Inter-brain synchrony in children scales with coordinated action during cooperative tangram solving

Inter-brain synchrony in children scales with coordinated action during cooperative tangram solving | 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 Research Article Inter-brain synchrony in children scales with coordinated action during cooperative tangram solving Raimundo Silva Soares, João Ricardo Sato¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9302137/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Cooperative problem solving plays a central role in children’s cognitive and social development, yet how neural activity relates to real-time peer coordination remains poorly understood. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we simultaneously recorded brain activity from 15 dyads of school-aged children (6–11 years) during a naturalistic Tangram puzzle task, including rest, solo problem-solving, observation, and collaboration. We examined task-related neural activation and inter-brain connectivity (IBC) to assess how interpersonal neural dynamics relate to task context and behavior. Group-level analyses revealed increased activation in the right posterior parietal cortex during collaborative problem solving, consistent with increased visuomotor coordination demands. In contrast, global IBC did not differ significantly across experimental conditions, indicating that inter-brain synchrony was not determined solely by cooperation or social presence. Critically, however, IBC during the collaborative condition was positively associated with children’s behavioral engagement, as indexed by the number of actions performed on the puzzle pieces, an effect absent in the rest, solo, and observational conditions. Together, these findings indicate that interpersonal neural synchrony in childhood scales with active, embodied engagement rather than shared task structure or co-presence alone. Beyond demonstrating the feasibility of dual-child portable fNIRS hyperscanning in an ecologically valid hands-on task, the results show that inter-brain measures primarily track the dynamics of coordinated behavior in real-world social interactions, constraining how such signals should be interpreted in naturalistic collaborative contexts. fNIRS hyperscanning Child development Peer collaboration Embodied cognition Ecological validity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Human cognition develops within deeply social environments. From early childhood, learning, problem-solving, and reasoning often occur through collaboration with others, in which shared attention, coordinated actions, and communication influence cognitive development (Rogoff 2003 ; Tomasello 2019 ). Cooperative problem solving is a key aspect of childhood social interaction and has been connected to improvements in executive function and academic progress (Warneken 2018 ; Doebel 2020 ). Despite extensive behavioral evidence, the neural processes underlying children's coordination of thoughts and actions during cooperation remain poorly understood. Understanding how children’s brains synchronize during real-world interactions is thus a challenge for developmental social neuroscience. Hyperscanning, a technique that records neural activity from two or more individuals simultaneously, provides a unique view of the neural processes underlying social interaction (Montague et al. 2002 ; Babiloni and Astolfi 2014 ). Instead of inferring social processes from isolated brains, hyperscanning allows measurement of inter-brain synchrony, showing how neural dynamics become aligned during communication, joint attention, and cooperative behavior. A solid body of adult research shows increases in inter-brain coupling during cooperation (Shaw et al. 2023 ), verbal communication (Jiang et al. 2012), coordinated movement (Konvalinka and Roepstorff 2012 ), and shared attention (Dumas et al., 2010 ). Such synchrony has been interpreted as reflecting interpersonal coordination processes, which are key parts of social cognition (Hasson et al. 2012 ; Redcay and Schilbach 2019 ). In adult hyperscanning literature, a critical distinction is often drawn between co-action (performing tasks side by side) and joint action (collaborating toward a shared goal). Evidence suggests that physical proximity or simultaneous action alone is insufficient to elicit robust inter-brain synchrony; rather, shared intentionality and reciprocal coordination drive neural alignment (Cui et al. 2012 ; Cheng et al. 2015 ). However, it remains unclear whether this dissociation applies to children. Since childhood development is heavily characterized by parallel play and observational learning, determining whether children's neural synchrony emerges from social presence or requires active behavioral engagement is a key open question. In children, research on hyperscanning remains limited (Bi et al. 2023 ). Most developmental studies have focused on adult-child interactions, revealing neural alignment during joint attention, emotional attunement, and teaching episodes (Harrist and Waugh 2002 ; Reindl et al. 2018 ; Alonso et al. 2024 ). Peer interactions have been relatively understudied, despite their crucial role in cognitive and social development. Existing research suggests that inter-brain synchrony may indicate shared engagement and cooperation in child dyads (Cheng et al. 2015 ; Zhou et al. 2025 ). However, the field lacks more investigations of naturalistic, hands-on tasks that more closely resemble real-world child-child collaboration scenarios. Functional near-infrared spectroscopy (fNIRS) is a technique used for developmental hyperscanning because of its tolerance for movement and comfort for young participants, making it suitable for ecologically valid settings (Piazza et al. 2020 ; Oku et al. 2022 , 2023 ). However, few fNIRS hyperscanning studies have investigated how children coordinate neural activity in natural environments (Zhou et al. 2025 ). There is a need to explore embodied cooperative problem-solving, in which children manipulate objects and communicate spontaneously, which is likely to elicit more dynamic neural responses. Additionally, emerging research employs functional connectivity and graph-theoretic methods to analyze inter-brain networks (Czeszumski et al. 2021). While traditional analyses focus on single-channel synchrony, network-based approaches offer a systems-level view of inter-brain interactions. This perspective helps explain the rapid development of cortical networks (Blakemore and Choudhury 2006 ). Despite advances in portable neuroimaging, relatively few studies have examined inter-brain dynamics in child dyads performing hands-on, naturalistic tasks. Also, most studies still rely on simplified or scripted paradigms, leaving unclear how inter-brain dynamics operate during unstructured activities typical of real-world environments. Understanding whether neural coupling reflects social presence or active collaborative engagement is therefore critical for interpreting portable neuroimaging findings in ecologically valid contexts. To bridge the gap between strictly controlled experiments and the dynamic nature of peer interaction, this study employs fNIRS hyperscanning during a naturalistic Tangram puzzle task, grounding its approach in embodied cognition theories, which posit that cognitive processes are rooted in physical interaction (Glenberg 2008 ). Investigating whether joint cognitive performance covaries with coordinated action, we recorded simultaneous activity in prefrontal and temporoparietal channels (Saxe and Kanwisher 2013; Barreto et al. 2021 ; da Silva Soares et al. 2024) across solo, observational, and cooperative conditions. Here, we pursued three specific objectives. I: To determine whether cooperative problem solving is associated with changes in frontoparietal cortical activation compared to non-interactive conditions. II: To evaluate whether inter-brain coupling differentiates task conditions (rest, solo, observation, cooperation) at a global level. III: To examine whether inter-brain coupling varies continuously with behavioral engagement during cooperation. 