Distinct developmental trajectories shape human sensitivity to rhythms in the environment

Distinct developmental trajectories shape human sensitivity to rhythms in the environment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Distinct developmental trajectories shape human sensitivity to rhythms in the environment Antoine Guinamard, Nicholas Foster, Sylvain Clément, Valentin Bégel, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7086372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rhythm is an omnipresent feature of our environment. Repetitive temporal patterns in sound and vision influence how we pay attention to the world, move and speak. Grasping these regularities is critical for development. Humans can track surrounding rhythms explicitly —like dancing to the beat of music- or implicitly, when rhythms guide perception and behavior without deliberate attention. Whether these abilities follow different developmental trajectories remains unknown. Here, we tested 98 children aged 7-13 using a novel gamified task measuring implicit rhythm processing, alongside assessments of explicit rhythmic abilities and cognition. For the first time, we show that explicit and implicit rhythmic abilities follow distinct developmental paths: although the former improve with age and musical experience, the latter remains stable. Both are modulated differently by cognitive control, yet are not fully disconnected. These findings offer new theoretical insights into rhythm development, with important implications for neurodevelopmental disorders and rhythm-based rehabilitation. Biological sciences/Neuroscience/Cognitive neuroscience/Perception Biological sciences/Neuroscience/Cognitive neuroscience/Attention Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Scientific community and society/Social sciences/Psychology/Human behaviour Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Rhythmic patterns are a ubiquitous feature of our environment. Temporal regularities occur in music and speech, in bodily movements, and across many everyday activities 1 . From the repetitive pulse of an ambulance siren to the complex temporal structure of language or music, humans display remarkable sensitivity to rhythm. The ability to detect and track such temporal regularities —broadly referred to as rhythmic abilities— is partly rooted in our genetics 2 – 4 . Far beyond musicality, these abilities support the prediction of future events and help optimize perception and action in a constantly changing environment 5 , 6 . In childhood, rhythmic abilities contribute to core developmental domains 7 such as language 8 , social interaction 9 , 10 and literacy 11 . In turn, poor rhythmic abilities have been linked to developmental conditions like dyslexia 12 or ADHD 13 , and are considered potential precursors of broader learning and developmental disorders 14 , 15 . Understanding how rhythmic abilities develop is therefore essential, both for identifying early signs of learning difficulties and informing targeted interventions. However, most developmental studies have focused on explicit rhythm processing, i.e., deliberate engagement with rhythmic patterns, typically involved when we dance or sing along with music. In everyday life, though, rhythm more often shapes perception and action implicitly. Whether reacting to traffic or engaging in conversation, we routinely benefit from temporal regularities without deliberately attending to them. This study addresses a critical gap by examining, for the first time, the joint development of explicit and implicit rhythmic abilities, and their relation to cognitive functions in children. Explicit rhythm processing is typically assessed using tasks that directly involve rhythmic structure, such as beat perception (judging whether a sound aligns with the underlying pulse of music) and beat synchronization (tapping in time with the beat). In contrast, implicit rhythm processing is evaluated through tasks where rhythm is not the focus —such as pitch discrimination— but where temporal regularity can still enhance performance, for example by speeding up reaction times 16 – 18 . The distinction between implicit and explicit processing has been extensively studied in domains such as memory 19 – 21 or single duration processing 22 – 24 , but has received much less attention in rhythm and beat processing research. Emerging evidence in adults suggests that implicit and explicit rhythm processing may rely on distinct mechanisms. In a multiple case-study, three individuals with poor explicit rhythm perception 25 , 26 nonetheless showed faster responses to temporally regular sequences in a pitch detection task 18 . Similarly, implicit rhythm processing appears more resilient to age-related decline than explicit perception 27 . However, to our knowledge, no study has examined this distinction in children, and it remains unknown whether implicit and explicit rhythmic abilities follow separate developmental trajectories or emerge in tandem during childhood. There is compelling evidence that explicit rhythmic abilities, such as beat perception and synchronization, improve steadily throughout childhood and adolescence 28 – 32 . Most individuals, regardless of musical education, eventually acquire the ability to perceive and move to a beat 33 , 34 . Still, substantial individual differences exist 35 , 36 , partly shaped by musical experience 31 , 35 and broader cognitive factors including cognitive control 37 , 38 . In contrast, very little is known about the development of implicit rhythm processing. Implicit processes more broadly are often assumed to emerge early and to be largely mature by childhood 39 – 41 . However, the only developmental study of implicit rhythmic abilities —conducted in the visual modality— reported increasing reaction time benefits from rhythm regularity between ages 5 and 12 42 . Whether similar patterns hold in the auditory domain remains unclear, given that studies in adults suggest that auditory and visual implicit rhythm processing rely on distinct mechanisms and show uncorrelated performance 43 , 44 . A key to understanding how explicit and implicit rhythmic abilities develop may lie in their relationship to executive functions, a set of higher-order cognitive control processes supporting goal-directed behavior, concentration, learning, and self-regulation 45 . These functions develop substantially during school years 45 – 47 , in parallel with improvements in explicit rhythmic abilities. Significant associations have been reported between explicit rhythmic abilities and various executive components, including controlled attention 48 , inhibition 13 , working memory 49 , 50 , and cognitive flexibility 12 . By contrast, implicit rhythmic abilities are thought to rely less on executive functions, operating largely automatically 51 . Evidence from adult studies suggests implicit rhythm processing can occur independently of cognitive control 52 , 53 , but whether this is also true in children remains unknown. Notably, the cognitive predictors of implicit rhythmic abilities remain largely unexplored. To summarize, it remains unclear whether implicit and explicit rhythmic abilities follow shared or distinct developmental trajectories in childhood, and whether they relate similarly to executive functions. Clarifying these relationships is essential for refining theoretical models of rhythm processing and informing interventions targeting rhythm-related difficulties. For this purpose, we examined the developmental trajectory of both implicit and explicit rhythmic abilities and their links to executive functions in school-aged children, a key developmental window for cognitive control and the emergence of learning difficulties. To evaluate implicit rhythm processing, we developed a novel gamified task in which children detected pitch changes following regular or irregular tone sequences to help a small astronaut navigate space. Crucially, neither the task goals nor instructions required attention to rhythmic regularity. Reaction time benefits from regularity indexed implicit rhythm processing. To assess explicit abilities, we used beat perception and synchronization tasks from a standardized rhythm battery (BAASTA) 54 , 55 . Finally, we evaluated executive functions using established tasks of controlled auditory attention, inhibitory control, working memory, and cognitive flexibility 56 – 58 . According to our predictions, explicit rhythmic abilities would be more closely linked to age, musical experience, and executive functions than implicit abilities. In the present within-subject study involving 98 children aged 7 to 13, we demonstrate that explicit rhythmic abilities improve with age and formal musical experience, whereas implicit rhythmic abilities remain remarkably stable, suggesting distinct developmental trajectories. Yet, the two types of rhythm processing are not entirely independent: their relationship is modulated by executive functions. Notably, executive functions predict performance in opposite directions for implicit and explicit tasks. These findings offer a nuanced picture of rhythm processing in childhood, revealing unexpected links between controlled and more automatic forms of rhythm processing. They further suggest that cognitive control influences not only intentional rhythmic behavior, but also the implicit tracking of temporal regularities. Results Children implicitly process the rhythm of auditory events To test whether children implicitly track rhythmic regularities in sound sequences, i.e., even when rhythm is not directly task-relevant, we examined whether they responded faster to auditory targets (detecting a pitch difference) following regular (isochronous) versus irregular (temporally random) tone sequences. Figure 1 a shows reaction times (RTs) per condition. RTs were significantly faster in the regular condition than in the irregular one (RT regular : M = 518.7 ms, SD = 78.1 ms; RT irregular : M = 538.7 ms, SD = 89.0 ms; two-sided Wilcoxon signed rank test: V = 1083, p < .001). The very large effect size (rank biserial correlation, r = − .55) indicates a consistent influence of temporal regularity on children’s performance. To further probe this effect, we examined whether regularity also influenced accuracy in detecting pitch changes. Children were slightly less accurate in the regular condition than in the irregular condition, as reflected by a drop in the sensitivity index ( d′ ; t (97) = -3.32, p = .001) with a very small effect size ( d = -0.16). This difference was primarily driven by an increase in the false alarm rate ( i.e. , responses to non-deviant targets; V = 2883.5, p < .001, r = .47), while the Hit rate ( i.e. , correct responses to pitch changes) did not differ between conditions ( V = 1929, p = .658, r = .47). This suggests that children were more biased toward target detection in regular sequences. To determine whether RT benefits were robust after accounting for individual differences in accuracy, we fitted a linear mixed-effects model including condition, accuracy benefits (Δ d′ = d′ regular - d′ irregular ), and their interaction as predictors (see Statistical analyses , Supplementary Note 1 ). While the model revealed a positive Condition × Δ d′ interaction ( β = 0.0162, F (1,96.5) = 5.49, p = .02) with a small effect size (ω p 2 = 0.05), it confirmed a main effect of condition ( β = -0.0125, F (1,95.0) = 10.29, p = .002) with a moderate effect size (ω p 2 = 0.09). The effect of Δ d′ on RT was not significant ( F (1,96.) = 0.72, p = .40, ω p 2 = 0.00). Thus, RT benefits remained significant when accounting for accuracy, indicating that temporal regularity facilitated processing beyond a mere speed-accuracy tradeoff. We then computed mean RT benefits from regularity (ΔRT = RT irregular - RT regular ) for each participant as the main measure of implicit rhythmic performance for the following analyses. The ΔRT (M = 20.0 ms, SD = 43.1 ms) ranged from − 111ms (i.e slowing of RT) to + 154ms (i.e speeding of RT) reflecting wide interindividual variability in implicit influence of rhythm among children (Fig. 1 b). Children’s performance in explicit rhythmic tasks To assess children’s explicit rhythmic abilities, we measured accuracy in beat perception, i.e., the ability to detect beat misalignments in the Beat Alignment Test (BAT), as well as accuracy and consistency in a series of audio-motor beat synchronization tasks (see Methods ). Descriptive statistics for raw task scores are presented in Supplementary Table 1 , with score distributions in Supplementary Figs. 1–2 , and inter-task correlations in Supplementary Fig. 3 . In the beat perception task, children were generally proficient at identifying beat misalignments, as reflected by a mean sensitivity index (d′) of 1.77 (SD = 1.19). Given that d′ = 0 reflects chance-level performance and values above 2 are typically considered good, this suggests moderately accurate beat perception, albeit with substantial individual variability. For beat synchronization, children were overall able to reliably align their movements to the beat, with a mean consistency score across tasks of 0.79 (SD = 0.30) (note: 0 = poor synchronization; to 1 = perfect synchronization). Accuracy scores further revealed a tendency to anticipate the beat, as shown by a negative mean vector angle (M = -21.9°, SD = 39.4), indicating early taps relative to the beat onset. As expected, beat perception and synchronization performance were strongly related: standardized BAT performance significantly predicted synchronization consistency scores (β = 0.64, p < .001), with a large effect size (ω p 2 = 0.4) accounting for 41% of the variance (R² = 0.41; Fig. 2 a). To capture individual differences in explicit rhythmic ability, we computed the Beat Tracking Index (BTI) combining standardized performance in both BAT and synchronization tasks 13 , 54 . The BTI scores ranged from − 1.86 to 1.60 (M = 0.001, SD = 0.88; Fig. 2 b). Note that is computed from z-scores but it is not itself a z-score; therefore, its mean is not exactly zero and its standard deviation is not exactly one. The BTI was used as the main index of explicit rhythmic abilities Explicit but not implicit abilities grow with age and music To test whether implicit and explicit rhythmic abilities follow distinct developmental trajectories, we examined how they varied with age (Fig. 3 a, 3 b) and formal musical experience (Fig. 3 c, 3 d), using multiple linear regression models with both variables as predictors. Implicit rhythmic performance, indexed by reaction time benefits (ΔRT) showed no significant association with either age ( β = − 0.01, p = .902; ω p 2 = 0.00) or formal musical experience ( β = 0.05, p = .603; ω p 2 = 0.00); R² = .003, F (2, 95) = 0.14, p = .868). We also examined whether these variables predicted accuracy differences between conditions (Δd′), but we found no effect of age ( β = 0.14, p = .177; ω p 2 = 0.01) or musical experience ( β = − 0.10, p = .348; ω p 2 = 0.00 ; R ² = .027; F (2, 95) = 1.32, p = .272). These results suggest that neither age nor musical experience significantly influences implicit rhythmic performance. In contrast, explicit rhythmic abilities (BTI) improved with both age ( β = 0.32, p = 0.001, ω p 2 = 0.13) and formal musical experience ( β = 0.42, p < 0.001; ω p 2 = 0.19), with respectively a medium and large effect size ( R ² = 0.30; F (2, 94) = 19.36, p < 0.001). This pattern held when examining the perceptual and sensorimotor components of the BTI separately, indicating that the positive associations were not driven by the motor demands of the tasks ( Supplementary Fig. 4 ). Different modulation by executive functions We next examined how rhythmic abilities relate to executive functions. To this aim, following the first exploration of the results, we computed the Executive Functioning Index (EFI), a composite score derived from standardized measures of auditory controlled attention, inhibitory control, working memory, and cognitive flexibility (see Methods for computation, and Supplementary Fig. 5 for correlations between the components and the distribution of the EFI). Notably, implicit and explicit rhythmic performance showed opposite associations with the Executive Functioning Index (Fig. 4 ). As expected, explicit rhythmic abilities (BTI) showed a positive association with the Executive Functioning Index (β = 0.22 , p = .033; R² = .04, ω p 2 = 0.04), in line with previous literature. In contrast, and unexpectedly, RT benefits in the implicit task (ΔRT) were negatively associated with EFI ( β = − 0.36 , p < .001; ω p 2 = 0.12; R² = .13) indicating that in children with higher executive functioning, response speed was less influenced by rhythmic regularity when rhythm was not directly relevant to the task, as was the case in our implicit timing paradigm. This negative relation was also found for all the four components of the EFI (see Supplementary Fig. 6 ). To test whether this unexpected link could be the consequence of a speed-accuracy tradeoff, we included accuracy differences in the model and an interaction term, but the EFI remained the only significant predictor of ΔRT ( Supplementary Note 2 ) suggesting that the link between RT benefits and executive functions cannot be merely explained by a speed-accuracy tradeoff effect. To further investigate the observed negative association with EFI, all EFI components were entered into