td>Stride length (m)</td><td>1.17 (0.10)</td><td>1.15 (0.12)</td><td>1.14 (0.09)</td><td>1.16 (0.09)</td><td>2.128</td><td>.109</td><td>.117</td></tr><tr><td>Stride time (s)</td><td><strong>1.30 (0.05)</strong></td><td><strong>1.28 (0.04)</strong></td><td><strong>1.28 (0.11)</strong></td><td><strong>1.28 (0.1)</strong></td><td><strong>3.44</strong></td><td><strong>.024*</strong></td><td><strong>.177</strong></td></tr><tr><td>CV stride length (%)</td><td>3.02 (2.1)</td><td>2.89 (1.3)</td><td>2.68 (0.56)</td><td>2.56 (0.81)</td><td>.954</td><td>.422</td><td>.056</td></tr><tr><td>CV stride time (%)</td><td>3.87 (3.8)</td><td>3.18 (3.15)</td><td>2.92 (2.14)</td><td>2.82 (2.09)</td><td>2.10</td><td>.113</td><td>.116</td></tr><tr><td>Trunk HR V</td><td><strong>2.66 (0.70)</strong></td><td><strong>2.66 (0.67)</strong></td><td><strong>2.75 (0.70)</strong></td><td><strong>2.82 (0.77)</strong></td><td><strong>4.41</strong></td><td><strong>.008*</strong></td><td><strong>.216</strong></td></tr><tr><td>Trunk HR ML</td><td>1.96 (0.53)</td><td>2.0 (0.52)</td><td>2.0 (0.52)</td><td>2.03 (0.56)</td><td>.895</td><td>.45</td><td>.053</td></tr><tr><td>Trunk HR AP</td><td>2.19 (0.65)</td><td>2.17 (0.60)</td><td>2.14 (0.59)</td><td>2.17 (0.68)</td><td>.383</td><td>.765</td><td>.023</td></tr><tr><td>Pelvis HR V</td><td>2.59 (0.70)</td><td>2.53 (0.65)</td><td>2.612 (0.62)</td><td>2.69 (0.76)</td><td>2.227</td><td>.092</td><td>.124</td></tr><tr><td>Pelvis HR ML</td><td>1.71 (0.28)</td><td>1.70 (0.26)</td><td>1.72 (0.27)</td><td>1.74 (0.32)</td><td>.446</td><td>.721</td><td>.027</td></tr><tr><td>Pelvis HR AP</td><td>3.33 (0.51)</td><td>3.26 (0.60)</td><td>3.32 (0.60)</td><td>3.28 (0.26)</td><td>.379</td><td>.769</td><td>.023</td></tr></tbody></table>
356 Dual task effects (DTEs)

357 A comparison of motor and cognitive DTEs revealed no significant differences in motor and
358 cognitive costs between the levels 1, 2 and 3. However, a positive effect of the dual-task walking on
359 motor performances (i.e., motor facilitation) was observed for 10 participants (N = 17) at cognitive
360 difficulty level 2 (Figure 4). For 7 of them, this motor facilitation was associated with cognitive
361 facilitation (i.e., mutual facilitation). Furthermore, the dual-task associated with cognitive difficulty
362 level 3 highlighted a positive effect of dual-task walking on dual-task motor performances in 12
363 participants, accompanied by a positive effect on walking performances (i.e., motor prioritization) for 7
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<figure><img src="image_27.png" /></figure>

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367 Figure 4: Profiles of cognitive-motor interference across motor and cognitive tasks (Dual Task Effect or DTE1 for level 1, DTE2 for level 2 and DTE3 for level 3) for the trunk vertical harmonic ratio HR V. DTE refers to dual task effect. Positive values for DTE indicate that performance improved in dual-task condition relative to single-task performance; negative values for DTE indicate that performance deteriorated in dual-task condition relative to single-task performance

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372 Evoked potentials

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375 The peak amplitudes of the P3a and P3b components in response to target stimuli, generated respectively at the frontal (P3a) and parietal (P3b) levels, are depicted in Figure 5 and Figure 6 as a function of their onset latency. The P3a wave was earlier and with a lower amplitude than the P3b.
<figure><img src="image_28.png" /><figcaption>Figure 5 : Topographies of frontal P3a and parietal P3b waves at the time (s) corresponding to their peak amplitudes (µV) as a function of motor complexity (DTS: sitting, DTW: walking) and cognitive difficulty level (levels 1, 2, and 3) of the dual task.
The onset of target stimuli (t=0 s) corresponds to the dashed line.</figcaption></figure>