2. Methods 2.1. Participants The study received approval from the local ethics committee, and written informed consent was obtained from parents/guardians and children's assent. The study recruited 30 children (15 pairs, 18 boys, 12 girls; mean age 9.17 and 9.92 years, respectively) aged 6 to 11 years from the local community. Participants with diagnosed neurological or psychiatric conditions were excluded, ensuring only typically developing children participated. 2.2. Task: Tangram Puzzle The Tangram task models collaborative problem solving, frequently used in early education to promote spatial reasoning and shared strategy formation (Ayaz et al. 2012 ). Each kit contained seven colored geometric pieces forming a 14.2cm x 14.2cm set, producing 24 predefined geometric shapes displayed on an iPad screen by physically manipulating the Tangram pieces on the table surface in front of them. This setup required children to translate the visual template from the tablet into a physical construction on the workspace, testing spatial thinking skills. If a pair failed to solve a puzzle within four minutes, the experimenter advanced to the next challenge. This approach was designed to ensure engagement while maintaining a suitable level of challenge. 2.3. fNIRS Task Procedure Each pair participated in one session lasting approximately 25 minutes. Participants received instructions on the task and puzzle-solving procedure beforehand. A camera recorded the puzzle-solving process to analyze behavior, including the number and timing of interactions with the pieces. Participants were randomly assigned seats (Child 1, Child 2) side by side at a table, allowing clear iPad visibility and easy manipulation of Tangram pieces without excessive head movement. A practice trial with a visual tip puzzle allowed familiarization. Once participants were comfortable, fNIRS caps with optodes were placed to continuously track brain hemodynamics related to cognition. The experimental protocol consisted of two runs, each containing four segments. Each segment included four 30-second blocks: rest (looking at a cross on the iPad), Child 1 solving (Child 2 observing), Child 2 solving (Child 1 observing), and duo (both children collaborating). The total experiment time was 16 minutes. Pre-recorded audio commands (Rest, Child 1, Child 2, Duo) directed participants. Block sequences within segments were varied to prevent anticipation. Upon solving a puzzle or exceeding the 4-minute limit, the next shape was presented. 2.4. Data Acquisition fNIRS signals were acquired using a portable NIRSPORTS 2 system (NIRx, Germany), acquisition sampling rate (10.17 Hz), with 16 emitters and 16 detectors (continuous wave), split between the two participants. Calibration and acquisition were performed using Aurora software (Brain Innovation, the Netherlands). Optode placement (Fig. 2 ) covered the frontal and parietal regions, based on the international 10–20 EEG system, using a cap with holders. Short-distance channels (one per source bundle) were used to regress out potential systemic artifacts. Regions of interest (ROIs) were defined as the right posterior parietal regions (channel 14 and 17) (Koessler et al. 2009 ), associated with reaching and grasping coordination, dorsolateral prefrontal cortex (dlPFC, channels 2, 5, 8, 11) related to spatial cognition (da Silva Soares et al. 2024)and the temporoparietal junction (TPJ, channels 15, 18, 21, 23) often implicated in social cognition in prior imaging literature (Barreto et al. 2021 ). 2.5. fNIRS Data Analysis The fNIRS data were pre-processed using Satori v1.8 software (Brain Innovation, the Netherlands). The modified Beer-Lambert law was applied to convert the optical signals of each wavelength (760 and 850 nm) into concentration changes of oxy-Hb and deoxy-Hb. Pre-processing routines included motion correction using Temporal Derivative Distribution Repair (TDDR) and high-frequency restoration. This process also involved spike removal and short-channel regression, in which a general linear model was fitted, using the highest-correlated short channel as a regressor for each channel signal. Additionally, temporal filtering was applied using a high-pass Butterworth filter at 0.01 Hz and a low-pass filter at 0.50 Hz to eliminate slow drift and high-frequency noise, such as systemic artifacts (Cui et al. 2010 ; Tachtsidis and Scholkmann 2016 ; Yücel et al. 2021 ). To quantify neural activation, a General Linear Model (GLM) was applied to the HbO time series (Huppert 2016 ). The design matrix included regressors for each condition (Rest, Solo, Observation, Duo) convolved with the canonical hemodynamic response function (HRF). The resulting beta values were used for two distinct analyses: (1) to generate group-level activation maps for visualizing the spatial distribution of activity, and (2) to extract mean activation values within the defined ROIs for statistical correlation with behavioral performance. Initially, statistical inference was performed at the level of anatomically defined regions of interest (ROIs). Three ROIs were defined a priori based on the experimental hypotheses and montage coverage: the dorsolateral prefrontal cortex (dlPFC), the right temporoparietal junction region (rTPJ), and the posterior parietal cortex. For confirmatory inference, mean beta values within each ROI were tested against baseline and corrected for multiple comparisons using Bonferroni correction across the three ROIs (α = 0.05/3). Channel-wise statistics are reported only to localize the spatial distribution of ROI effects and are treated as descriptive rather than independent inferential tests, as neighboring fNIRS channels are spatially correlated. For the peak activation channel, we report the uncorrected p -value, the standardized effect size (Cohen's d ), and the 95% Confidence Interval (CI) of the beta estimates to characterize the magnitude and reliability of the effect independent of multiple-comparison penalties. Statistical analysis involved calculating Pearson correlation coefficients between ROIs for each pair and drawing inferences about inter-subject functional connectivity. Behavioral analysis counted the number of solved challenges and the number of touches (interactions/actions) with Tangram pieces. 2.6. Preprocessing of fNIRS Time Series For each dyad, raw oxyhemoglobin (HbO) signals were imported separately for each participant and processed according to the analysis pipeline described in the supplementary material. Sixteen long-distance channels were selected for functional connectivity analysis. To ensure comparability across individuals and dyads, all HbO time series were standardized using within-participant z-scoring. Standardized signals were then organized into two three-dimensional matrices, with dimensions of 15 dyads × 9,765 time points × 16 channels. This structure enabled the subsequent concatenation of channels across participants, which was required for the connectivity analysis. 