a multiple regression model ( R² = .15; F (4, 93) = 4.18, p = .004), in which auditory controlled attention emerged as the strongest predictor (β = − 0.27 , p = .015) with a medium effect size (ω p 2 = 0.11), while the others did not reach significance (inhibitory control: β = − 0.02 , p = .853; working memory: β = − 0.09 , p = .383; cognitive flexibility: β = − 0.12 , p = .264). These results suggest a potentially more central role of auditory attentional control in the implicit effect of temporal regularities on children’s auditory processing. Following these results, we made a post hoc analysis of late RT outliers excluded during the preprocessing (see Methods ). A slightly higher proportion of late responses was observed in the irregular condition, potentially reflecting increased attentional disengagement under temporal uncertainty (regular: M = 1.25%, SD = 1.54; irregular: M = 1.96%, SD = 1.54; V = 2312.5, p = .002). Link between implicit and explicit rhythmic abilities In order to test the independence of implicit and explicit rhythmic abilities, we examined whether RT benefits from regularity in the implicit task (ΔRT) were related to explicit rhythmic abilities. A simple regression analysis showed that ΔRT was not related to the Beat Tracking Index (BTI) ( β = 0.14, p = .185, ω p 2 = 0.01; R² = 0.02; see scatterplots in Supplementary Fig. 7 ). This result points towards an independence between these abilities. However, a relationship between implicit and explicit rhythmic performance emerged when taking into account executive functioning. We fitted a multiple linear regression model with ΔRT as the outcome variable and BTI, EFI, and their interaction term as predictors ( R formula: ΔRT ~ BTI * EFI). The model was significant, explaining 23% of the variance ( R² = 0.23; F (3, 93) = 9.42, p < .001). Explicit rhythmic abilities positively predicted RT benefits in the implicit task ( β = 0.25, p = .009; ω p 2 = 0.01), and this effect was stronger in children with higher executive functioning, as reflected by a significant interaction ( β = 0.23, p = .013; ω p 2 = 0.05). Meanwhile, executive functioning negatively predicted RT benefits overall ( β = − 0.39, p < .001; ω p 2 = 0.16). To illustrate this interaction and clarify the direction of effects, Fig. 5 presents individual mean RTs for both conditions, rather than ΔRT values. Participants were grouped into three executive functioning levels (low, medium, high) based on the 1/3 and 2/3 quantiles of the EFI. Discussion Understanding how rhythmic abilities develop and shape sensitivity to environmental regularities requires disentangling their implicit and explicit components. In this study, we directly compared implicit and explicit rhythmic performances in approximately 100 children aged 7 to 13 —a critical window for cognitive maturation and learning 45 , 46 . We found that explicit, deliberate rhythm processing improves markedly with age and formal musical experience, whereas implicit rhythm sensitivity remains remarkably stable across development. Yet these distinct developmental trajectories do not reflect complete independence. Instead, we identified a hidden connection between the two processes, modulated by executive functions. These findings offer new insights into the boundaries and potential bridges between implicit and explicit cognition during human development. This study is the first to examine the development of both implicit and explicit rhythm processing, shedding new light on how rhythmic abilities emerge and interact during childhood. Previous developmental studies have primarily focused on explicit, deliberate rhythm processing, which relies on cognitive control to direct attention toward rhythm, maintain an internal beat, and align actions accordingly. However, rhythm often influences behavior in more subtle ways 5 . In everyday contexts, rhythm per se is rarely the focus of attention, yet it can implicitly shape perception, prediction, and response to events 6 , 59 , 60 . Unlike explicit tasks, our novel implicit task did not require participants to attend to rhythm. Instead, children played a gamified task in which they had to detect pitch changes to help a small astronaut navigate through space. Crucially, children were faster to detect pitch targets when these followed temporally regular sequences, revealing implicit sensitivity to rhythm —even when rhythm was not directly relevant to the task. A pivotal finding of this study is the clear dissociation in developmental trajectories. Explicit rhythmic abilities improved with age and formal musical experience, consistent with previous findings 28 , 30 – 32 , whereas implicit rhythm sensitivity remained remarkably stable between ages 7 and 13. This lack of age or training effects, observed in a relatively large sample size for this developmental window, suggests that the ability to benefit from rhythmic regularities is already well established by early childhood. In contrast, the deliberate use of rhythm appears to develop more gradually, shaped by cognitive maturation and musical experience. Our results contrast with prior work reporting age-related increases in visual implicit rhythmic effects 42 . A likely explanation lies in the used modality: humans show greater sensitivity to rhythmic regularity in the auditory modality 43 , and auditory rhythms engage stronger auditory-motor coupling than visual rhythms 61 – 63 . Notably, auditory beat perception begins to emerge in utero and is already observable in premature newborns 64 , 65 . Infants are then exposed to the rhythmic structure of lullabies, nursery rhymes, and speech prosody 66 , 67 , which may accelerate implicit auditory rhythm processing relative to visual rhythms, contributing to the auditory advantage reported in adults 43 . Future research directly comparing auditory and visual modalities using matched paradigms will be needed to test this hypothesis. Nevertheless, our findings echo results from other domains such as memory 19 or auditory single-duration processing 23 , where implicit and explicit systems follow distinct developmental trajectories, with implicit processes appearing more stable across ages. Another key and unexpected finding is that, although implicit and explicit rhythmic abilities follow distinct developmental trajectories -suggesting reliance on partly dissociable mechanisms- a link between them emerges when executive functions are taken into account. Specifically, a positive association was observed in children with higher executive functioning. This finding is non-trivial: it suggests that benefiting from temporal regularities in everyday contexts may still rely on rhythm skills typically assessed through explicit musical tasks, but that this relationship is shaped by cognitive control. Such an executive function-dependent link challenges the notion of a strict dichotomy between implicit and explicit rhythm processing. Instead, it supports a more dynamic framework in which the two systems interact and may draw on shared mechanisms depending on individual cognitive profiles. In adults, neuroimaging studies show that both forms of rhythm processing engage overlapping motor-related structures, including the basal ganglia, cerebellum, and supplementary motor area 68 – 72 . Crucially, implicit rhythm processing also specifically recruits the left inferior parietal cortex -a region essential for temporal orienting of attention 73 . Whether these brain structures are similarly recruited in children, and whether shared or distinct regions are engaged depending on executive functioning, could be investigated in future developmental neuroimaging studies. Perhaps most intriguingly, we found that global executive functioning levels modulate implicit and explicit rhythm abilities in opposite directions. As expected, higher executive functions predicted better performance in explicit rhythm tasks, consistent with previous reports 12 , 13 , 48 , 50 . Yet paradoxically, they were associated with smaller RT benefits in the implicit task. This novel finding suggests that executive functions influence rhythmic processing in fundamentally different ways depending on task demands. When rhythm is incidental -as in our implicit task- stronger executive functioning may reduce reliance on temporal regularities. According to entrainment models 59 , 74 , particularly the Dynamic Attending Theory 75 – 77 , rhythmic temporal regularity facilitates perception by aligning internal neural oscillations with external periodicities 78 – 80 . Although typically considered automatic and bottom-up 51 , 52 , 81 , entrainment can be modulated by top-down control depending on task demand 6 , 82 . One possibility is that children with higher executive functions rely less on spontaneous entrainment or override it in favor of more controlled strategies. Such modulation may be beneficial in everyday environments, where rhythm-based cues are not always informative 83 . In our task, children with high executive functioning may have selectively focused on pitch changes, resisting the influence of the preceding rhythmic sequence. In contrast, children with lower executive functioning may have been more easily entrained, leading to greater RT benefits in the regular condition. Alternatively, auditory rhythmic regularity may exert a less temporally specific effect, broadly supporting attentional control without necessarily involving entrainment mechanisms 84 . This general facilitation could particularly benefit children with weaker global executive functioning, potentially explaining the negative association between RT benefits and EF. Supporting this, auditory controlled attention emerged as the strongest negative predictor of RT benefit among all EF components. Moreover, irregular trials elicited more late outlier responses, suggesting greater attentional disengagement under temporal uncertainty. These findings align with the idea that unpredictability imposes a higher cognitive cost 85 , a possibility that future studies could further investigate. Another contributing factor may be impulsivity, or a strategic shift toward speed over accuracy in children with lower executive functioning. Overall, children made more false alarms after regular sequences —a pattern also observed in adults with autism 86 — suggesting that rhythmic cues may bias responses in individuals with reduced inhibitory control. However, the negative link between EF and RT benefit remained significant after controlling for accuracy, indicating that impulsivity alone does not fully explain this relation. These explanations are not mutually exclusive. The broad variability in RT benefits likely reflects the recruitment of different processes and a dynamic interplay between cognitive control, individual strategies, and sensitivity to temporal regularities. Substantial inter-individual variability has also been reported in adults 44 , 87 – 89 . Our findings extend this perspective developmentally, showing that such variability emerges early in life and may reflect differences in cognitive profiles. Variability is a hallmark of the human sense of rhythm 35 , 87 , 90 . Understanding why individuals do -or do not- rely on rhythmic regularities in their environment remains a compelling avenue for future research 91 . Our findings underscore the need to clarify the mechanisms underlying this variability and highlight the distinct yet complementary role that implicit rhythm processing may play in development and learning 92 , 93 . Promising directions include paradigms that dissociate general attentional benefits from entrainment-specific effects, as well as neurophysiological methods to probe the temporal dynamics of rhythm-related facilitation 87 , 88 , 94 . Longitudinal or cross-sectional comparisons using identical paradigms in children and adults may further elucidate how implicit-explicit interactions evolve beyond adolescence. Critically, extending this work to neurodevelopmental conditions such as ADHD or speech and language disorders known to involve explicit rhythm deficits 12 – 15 , 95 could determine whether implicit rhythm processing is similarly affected, and how such impairments impact everyday functioning. These questions are not only theoretically interesting but hold translational promise: while training explicit rhythmic abilities has demonstrated transfer effects on executive functioning in children 96 – 99 , exploring whether implicit rhythm sensitivity can also be trained -and whether such training might yield broader cognitive benefits- represents an exciting direction for future studies. Developmental research offers valuable insights into the architecture of rhythm processing. By demonstrating that implicit and explicit rhythm processing follow distinct developmental trajectories yet remain functionally linked via cognitive control, our study opens new avenues for clinical and educational applications, contributes to theoretical models of rhythm, and may prompt further reflection on evolutionary and developmental roots of rhythmic cognition. Method Participants A total of 106 French-speaking children aged 7 to 13 participated in the study. They were recruited in Lille (France) and Montréal (Canada). The following non-inclusion criteria were applied: (1) a reported diagnosis of a neurodevelopmental or psychiatric disorder, (2) a history of neurological events, (3) reported hearing impairments, and (4) uncorrected vision deficits. To control for general intellectual and sensori-motor functioning, we assessed fluid reasoning and processing speed using the Matrix Reasoning and Coding subtests of the Wechsler Intelligence Scale for Children − 5th edition 57 as control tests of general intellectual and sensori-motor functioning. A below-threshold score (i.e., standard score < 5) on either subtest was defined as an exclusion criterion. Additionally, a data quality check was performed on the implicit rhythmic task to ensure that children adequately understood and performed the task (see the Preprocessing section). A high number of invalid trials or performance close to chance level was considered an exclusion criterion (see the Preprocessing section). In total, eight children were excluded: two due to a low score on one of the control tests, four because they did not meet the quality criteria for the implicit rhythmic task, and two due to behavioral issues or technical problems during testing. The final sample consisted of 98 children aged 7 to 13 ( M = 10.1, SD = 1.6), including 62 children from France and 36 from Canada, with 58% being female. Among them, 35 had at least one year of formal musical experience (for the whole group: M = 0.95 years, SD = 1.5, range = [0; 5]). The occupational categories reported by the children’s parents were professionals and managers (41%), office and service workers (45%), skilled and unskilled manual workers (6%), and unemployed (6%). Mean standard scores on the control tests were as follows: Matrix Reasoning ( M = 10.5, SD = 2.53) and Coding ( M = 11.5, SD = 2.31). Spontaneous motor tempo was also assessed using an unpaced tapping task administered prior to the rhythmic tasks. As this measure was not central to our hypotheses and showed no significant association with implicit rhythmic performance or age, it is not included in the main analysis. For completeness, we report the distribution of mean inter-tap interval ( M = 549ms, SD = 186) and its coefficient of variation ( Supplementary Fig. 8 ), along with scatterplots showing no relationship with age, ΔRT, or paced tapping performance at 600 ms ( Supplementary Figs. 9, 10, 11 ). Written informed consent was obtained from all participants’ parents, in accordance with the Declaration of Helsinki. Children also gave assent prior to testing. The study was approved by the ethics committees of the University of Montreal (CEREP-22-052-D) and the University of Lille (CER-2021-563-S101). Materials and tasks Implicit rhythmic task In the implicit rhythmic task, children listened to sequences of tones (standard sounds of identical pitch), each followed by a target sound that was either identical in pitch (non-deviant) or higher in pitch (deviant) (Fig. 6 ). Children were instructed to respond as quickly as possible only if the target sound was deviant. Stimulus materials. Each sequence consisted of eight standard sounds (600 Hz), followed by a target sound, which was either higher in pitch than the standard sounds (deviant, 2/3 of target sounds) or identical in pitch (non-deviant, 1/3 of target sounds). The pitch deviation of the target sounds was individually adjusted to each child's pitch detection ability (see Adaptation of Task Difficulty below). All the sounds were pure tones (duration: 150 ms, including 10 ms rise and fall times) and were computer-generated using the scipy.signal python library 100 in Python 3.10. Adaptation of task difficulty . An adaptive procedure was implemented to tailor task difficulty for each child's pitch discrimination ability and learning speed. At the beginning of the experiment, a brief staircase method (1 up, 1 down) was used to estimate each child’s pitch discrimination threshold and define the initial pitch difference for deviant tones, setting the starting difficulty level. Throughout the experiment, task difficulty was adjusted after each block based on the child’s performance: if accuracy exceeded 85%, the pitch difference was decreased to increase difficulty; if