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<figure><img src="image_29.png" /><figcaption>Figure 6: Averaging of evoked potentials by target stimuli on electrodes of the frontal cluster Fz (top) and parietal Pz (bottom) as a function of motor complexity (DTS: sitting, DTW: walking) and cognitive difficulty level (levels 1, 2, and 3) of the dual task. The onset of auditory stimuli corresponds to t=0 s (dashed line).</figcaption></figure>

The ANOVA revealed that the difficulty level of the cognitive task modulated the amplitude of the P3a, regardless of motor complexity ($F_{1,16} = 8.065, p<0.001, \eta^2_p = 0.335$). The amplitude of the P3a was significantly lower for cognitive difficulty level 1 compared to cognitive difficulty levels 2 and 3 ($p = .003$ and $p = .001$, respectively). Regarding the latency of the P3a, an effect of motor complexity was observed ($F_{1,16} = 10.53, p=0.005, \eta^2_p = 0.397$). The P3a wave appeared later when cognitive tasks
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were performed in dual-task walking compared to the sitting position, regardless of cognitive difficulty level (p = .005).

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No modification of the amplitude of the P3b was observed. However, the ANOVA indicated an interaction between cognitive and motor difficulty levels on the latency of the P3b ($F_{2,32} = 4.05, p=0.027, \eta^2_p = 0.202$). The latency of the P3b wave was longer when the cognitive task of difficulty level 1 was performed during walking (DTW1) compared to the sitting position (DTS1; $p = .001$). However, no effect was observed for cognitive difficulty levels 2 and 3. A simple effect of motor complexity was also found ($F_{1,16} = 10.63, p=0.005, \eta^2_p = 0.399$). Dual-task walking significantly increased the latency of the P3b compared to the sitting position (p = .027) (Figure 7).

<figure><img src="image_30.png" /></figure>

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Figure 7: Means and standard deviations of latency and amplitude of frontal P3a and P3b as a function of motor complexity (DTS: sitting, DTW: walking) and cognitive difficulty level (level 1, 2, and 3) of the dual task. \*: $p<.05$.

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Discussion

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This study aimed to understand cognitive-motor interferences (CMI) with increase of cognitive and motor task difficulty. An auditory oddball task was performed with three levels of cognitive
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difficulty and two levels of motor complexity. No instruction regarding prioritization of either task was given. Cognitive and motor performances, as well as the two components of the P3 brainwave (P3a and P3b) induced by target stimuli, were analyzed to identify associated cerebral modifications.

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## Cognitive difficulty and attentional demand

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As expected, our study revealed an increase in cognitive error during the auditory oddball task with increasing cognitive task difficulty in the seated dual-task condition. This result is consistent with literature reporting that a sufficiently complex task leads to a degradation of cognitive performance (Al-Yahya et al., 2011; Patel et al., 2014). Consistent with our hypotheses, increasing cognitive difficulty induced an increase in P3a amplitude without modification of P3b amplitude. P3a amplitude notably reflects the amount of resources allocated automatically to the task (Kok, 2001; Muller-Gass et al., 2007; Polich, 2007; Wronka et al., 2012). This P3a is generated in the frontal region in response to a target stimulus and is generally associated with the FPV network (Kim, 2014; Volpe et al., 2007; Wronka et al., 2012), particularly via the anterior cingulate cortex (ACC). The ACC is activated during the detection of sensory conflicts or errors resulting from the difficulty in discriminating a relevant (target) stimulus among irrelevant stimuli. The more posterior P3b has generators located at the parietal level and is associated with a more controlled allocation of attentional resources necessary for the voluntary maintenance of information in memory to respond to the task objective (Kim, 2014; Polich, 2007; Wronka et al., 2012). This P3b might be associated with the DFP attentional network (Justen & Herbert, 2018; Volpe et al., 2007; Wronka et al., 2012).