2.7. Inter-Brain Connectivity (IBC) Computation The 16 channels from participant A and the 16 channels from participant B were concatenated into a single matrix for each block to estimate functional connectivity. A full 32 × 32 Pearson correlation matrix was computed for every 30-second block, and then averaged across the eight blocks of each condition to yield a representative functional connectivity matrix for each dyad. Although this computation generated both intra- and inter-brain correlations, our analysis focused exclusively on the inter-brain component. From each averaged 32 × 32 matrix, we extracted the submatrix corresponding to the cross-brain correlations (rows 0–15 from participant 1 vs. columns 16–31 from participant 2), yielding a 16 × 16 inter-brain correlation matrix. These 256 correlation coefficients were then flattened into a 1-dimensional vector and summed to generate a single scalar IBC score for each condition and dyad. This procedure yielded four IBC indices per dyad: Rest, Child 1, Child 2, and Duo. This scalar provides a montage-level summary of cross-participant temporal covariance. A non-parametric Friedman test was used to evaluate differences in IBC across Rest, Child 1, Child 2, and Duo as each dyad contributed repeated measures across all four conditions, and the IBC distributions deviated from normality. The significance threshold was set to α = 0.05. 2.8. Brain-Behavior Correlation Analysis Behavioral measures, including the total number of actions on Tangram pieces and other performance indices, were imported from an external behavioral database. To examine the relationship between behavioral engagement and neural coupling, Spearman's rank correlation was computed for each condition between the IBC and the behavioral variables. Spearman correlations were selected due to the small sample size and the presence of monotonic, non-Gaussian relationships. 3. Results 3.1. Feasibility of Dual-Child fNIRS Hyperscanning All dyads successfully completed the collaborative problem-solving activity. The study confirmed the feasibility of using fNIRS hyperscanning for naturalistic cooperative tasks involving motor and cognitive processing in children, as data were acquired from 30 children during collaborative Tangram problem-solving. Each participant pair completed the full experimental protocol, and no datasets required exclusion due to motion artifacts or technical failure. These observations demonstrate successful acquisition across all dyads in a movement-rich context. Descriptive statistics for performance are shown in Table 1. Table 1. Descriptive Measures of Task Performance Across Dyads . Summary of task performance metrics for each dyad, including Acc: number of puzzles solved (count), N_interact: number of physical interactions/touches with pieces. Behavioral variables were used in correlational analyses with neural coupling. Comparisons between children within dyads showed similar patterns in the number of solved challenges (Acc) and interactions with pieces (N_interact). Overall, performance metrics indicated comparable engagement across partners. 3.2. ROI Correlations Correlation analysis of HbO variation in ROIs between dyads during the solo condition (one child solving while the other observed) revealed a significant inverse correlation between activation in the dlPFC and temporoparietal regions (Pearson's r = -0.400, p = 0.029). Additionally, dlPFC activation correlated significantly with the number of solved challenges (z(P)_Acc) (Pearson's r = 0.469, p = 0.009). Comparing average ROI activation between the Solo and Duo (collaborative) conditions also showed a significant inverse correlation between the dlPFC and the temporoparietal regions (Pearson's r = -0.390, p = 0.033). Similar to the solo condition, dlPFC activation correlated positively with task accuracy (z(P)_Acc) (Pearson's r = 0.439, p = 0.015). 3.3. Task-Related Neural Activation Group-level GLM analysis revealed regional activation within the posterior parietal ROI, with a peak at channel 17 (CP4–CP6), during the cooperative condition relative to baseline (t(29) = 2.78, p_uncorr = 0.009). While this specific channel did not survive a highly conservative whole-probe Bonferroni correction across all 16 measured channels (p_adjusted > 0.05), it remained significant when correcting for our three a priori-defined theoretical regions (p baseline contrast (Figure 3), while statistical inference is based on channel-wise beta estimates. Importantly, exploratory evaluation of the activation magnitude revealed a medium effect size (Cohen's d = 0.51), and the 95% Bootstrap Confidence Interval of the beta estimates ([7.55, 36.39]) excluded zero. Together, the effect size and the focal spatial distribution support the parietal involvement during the task. In contrast, activation in the dlPFC and the TPJ did not reach statistical significance (p > 0.05). 3.4. Inter-Brain Connectivity Across Task Conditions Visual inspection of IBC distributions via boxplots indicated comparable ranges across Rest, Child 1, Child 2, and Duo conditions (Figure 4). The non-parametric Friedman test did not reveal a statistically significant effect of condition on global IBC. Global IBC, as measured by large-scale cross-brain correlation, did not differ significantly between rest, single-agent problem solving, observation, and collaborative interaction. 3.5. Brain-Behavior Associations The primary behavioral index, the number of actions performed on the Tangram pieces, served as an indicator of task engagement and collaborative interaction. Spearman correlations revealed no significant association between behavior and IBC in the Rest, Child 1, or Child 2 conditions (all p > 0.05). In contrast, a significant positive correlation emerged in the Duo condition, such that dyads who interacted more frequently with the puzzle pieces exhibited stronger inter-brain coupling during collaboration. The scatterplot shows a monotonic relationship between DUO and behavioral engagement. The scatterplot shows a monotonic association in the Duo condition (Figure 5). 4. Discussion The present study investigated the neural correlates of cooperative problem solving in children using fNIRS hyperscanning, integrating whole-brain inter-brain connectivity metrics with behavioral measures of engagement during a hands-on Tangram task. To our knowledge, this is one of the first demonstrations of two-child hyperscanning during a physically embodied, cooperative visuospatial task. This naturalistic design provides insights into how developing brains coordinate neural activity during real collaboration. Importantly, the present findings do not indicate that neural synchrony directly indexes learning success. Rather, they suggest that hyperscanning measures in children may be sensitive to the degree of coordinated engagement required for collaborative knowledge construction. This distinction is central for educational neuroscience, where portable neuroimaging is increasingly used to study real-world learning environments. The central findings of this study are threefold: (I) dual-child hyperscanning proved methodologically feasible even in a dynamic, movement-rich context; (II) global inter-brain connectivity did not differ significantly across rest, solo, observational, and cooperative conditions; and (III) during cooperative problem solving, neural measures were selectively sensitive to active engagement, such that children coordinating physical actions with a partner exhibited both increased right posterior parietal activation and stronger inter-brain coupling as a function of behavioral engagement. Together, these results provide novel insights into how children’s brains coordinate during naturalistic social interactions and offer important implications for developmental social neuroscience. 