accuracy fell below 70%, the pitch difference was increased to make the task easier; and if accuracy remained between 70% and 84%, the pitch difference was left unchanged. These threshold values were determined in a pilot phase to ensure smooth progression of the task. This adaptive design aimed to maintain an optimal challenge, ensuring sustained engagement of the children throughout the task. Task description and procedure. We manipulated the temporal regularity of the standard sound sequences. In regular trials (50%), standard sounds occurred at a fixed inter-onset interval (IOI) of 600 ms (i.e., isochronously). In irregular trials (50%), IOIs varied randomly between 300 and 900 ms (flat distribution). In both cases, the target sound followed the last standard sound after 600 ms. The task consisted of six blocks, each containing 20 deviant target trials and 10 non-deviant target trials. To enhance the child's engagement and motivation, the task was gamified (Fig. 7 ). The game featured Marius , a lost astronaut navigating his way back to Earth using sound cues. Higher-pitched sounds indicated that Marius was moving away from Earth, prompting children to redirect him by pressing a button. Each level represented the discovery of a new planet. During trials, children viewed a black screen and were instructed to fixate on a central red rocket while listening to the sequences. They were asked to press a button only if the final sound in a sequence was higher in pitch than the preceding tones. As a warning signal, the red rocket turned green on the 8th sound, indicating that the target sound was about to be presented. Instructions emphasized responding exclusively to deviant target sounds while ignoring non-deviant targets and standard sounds. As the task focused on pitch discrimination, it did not require explicit attention to the temporal regularity of the sequences, and the child was not informed that rhythm was manipulated. The main dependent measure was reaction time (RT) on deviant targets. Additionally, response types were recorded: Hits (button presses on deviant targets), False Alarms (FA; button presses on non-deviant targets), Correct Rejections (CR; correctly withholding a response to a non-deviant target), and Misses (failure to respond to a deviant target). To maintain engagement, visual feedback encouraged rapid and accurate responses: quick reactions were rewarded with a diamond star (RT < 450 ms), gold star (RT between 450 and 549 ms), silver star (RT between 550 and 649 ms), or bronze star (slower RTs). False alarms triggered a no-go sign to discourage random button presses. At the end of each block, the child received feedback on their overall accuracy, represented by 1, 2, or 3 stars. Regardless of performance, all children successfully "reached Earth" at the end of the game to ensure a positive and engaging experience. The implicit rhythmic task was developed using PsychoPy 101 . Game images were sourced from Shutterstock.com (freely available for non-commercial use) and edited using GIMP software ( https://www.gimp.org/ ). The experiment took place in a quiet room, with the child being seated in front of a computer next to the experimenter. They responded using their dominant hand via a response button (Buddy Button). The entire task, including instructions, training, and short breaks between blocks, lasted approximately 30 minutes. Explicit Rhythmic Tasks Explicit rhythmic tasks were selected from the mobile version of the Battery for the Assessment of Auditory and Sensorimotor Timing Abilities (BAASTA 54 , 55 ) and administered using a tablet with headphones. BAASTA includes various tests designed to evaluate both perceptual and sensorimotor explicit rhythmic abilities. All sensorimotor tests were performed using the index finger of the participants' dominant hand. Instructions explicitly emphasized temporal regularity, and all auditory stimuli were computer-generated with a piano timbre. This battery was previously used in studies involving both typically and atypically developing children within this age range 12 , 13 , 97 . Beat perception was assessed using the Beat Alignment Test (BAT) . In this task, children judged whether a metronome was correctly aligned with the beat of a musical excerpt or not. Musical excerpts were presented at a tempo of 600 ms per beat. After seven beats, a metronome was superimposed on the music, either aligned or misaligned to the beat. The test contained two-thirds of misaligned trials and one-third of aligned trials. Beat synchronization was evaluated through four paced tapping tasks , in which children tapped along with either isochronous sequences (two conditions: 450 ms and 600 ms IOIs) or musical excerpts (two pieces: Badinerie by Bach (music 1) and the William Tell Overture by Rossini (music 2), both featuring an inter-beat interval of 600 ms). Each task consisted of two trials. Executive Functions Tests The tests were selected from the Wechsler Intelligence Scale for Children − 5th edition (WISV-V 57 ), the Test of Everyday Attention for Children (TEA-ch 56 ), and the FÉE battery for executive function in children 58 . Auditory controlled attention was assessed using the Coups de Fusil task from the French version of the TEA-Ch battery. This multicomponent task taps into both sustained attention and executive functioning, notably auditory working memory and cognitive control. Children listened to ten series of sounds presented in a temporally irregular sequence. Their task was to silently count the number of sounds in each series (ranging from 9 to 15) without using their fingers. Inhibitory control was assessed using the Tapping Enfant task from the FÉE. In this test, children were instructed to tap on the table with their index finger, either once or twice, depending on the experimenter's actions. The task consisted of three parts with increasing difficulty. In the first part, children simply had to reproduce the experimenter's gestures: tapping once when the experimenter tapped once and tapping twice when the experimenter tapped twice. The second part introduced a go/no-go paradigm, where children still had to tap once when the experimenter tapped once but were required to refrain from tapping if the experimenter tapped twice. In the third and most complex part, the rules were reversed: children had to tap twice when the experimenter tapped once and tap once when the experimenter tapped twice. Additionally, if the experimenter used two fingers instead of just the index finger -regardless of whether they tapped once or twice- children had to withhold their response. Working memory was assessed using the Mémoire des Chiffres test from the WISC-V. In this digit-span task, children were asked to repeat sequences of numbers presented aloud by the examiner. The task consisted of three conditions: in the forward digit span, children repeated the numbers in the same order as presented; in the backward digit span, they repeated them in reverse order; and in the ascending digit span, they reordered the numbers in ascending order. Cognitive flexibility was evaluated with the task Les petits Hommes Verts from the French version TEA-Ch. In this task, children counted small green monsters appearing along burrows. Arrows placed between the creatures indicated the counting direction: children started by counting forward one by one, but whenever they encountered an arrow pointing downward, they had to switch directions and count backward until reaching the final creature. 4.3| Procedure Children participated in two sessions, conducted either at the BRAMS laboratory in Montreal, Canada or in a quiet room at their school in Lille, France. One session focused on rhythmic abilities (implicit and explicit, ≈1h15), while the other assessed cognitive abilities (≈ 45 minutes). During the rhythm session, children first completed a spontaneous motor tempo assessment, followed by the implicit rhythmic task and the perceptual and paced tapping tasks from the BAASTA. In the cognitive session, they began with subtests from the WISC-V battery evaluating reasoning and processing speed, used as control measures of intellectual and sensory-motor efficiency, before completing tests from the WISC-V, TEA-CH and FÉE batteries assessing attention, inhibition, working memory, and cognitive flexibility. To minimize fatigue, sessions were scheduled on two different half-days, either on separate days or with one in the morning and the other in the afternoon. The order of the sessions was randomized, with 40% of the children starting with the cognitive session and 60% beginning with the rhythm session. No significant effect of session order was found for any of the variables of interest (all p > .05, lowest p = .55). Data pre-processing and analysis Implicit rhythmic task pre-processing The following steps outline the data pre-processing procedure 102 . A quality check was performed to ensure data reliability. For each participant, trials were classified as valid or invalid. A trial was considered valid if no button press occurred before target onset, in line with the instruction to respond only to the target sound. Although a small number of invalid trials can reflect impulsive or anticipatory responses, a high proportion may indicate poor task compliance. The percentage of valid trials was computed for each participant (mean = 95.2%), with no significant difference between conditions ( p = .401). Participants with fewer than 60% valid trials were excluded, which applied to only one participant. For the remaining participants, invalid trials were removed from further analyses. We then computed the proportion of correct responses. Participants performing near chance level (accuracy < 60%) were excluded from the analysis (n = 3). In a second step, trials with outlier reaction times (RTs) were removed. Outliers were defined as RTs falling more than ± 3 SD from the participant’s mean RT 103 . Outliers identification was conducted independently of conditions and included both correct responses and false alarms 104 . Overall, 1.69% of trials were classified as outliers. Condition differences were examined post hoc (see the Results section). While no significant difference was found for early outliers (i.e., fast responses; p = .308), a slightly higher proportion of late outliers was observed in the irregular condition (regular: 1.25%, irregular: 1.96%, p = .002). Finally, for each participant and each condition (regular and irregular), we computed the mean correct RT (i.e., RTs from Hit trials). As a secondary measure, accuracy was assessed using a sensitivity index ( d ′), calculated as the difference between the z-transformed Hits and FA rates. In accordance with Macmillan and Kaplan’s correction (Macmillan & Kaplan, 1985), Hit and FA rates of 0 or 1 were adjusted to l/(2N) and 1–l/(2N), respectively, where N corresponds to the number of signal (for Hits) or noise (for FA) trials. Implicit rhythmic ability was quantified as the difference in mean reaction time (ΔRT) between the two conditions, with positive values indicating better performance in the regular condition than the irregular one (ΔRT = RT irregular – RT regular ). As a secondary measure, we computed the mean difference in accuracy (Δ d ′), where positive values also indicated better performance in the regular condition (Δ d ′ = d’ regular – d’ irregular ). Explicit rhythmic tasks: calculation of the Beat Tracking Index (BTI) To summarize rhythm perception and synchronization abilities, we computed the Beat Tracking Index (BTI), which combined beat perception and synchronization scores 13 , 54 . The beat perception score was derived from the sensitivity index ( d′ ) in the Beat Alignment Test (BAT), calculated based on hit rates (correct detections of misaligned metronomes) and false alarms rates (incorrect detections of misalignment in aligned metronomes). Individual d′ values were converted to z-scores based on the full sample distribution. The beat synchronization score was based on synchronization consistency across the four paced tapping tasks. Consistency reflects the temporal regularity of tapping intervals relative to the beat within a trial - that is, how stably participants maintained a periodic response pattern. It was computed using circular statistics 54 , 55 yielding values from 0 (no consistency) to 1 (perfect consistency). To correct for the typical skewness of synchronization data, consistency scores were logit-transformed 33 , 54 producing values theoretically ranging from -∞ (logit(0)) to +∞ (logit(1)). These transformed scores were then converted into z-scores for each task and averaged to obtain a composite beat synchronization score. The BTI was computed as the mean of each participant’s beat perception and beat synchronization z-scores. For one participant, synchronization could not be computed due to insufficient tapping signal in the music trials; as such, BTI analyses include 97 participants. Additionally, for each synchronization task, accuracy was computed. Accuracy is expressed as the angle of the vector R (θ, or relative phase, in degrees), indicating whether participants tapped before (negative values) or after (positive values) the pacing event. Accuracy was computed only if synchronization performance exceeded chance level, as determined by the Rayleigh test for circular uniformity 33 , 54 , 55 . Cognitive tasks: calculation of the Executive Functioning Index (EFI) An initial exploration of the results revealed correlations between implicit performance and all executive function measures. Given that these functions are interrelated and build upon each other (Diamond, 2013), we computed a composite Executive Functioning Index (EFI) by combining scores from sustained attention, inhibitory control, working memory, and cognitive flexibility tests. This approach aimed to reduce the number of statistical tests and minimizing the risk of an inflated alpha error in subsequent analyses. The auditory attention score was derived from the raw score of the Score! test, representing the number of correctly counted sound sequences. For the inhibition task, a raw performance score was computed by summing the number of correct responses across the three parts of the Tapping Enfant test. The auditory working memory score corresponded to the total raw score obtained on the Digit test, reflecting the number of correctly recalled sequences across all three conditions (forward, backward, and ascending). Finally, cognitive flexibility was measured using the raw accuracy score from the Creature Counting test, which corresponded to the number of sequences correctly completed despite changes in counting direction. All raw scores were converted into z-scores, using the mean and standard deviation of the full sample. These z-scores were then averaged to compute the EFI. The distribution of EFI scores is provided in the Supplementary Fig. 5 . Statistical analyses Data processing and statistical analyses were conducted using R version 4.4.0 105 . Implicit rhythmic abilities were assessed through pairwise comparisons of mean reaction times between the temporally regular and irregular conditions. Additional comparisons were performed on mean accuracy, measured by the sensitivity index ( d’ ). Prior to analysis, variable distributions were tested for normality using the jmv package 106 ; a variable was considered normally distributed if both skewness and excess kurtosis fell within the [-1,1] range. For normally distributed variables, two-sided paired-sample t-tests were used; otherwise, two-sided Wilcoxon signed-rank tests were applied. Effect sizes were computed using the R effect size package 107 : Cohen’s d for t-tests, rank-biserial correlation (r) for Wilcoxon tests, and partial omega squared (ω²ₚ) for F-tests in regressions. Effect sizes are reported and interpreted according to established benchmarks: Funder and Ozer 108 for rank biserial r , Sawilowsky 109 for Cohen’s d, and Field 110 for ω²ₚ. To further explore reaction time differences in the implicit rhythmic task while accounting for variations in accuracy between conditions, we conducted a mixed-effects model analysis. This analysis was implemented using the lmerTest 111 and lme4 112 R packages. The model included log10-transformed correct RTs per trial as the dependent variable to better normalize residuals. Fixed effects comprised condition (with the irregular condition as the reference level) and the accuracy difference between conditions ( Δd’ ). To account for the hierarchical structure of the data, we included random intercepts for participants and random slopes for the condition effect. Omnibus tests for main effects and interactions were performed using type III sum of squares, with F and p-values computed via the Kenward-Roger approximation for degrees of freedom 113 , 114 Finally, relationships between rhythmic abilities (both implicit and explicit) and variables such as age, formal musical experience, and executive functions were explored using simple and multiple linear regressions. Models were fitted using the lm function in R, with ΔRT (from the implicit task) serving as the primary measure of implicit rhythmic abilities and Δd’ included as additional information on accuracy differences. The BTI was used as a measure of explicit rhythmic abilities. All predictors were standardized prior to inclusion in the models, except for Δd′, which was left unstandardized so that