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The combination of the increase in P3a amplitude observed between the first level of cognitive difficulty (level 1) and those of higher difficulties (levels 2 and 3), with similar cognitive error for the first two levels of cognitive difficulty (levels 1 and 2) and higher for level 3, suggests a form of "decoupling" between behavioral and cerebral responses. Although cognitive error is similar, the increase in P3a amplitude suggests that increased cognitive difficulty nevertheless led to a greater attentional demand to ensure discrimination of relevant stimuli. When cognitive difficulty further increased (level 3), cognitive error also increased, but P3a amplitude remained the same, suggesting a threshold effect on P3a. In other words, with the evolution of cognitive difficulty, as long as the demand for attentional resources remains lower than the resources that can be mobilized for the task, cognitive performance can be maintained through recruitment of additional attentional resources. However, consistent with the Central Capacity Sharing Model (Tombu & Jolicoeur, 2003; Wickens, 2008), when the necessary resources become greater than those available, all of these resources are then mobilized and lead to degradation of cognitive performance. This threshold effect can therefore be explained by 1) reaching the total volume of resources or 2) how these resources are managed. Let's take the example of managing our wallet. During a purchase, we can reach the maximum value of our wallet by spending all of our money or by deciding not to use all of our money to anticipate a future purchase or if the
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interest of that purchase is lower. It is very likely that adults may decide not to allocate all of their
available attentional resources by setting an implicit threshold to maintain a reserve of attentional
resources depending on the task. In any case, there would be a non-linear relationship between the
amount of cognitive error and the amount of attentional resources recruited.

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Furthermore, contrary to our initial hypothesis and in opposition to other works (Kok, 2001; Polich, 2004), increasing cognitive difficulty was not accompanied by an increase in latency of either P3a or P3b. This result suggests that the cognitive difficulty of our task did not modify the recruitment delay of attentional resources involved in the detection and response to target stimuli. In our protocol, the concept of increasing the difficulty of the oddball task by the number of target stimuli to discriminate remains subjective and difficult to quantify. This issue, related to the quantification of cognitive difficulty, is found in most protocols that use, for example, counting down by 2, 3, 4, or 7 (Hoang et al., 2020; Verrel et al., 2009). Nevertheless, taken together, our results validate that increasing the difficulty of an oddball task with the number of stimuli to count (1, 2, or 3) leads to an increase in attentional demand, but without necessarily inducing an increase in the recruitment delay of these resources.

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The oddball task paradigm involves two modes of attention allocation. Based on the regularity of appearance of irrelevant stimuli, the brain would integrate a predictive model of sensory inputs. When a rare stimulus (target) appears that does not match this model, it will be automatically detected as deviant. This first step would involve automatic allocation of attentional resources in a bottom-up manner via the FPV network. Subsequently, processes of responding to target stimuli would require more voluntary and controlled attentional allocation in a top-down manner via the FPD network (Kim, 2014; Naatanen et al., 2007). In our study, the increase in P3a amplitude generated by the FPV network, without modification of that of P3b generated by the FPD network, suggests that increasing the difficulty of the oddball task primarily affected the amount of attentional resources allocated automatically to the detection and discrimination of relevant stimuli rather than to processes of responding to target stimuli. It is reasonable to assume that an additional level of difficulty in the oddball task would ultimately lead to an increase in the latency of P3b.

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*Motor Complexity (Sitting vs Walking) in Dual-Task and Attentional Demand*

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The three levels of cognitive difficulty of the oddball task were performed in both sitting and
walking dual-task conditions. With increasing motor complexity, we observed a lengthening of the
latency of the frontal P3a and parietal P3b appearance following the appearance of target stimuli, without
modification of their amplitude regardless of the level of cognitive difficulty.

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On one hand, the lack of modification in the amplitude of P3a and P3b waves with increasing
motor complexity contradicts numerous studies investigating cognitive-motor dual-task situations in
which walking induced cognitive-motor interferences (i.e., decreased cognitive performance) resulting from the engagement of new attentional resources for locomotion. Moreover, in De Sanctis et al.'s study (2014), a reduction in P3a amplitude was observed when a walking task (primary task) was performed with a go-nogo cognitive task (secondary task) compared to a sitting condition. This reduction was even more pronounced when the complexity of the primary task was increased (normal speed walking vs fast walking). These authors concluded that increasing the complexity of the primary motor task reduced the attentional resources available for the secondary cognitive task. Indeed, most dual-task studies suggest that when the P3 wave is generated by the secondary cognitive task, its amplitude decreases as the primary task becomes more complex, without modifying its latency of appearance. In these studies where the performance of the primary motor task had to be maintained, it seems that, by default, more attentional resources were allocated to the primary task to meet the demands of the experimental instructions. In our study, the absence of modification in the amplitude of P3a and P3b could indicate that walking, compared to sitting, did not require additional attentional resources or, due to the absence of a priori prioritization of one of the two tasks in our protocol, this may have led to a different management strategy of attentional resources. On the other hand, the lengthening of the latency of appearance of the frontal P3a and parietal P3b suggests that the motor complexity of the dual-task resulted in a delay in detection and discrimination processes (P3a) as well as response processes (P3b) to the oddball task. In other words, increasing the motor complexity of the dual-task led to a recruitment delay of attentional resources to allow sufficient allocation to both cognitive and motor tasks, in accordance with Cortney Bradford et al.'s study (2019).