4.1. Feasibility of Dual-Child Hyperscanning in Naturalistic Tasks A primary contribution of this work is the demonstration that fNIRS hyperscanning can be deployed in pairs of school-aged children engaged in a cognitively demanding, physically interactive task. This extends feasibility demonstrations previously shown in adult dyads during cooperative or competitive tasks (Baker et al. 2016 ; Czeszumski et al. 2021) and aligns with emerging evidence that fNIRS is particularly well suited to naturalistic child-child paradigms (Piazza et al. 2020 ). By preserving ecological validity while maintaining data quality, our findings support ongoing shifts in developmental neuroscience toward studying children in social contexts rather than artificial laboratory constraints. This methodological advance may facilitate broader adoption of hyperscanning in research on collaborative learning, joint attention, and peer interaction. In addition, these findings provide a step toward integrating hyperscanning approaches into classroom research frameworks, where embodied collaboration is central to learning dynamics. 4.2. Neural Activation During Cooperative Problem Solving The strongest task-related activation was observed within the right posterior parietal ROI during cooperative problem solving. Given the spatial resolution nd coverage of the present fNIRS montage, this finding should be interpreted as regional engagement of a visuomotor integration area rather than evidence for a specific cortical locus. The peak channel (CH 17, CP4-CP6), therefore, serves to localize the spatial distribution of the regional effect, not as an independent inferential result. Posterior parietal regions are widely implicated in integrating perception and action during object manipulation and spatially guided behavior (Wilson and Knoblich 2005 ; Sebanz et al. 2006 ; Glenberg 2008 ), and the parieto-frontal mirror system has been involved in executing actions, predicting, and interpreting others' physical actions in shared space (Rizzolatti and Sinigaglia 2010 ). The Tangram task required participants to jointly manipulate pieces, monitor a partner’s ongoing movements, and continuously adjust their own actions in response. The observed activation is therefore consistent with increased visuomotor coordination demands during cooperative manipulation compared to baseline. Within the joint action literature, coordinated behavior has been proposed to rely on the alignment between observed and executed movements (Wilson and Knoblich 2005 ; Sebanz et al. 2006 ). In this framework, posterior parietal activity may reflect the integration of visual information about a partner’s actions with one’s own motor planning processes, supporting temporally organized interaction. Importantly, the present data do not allow attribution to a specific functional network. fNIRS measures superficial hemodynamic signals with limited spatial specificity and substantial inter-channel covariance (Pinti et al. 2020 ; Yücel et al. 2021 ). Therefore, the current findings are compatible with accounts in which social coordination emerges from domain-general sensorimotor coupling during joint behavior (Hari et al. 2015 ; Redcay and Schilbach 2019 ). Finally, the peak channel's sensitivity to the multiple-comparisons strategy further supports a regional interpretation. The posterior parietal ROI reached significance, whereas individual channels did not survive channel-wise correction. We therefore interpret the effect as recruitment of a posterior parietal processing region, while precise localization will require higher-density coverage or image reconstruction approaches in future studies. 4.3. Condition-Level Stability in Inter-Brain Connectivity Contrary to initial expectations, global IBC did not vary significantly across experimental conditions. Rather than contradicting prior hyperscanning findings in adults (Nozawa et al. 2016 ), this result suggests that inter-brain coupling is not necessarily a categorical marker of task type. Instead, it may depend on the temporal structure of interaction, consistent with evidence that IBC reflects dynamic social alignment over time (Dai et al. 2018 ; Wass et al. 2020 ). Several factors may contribute to the absence of condition-level effects. First, developmental variability may increase heterogeneity in neural coordination across dyads, making global condition contrasts less sensitive in children (Sheridan, 2014) (Sheridan et al. 2014 ). Second, the large-scale, channel-agnostic IBC metric used here summarizes widespread cross-brain covariance, which likely includes both interaction-related and condition-invariant components. Such aggregation can reduce sensitivity to categorical contrasts while remaining responsive to moment-to-moment behavioral coupling. Accordingly, cooperative interaction may not induce uniform synchrony across all dyads, but rather produce coupling contingent on interaction dynamics. This interpretation aligns with the present observation that brain–behavior associations were detectable even in the absence of condition-level differences, suggesting that IBC is better understood as a continuous index of coordination rather than a binary effect of cooperation. 4.4. Behaviorally Driven Inter-Brain Synchrony in Collaborative Problem Solving A central finding of the present study was that IBC during the collaborative condition was positively associated with behavioral engagement, specifically the number of actions performed on the Tangram pieces. This pattern is consistent with accounts proposing that neural synchrony reflects interactional attunement rather than mere co-presence or shared task structure (Hasson et al. 2012 ; Redcay and Schilbach 2019 ). In developmental contexts, where synchrony has been discussed in relation to scaffolding and co-regulation (Hoehl et al. 2021), the present results indicate that neural coupling increased when children actively manipulated the environment together. Importantly, this association suggests that dyads engaging more actively in joint manipulation exhibited stronger temporal coordination of neural activity. Such a pattern is compatible with frameworks emphasizing embodied coordination and perception–action coupling (Glenberg 2008 ; Gallese 2014 ; Kontra et al. 2015 ) as well as interactive approaches to social neuroscience (Schilbach et al. 2013 ), without implying that identical cognitive processes occurred simultaneously in both participants. Our results also relate to the distinction between co-action and joint action described in adult hyperscanning research. Previous studies indicate that simultaneous presence or parallel activity alone may be insufficient to produce robust inter-brain synchrony (Cui et al. 2012 ; Cheng et al. 