a value of 0 represented no difference in accuracy between conditions. Model diagnostics were performed using the performance package 115 to ensure appropriate fit. Declarations Reporting Summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability To ensure reproducibility, data used in the analyses will be made publicly available along the R project for the analyses. Code availability R code used for the analyses will be made publicly available in a dedicated GitHub repository associated with this article. Acknowledgements This study was supported by a Canada Research Chair awarded to S.D.B.; by grants from the University of Lille, the Académie Française, and ISITE-ULNE awarded to A.G.; by an INSPE grant awarded to D.D.; and by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program (reference ANR-23-IAHU-0003) awarded to S.S. We are grateful to the children and their families for their participation, and to Océane Martin, Margaux Dos Santos, Célina Zenag, and Noémie Beauduin for their assistance with recruitment and data collection. Author contributions A.G. contributed to the conceptualization, development of the implicit task, methodology, participant testing, data analysis, and writing -original draft. D.D., S.D.B. and S.S. contributed to the conceptualization, development of the implicit task, methodology, supervision, data analysis, and writing -review and editing. N.E.V. contributed to the development of the implicit task, data analysis, and writing -review and editing. S.C., V.B., and S.A.K. contributed to the development of the implicit task. D.D. and S.D.B. jointly supervised the project. All authors reviewed and approved the final manuscript. Competing interests The authors declare the following competing interests: SDB is on the board of the BeatHealth company dedicated to the design and commercialization of technological tools for assessing rhythmic abilities such as BAASTA tablet and implementing rhythm-based interventions. Other authors have no competing interest to disclose. Materials & Correspondence Correspondence and material requests should be addressed to A.G. ( [email protected] ), D.D ( [email protected] ) and S.D.B. ( [email protected] ). References Doelling, K., Herbst, S., Arnal, L. & van Wassenhove, V. Psychological and Neuroscientific Foundations of Rhythms and Timing. in Performing Time: Synchrony and Temporal Flow in Music and Dance (eds. Wöllner, C. & London, J.) 0 (Oxford University Press, 2023). doi:10.1093/oso/9780192896254.003.0005. Alagöz, G. et al. The shared genetic architecture and evolution of human language and musical rhythm. Nat Hum Behav 9 , 376–390 (2025). Niarchou, M. et al. 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The implicit learning of metrical and nonmetrical temporal patterns. Quarterly Journal of Experimental Psychology 66 , 360–380 (2013). Breska, A. & Deouell, L. Y. Mechanisms of Implicit Timing: Cognitive and Electrophysiological Manifestations of Temporal Expectations Following Rhythmic Visual Input. Procedia - Social and Behavioral Sciences 126 , 45–46 (2014). Guinamard, A. et al. Musical abilities in children with developmental cerebellar anomalies. Frontiers in Systems Neuroscience 16 , (2022). Frischen, U., Schwarzer, G. & Degé, F. Comparing the Effects of Rhythm-Based Music Training and Pitch-Based Music Training on Executive Functions in Preschoolers. Frontiers in Integrative Neuroscience 13 , (2019). Bégel, V. et al. Dance Improves Motor, Cognitive, and Social Skills in Children With Developmental Cerebellar Anomalies. Cerebellum 21 , 264–279 (2022). Jamey, K. et al. Enhancing sensorimotor and executive functioning in autistic children with a rhythmic videogame: A pilot study. Preprint at https://doi.org/10.21203/rs.3.rs-5790839/v1 (2025). Jamey, K. et al. Can You Beat the Music? Validation of a Gamified Rhythmic Training in Children with ADHD. 2024.03.19.24304539 Preprint at https://doi.org/10.1101/2024.03.19.24304539 (2024). Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17 , 261–272 (2020). Peirce, J. et al. PsychoPy2: Experiments in behavior made easy. Behav Res 51 , 195–203 (2019). Lönneker, H. et al. We Don’t Know What You Did Last Summer. On the Importance of Transparent Reporting of Reaction Time Data Pre-processing. Cortex 172 , 14–37 (2024). Berger, A. & Kiefer, M. Comparison of Different Response Time Outlier Exclusion Methods: A Simulation Study. Frontiers in Psychology 12 , (2021). André, Q. Outlier exclusion procedures must be blind to the researcher’s hypothesis. J Exp Psychol Gen 151 , 213–223 (2022). R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2024). Selker, R., Love, J., Dropmann, D., Moreno, V. & Agosti, M. jmv: The ‘jamovi’ Analyses. (2023). Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: Estimation of Effect Size Indices and Standardized Parameters. Journal of Open Source Software 5 , 2815 (2020). Funder, D. C. & Ozer, D. J. Evaluating Effect Size in Psychological Research: Sense and Nonsense. Advances in Methods and Practices in Psychological Science 2 , 156–168 (2019). Sawilowsky, S. S. New Effect Size Rules of Thumb. Journal of Modern Applied Statistical Methods 8 , 597–599 (2009). Field, A. Discovering Statistics Using IBM SPSS Statistics . (SAGE, 2013). Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software 82 , 1–26 (2017). Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67 , 1–48 (2015). Halekoh, U. & Højsgaard, S. A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models – The R Package pbkrtest. Journal of Statistical Software 59 , 1–32 (2014). Luke, S. G. Evaluating significance in linear mixed-effects models in R. Behav Res 49 , 1494–1502 (2017). Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software 6 , 3139 (2021). Additional Declarations Yes there is potential Competing Interest. The authors declare the following competing interests: SDB is on the board of the BeatHealth company dedicated to the design and commercialization of technological tools for assessing rhythmic abilities such as BAASTA tablet and implementing rhythm-based interventions. Other authors have no competing interest to disclose. Supplementary Files SUPPLEMENTARYINFORMATIONDevTrajectoriesRhythm.docx Supplementary Information for: Distinct developmental trajectories shape human sensitivity to rhythms in the environment reportingsummaryDevTrajectoriesRhythmflat.pdf Reporting Summary Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7086372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486953990,"identity":"793d9ba7-7cfc-4b19-bc42-821e5c954637","order_by":0,"name":"Antoine Guinamard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYLCCBBiDpwJIMDM3EFDPjKzlDEiAkQgtcMDbBiIJaOGf3X/wwcMdNgzy0d1pH97Oq43mbwdq+VGxDacWiTuHmQ0Sz6QxGN45u3nm3G3Hc2ccZmxg7DlzG7c1N5LZJBLbDjMYzsjdzMy77VhuA1ALM2Mbbi3yEC3/oVrmHMudT0iLAUTLAQZ5CZCWhprcDYS0GN5INjZIbEvmMQBqYZxz7EDuRqCWg/j8Incj8eHDn212cvJAhzG8qanLnXf+8MEHPyrweB8KeAwOgOnDYPIAQfUgIN8ApuqIUjwKRsEoGAUjCwAA88Va1ta1yPsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7721-7692","institution":"University of Lille","correspondingAuthor":true,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Guinamard","suffix":""},{"id":486953991,"identity":"0b938108-79ce-4dbc-8873-19af38787257","order_by":1,"name":"Nicholas Foster","email":"","orcid":"https://orcid.org/0000-0002-5406-3109","institution":"University of Montreal","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Foster","suffix":""},{"id":486953992,"identity":"ca185ae1-5255-4097-829e-054e07516059","order_by":2,"name":"Sylvain Clément","email":"","orcid":"https://orcid.org/0000-0003-4965-311X","institution":"University of Lille","correspondingAuthor":false,"prefix":"","firstName":"Sylvain","middleName":"","lastName":"Clément","suffix":""},{"id":486953993,"identity":"aa5e38b3-eaea-40f1-8f20-02d5bf14b2da","order_by":3,"name":"Valentin Bégel","email":"","orcid":"https://orcid.org/0000-0003-4049-1126","institution":"Université Paris Cité","correspondingAuthor":false,"prefix":"","firstName":"Valentin","middleName":"","lastName":"Bégel","suffix":""},{"id":486953994,"identity":"3de71a5e-8c99-47a1-adf8-e91f61065b4b","order_by":4,"name":"Sonja Kotz","email":"","orcid":"https://orcid.org/0000-0002-5894-4624","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Sonja","middleName":"","lastName":"Kotz","suffix":""},{"id":486953995,"identity":"95779788-1178-43b1-b58e-fa9641e120c4","order_by":5,"name":"Séverine Samson","email":"","orcid":"https://orcid.org/0000-0002-4507-9440","institution":"Institut Pasteur","correspondingAuthor":false,"prefix":"","firstName":"Séverine","middleName":"","lastName":"Samson","suffix":""},{"id":486953996,"identity":"b813f6ca-b6e3-4442-8517-a620ad4b6289","order_by":6,"name":"Simone Dalla Bella","email":"","orcid":"https://orcid.org/0000-0001-9813-7408","institution":"University of Montreal","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"Dalla","lastName":"Bella","suffix":""},{"id":486953997,"identity":"04463961-81bb-44c7-b83b-4390a5218002","order_by":7,"name":"Delphine Dellacherie","email":"","orcid":"https://orcid.org/0000-0002-3543-8606","institution":"University of Lille","correspondingAuthor":false,"prefix":"","firstName":"Delphine","middleName":"","lastName":"Dellacherie","suffix":""}],"badges":[],"createdAt":"2025-07-09 17:25:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7086372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7086372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87048637,"identity":"80e67e11-2fe3-4163-85b4-3e2a8fb4fa6a","added_by":"auto","created_at":"2025-07-18 14:51:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance in the implicit rhythmic task. (a)\u003c/strong\u003e: Mean reaction time (RT) for auditory targets following either temporally regular or irregular tone sequences. Central diamonds indicate the mean RT; error bars represent the standard error of the mean (s.e.m.). ***p \u0026lt; .001 (two-tailed Wilcoxon signed-rank test). \u003cstrong\u003e(b)\u003c/strong\u003e: Distribution of reaction time benefits from temporal regularity (ΔRT = RT\u003csub\u003eirregular\u003c/sub\u003e - RT\u003csub\u003eregular\u003c/sub\u003e). Boxes show the interquartile range (IQR; 25\u003csup\u003eth\u003c/sup\u003e-75\u003csup\u003eth\u003c/sup\u003e percentiles) with the median as the central line; whiskers extend to 1.5×IQR. Black dots indicate the mean with error bars representing the s.e.m.; purple dots represent individual observations.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/bf6288c63db460e227670f8d.png"},{"id":87048648,"identity":"d8bd443c-0115-4f9d-ad71-5067a5e0a907","added_by":"auto","created_at":"2025-07-18 14:51:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExplicit rhythmic abilities summarized in the Beat Tracking index (BTI). (a):\u003c/strong\u003e Relationship between perceptual (BAT z-score) and sensorimotor (synchronization consistency z-score) in explicit rhythmic tasks. The regression line is shown with its shaded areas representing 95% confidence intervals. \u003cstrong\u003e(b)\u003c/strong\u003e: Distribution of BTI scores, obtained by averaging the perceptual and sensorimotor scores. Boxes show the interquartile range (IQR; 25\u003csup\u003eth\u003c/sup\u003e-75\u003csup\u003eth\u003c/sup\u003e percentiles) with the median as the central line; whiskers extend to 1.5×IQR. Black dots indicate the mean with error bars representing the s.e.m.; blue dots represent individual observations.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/fdc0a1ed2ec6e35531ed6cfa.png"},{"id":87050846,"identity":"d1e2c5dc-2f75-437f-8b72-5a41d258e5b8","added_by":"auto","created_at":"2025-07-18 14:59:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance in implicit and explicit rhythmic tasks and their relationship with age and formal musical \u003c/strong\u003eexperience\u003cstrong\u003e. \u003c/strong\u003ePanels (a) and (b): RT benefits in the implicit task (ΔRT = RT\u003csub\u003eirregular\u003c/sub\u003e – RT\u003csub\u003eregular\u003c/sub\u003e) as a function of participants' age (a) and formal musical experience (b). Panels (c) and (d): Explicit beat tracking abilities (BTI) as a function of age (c) and formal musical experience (d). Regression lines are shown with shaded areas representing 95% confidence intervals. \u003cem\u003ep\u003c/em\u003e-values are derived from multiple linear regressions including both age and musical experience as predictors. 'n.s.' indicates non-significant relationship (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/44bb302f96c223494678e0e3.png"},{"id":87048642,"identity":"932c4e68-df9b-4fe5-9d74-a8601a996f99","added_by":"auto","created_at":"2025-07-18 14:51:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance in implicit and explicit rhythmic tasks and their relationship with executive functions. (a)\u003c/strong\u003e: RT benefits in the implicit task (ΔRT = RT\u003csub\u003eirregular\u003c/sub\u003e – RT\u003csub\u003eregular\u003c/sub\u003e); \u003cstrong\u003e(b)\u003c/strong\u003e: Explicit rhythmic abilities (BTI), plotted as a function of participants’ general level of executive functioning (EFI). Regression lines are shown with shaded areas representing 95% confidence intervals for the prediction.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/f9bb285170ab0143565c6562.png"},{"id":87048644,"identity":"6e131ff9-f496-423b-a504-6fc75a717239","added_by":"auto","created_at":"2025-07-18 14:51:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":177480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModulation of reaction times in the implicit rhythmic task as a function of explicit rhythmic abilities and executive functions. \u003c/strong\u003eThis figure illustrates the interaction between explicit rhythmic abilities and executive functions in predicting RT benefits from temporal regularity in the implicit task. Individual mean RTs for correct target detections are plotted for both regular and irregular conditions, as a function of explicit rhythmic abilities (Beat Tracking Index, BTI). Separate regression lines are shown for each condition. To visualize the interaction with executive functions, participants were grouped into low, medium, and high Executive Functioning Index levels based on terciles of the EFI.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/493fc63ec7cb1fbb844b4811.png"},{"id":87048643,"identity":"6a893bb2-7691-4e00-826c-549c46698a2b","added_by":"auto","created_at":"2025-07-18 14:51:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchema of the implicit rhythmic task. \u003c/strong\u003eIn this task, children were instructed to press a button when they detected a deviant pitch following an 8-tone sequence, which could be either temporally regular or irregular. Crucially, rhythmic regularity was never mentioned in the instructions. During each trial, children viewed a black screen and were asked to fixate on a red rocket at the center, which turned green on the last standard tone before the target appeared. Star-shaped feedback was provided after correct responses, with faster reaction times earning more stars. A “no-go” sign appeared following false alarms.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/52eadf1f5695e166a1fe6871.png"},{"id":87048654,"identity":"b3bfe2f7-dac0-4af4-aeb6-f69856418b59","added_by":"auto","created_at":"2025-07-18 14:51:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":680868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreenshots of the gamified implicit task:\u003c/strong\u003e (a) Introduction screen; (b–d) transition screens between blocks; (e) end of block; (f) end of the task. In this game, children helped a small astronaut return to Earth by detecting deviant sounds. Each block corresponded to a game level, at the end of which a new planet was discovered. To sustain motivation, performance at each level was rewarded with 1, 2, or 3 stars based on overall accuracy.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/28e054c604069c3eb563e154.png"},{"id":105035795,"identity":"e83f6ef0-358b-4a0d-b0df-f75b07b0b427","added_by":"auto","created_at":"2026-03-20 07:26:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2917935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/83cd1123-3a69-4f1e-b67e-3d4a8477d96b.pdf"},{"id":87050844,"identity":"731723b6-8c11-43f0-8610-76a46f416354","added_by":"auto","created_at":"2025-07-18 14:59:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1486266,"visible":true,"origin":"","legend":"Supplementary Information for: Distinct developmental trajectories shape human sensitivity to rhythms in the environment","description":"","filename":"SUPPLEMENTARYINFORMATIONDevTrajectoriesRhythm.docx","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/cabe5e9f597557ccba0e8e33.docx"},{"id":87048640,"identity":"9695f8e7-e214-4831-b1f6-1c8275e0e0eb","added_by":"auto","created_at":"2025-07-18 14:51:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2262614,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"reportingsummaryDevTrajectoriesRhythmflat.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7086372/v1/b7c5ea88126b2ec58f332ab2.