Additionally, with increasing motor complexity, cognitive error was higher when walking was associated with the most difficult cognitive level (level 3) compared to sitting. We have previously seen that increasing cognitive difficulty increases attentional demand and that the attentional resources required for executing this last level of cognitive difficulty become superior to those mobilized by a threshold effect. With increasing motor complexity, our results did not show any modification in cognitive performance for the first two levels of cognitive difficulty (levels 1 and 2). This suggests that the available resources were sufficient to manage the increase in motor complexity of the dual-task. However, when the limit of attentional resources was reached for the execution of the cognitive task, the increase in motor complexity was associated with a degradation of cognitive error. This decrease in dual-task walking performance compared to sitting suggests that walking also requires attentional resources, more important than in sitting. Thus, we hypothesize that the absence of modification in the amplitude of P3a and P3b is not related to the fact that walking does not require additional attentional resources, but rather that considering the absence of instruction regarding task prioritization in our protocol, a different attentional resource management strategy was implemented.

Taken together, these results suggest that attentional management of motor complexity results in a longer recruitment delay of resources to allocate enough resources to all tasks. Thus, without
defining a primary or secondary task, when motor difficulty increases in a dual-task during walking, the
use of additional attentional resources to respond to motor demand would induce a recruitment delay of
those necessary to respond to the cognitive task. The lengthening of the latency of appearance of P3a
and P3b with motor complexity would therefore provide an indication of this attentional resource
management in dual-task.

## 518 Cognitive Complexity Associated with Walking and Attentional Demand

On a treadmill, it is necessary to maintain walking speed no matter what happens. Thus, if
changes in task constraints (increased task complexity, instructions, risk of falling, etc.) push us to adapt
our walking, other motor parameters than speed have to be modified. We observed a reduction in stride
duration when a cognitive task was performed while walking compared to walking alone, regardless of
the complexity of the cognitive task. This reduction in stride duration suggests that individuals modified
their walking pattern to adapt to additional constraints. It is possible that this adaptation of walking
translates into other modifications such as an increase in step width. These results are in agreement with
what was found in a recent study (Arpaia et al., 2024). However, our walking analysis device did not
allow measurement of this parameter. This reduction in stride duration would not be a unique element
highlighting an adaptation of the locomotor pattern to situational constraints but rather an indicator
among others. Some authors suggest that a shorter stride duration is less demanding in terms of
locomotor control because the projection of the center of gravity remains outside the base of support for
a shorter duration. However, other authors have interpreted this motor adaptation as an illustration of
increased executive locomotor control. Analysis of spatiotemporal parameters alone is hardly conclusive
about the degradation or improvement of walking. Indeed, these parameters provide information about
the overall organization of walking but do not highlight more subtle modifications of the locomotor
pattern. Therefore, the analysis of spatiotemporal parameters is commonly coupled with that of
variability and stability parameters of walking. Taken together, these parameters allow a better
understanding of the organization of locomotion.

Many studies have focused on the variability of spatiotemporal parameters of walking in dual-task situations. This variability would reflect the amount of attentional resources allocated to locomotor control. A decrease in variability would indicate a reduction in the involvement of attentional resources needed for locomotor control and would allow the performance of another task simultaneously without a decrease in locomotor performance. Our results showed that when a cognitive task was associated with walking, regardless of its complexity, it did not lead to a modification of the variability of stride length or duration compared to walking alone. These results contradict the conclusions of the meta-analysis conducted by Al-Yahya et al. (2011), which reports an increase in variability in most studies with the addition of a cognitive task. However, all studies in this meta-analysis focused on the analysis of natural walking and not on a treadmill. Indeed, some studies investigating walking on a treadmill have also
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reported no modifications in the variability of walking in dual-task situations. Thus, the absence of
modification in the variability of walking in our study could be explained by the fact that the mechanical
training of the treadmill, and thus the constant support of walking it imposes, could lead to a reduction
in inter-condition variability.