2015 ). Similarly, the absence of significant coupling during the observational condition in the present study suggests that social presence alone did not reliably produce neural alignment in children. Instead, coupling was most evident when participants continuously adjusted their behavior to each other during shared manipulation. This observation is compatible with the proposal that social interaction can be understood in terms of coordinated action dynamics (Konvalinka and Roepstorff 2012 ). From a developmental perspective, engagement-dependent neural coupling may reflect interactional processes relevant for collaborative cognition, although the present data do not directly measure shared mental representations or learning outcomes. Given that cooperation supports learning in children (Warneken 2018 ; Tomasello 2019 ), these findings may help constrain hypotheses about when alignment-like neural signatures emerge during peer interaction, potentially during hands-on, jointly coordinated activity. Accordingly, the results motivate future classroom research but should not be interpreted as direct evidence for learning. 4.5. Limitations and Future Directions It should be mentioned that this study has some limitations. First, although fNIRS is relatively resistant to motion, the task's natural, hands-on nature likely led to head movements that could not be fully prevented. Second, the participant age range covers a period of significant neurocognitive development, which may have introduced variability in executive function, social cognition, and cooperative skills. Third, the difficulty levels of the Tangram puzzles varied, possibly resulting in different cognitive demands across dyads and conditions. Given the partial sensitivity of the CH17 finding to the multiple-comparisons strategy, we treat this localization as a promising but provisional result. The moderate effect size and anatomical plausibility motivate targeted follow-up studies to confirm localized posterior-parietal recruitment during cooperative embodied tasks. Furthermore, the lack of independent measures of executive function or social-cognitive skills limited our ability to connect neural coupling to individual developmental differences. Lastly, the small sample size limits statistical power and the generalizability of the findings. Moreover, a recent meta-analysis suggests that children's age and brain regions are significant predictors of effect size in parent-child research (Zhao et al. 2024 ). Future research should involve larger, developmentally focused samples and incorporate additional behavioral assessments to refine and expand these findings. It should also examine developmental trajectories by including a broader age range of children and comparing different contexts, such as cooperative versus competitive settings, to evaluate how neural response coupling varies with the task. 5. Conclusion The present study suggests that the inter-brain coupling in children covaries with behavioral engagement during cooperative action. This pattern indicates that hyperscanning signals in naturalistic contexts primarily reflect the temporal organization of shared activity. Using dual-child portable fNIRS hyperscanning in an ecologically valid cooperative learning task, we show that global inter-brain connectivity does not differentiate between rest, solo, observational, and collaborative conditions. Critically, however, neural coupling during collaboration scales with children’s behavioral engagement, emerging most strongly when partners actively coordinate their actions toward a shared goal. Within this framework, coordinated action provides the context in which coordinated neural dynamics become observable, particularly during hands-on collaborative behavior. Accordingly, the present results contribute to ongoing efforts to characterize how interpersonal neural measures relate to real-world interaction, suggesting that in developmental settings, neural coupling may emerge most robustly when children actively structure behavior together. These findings inform future research on real-world social interaction and peer learning while remaining agnostic about specific cognitive or learning processes, which will require designs directly targeting representational change. Declarations Funding The author’s work cited here was funded by the State of São Paulo Research Foundation (grant numbers 2018/21934-5, 2023/13418-5, 2023/12217-6) and by the D’Or Institute for Research and Education (IDOR). Contributions R.S.S.J.: Conceptualization, Methodology, Formal analysis, Writing - original draft. J.R.S.: Supervision, Methodology, Writing - review & editing. Data Availability The datasets generated during the current study are available from the corresponding author upon reasonable request. Ethics declarations The study received approval from the local ethics committee, and written informed consent was obtained from parents/guardians and children's assent. Competing interests The authors report no competing interests. Declaration of Generative AI Use The authors declare that AI-assisted tools were used solely for language editing and grammatical revision. The authors take full responsibility for the content of the manuscript. References Alonso, A., McDorman, S. A., & Romeo, R. R. (2024). How parent–child brain‐to‐brain synchrony can inform the study of child development. Child development perspectives , 18 (1), 26–35. Ayaz, H., Shewokis, P. A., Izzetoğlu, M., Çakir, M. P., & Onaral, B. (2012). Tangram solved? Prefrontal cortex activation analysis during geometric problem solving. 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A., Hasenfratz, L., Hasson, U., & Lew-Williams, C. (2020). Infant and adult brains are coupled to the dynamics of natural communication. Psychological Science, 31 (1), 6–17. Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., & Burgess, P. W. (2020). The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464 (1), 5–29. https://doi.org/10.1111/nyas.13948 Redcay, E., & Schilbach, L. (2019). Using second-person neuroscience to elucidate mechanisms of social interaction. Nature Reviews Neuroscience, 20 (8), 495–505. Reindl, V., Gerloff, C., Scharke, W., & Konrad, K. (2018). Brain-to-brain synchrony in parent–child dyads and the relationship with emotion regulation and cooperation. NeuroImage, 178 , 493–502. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27 , 169–192. https://doi.org/10.1146/annurev.neuro.27.070203.144230 Rizzolatti, G., & Sinigaglia, C. (2010). The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nature Reviews Neuroscience , 11 (4), 264–274. Rogoff, B. (2003). The cultural nature of human development . Oxford University Press. Saxe, R., & Kanwisher, N. (2003). People thinking about thinking people: The role of the temporo-parietal junction in “theory of mind.” NeuroImage, 19 (4), 1835–1842. Schilbach, L., Timmermans, B., Reddy, V., Costall, A., Bente, G., Schlicht, T., & Vogeley, K. (2013). Toward a second-person neuroscience. Behavioral and Brain Sciences, 36 , 393–414. Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: bodies and minds moving together. Trends in cognitive sciences , 10 (2), 70–76. Shaw, D. J., Czekoova, K., Mareček, R., Špiláková, B. H., & Brázdil, M. (2023). The interacting brain: dynamic functional connectivity among canonical brain networks dissociates cooperative from competitive social interactions: NeuroImage, 269 , 119933. Sheridan, M., Kharitonova, M., Martin, R. E., Chatterjee, A., & Gabrieli, J. D. (2014). Neural substrates of cognitive control development in children ages 5–10 years. Journal of cognitive neuroscience, 26(8), 1840-1850. Su, W. C., Culotta, M., Mueller, J., Tsuzuki, D., & Bhat, A. (2023). fNIRS-Based differences in cortical activation during tool use, pantomimed actions, and meaningless actions between children with and without Autism Spectrum Disorder (ASD). Brain Sciences , 13 (6), 876. Tachtsidis, I. & Scholkmann, F. False positives and false negatives in functional near-infraredspectroscopy: issues, challenges, and the way forward. Neurophotonics 3, 031405 (2016). Tomasello, M. (2019). Becoming human: A theory of ontogeny . Harvard University Press. Warneken, F. (2018). How children solve the two problems of cooperation. Annual Review of Psychology, 69 , 205–229. Wass, S. V., Whitehorn, M., Haresign, I. M., Phillips, E., & Leong, V. (2020). Interpersonal neural entrainment during early social interaction. Trends in cognitive sciences , 24 (4), 329–342. Wilson, M., & Knoblich, G. (2005). The case for motor involvement in perceiving conspecifics. Psychological Bulletin , 131 (3), 460–473. Yücel, M. A., Lühmann, A. V., Scholkmann, F., Gervain, J., Dan, I., Ayaz, H., ... & Wolf, M. (2021). Best practices for fNIRS publications. Neurophotonics , 8 (1), 012101. Zhao, Q., Zhao, W., Lu, C., Du, H., & Chi, P. (2024). Interpersonal neural synchronization during social interactions in close relationships: A systematic review and meta-analysis of fNIRS hyperscanning studies. Neuroscience & Biobehavioral Reviews , 158 , 105565. Zhou, S., Zhang, Y., Zhang, M., & Li, D. (2025). The Role of Peer Relationship on Children's Creativity During Cooperative and Competitive Interactions: An fNIRS-based Hyperscanning Study. Developmental Cognitive Neuroscience , 101592. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 02 Apr, 2026 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. 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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-9302137","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633712518,"identity":"ee6e5d0e-6771-4390-81f2-20bcee0d4b0d","order_by":0,"name":"Raimundo Silva Soares","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFACxgYgwQznyrExJJCoxZgILWCA0JLYQEiLefvh1s28O6zlGPgPH/xcUVGX3seefOwDQ8U9uwYcWmTOJLbd5j2TbswgkZYseebM4dw2nmfJMxjOFCfj0iLBANLSdjixQYLHQLKx7UBum0SOMQNjW0IyLodJ8D+EauE/Y/yz8V9dOhtBLRIwWxhyzCQbG5gTYFrscGt52HZzblu6MZtEWpplw7HDhiC/MCScSUjA7bD0ZzfetlnL8fMfPnyzoaZOXr49+TDDh4oEe1xa4IANhZcAjiBSAWFbRsEoGAWjYKQAALp4Uh8pb1VvAAAAAElFTkSuQmCC","orcid":"","institution":"D’Or Institute for Research and Education","correspondingAuthor":true,"prefix":"","firstName":"Raimundo","middleName":"Silva","lastName":"Soares","suffix":""},{"id":633712519,"identity":"a48a7eff-1f54-4662-b787-ddcdb2b771cc","order_by":1,"name":"João Ricardo Sato¹","email":"","orcid":"","institution":"Universidade Federal do ABC","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Ricardo","lastName":"Sato¹","suffix":""}],"badges":[],"createdAt":"2026-04-02 10:53:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9302137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9302137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108793719,"identity":"6b2ee6f9-c5ee-4316-b837-5c31f109e0f1","added_by":"auto","created_at":"2026-05-08 12:59:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119024,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the experimental setup showing two children seated side-by-side wearing fNIRS caps during the Tangram task. The right panel shows the sequence of blocks for each segment: REST (fixation), Child 1 (child 1 solves; child 2 observes), Child 2 (child 2 solves; child 1 observes), and Duo (Double as collaborative problem solving). Each block lasted 30 seconds, and each condition was repeated across eight segments.\u003c/p\u003e","description":"","filename":"Figure1HyperTangramv4.png","url":"https://assets-eu.researchsquare.com/files/rs-9302137/v1/0a56dfdee13feca04b67ae01.png"},{"id":108807765,"identity":"3320106b-d03d-4fab-bd06-9b3c1e0fc737","added_by":"auto","created_at":"2026-05-08 15:31:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3162383,"visible":true,"origin":"","legend":"\u003cp\u003eOptode placement covered the prefrontal cortex and the right temporoparietal regions based on the 10-20 EEG system using a cap with holders. The setup used in the research shows blue circles for detectors, red circles for sensors, and short channels as the black circles around the sources. The detectors and sensors form channels, which are represented by blue lines.\u003c/p\u003e","description":"","filename":"Figure2TanGramv2.png","url":"https://assets-eu.researchsquare.com/files/rs-9302137/v1/18a2b2bd5a73b274b6e581c3.png"},{"id":108793721,"identity":"97720ff4-a7b0-4c02-b7f8-560d9c7ffeb1","added_by":"auto","created_at":"2026-05-08 12:59:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1547185,"visible":true,"origin":"","legend":"\u003cp\u003eUnthresholded t-map. Group-level activation maps contrasting Duo \u0026gt; Baseline. Maps are visualized on a cortical surface, including the dorsolateral prefrontal cortex (dlPFC), the temporoparietal junction (TPJ), and the right posterior parietal region. Statistical comparisons were performed based on beta values. The posterior parietal ROI reached significance; channel 17 (CP4–CP6) showed the peak t-value within this region.\u003c/p\u003e","description":"","filename":"Figure3TMap.png","url":"https://assets-eu.researchsquare.com/files/rs-9302137/v1/75ac9af56c9fbf4c0ce36877.png"},{"id":108793723,"identity":"078cf4a0-2980-46b2-ba1d-06ad81ca3a3c","added_by":"auto","created_at":"2026-05-08 12:59:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20498,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of Inter-Brain Connectivity Across Conditions. Distribution of IBC values for Rest, Child 1, Child 2, and Duo conditions. Boxplots indicate medians, interquartile ranges, and variability across dyads.\u003c/p\u003e","description":"","filename":"Figure4IBC.png","url":"https://assets-eu.researchsquare.com/files/rs-9302137/v1/8b700a9e8ea51e714dd80124.png"},{"id":108793722,"identity":"499fcc23-8174-49d8-a511-137c058e817b","added_by":"auto","created_at":"2026-05-08 12:59:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13980,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship Between Behavioral Engagement and Inter-Brain Connectivity During Collaboration. Scatterplot depicting the association between the number of Tangram piece interactions (behavioral engagement) and the IBC score during the DUO condition.