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nThe authors declare the following competing interests: SDB is on the board of the BeatHealth company dedicated to the design and commercialization of technological tools for assessing rhythmic abilities such as BAASTA tablet and implementing rhythm-based interventions. Other authors have no competing interest to disclose.","formattedTitle":"Distinct developmental trajectories shape human sensitivity to rhythms in the environment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRhythmic patterns are a ubiquitous feature of our environment. Temporal regularities occur in music and speech, in bodily movements, and across many everyday activities\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. From the repetitive pulse of an ambulance siren to the complex temporal structure of language or music, humans display remarkable sensitivity to rhythm. The ability to detect and track such temporal regularities \u0026mdash;broadly referred to as rhythmic abilities\u0026mdash; is partly rooted in our genetics\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Far beyond musicality, these abilities support the prediction of future events and help optimize perception and action in a constantly changing environment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In childhood, rhythmic abilities contribute to core developmental domains\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e such as language\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, social interaction\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and literacy\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In turn, poor rhythmic abilities have been linked to developmental conditions like dyslexia\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e or ADHD\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and are considered potential precursors of broader learning and developmental disorders\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnderstanding how rhythmic abilities develop is therefore essential, both for identifying early signs of learning difficulties and informing targeted interventions. However, most developmental studies have focused on explicit rhythm processing, i.e., deliberate engagement with rhythmic patterns, typically involved when we dance or sing along with music. In everyday life, though, rhythm more often shapes perception and action implicitly. Whether reacting to traffic or engaging in conversation, we routinely benefit from temporal regularities without deliberately attending to them. This study addresses a critical gap by examining, for the first time, the joint development of explicit and implicit rhythmic abilities, and their relation to cognitive functions in children.\u003c/p\u003e\u003cp\u003eExplicit rhythm processing is typically assessed using tasks that directly involve rhythmic structure, such as beat perception (judging whether a sound aligns with the underlying pulse of music) and beat synchronization (tapping in time with the beat). In contrast, implicit rhythm processing is evaluated through tasks where rhythm is not the focus \u0026mdash;such as pitch discrimination\u0026mdash; but where temporal regularity can still enhance performance, for example by speeding up reaction times\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The distinction between implicit and explicit processing has been extensively studied in domains such as memory\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e or single duration processing\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, but has received much less attention in rhythm and beat processing research. Emerging evidence in adults suggests that implicit and explicit rhythm processing may rely on distinct mechanisms. In a multiple case-study, three individuals with poor explicit rhythm perception\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e nonetheless showed faster responses to temporally regular sequences in a pitch detection task\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Similarly, implicit rhythm processing appears more resilient to age-related decline than explicit perception\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, no study has examined this distinction in children, and it remains unknown whether implicit and explicit rhythmic abilities follow separate developmental trajectories or emerge in tandem during childhood.\u003c/p\u003e\u003cp\u003eThere is compelling evidence that explicit rhythmic abilities, such as beat perception and synchronization, improve steadily throughout childhood and adolescence\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Most individuals, regardless of musical education, eventually acquire the ability to perceive and move to a beat\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Still, substantial individual differences exist\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, partly shaped by musical experience\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and broader cognitive factors including cognitive control\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In contrast, very little is known about the development of implicit rhythm processing. Implicit processes more broadly are often assumed to emerge early and to be largely mature by childhood\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, the only developmental study of implicit rhythmic abilities \u0026mdash;conducted in the visual modality\u0026mdash; reported increasing reaction time benefits from rhythm regularity between ages 5 and 12\u003csup\u003e42\u003c/sup\u003e. Whether similar patterns hold in the auditory domain remains unclear, given that studies in adults suggest that auditory and visual implicit rhythm processing rely on distinct mechanisms and show uncorrelated performance\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA key to understanding how explicit and implicit rhythmic abilities develop may lie in their relationship to executive functions, a set of higher-order cognitive control processes supporting goal-directed behavior, concentration, learning, and self-regulation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. These functions develop substantially during school years\u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, in parallel with improvements in explicit rhythmic abilities. Significant associations have been reported between explicit rhythmic abilities and various executive components, including controlled attention\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, inhibition\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, working memory\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and cognitive flexibility\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. By contrast, implicit rhythmic abilities are thought to rely less on executive functions, operating largely automatically\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Evidence from adult studies suggests implicit rhythm processing can occur independently of cognitive control\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, but whether this is also true in children remains unknown. Notably, the cognitive predictors of implicit rhythmic abilities remain largely unexplored. To summarize, it remains unclear whether implicit and explicit rhythmic abilities follow shared or distinct developmental trajectories in childhood, and whether they relate similarly to executive functions. Clarifying these relationships is essential for refining theoretical models of rhythm processing and informing interventions targeting rhythm-related difficulties.\u003c/p\u003e\u003cp\u003eFor this purpose, we examined the developmental trajectory of both implicit and explicit rhythmic abilities and their links to executive functions in school-aged children, a key developmental window for cognitive control and the emergence of learning difficulties. To evaluate implicit rhythm processing, we developed a novel gamified task in which children detected pitch changes following regular or irregular tone sequences to help a small astronaut navigate space. Crucially, neither the task goals nor instructions required attention to rhythmic regularity. Reaction time benefits from regularity indexed implicit rhythm processing. To assess explicit abilities, we used beat perception and synchronization tasks from a standardized rhythm battery (BAASTA)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Finally, we evaluated executive functions using established tasks of controlled auditory attention, inhibitory control, working memory, and cognitive flexibility\u003csup\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. According to our predictions, explicit rhythmic abilities would be more closely linked to age, musical experience, and executive functions than implicit abilities.\u003c/p\u003e\u003cp\u003eIn the present within-subject study involving 98 children aged 7 to 13, we demonstrate that explicit rhythmic abilities improve with age and formal musical experience, whereas implicit rhythmic abilities remain remarkably stable, suggesting distinct developmental trajectories. Yet, the two types of rhythm processing are not entirely independent: their relationship is modulated by executive functions. Notably, executive functions predict performance in opposite directions for implicit and explicit tasks. These findings offer a nuanced picture of rhythm processing in childhood, revealing unexpected links between controlled and more automatic forms of rhythm processing. They further suggest that cognitive control influences not only intentional rhythmic behavior, but also the implicit tracking of temporal regularities.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eChildren implicitly process the rhythm of auditory events\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo test whether children implicitly track rhythmic regularities in sound sequences, i.e., even when rhythm is not directly task-relevant, we examined whether they responded faster to auditory targets (detecting a pitch difference) following regular (isochronous) versus irregular (temporally random) tone sequences. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea shows reaction times (RTs) per condition. RTs were significantly faster in the regular condition than in the irregular one (RT\u003csub\u003eregular\u003c/sub\u003e : M\u0026thinsp;=\u0026thinsp;518.7 ms, SD\u0026thinsp;=\u0026thinsp;78.1 ms; RT\u003csub\u003eirregular\u003c/sub\u003e : M\u0026thinsp;=\u0026thinsp;538.7 ms, SD\u0026thinsp;=\u0026thinsp;89.0 ms; two-sided Wilcoxon signed rank test: V\u0026thinsp;=\u0026thinsp;1083, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The very large effect size (rank biserial correlation, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.55) indicates a consistent influence of temporal regularity on children\u0026rsquo;s performance.\u003c/p\u003e\u003cp\u003eTo further probe this effect, we examined whether regularity also influenced accuracy in detecting pitch changes. Children were slightly less accurate in the regular condition than in the irregular condition, as reflected by a drop in the sensitivity index (\u003cem\u003ed\u0026prime;\u003c/em\u003e; \u003cem\u003et\u003c/em\u003e\u003csub\u003e(97)\u003c/sub\u003e = -3.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) with a very small effect size (\u003cem\u003ed\u003c/em\u003e = -0.16). This difference was primarily driven by an increase in the false alarm rate (\u003cem\u003ei.e.\u003c/em\u003e, responses to non-deviant targets; \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2883.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.47), while the Hit rate (\u003cem\u003ei.e.\u003c/em\u003e, correct responses to pitch changes) did not differ between conditions (\u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1929, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.658, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.47). This suggests that children were more biased toward target detection in regular sequences.\u003c/p\u003e\u003cp\u003eTo determine whether RT benefits were robust after accounting for individual differences in accuracy, we fitted a linear mixed-effects model including condition, accuracy benefits (Δ\u003cem\u003ed\u0026prime; = d\u0026prime;\u003c/em\u003e\u003csub\u003eregular\u003c/sub\u003e - \u003cem\u003ed\u0026prime;\u003c/em\u003e\u003csub\u003eirregular\u003c/sub\u003e), and their interaction as predictors (see \u003cb\u003eStatistical analyses\u003c/b\u003e, \u003cb\u003eSupplementary Note 1\u003c/b\u003e). While the model revealed a positive Condition\u0026thinsp;\u0026times;\u0026thinsp;Δ\u003cem\u003ed\u0026prime;\u003c/em\u003e interaction (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0162, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,96.5)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02) with a small effect size (ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.05), it confirmed a main effect of condition (\u003cem\u003eβ\u003c/em\u003e = -0.0125, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,95.0)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;10.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) with a moderate effect size (ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.09). The effect of Δ\u003cem\u003ed\u0026prime;\u003c/em\u003e on RT was not significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,96.)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.40, ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.00). Thus, RT benefits remained significant when accounting for accuracy, indicating that temporal regularity facilitated processing beyond a mere speed-accuracy tradeoff.\u003c/p\u003e\u003cp\u003eWe then computed mean RT benefits from regularity (ΔRT\u0026thinsp;=\u0026thinsp;RT\u003csub\u003eirregular\u003c/sub\u003e - RT\u003csub\u003eregular\u003c/sub\u003e) for each participant as the main measure of implicit rhythmic performance for the following analyses. The ΔRT (M\u0026thinsp;=\u0026thinsp;20.0 ms, SD\u0026thinsp;=\u0026thinsp;43.1 ms) ranged from \u0026minus;\u0026thinsp;111ms (i.e slowing of RT) to +\u0026thinsp;154ms (i.e speeding of RT) reflecting wide interindividual variability in implicit influence of rhythm among children (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eChildren\u0026rsquo;s performance in explicit rhythmic tasks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess children\u0026rsquo;s explicit rhythmic abilities, we measured accuracy in beat perception, i.e., the ability to detect beat misalignments in the Beat Alignment Test (BAT), as well as accuracy and consistency in a series of audio-motor beat synchronization tasks (see \u003cb\u003eMethods\u003c/b\u003e). Descriptive statistics for raw task scores are presented in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e, with score distributions in \u003cb\u003eSupplementary Figs.\u0026nbsp;1\u0026ndash;2\u003c/b\u003e, and inter-task correlations in \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn the beat perception task, children were generally proficient at identifying beat misalignments, as reflected by a mean sensitivity index (d\u0026prime;) of 1.77 (SD\u0026thinsp;=\u0026thinsp;1.19). Given that d\u0026prime; = 0 reflects chance-level performance and values above 2 are typically considered good, this suggests moderately accurate beat perception, albeit with substantial individual variability. For beat synchronization, children were overall able to reliably align their movements to the beat, with a mean consistency score across tasks of 0.79 (SD\u0026thinsp;=\u0026thinsp;0.30) (note: 0\u0026thinsp;=\u0026thinsp;poor synchronization; to 1\u0026thinsp;=\u0026thinsp;perfect synchronization). Accuracy scores further revealed a tendency to anticipate the beat, as shown by a negative mean vector angle (M = -21.9\u0026deg;, SD\u0026thinsp;=\u0026thinsp;39.4), indicating early taps relative to the beat onset.\u003c/p\u003e\u003cp\u003eAs expected, beat perception and synchronization performance were strongly related: standardized BAT performance significantly predicted synchronization consistency scores (β\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with a large effect size (ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.4) accounting for 41% of the variance (R\u0026sup2; = 0.41; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). To capture individual differences in explicit rhythmic ability, we computed the Beat Tracking Index (BTI) combining standardized performance in both BAT and synchronization tasks\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The BTI scores ranged from \u0026minus;\u0026thinsp;1.86 to 1.60 (M\u0026thinsp;=\u0026thinsp;0.001, SD\u0026thinsp;=\u0026thinsp;0.88; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Note that is computed from z-scores but it is not itself a z-score; therefore, its mean is not exactly zero and its standard deviation is not exactly one. The BTI was used as the main index of explicit rhythmic abilities\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExplicit but not implicit abilities grow with age and music\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo test whether implicit and explicit rhythmic abilities follow distinct developmental trajectories, we examined how they varied with age (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and formal musical experience (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), using multiple linear regression models with both variables as predictors.