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When examining gait stability through harmonic ratios (HR), our findings contribute to a better understanding of the evolution of locomotor control in relation to the cognitive demands associated with walking. HR are derived measures from acceleration data and reflect the stability of walking through the repetition of locomotor patterns during strides. Variations in trunk and pelvis accelerations along the vertical and antero-posterior axes particularly reflect the regularity of accelerations/decelerations of these body segments prior to foot contact and during the propulsion phase (Menz et al., 2003). Our results showed an increase in trunk HR along the vertical axis when walking was performed with the most challenging cognitive task. This stabilization of the trunk plays a key role in the subject's dynamic balance (Assaïante & Amblard, 1995; Bae et al., 2013). Therefore, our participants exhibited a more stable gait, i.e., trunk stabilization, with increasing cognitive complexity (Bellanca et al., 2013; Lowry et al., 2012). Consistent with our hypothesis, a sufficiently complex dual-task can thus lead to an improvement in walking performance in adults. These recent findings underscore the importance of integrating measures that identify more subtle motor changes. More generally, this suggests that locomotion should be considered in a multi-parametric manner given the wide range of adaptation dimensions and, consequently, the study of walking. Thus, as the complexity of the cognitive task associated with walking increases, motor performance may improve compared to walking performed with a less challenging cognitive task or walking alone. However, it is reasonable to assume that an additional level of difficulty in the oddball task would have ultimately led to a degradation of motor performance, returning to the same level as walking alone, or possibly worsening further, in accordance with predictions from the U-shaped non-linear interaction model (Lacour et al., 2008). Therefore, it seems that there is a U-shaped non-linear relationship in the management of attentional resources required for motor stability depending on the difficulty of the associated cognitive task.

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Cognitive-Motor Interference (CMI) and Attention Resource Management

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Our results showed that when walking was associated with a task of low cognitive difficulty,
dual-tasking did not induce a CMI. However, the increase in the amplitude of P3a in response to
increasing cognitive complexity and the lengthening of the onset of P3a and P3b with motor complexity
suggest a management of available attention resources to maintain identical cognitive and motor
performance. Furthermore, we demonstrated the emergence of CMI with increasing motor (walking)
and cognitive (level 3) complexity of the dual task, resulting in increased cognitive error associated with
improved gait stability without the need for additional attentional resources because the amplitude of
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P3a and P3b remained similar. Two explanatory hypotheses can be considered to interpret these results: 1) a motor prioritization due to dual-task constraints (integrated model of task prioritization, Yogev-Seligmann 2012) or 2) a motor facilitation induced by external attention focus on the cognitive task as proposed by McNevin et al. (2002) and Wulf et al. (2004).

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According to the first hypothesis, motor prioritization was accompanied by degradation of cognitive performance (Plummer et al., 2013). Motor task prioritization occurred when walking was associated with the most complex cognitive task, for which attentional demand became high, as suggested by the threshold effect of P3a amplitude. In our study, no instructions were given regarding the prioritization of cognitive or motor tasks in terms of primary or secondary tasks (Huang et al., 2003). The management of dual cognitive-motor tasks could lead to task prioritization when the attentional resources limit of one of the tasks was reached. This could be explained by the fact that walking was performed on a treadmill. Given the motor constraint generated by continuous treadmill training, participants prioritized gait stability to reduce the risk of falling by allocating more attention to gait control compared to the overall demands of this complex dual task (Clark, 2015). This more executive control involved more attentional resources that would thus compete with the cognitive task. Furthermore, motor prioritization could partly explain the lengthening of the recruitment delay of attentional resources indicated by the increase in P3b at the parietal level. Indeed, the posterior parietal cortex plays a role in evaluating the context as well as in adapting locomotor patterns (Gréa 2002; Mutha et al. 2011; Young et al. 2020b). Moreover, this P3b is notably generated by the DFP network which plays a major role in attentional engagement and disengagement processes in a top-down mode (Blundon & Ward, 2019; Wronka et al., 2012). According to this first hypothesis, prioritizing walking could lead to a more significant allocation of resources for evaluating the situation we are in through a more executive attentional management. However, this prioritization strategy does not explain the amplitude changes observed on the P3a component generated by the VFP network, which has a role in the more automatic allocation of attentional resources in a stimulus-driven mode. This leads us to consider the second hypothesis based on external attention focus.