\u003c/p\u003e","description":"","filename":"Figure5v3.png","url":"https://assets-eu.researchsquare.com/files/rs-9302137/v1/b31fda26572f932e58e090d3.png"},{"id":108809802,"identity":"1f21c5be-a4ba-4c07-9c74-aa48b33fa0da","added_by":"auto","created_at":"2026-05-08 15:55:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5528638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9302137/v1/de2848df-1e67-4a7c-98ee-377786a8be1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inter-brain synchrony in children scales with coordinated action during cooperative tangram solving","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHuman cognition develops within deeply social environments. From early childhood, learning, problem-solving, and reasoning often occur through collaboration with others, in which shared attention, coordinated actions, and communication influence cognitive development (Rogoff \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tomasello \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cooperative problem solving is a key aspect of childhood social interaction and has been connected to improvements in executive function and academic progress (Warneken \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Doebel \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite extensive behavioral evidence, the neural processes underlying children's coordination of thoughts and actions during cooperation remain poorly understood. Understanding how children\u0026rsquo;s brains synchronize during real-world interactions is thus a challenge for developmental social neuroscience.\u003c/p\u003e \u003cp\u003eHyperscanning, a technique that records neural activity from two or more individuals simultaneously, provides a unique view of the neural processes underlying social interaction (Montague et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Babiloni and Astolfi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Instead of inferring social processes from isolated brains, hyperscanning allows measurement of inter-brain synchrony, showing how neural dynamics become aligned during communication, joint attention, and cooperative behavior. A solid body of adult research shows increases in inter-brain coupling during cooperation (Shaw et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), verbal communication (Jiang et al. 2012), coordinated movement (Konvalinka and Roepstorff \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and shared attention (Dumas et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Such synchrony has been interpreted as reflecting interpersonal coordination processes, which are key parts of social cognition (Hasson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Redcay and Schilbach \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn adult hyperscanning literature, a critical distinction is often drawn between co-action (performing tasks side by side) and joint action (collaborating toward a shared goal). Evidence suggests that physical proximity or simultaneous action alone is insufficient to elicit robust inter-brain synchrony; rather, shared intentionality and reciprocal coordination drive neural alignment (Cui et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, it remains unclear whether this dissociation applies to children. Since childhood development is heavily characterized by parallel play and observational learning, determining whether children's neural synchrony emerges from social presence or requires active behavioral engagement is a key open question.\u003c/p\u003e \u003cp\u003eIn children, research on hyperscanning remains limited (Bi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Most developmental studies have focused on adult-child interactions, revealing neural alignment during joint attention, emotional attunement, and teaching episodes (Harrist and Waugh \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Reindl et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Peer interactions have been relatively understudied, despite their crucial role in cognitive and social development. Existing research suggests that inter-brain synchrony may indicate shared engagement and cooperation in child dyads (Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the field lacks more investigations of naturalistic, hands-on tasks that more closely resemble real-world child-child collaboration scenarios.\u003c/p\u003e \u003cp\u003eFunctional near-infrared spectroscopy (fNIRS) is a technique used for developmental hyperscanning because of its tolerance for movement and comfort for young participants, making it suitable for ecologically valid settings (Piazza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oku et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, few fNIRS hyperscanning studies have investigated how children coordinate neural activity in natural environments (Zhou et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). There is a need to explore embodied cooperative problem-solving, in which children manipulate objects and communicate spontaneously, which is likely to elicit more dynamic neural responses. Additionally, emerging research employs functional connectivity and graph-theoretic methods to analyze inter-brain networks (Czeszumski et al. 2021). While traditional analyses focus on single-channel synchrony, network-based approaches offer a systems-level view of inter-brain interactions. This perspective helps explain the rapid development of cortical networks (Blakemore and Choudhury \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite advances in portable neuroimaging, relatively few studies have examined inter-brain dynamics in child dyads performing hands-on, naturalistic tasks. Also, most studies still rely on simplified or scripted paradigms, leaving unclear how inter-brain dynamics operate during unstructured activities typical of real-world environments. Understanding whether neural coupling reflects social presence or active collaborative engagement is therefore critical for interpreting portable neuroimaging findings in ecologically valid contexts.\u003c/p\u003e \u003cp\u003eTo bridge the gap between strictly controlled experiments and the dynamic nature of peer interaction, this study employs fNIRS hyperscanning during a naturalistic Tangram puzzle task, grounding its approach in embodied cognition theories, which posit that cognitive processes are rooted in physical interaction (Glenberg \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Investigating whether joint cognitive performance covaries with coordinated action, we recorded simultaneous activity in prefrontal and temporoparietal channels (Saxe and Kanwisher 2013; Barreto et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; da Silva Soares et al. 2024) across solo, observational, and cooperative conditions.\u003c/p\u003e \u003cp\u003eHere, we pursued three specific objectives. I: To determine whether cooperative problem solving is associated with changes in frontoparietal cortical activation compared to non-interactive conditions. II: To evaluate whether inter-brain coupling differentiates task conditions (rest, solo, observation, cooperation) at a global level. III: To examine whether inter-brain coupling varies continuously with behavioral engagement during cooperation.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003e The study received approval from the local ethics committee, and written informed consent was obtained from parents/guardians and children's assent. The study recruited 30 children (15 pairs, 18 boys, 12 girls; mean age 9.17 and 9.92 years, respectively) aged 6 to 11 years from the local community. Participants with diagnosed neurological or psychiatric conditions were excluded, ensuring only typically developing children participated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Task: Tangram Puzzle\u003c/h2\u003e \u003cp\u003eThe Tangram task models collaborative problem solving, frequently used in early education to promote spatial reasoning and shared strategy formation (Ayaz et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Each kit contained seven colored geometric pieces forming a 14.2cm x 14.2cm set, producing 24 predefined geometric shapes displayed on an iPad screen by physically manipulating the Tangram pieces on the table surface in front of them. This setup required children to translate the visual template from the tablet into a physical construction on the workspace, testing spatial thinking skills. If a pair failed to solve a puzzle within four minutes, the experimenter advanced to the next challenge. This approach was designed to ensure engagement while maintaining a suitable level of challenge.