\u003c/p\u003e\u003cp\u003eImplicit rhythmic performance, indexed by reaction time benefits (ΔRT) showed no significant association with either age (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;.902; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.00) or formal musical experience (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.603; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.00); \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .003, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 95)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.868). We also examined whether these variables predicted accuracy differences between conditions (Δd\u0026prime;), but we found no effect of age (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.177; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01) or musical experience (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.348; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.00 ; \u003cem\u003eR\u003c/em\u003e\u0026sup2; = .027; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 95)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.272). These results suggest that neither age nor musical experience significantly influences implicit rhythmic performance.\u003c/p\u003e\u003cp\u003eIn contrast, explicit rhythmic abilities (BTI) improved with both age (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.13) and formal musical experience (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.19), with respectively a medium and large effect size (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.30; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 94)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;19.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This pattern held when examining the perceptual and sensorimotor components of the BTI separately, indicating that the positive associations were not driven by the motor demands of the tasks (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferent modulation by executive functions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe next examined how rhythmic abilities relate to executive functions. To this aim, following the first exploration of the results, we computed the Executive Functioning Index (EFI), a composite score derived from standardized measures of auditory controlled attention, inhibitory control, working memory, and cognitive flexibility (see \u003cb\u003eMethods\u003c/b\u003e for computation, and \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e for correlations between the components and the distribution of the EFI).\u003c/p\u003e\u003cp\u003eNotably, implicit and explicit rhythmic performance showed opposite associations with the Executive Functioning Index (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As expected, explicit rhythmic abilities (BTI) showed a positive association with the Executive Functioning Index (β\u0026thinsp;=\u0026thinsp;\u003cem\u003e0.22\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.033; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .04, ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.04), in line with previous literature. In contrast, and unexpectedly, RT benefits in the implicit task (ΔRT) were negatively associated with EFI (\u003cem\u003eβ\u003c/em\u003e = \u003cem\u003e\u0026minus;\u0026thinsp;0.36\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .13) indicating that in children with higher executive functioning, response speed was less influenced by rhythmic regularity when rhythm was not directly relevant to the task, as was the case in our implicit timing paradigm. This negative relation was also found for all the four components of the EFI (see \u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo test whether this unexpected link could be the consequence of a speed-accuracy tradeoff, we included accuracy differences in the model and an interaction term, but the EFI remained the only significant predictor of ΔRT (\u003cb\u003eSupplementary Note 2\u003c/b\u003e) suggesting that the link between RT benefits and executive functions cannot be merely explained by a speed-accuracy tradeoff effect.\u003c/p\u003e\u003cp\u003eTo further investigate the observed negative association with EFI, all EFI components were entered into a multiple regression model (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = .15; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(4, 93)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004), in which auditory controlled attention emerged as the strongest predictor (β = \u003cem\u003e\u0026minus;\u0026thinsp;0.27\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.015) with a medium effect size (ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.11), while the others did not reach significance (inhibitory control: β = \u003cem\u003e\u0026minus;\u0026thinsp;0.02\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.853; working memory: β = \u003cem\u003e\u0026minus;\u0026thinsp;0.09\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.383; cognitive flexibility: β = \u003cem\u003e\u0026minus;\u0026thinsp;0.12\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.264). These results suggest a potentially more central role of auditory attentional control in the implicit effect of temporal regularities on children\u0026rsquo;s auditory processing. Following these results, we made a post hoc analysis of late RT outliers excluded during the preprocessing (see \u003cb\u003eMethods\u003c/b\u003e). A slightly higher proportion of late responses was observed in the irregular condition, potentially reflecting increased attentional disengagement under temporal uncertainty (regular: M\u0026thinsp;=\u0026thinsp;1.25%, SD\u0026thinsp;=\u0026thinsp;1.54; irregular: M\u0026thinsp;=\u0026thinsp;1.96%, SD\u0026thinsp;=\u0026thinsp;1.54; \u003cem\u003eV\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2312.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLink between implicit and explicit rhythmic abilities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn order to test the independence of implicit and explicit rhythmic abilities, we examined whether RT benefits from regularity in the implicit task (ΔRT) were related to explicit rhythmic abilities. A simple regression analysis showed that ΔRT was not related to the Beat Tracking Index (BTI) (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.185, ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.02; see scatterplots in \u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e). This result points towards an independence between these abilities. However, a relationship between implicit and explicit rhythmic performance emerged when taking into account executive functioning. We fitted a multiple linear regression model with ΔRT as the outcome variable and BTI, EFI, and their interaction term as predictors (\u003cem\u003eR\u003c/em\u003e formula: ΔRT\u0026thinsp;~\u0026thinsp;BTI * EFI). The model was significant, explaining 23% of the variance (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.23; \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(3, 93)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Explicit rhythmic abilities positively predicted RT benefits in the implicit task (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01), and this effect was stronger in children with higher executive functioning, as reflected by a significant interaction (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.013; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.05). Meanwhile, executive functioning negatively predicted RT benefits overall (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; ω\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.16). To illustrate this interaction and clarify the direction of effects, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents individual mean RTs for both conditions, rather than ΔRT values. Participants were grouped into three executive functioning levels (low, medium, high) based on the 1/3 and 2/3 quantiles of the EFI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding how rhythmic abilities develop and shape sensitivity to environmental regularities requires disentangling their implicit and explicit components. In this study, we directly compared implicit and explicit rhythmic performances in approximately 100 children aged 7 to 13 —a critical window for cognitive maturation and learning\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We found that explicit, deliberate rhythm processing improves markedly with age and formal musical experience, whereas implicit rhythm sensitivity remains remarkably stable across development. Yet these distinct developmental trajectories do not reflect complete independence. Instead, we identified a hidden connection between the two processes, modulated by executive functions. These findings offer new insights into the boundaries and potential bridges between implicit and explicit cognition during human development.\u003c/p\u003e\u003cp\u003eThis study is the first to examine the development of both implicit and explicit rhythm processing, shedding new light on how rhythmic abilities emerge and interact during childhood. Previous developmental studies have primarily focused on explicit, deliberate rhythm processing, which relies on cognitive control to direct attention toward rhythm, maintain an internal beat, and align actions accordingly. However, rhythm often influences behavior in more subtle ways\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In everyday contexts, rhythm per se is rarely the focus of attention, yet it can implicitly shape perception, prediction, and response to events\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Unlike explicit tasks, our novel implicit task did not require participants to attend to rhythm. Instead, children played a gamified task in which they had to detect pitch changes to help a small astronaut navigate through space. Crucially, children were faster to detect pitch targets when these followed temporally regular sequences, revealing implicit sensitivity to rhythm —even when rhythm was not directly relevant to the task.\u003c/p\u003e\u003cp\u003eA pivotal finding of this study is the clear dissociation in developmental trajectories. Explicit rhythmic abilities improved with age and formal musical experience, consistent with previous findings\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e–\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, whereas implicit rhythm sensitivity remained remarkably stable between ages 7 and 13. This lack of age or training effects, observed in a relatively large sample size for this developmental window, suggests that the ability to benefit from rhythmic regularities is already well established by early childhood. In contrast, the deliberate use of rhythm appears to develop more gradually, shaped by cognitive maturation and musical experience.\u003c/p\u003e\u003cp\u003eOur results contrast with prior work reporting age-related increases in visual implicit rhythmic effects\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. A likely explanation lies in the used modality: humans show greater sensitivity to rhythmic regularity in the auditory modality\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and auditory rhythms engage stronger auditory-motor coupling than visual rhythms\u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e–\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Notably, auditory beat perception begins to emerge in utero and is already observable in premature newborns\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Infants are then exposed to the rhythmic structure of lullabies, nursery rhymes, and speech prosody\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, which may accelerate implicit auditory rhythm processing relative to visual rhythms, contributing to the auditory advantage reported in adults\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Future research directly comparing auditory and visual modalities using matched paradigms will be needed to test this hypothesis. Nevertheless, our findings echo results from other domains such as memory\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e or auditory single-duration processing\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, where implicit and explicit systems follow distinct developmental trajectories, with implicit processes appearing more stable across ages.\u003c/p\u003e\u003cp\u003eAnother key and unexpected finding is that, although implicit and explicit rhythmic abilities follow distinct developmental trajectories -suggesting reliance on partly dissociable mechanisms- a link between them emerges when executive functions are taken into account. Specifically, a positive association was observed in children with higher executive functioning. This finding is non-trivial: it suggests that benefiting from temporal regularities in everyday contexts may still rely on rhythm skills typically assessed through explicit musical tasks, but that this relationship is shaped by cognitive control. Such an executive function-dependent link challenges the notion of a strict dichotomy between implicit and explicit rhythm processing. Instead, it supports a more dynamic framework in which the two systems interact and may draw on shared mechanisms depending on individual cognitive profiles. In adults, neuroimaging studies show that both forms of rhythm processing engage overlapping motor-related structures, including the basal ganglia, cerebellum, and supplementary motor area\u003csup\u003e\u003cspan additionalcitationids=\"CR69 CR70 CR71\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e–\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Crucially, implicit rhythm processing also specifically recruits the left inferior parietal cortex -a region essential for temporal orienting of attention\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Whether these brain structures are similarly recruited in children, and whether shared or distinct regions are engaged depending on executive functioning, could be investigated in future developmental neuroimaging studies.\u003c/p\u003e\u003cp\u003ePerhaps most intriguingly, we found that global executive functioning levels modulate implicit and explicit rhythm abilities in opposite directions. As expected, higher executive functions predicted better performance in explicit rhythm tasks, consistent with previous reports\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Yet paradoxically, they were associated with smaller RT benefits in the implicit task. This novel finding suggests that executive functions influence rhythmic processing in fundamentally different ways depending on task demands. When rhythm is incidental -as in our implicit task- stronger executive functioning may reduce reliance on temporal regularities. According to entrainment models\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, particularly the Dynamic Attending Theory\u003csup\u003e\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e–\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, rhythmic temporal regularity facilitates perception by aligning internal neural oscillations with external periodicities\u003csup\u003e\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e–\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Although typically considered automatic and bottom-up\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, entrainment can be modulated by top-down control depending on task demand\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. One possibility is that children with higher executive functions rely less on spontaneous entrainment or override it in favor of more controlled strategies. Such modulation may be beneficial in everyday environments, where rhythm-based cues are not always informative\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. In our task, children with high executive functioning may have selectively focused on pitch changes, resisting the influence of the preceding rhythmic sequence. In contrast, children with lower executive functioning may have been more easily entrained, leading to greater RT benefits in the regular condition.\u003c/p\u003e\u003cp\u003eAlternatively, auditory rhythmic regularity may exert a less temporally specific effect, broadly supporting attentional control without necessarily involving entrainment mechanisms\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. This general facilitation could particularly benefit children with weaker global executive functioning, potentially explaining the negative association between RT benefits and EF. Supporting this, auditory controlled attention emerged as the strongest negative predictor of RT benefit among all EF components. Moreover, irregular trials elicited more late outlier responses, suggesting greater attentional disengagement under temporal uncertainty. These findings align with the idea that unpredictability imposes a higher cognitive cost\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, a possibility that future studies could further investigate. Another contributing factor may be impulsivity, or a strategic shift toward speed over accuracy in children with lower executive functioning. Overall, children made more false alarms after regular sequences —a pattern also observed in adults with autism\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e— suggesting that rhythmic cues may bias responses in individuals with reduced inhibitory control. However, the negative link between EF and RT benefit remained significant after controlling for accuracy, indicating that impulsivity alone does not fully explain this relation. These explanations are not mutually exclusive. The broad variability in RT benefits likely reflects the recruitment of different processes and a dynamic interplay between cognitive control, individual strategies, and sensitivity to temporal regularities. Substantial inter-individual variability has also been reported in adults\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan additionalcitationids=\"CR88\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e–\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Our findings extend this perspective developmentally, showing that such variability emerges early in life and may reflect differences in cognitive profiles.