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This second hypothesis suggests that part of the attention could be focused on the cognitive task during dual cognitive-motor tasks and allow for a more automated and spontaneous gait control resulting in improved locomotor performance. This hypothesis of motor facilitation, resulting here in gait stabilization, has also been reported in several studies examining dual-task walking with a dichotic listening or n-back cognitive task (Decker et al., 2016; Lovden et al., 2008; Rochester et al., 2007; Verrel et al., 2009). External attention focus on a cognitive task differs from cognitive prioritization in that it does not involve task hierarchy similar to determining a primary and secondary task. As described earlier, cognitive prioritization would lead to improved performance in the defined "primary" cognitive task (Plummer et al., 2013). This was not the case in our study. External attention focus refers to the cognitive task partly diverting attention from the motor task. When walking is performed alone, we can
focus all our attention on how we walk in the form of executive gait control, which can create
interference with automated locomotor adjustments at the spinal level (Clark, 2015). Furthermore, while
automatic control allows for more rapid integration of environmental information via spinal reflex
networks for finer movement adaptation, executive control of locomotion is less suited to managing the
complexity of multiple inter-articular movements (Clark, 2015). Thus, when walking is performed in a
dual cognitive-motor task, the cognitive task would then divert the attention necessary for executive gait
control by demanding more attentional resources. This diversion of attention, by reducing the amount
of attentional resources, would make motor control more automatic, allowing for finer gait pattern
adaptation as suggested by the improvement in harmonic ratios (Hamacher et al., 2015; Hausdorff,
2005). This second hypothesis could partly explain the threshold effect found for the increase in P3a
with the most complex walking dual task.

These two hypotheses differ notably in their mode of allocation of attentional resources: more
controlled according to the first and more automatic according to the second. From this, we propose that
it would be judicious to characterize dual-task performance based on the balance between executive and
automatic control, and not only in terms of performance degradation or improvement (Clark, 2015).
This distinction is particularly significant when considering motor performance. An increase in gait
variability can be considered an indicator of degraded gait performance due to less efficient motor
control or, conversely, improved motor performance due to a high degree of flexibility in the locomotor
system. This difficulty in characterizing gait in terms of performance degradation or improvement may
also arise for spatiotemporal parameters (stride length, width, and duration) or stability parameters.
Indeed, a system that is too stable may be difficult to adapt, while a system that is too unstable may be
very costly in terms of locomotor control.

In conclusion, it appears that the evolution of cognitive and motor complexity is not solely based
on a U-shaped relationship of attentional resources needed for cognitive and motor task execution, but
rather on an adjustment of their management according to the overall complexity of the dual task. We
have shown that with increasing complexity of the dual task, young adults seem to implement
mechanisms to better manage the increased attentional demand. Furthermore, this study highlights the
importance of not giving instructions regarding the prioritization of one of the tasks to better understand
how our system organizes itself in response to task constraints. In other words, a dual task would not
simply correspond to adding a secondary task to a primary task, but should be considered more as a
new, integrated, unique, and inherently complex task different from the simple addition of two tasks.
This view is consistent with the study by Wu et al. (2013), in which the authors showed that when two
different tasks were executed simultaneously, the neural networks of each task were integrated into a
single network. The ability to integrate and adjust these different brain networks seems essential for the
execution of a "higher-order" task (Leone et al., 2017). Thus, we propose to rethink the dual task through
a complex task rather than simply the sum of two elementary tasks.
655 **Limitations of the study**

656 The study faced several limitations that should be considered when interpreting the results.
657 Firstly, the small sample size may limit the generalizability of the findings, as a larger cohort could
658 provide more robust data. The study also did not utilize wireless EEG, which could have provided more
659 comprehensive data on brain activity during gait. Furthermore, the use of treadmill walking as a proxy
660 for spontaneous walking may not accurately represent natural gait patterns, potentially affecting the
661 study's ecological validity.

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663 **Declarations of interest:** none

664 **Authors' contribution:**

665 D. Fauvel: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software;
666 Validation; Visualization; Writing - original draft.

667 O. Daniel: Formal analysis; Software; Writing - review & editing

668 L. Struber: Software; Writing - review & editing

669 E. Palluel: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration;
670 Supervision; Validation; Visualization; Writing - original draft

671

672 **References**

673 Al-Yahya, E., Dawes, H., Smith, L., Dennis, A., Howells, K., & Cockburn, J. (2011). Cognitive motor interference while walking: a systematic review and meta-analysis [Meta-Analysis Research Support, Non-U.S. Gov't Review]. *Neurosci Biobehav Rev*, *35*(3), 715-728.
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676 https://doi.org/10.1016/j.neubiorev.2010.08.008

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