\u003c/p\u003e\u003cp\u003eVariability is a hallmark of the human sense of rhythm\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Understanding why individuals do -or do not- rely on rhythmic regularities in their environment remains a compelling avenue for future research\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. Our findings underscore the need to clarify the mechanisms underlying this variability and highlight the distinct yet complementary role that implicit rhythm processing may play in development and learning\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Promising directions include paradigms that dissociate general attentional benefits from entrainment-specific effects, as well as neurophysiological methods to probe the temporal dynamics of rhythm-related facilitation\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Longitudinal or cross-sectional comparisons using identical paradigms in children and adults may further elucidate how implicit-explicit interactions evolve beyond adolescence. Critically, extending this work to neurodevelopmental conditions such as ADHD or speech and language disorders known to involve explicit rhythm deficits\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e could determine whether implicit rhythm processing is similarly affected, and how such impairments impact everyday functioning. These questions are not only theoretically interesting but hold translational promise: while training explicit rhythmic abilities has demonstrated transfer effects on executive functioning in children\u003csup\u003e\u003cspan additionalcitationids=\"CR97 CR98\" citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e–\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e, exploring whether implicit rhythm sensitivity can also be trained -and whether such training might yield broader cognitive benefits- represents an exciting direction for future studies.\u003c/p\u003e\u003cp\u003eDevelopmental research offers valuable insights into the architecture of rhythm processing. By demonstrating that implicit and explicit rhythm processing follow distinct developmental trajectories yet remain functionally linked via cognitive control, our study opens new avenues for clinical and educational applications, contributes to theoretical models of rhythm, and may prompt further reflection on evolutionary and developmental roots of rhythmic cognition.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 106 French-speaking children aged 7 to 13 participated in the study. They were recruited in Lille (France) and Montréal (Canada). The following non-inclusion criteria were applied: (1) a reported diagnosis of a neurodevelopmental or psychiatric disorder, (2) a history of neurological events, (3) reported hearing impairments, and (4) uncorrected vision deficits. To control for general intellectual and sensori-motor functioning, we assessed fluid reasoning and processing speed using the Matrix Reasoning and Coding subtests of the Wechsler Intelligence Scale for Children − 5th edition \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e as control tests of general intellectual and sensori-motor functioning. A below-threshold score (i.e., standard score \u0026lt; 5) on either subtest was defined as an exclusion criterion. Additionally, a data quality check was performed on the implicit rhythmic task to ensure that children adequately understood and performed the task (see the Preprocessing section). A high number of invalid trials or performance close to chance level was considered an exclusion criterion (see the \u003cb\u003ePreprocessing\u003c/b\u003e section).\u003c/p\u003e\u003cp\u003eIn total, eight children were excluded: two due to a low score on one of the control tests, four because they did not meet the quality criteria for the implicit rhythmic task, and two due to behavioral issues or technical problems during testing.\u003c/p\u003e\u003cp\u003eThe final sample consisted of 98 children aged 7 to 13 (\u003cem\u003eM\u003c/em\u003e = 10.1, \u003cem\u003eSD\u003c/em\u003e = 1.6), including 62 children from France and 36 from Canada, with 58% being female. Among them, 35 had at least one year of formal musical experience (for the whole group: \u003cem\u003eM\u003c/em\u003e = 0.95 years, \u003cem\u003eSD\u003c/em\u003e = 1.5, range = [0; 5]). The occupational categories reported by the children’s parents were professionals and managers (41%), office and service workers (45%), skilled and unskilled manual workers (6%), and unemployed (6%).\u003c/p\u003e\u003cp\u003eMean standard scores on the control tests were as follows: Matrix Reasoning (\u003cem\u003eM\u003c/em\u003e = 10.5, \u003cem\u003eSD\u003c/em\u003e = 2.53) and Coding (\u003cem\u003eM\u003c/em\u003e = 11.5, \u003cem\u003eSD\u003c/em\u003e = 2.31). Spontaneous motor tempo was also assessed using an unpaced tapping task administered prior to the rhythmic tasks. As this measure was not central to our hypotheses and showed no significant association with implicit rhythmic performance or age, it is not included in the main analysis. For completeness, we report the distribution of mean inter-tap interval (\u003cem\u003eM\u003c/em\u003e = 549ms, \u003cem\u003eSD\u003c/em\u003e = 186) and its coefficient of variation (\u003cb\u003eSupplementary Fig.\u0026nbsp;8\u003c/b\u003e), along with scatterplots showing no relationship with age, ΔRT, or paced tapping performance at 600 ms (\u003cb\u003eSupplementary Figs.\u0026nbsp;9, 10, 11\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e Written informed consent was obtained from all participants’ parents, in accordance with the Declaration of Helsinki. Children also gave assent prior to testing. The study was approved by the ethics committees of the University of Montreal (CEREP-22-052-D) and the University of Lille (CER-2021-563-S101).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterials and tasks\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplicit rhythmic task\u003c/b\u003e\u003c/p\u003e\u003cp\u003e In the implicit rhythmic task, children listened to sequences of tones (standard sounds of identical pitch), each followed by a target sound that was either identical in pitch (non-deviant) or higher in pitch (deviant) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Children were instructed to respond as quickly as possible only if the target sound was deviant.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStimulus materials.\u003c/em\u003e Each sequence consisted of eight standard sounds (600 Hz), followed by a target sound, which was either higher in pitch than the standard sounds (deviant, 2/3 of target sounds) or identical in pitch (non-deviant, 1/3 of target sounds). The pitch deviation of the target sounds was individually adjusted to each child's pitch detection ability (see \u003cb\u003eAdaptation of Task Difficulty\u003c/b\u003e below). All the sounds were pure tones (duration: 150 ms, including 10 ms rise and fall times) and were computer-generated using the \u003cem\u003escipy.signal\u003c/em\u003e python library\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e in Python 3.10.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAdaptation of task difficulty\u003c/em\u003e. An adaptive procedure was implemented to tailor task difficulty for each child's pitch discrimination ability and learning speed. At the beginning of the experiment, a brief staircase method (1 up, 1 down) was used to estimate each child’s pitch discrimination threshold and define the initial pitch difference for deviant tones, setting the starting difficulty level. Throughout the experiment, task difficulty was adjusted after each block based on the child’s performance: if accuracy exceeded 85%, the pitch difference was decreased to increase difficulty; if accuracy fell below 70%, the pitch difference was increased to make the task easier; and if accuracy remained between 70% and 84%, the pitch difference was left unchanged. These threshold values were determined in a pilot phase to ensure smooth progression of the task. This adaptive design aimed to maintain an optimal challenge, ensuring sustained engagement of the children throughout the task.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTask description and procedure.\u003c/em\u003e We manipulated the temporal regularity of the standard sound sequences. In regular trials (50%), standard sounds occurred at a fixed inter-onset interval (IOI) of 600 ms (i.e., isochronously). In irregular trials (50%), IOIs varied randomly between 300 and 900 ms (flat distribution). In both cases, the target sound followed the last standard sound after 600 ms. The task consisted of six blocks, each containing 20 deviant target trials and 10 non-deviant target trials.\u003c/p\u003e\u003cp\u003eTo enhance the child's engagement and motivation, the task was gamified (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The game featured \u003cem\u003eMarius\u003c/em\u003e, a lost astronaut navigating his way back to Earth using sound cues. Higher-pitched sounds indicated that Marius was moving away from Earth, prompting children to redirect him by pressing a button. Each level represented the discovery of a new planet. During trials, children viewed a black screen and were instructed to fixate on a central red rocket while listening to the sequences. They were asked to press a button only if the final sound in a sequence was higher in pitch than the preceding tones. As a warning signal, the red rocket turned green on the 8th sound, indicating that the target sound was about to be presented. Instructions emphasized responding exclusively to deviant target sounds while ignoring non-deviant targets and standard sounds. As the task focused on pitch discrimination, it did not require explicit attention to the temporal regularity of the sequences, and the child was not informed that rhythm was manipulated.\u003c/p\u003e\u003cp\u003eThe main dependent measure was reaction time (RT) on deviant targets. Additionally, response types were recorded: Hits (button presses on deviant targets), False Alarms (FA; button presses on non-deviant targets), Correct Rejections (CR; correctly withholding a response to a non-deviant target), and Misses (failure to respond to a deviant target). To maintain engagement, visual feedback encouraged rapid and accurate responses: quick reactions were rewarded with a diamond star (RT \u0026lt; 450 ms), gold star (RT between 450 and 549 ms), silver star (RT between 550 and 649 ms), or bronze star (slower RTs). False alarms triggered a no-go sign to discourage random button presses. At the end of each block, the child received feedback on their overall accuracy, represented by 1, 2, or 3 stars. Regardless of performance, all children successfully \"reached Earth\" at the end of the game to ensure a positive and engaging experience.\u003c/p\u003e\u003cp\u003eThe implicit rhythmic task was developed using PsychoPy\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Game images were sourced from Shutterstock.com (freely available for non-commercial use) and edited using GIMP software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gimp.org/\u003c/span\u003e\u003cspan address=\"https://www.gimp.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The experiment took place in a quiet room, with the child being seated in front of a computer next to the experimenter. They responded using their dominant hand via a response button (Buddy Button). The entire task, including instructions, training, and short breaks between blocks, lasted approximately 30 minutes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExplicit Rhythmic Tasks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExplicit rhythmic tasks were selected from the mobile version of the Battery for the Assessment of Auditory and Sensorimotor Timing Abilities (BAASTA\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e) and administered using a tablet with headphones. BAASTA includes various tests designed to evaluate both perceptual and sensorimotor explicit rhythmic abilities. All sensorimotor tests were performed using the index finger of the participants' dominant hand. Instructions explicitly emphasized temporal regularity, and all auditory stimuli were computer-generated with a piano timbre. This battery was previously used in studies involving both typically and atypically developing children within this age range \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBeat perception\u003c/b\u003e was assessed using the \u003cem\u003eBeat Alignment Test (BAT)\u003c/em\u003e. In this task, children judged whether a metronome was correctly aligned with the beat of a musical excerpt or not. Musical excerpts were presented at a tempo of 600 ms per beat. After seven beats, a metronome was superimposed on the music, either aligned or misaligned to the beat. The test contained two-thirds of misaligned trials and one-third of aligned trials.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBeat synchronization\u003c/b\u003e was evaluated through four \u003cem\u003epaced tapping tasks\u003c/em\u003e, in which children tapped along with either isochronous sequences (two conditions: 450 ms and 600 ms IOIs) or musical excerpts (two pieces: \u003cem\u003eBadinerie\u003c/em\u003e by Bach (music 1) and the \u003cem\u003eWilliam Tell Overture\u003c/em\u003e by Rossini (music 2), both featuring an inter-beat interval of 600 ms). Each task consisted of two trials.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExecutive Functions Tests\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe tests were selected from the Wechsler Intelligence Scale for Children − 5th edition (WISV-V\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e), the Test of Everyday Attention for Children (TEA-ch\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e), and the FÉE battery for executive function in children\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAuditory controlled attention\u003c/b\u003e was assessed using the \u003cem\u003eCoups de Fusil\u003c/em\u003e task from the French version of the TEA-Ch battery. This multicomponent task taps into both sustained attention and executive functioning, notably auditory working memory and cognitive control. Children listened to ten series of sounds presented in a temporally irregular sequence. Their task was to silently count the number of sounds in each series (ranging from 9 to 15) without using their fingers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInhibitory control\u003c/b\u003e was assessed using the \u003cem\u003eTapping Enfant\u003c/em\u003e task from the FÉE. In this test, children were instructed to tap on the table with their index finger, either once or twice, depending on the experimenter's actions. The task consisted of three parts with increasing difficulty. In the first part, children simply had to reproduce the experimenter's gestures: tapping once when the experimenter tapped once and tapping twice when the experimenter tapped twice. The second part introduced a go/no-go paradigm, where children still had to tap once when the experimenter tapped once but were required to refrain from tapping if the experimenter tapped twice. In the third and most complex part, the rules were reversed: children had to tap twice when the experimenter tapped once and tap once when the experimenter tapped twice. Additionally, if the experimenter used two fingers instead of just the index finger -regardless of whether they tapped once or twice- children had to withhold their response.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWorking memory\u003c/b\u003e was assessed using the \u003cem\u003eMémoire des Chiffres\u003c/em\u003e test from the WISC-V. In this digit-span task, children were asked to repeat sequences of numbers presented aloud by the examiner. The task consisted of three conditions: in the forward digit span, children repeated the numbers in the same order as presented; in the backward digit span, they repeated them in reverse order; and in the ascending digit span, they reordered the numbers in ascending order.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCognitive flexibility\u003c/b\u003e was evaluated with the task \u003cem\u003eLes petits Hommes Verts\u003c/em\u003e from the French version TEA-Ch. In this task, children counted small green monsters appearing along burrows. Arrows placed between the creatures indicated the counting direction: children started by counting forward one by one, but whenever they encountered an arrow pointing downward, they had to switch directions and count backward until reaching the final creature.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.3| Procedure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChildren participated in two sessions, conducted either at the BRAMS laboratory in Montreal, Canada or in a quiet room at their school in Lille, France. One session focused on rhythmic abilities (implicit and explicit, ≈1h15), while the other assessed cognitive abilities (≈ 45 minutes). During the rhythm session, children first completed a spontaneous motor tempo assessment, followed by the implicit rhythmic task and the perceptual and paced tapping tasks from the BAASTA. In the cognitive session, they began with subtests from the WISC-V battery evaluating reasoning and processing speed, used as control measures of intellectual and sensory-motor efficiency, before completing tests from the WISC-V, TEA-CH and FÉE batteries assessing attention, inhibition, working memory, and cognitive flexibility. To minimize fatigue, sessions were scheduled on two different half-days, either on separate days or with one in the morning and the other in the afternoon. The order of the sessions was randomized, with 40% of the children starting with the cognitive session and 60% beginning with the rhythm session. No significant effect of session order was found for any of the variables of interest (all \u003cem\u003ep\u003c/em\u003e \u0026gt; .05, lowest \u003cem\u003ep\u003c/em\u003e = .55).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData pre-processing and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplicit rhythmic task pre-processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following steps outline the data pre-processing procedure\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. A quality check was performed to ensure data reliability. For each participant, trials were classified as valid or invalid. A trial was considered valid if no button press occurred before target onset, in line with the instruction to respond only to the target sound. Although a small number of invalid trials can reflect impulsive or anticipatory responses, a high proportion may indicate poor task compliance. The percentage of valid trials was computed for each participant (mean = 95.2%), with no significant difference between conditions (\u003cem\u003ep\u003c/em\u003e = .401). Participants with fewer than 60% valid trials were excluded, which applied to only one participant. For the remaining participants, invalid trials were removed from further analyses. We then computed the proportion of correct responses. Participants performing near chance level (accuracy \u0026lt; 60%) were excluded from the analysis (n = 3).\u003c/p\u003e\u003cp\u003eIn a second step, trials with outlier reaction times (RTs) were removed. Outliers were defined as RTs falling more than ± 3 SD from the participant’s mean RT\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. Outliers identification was conducted independently of conditions and included both correct responses and false alarms\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Overall, 1.69% of trials were classified as outliers. Condition differences were examined post hoc (see the \u003cb\u003eResults\u003c/b\u003e section). While no significant difference was found for early outliers (i.e., fast responses; \u003cem\u003ep\u003c/em\u003e = .308), a slightly higher proportion of late outliers was observed in the irregular condition (regular: 1.25%, irregular: 1.96%, \u003cem\u003ep\u003c/em\u003e = .002).\u003c/p\u003e\u003cp\u003eFinally, for each participant and each condition (regular and irregular), we computed the mean correct RT (i.e., RTs from Hit trials). As a secondary measure, accuracy was assessed using a sensitivity index (\u003cem\u003ed\u003c/em\u003e′), calculated as the difference between the z-transformed Hits and FA rates. In accordance with Macmillan and Kaplan’s correction (Macmillan \u0026amp; Kaplan, 1985), Hit and FA rates of 0 or 1 were adjusted to l/(2N) and 1–l/(2N), respectively, where N corresponds to the number of signal (for Hits) or noise (for FA) trials.\u003c/p\u003e\u003cp\u003eImplicit rhythmic ability was quantified as the difference in mean reaction time (ΔRT) between the two conditions, with positive values indicating better performance in the regular condition than the irregular one (ΔRT = RT\u003csub\u003eirregular\u003c/sub\u003e – RT\u003csub\u003eregular\u003c/sub\u003e). As a secondary measure, we computed the mean difference in accuracy (Δ\u003cem\u003ed\u003c/em\u003e′), where positive values also indicated better performance in the regular condition (Δ\u003cem\u003ed\u003c/em\u003e′ = \u003cem\u003ed’\u003c/em\u003e\u003csub\u003eregular\u003c/sub\u003e – \u003cem\u003ed’\u003c/em\u003e\u003csub\u003eirregular\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eExplicit rhythmic tasks: calculation of the Beat Tracking Index (BTI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo summarize rhythm perception and synchronization abilities, we computed the Beat Tracking Index (BTI), which combined beat perception and synchronization scores\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe beat perception score was derived from the sensitivity index (\u003cem\u003ed′\u003c/em\u003e) in the Beat Alignment Test (BAT), calculated based on hit rates (correct detections of misaligned metronomes) and false alarms rates (incorrect detections of misalignment in aligned metronomes). Individual d′ values were converted to z-scores based on the full sample distribution.\u003c/p\u003e\u003cp\u003eThe beat synchronization score was based on synchronization consistency across the four paced tapping tasks. Consistency reflects the temporal regularity of tapping intervals relative to the beat within a trial - that is, how stably participants maintained a periodic response pattern. It was computed using circular statistics\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e yielding values from 0 (no consistency) to 1 (perfect consistency). To correct for the typical skewness of synchronization data, consistency scores were logit-transformed\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e producing values theoretically ranging from -∞ (logit(0)) to +∞ (logit(1)). These transformed scores were then converted into z-scores for each task and averaged to obtain a composite beat synchronization score.\u003c/p\u003e\u003cp\u003eThe BTI was computed as the mean of each participant’s beat perception and beat synchronization z-scores. For one participant, synchronization could not be computed due to insufficient tapping signal in the music trials; as such, BTI analyses include 97 participants.\u003c/p\u003e\u003cp\u003eAdditionally, for each synchronization task, accuracy was computed. Accuracy is expressed as the angle of the vector R (θ, or relative phase, in degrees), indicating whether participants tapped before (negative values) or after (positive values) the pacing event. Accuracy was computed only if synchronization performance exceeded chance level, as determined by the Rayleigh test for circular uniformity\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCognitive tasks: calculation of the Executive Functioning Index (EFI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn initial exploration of the results revealed correlations between implicit performance and all executive function measures. Given that these functions are interrelated and build upon each other (Diamond, 2013), we computed a composite Executive Functioning Index (EFI) by combining scores from sustained attention, inhibitory control, working memory, and cognitive flexibility tests. This approach aimed to reduce the number of statistical tests and minimizing the risk of an inflated alpha error in subsequent analyses.\u003c/p\u003e\u003cp\u003eThe auditory attention score was derived from the raw score of the \u003cem\u003eScore!\u003c/em\u003e test, representing the number of correctly counted sound sequences. For the inhibition task, a raw performance score was computed by summing the number of correct responses across the three parts of the \u003cem\u003eTapping Enfant\u003c/em\u003e test. The auditory working memory score corresponded to the total raw score obtained on the \u003cem\u003eDigit\u003c/em\u003e test, reflecting the number of correctly recalled sequences across all three conditions (forward, backward, and ascending). Finally, cognitive flexibility was measured using the raw accuracy score from the \u003cem\u003eCreature Counting\u003c/em\u003e test, which corresponded to the number of sequences correctly completed despite changes in counting direction.\u003c/p\u003e\u003cp\u003eAll raw scores were converted into z-scores, using the mean and standard deviation of the full sample. These z-scores were then averaged to compute the EFI. The distribution of EFI scores is provided in the \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData processing and statistical analyses were conducted using R version 4.4.0\u003csup\u003e105\u003c/sup\u003e. Implicit rhythmic abilities were assessed through pairwise comparisons of mean reaction times between the temporally regular and irregular conditions. Additional comparisons were performed on mean accuracy, measured by the sensitivity index (\u003cem\u003ed’\u003c/em\u003e). Prior to analysis, variable distributions were tested for normality using the \u003cem\u003ejmv\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e; a variable was considered normally distributed if both skewness and excess kurtosis fell within the [-1,1] range. For normally distributed variables, two-sided paired-sample t-tests were used; otherwise, two-sided Wilcoxon signed-rank tests were applied. Effect sizes were computed using the R \u003cem\u003eeffect size\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e: Cohen’s d for t-tests, rank-biserial correlation (r) for Wilcoxon tests, and partial omega squared (ω²ₚ) for F-tests in regressions. Effect sizes are reported and interpreted according to established benchmarks: Funder and Ozer\u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e for rank biserial \u003cem\u003er\u003c/em\u003e, Sawilowsky\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e for Cohen’s d, and Field\u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e for ω²ₚ.\u003c/p\u003e\u003cp\u003eTo further explore reaction time differences in the implicit rhythmic task while accounting for variations in accuracy between conditions, we conducted a mixed-effects model analysis. This analysis was implemented using the \u003cem\u003elmerTest\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003elme4\u003c/em\u003e\u003csup\u003e112\u003c/sup\u003e R packages. The model included log10-transformed correct RTs per trial as the dependent variable to better normalize residuals. Fixed effects comprised condition (with the irregular condition as the reference level) and the accuracy difference between conditions (\u003cem\u003eΔd’\u003c/em\u003e). To account for the hierarchical structure of the data, we included random intercepts for participants and random slopes for the condition effect. Omnibus tests for main effects and interactions were performed using type III sum of squares, with F and p-values computed via the Kenward-Roger approximation for degrees of freedom\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e,\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eFinally, relationships between rhythmic abilities (both implicit and explicit) and variables such as age, formal musical experience, and executive functions were explored using simple and multiple linear regressions. Models were fitted using the \u003cem\u003elm\u003c/em\u003e function in R, with ΔRT (from the implicit task) serving as the primary measure of implicit rhythmic abilities and Δd’ included as additional information on accuracy differences. The BTI was used as a measure of explicit rhythmic abilities. All predictors were standardized prior to inclusion in the models, except for Δd′, which was left unstandardized so that a value of 0 represented no difference in accuracy between conditions. Model diagnostics were performed using the \u003cem\u003eperformance\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e to ensure appropriate fit.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eReporting Summary\u003c/p\u003e\n\u003cp\u003eFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eTo ensure reproducibility, data used in the analyses will be made publicly available along the R project for the analyses.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eR code used for the analyses will be made publicly available in a dedicated GitHub repository associated with this article.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis study was supported by a Canada Research Chair awarded to S.D.B.; by grants from the University of Lille, the Acad\u0026eacute;mie Fran\u0026ccedil;aise, and ISITE-ULNE awarded to A.G.; by an INSPE grant awarded to D.D.; and by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program (reference ANR-23-IAHU-0003) awarded to S.S. We are grateful to the children and their families for their participation, and to Oc\u0026eacute;ane Martin, Margaux Dos Santos, C\u0026eacute;lina Zenag, and No\u0026eacute;mie Beauduin for their assistance with recruitment and data collection.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eA.G. contributed to the conceptualization, development of the implicit task, methodology, participant testing, data analysis, and writing -original draft. D.D., S.D.B. and S.S. contributed to the conceptualization, development of the implicit task, methodology, supervision, data analysis, and writing -review and editing. N.E.V. contributed to the development of the implicit task, data analysis, and writing -review and editing. S.C., V.B., and S.A.K. contributed to the development of the implicit task. D.D. and S.D.B. jointly supervised the project. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests \u003c/p\u003e\n\u003cp\u003eThe authors declare the following competing interests: SDB is on the board of the BeatHealth company dedicated to the design and commercialization of technological tools for assessing rhythmic abilities such as BAASTA tablet and implementing rhythm-based interventions. Other authors have no competing interest to disclose.\u003c/p\u003e\n\u003cp\u003eMaterials \u0026amp; Correspondence\u003c/p\u003e\n\u003cp\u003eCorrespondence and material requests should be addressed to A.G. ([email protected]), D.D ( [email protected] ) and S.D.B. ([email protected] ).\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDoelling, K., Herbst, S., Arnal, L. \u0026amp; van Wassenhove, V. 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S., Patil, I., Waggoner, P. \u0026amp; Makowski, D. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. \u003cem\u003eJournal of Open Source Software\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 3139 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7086372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7086372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rhythm is an omnipresent feature of our environment. Repetitive temporal patterns in sound and vision influence how we pay attention to the world, move and speak. Grasping these regularities is critical for development. Humans can track surrounding rhythms explicitly —like dancing to the beat of music- or implicitly, when rhythms guide perception and behavior without deliberate attention. Whether these abilities follow different developmental trajectories remains unknown. Here, we tested 98 children aged 7-13 using a novel gamified task measuring implicit rhythm processing, alongside assessments of explicit rhythmic abilities and cognition. For the first time, we show that explicit and implicit rhythmic abilities follow distinct developmental paths: although the former improve with age and musical experience, the latter remains stable. Both are modulated differently by cognitive control, yet are not fully disconnected. These findings offer new theoretical insights into rhythm development, with important implications for neurodevelopmental disorders and rhythm-based rehabilitation.","manuscriptTitle":"Distinct developmental trajectories shape human sensitivity to rhythms in the environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:51:46","doi":"10.21203/rs.3.rs-7086372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4d71afd-c2ea-4aa4-9d18-968330471901","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51699840,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Perception"},{"id":51699841,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Attention"},{"id":51699842,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control"},{"id":51699843,"name":"Scientific community and society/Social sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2026-03-19T18:48:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:51:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7086372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7086372","identity":"rs-7086372","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}