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. fNIRS Task Procedure\u003c/h2\u003e \u003cp\u003eEach pair participated in one session lasting approximately 25 minutes. Participants received instructions on the task and puzzle-solving procedure beforehand. A camera recorded the puzzle-solving process to analyze behavior, including the number and timing of interactions with the pieces. Participants were randomly assigned seats (Child 1, Child 2) side by side at a table, allowing clear iPad visibility and easy manipulation of Tangram pieces without excessive head movement. A practice trial with a visual tip puzzle allowed familiarization. Once participants were comfortable, fNIRS caps with optodes were placed to continuously track brain hemodynamics related to cognition.\u003c/p\u003e \u003cp\u003eThe experimental protocol consisted of two runs, each containing four segments. Each segment included four 30-second blocks: rest (looking at a cross on the iPad), Child 1 solving (Child 2 observing), Child 2 solving (Child 1 observing), and duo (both children collaborating). The total experiment time was 16 minutes. Pre-recorded audio commands (Rest, Child 1, Child 2, Duo) directed participants. Block sequences within segments were varied to prevent anticipation. Upon solving a puzzle or exceeding the 4-minute limit, the next shape was presented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data Acquisition\u003c/h2\u003e \u003cp\u003efNIRS signals were acquired using a portable NIRSPORTS 2 system (NIRx, Germany), acquisition sampling rate (10.17 Hz), with 16 emitters and 16 detectors (continuous wave), split between the two participants. Calibration and acquisition were performed using Aurora software (Brain Innovation, the Netherlands). Optode placement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) covered the frontal and parietal regions, based on the international 10\u0026ndash;20 EEG system, using a cap with holders. Short-distance channels (one per source bundle) were used to regress out potential systemic artifacts. Regions of interest (ROIs) were defined as the right posterior parietal regions (channel 14 and 17) (Koessler et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), associated with reaching and grasping coordination, dorsolateral prefrontal cortex (dlPFC, channels 2, 5, 8, 11) related to spatial cognition (da Silva Soares et al. 2024)and the temporoparietal junction (TPJ, channels 15, 18, 21, 23) often implicated in social cognition in prior imaging literature (Barreto et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. fNIRS Data Analysis\u003c/h2\u003e \u003cp\u003eThe fNIRS data were pre-processed using Satori v1.8 software (Brain Innovation, the Netherlands). The modified Beer-Lambert law was applied to convert the optical signals of each wavelength (760 and 850 nm) into concentration changes of oxy-Hb and deoxy-Hb. Pre-processing routines included motion correction using Temporal Derivative Distribution Repair (TDDR) and high-frequency restoration. This process also involved spike removal and short-channel regression, in which a general linear model was fitted, using the highest-correlated short channel as a regressor for each channel signal. Additionally, temporal filtering was applied using a high-pass Butterworth filter at 0.01 Hz and a low-pass filter at 0.50 Hz to eliminate slow drift and high-frequency noise, such as systemic artifacts (Cui et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tachtsidis and Scholkmann \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Y\u0026uuml;cel et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo quantify neural activation, a General Linear Model (GLM) was applied to the HbO time series (Huppert \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The design matrix included regressors for each condition (Rest, Solo, Observation, Duo) convolved with the canonical hemodynamic response function (HRF). The resulting beta values were used for two distinct analyses: (1) to generate group-level activation maps for visualizing the spatial distribution of activity, and (2) to extract mean activation values within the defined ROIs for statistical correlation with behavioral performance.\u003c/p\u003e \u003cp\u003eInitially, statistical inference was performed at the level of anatomically defined regions of interest (ROIs). Three ROIs were defined a priori based on the experimental hypotheses and montage coverage: the dorsolateral prefrontal cortex (dlPFC), the right temporoparietal junction region (rTPJ), and the posterior parietal cortex. For confirmatory inference, mean beta values within each ROI were tested against baseline and corrected for multiple comparisons using Bonferroni correction across the three ROIs (α\u0026thinsp;=\u0026thinsp;0.05/3). Channel-wise statistics are reported only to localize the spatial distribution of ROI effects and are treated as descriptive rather than independent inferential tests, as neighboring fNIRS channels are spatially correlated. For the peak activation channel, we report the uncorrected \u003cem\u003ep\u003c/em\u003e-value, the standardized effect size (Cohen's \u003cem\u003ed\u003c/em\u003e), and the 95% Confidence Interval (CI) of the beta estimates to characterize the magnitude and reliability of the effect independent of multiple-comparison penalties.\u003c/p\u003e \u003cp\u003eStatistical analysis involved calculating Pearson correlation coefficients between ROIs for each pair and drawing inferences about inter-subject functional connectivity. Behavioral analysis counted the number of solved challenges and the number of touches (interactions/actions) with Tangram pieces.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Preprocessing of fNIRS Time Series\u003c/h2\u003e \u003cp\u003eFor each dyad, raw oxyhemoglobin (HbO) signals were imported separately for each participant and processed according to the analysis pipeline described in the supplementary material. Sixteen long-distance channels were selected for functional connectivity analysis. To ensure comparability across individuals and dyads, all HbO time series were standardized using within-participant z-scoring. Standardized signals were then organized into two three-dimensional matrices, with dimensions of 15 dyads \u0026times; 9,765 time points \u0026times; 16 channels. This structure enabled the subsequent concatenation of channels across participants, which was required for the connectivity analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Inter-Brain Connectivity (IBC) Computation\u003c/h2\u003e \u003cp\u003eThe 16 channels from participant A and the 16 channels from participant B were concatenated into a single matrix for each block to estimate functional connectivity. A full 32 \u0026times; 32 Pearson correlation matrix was computed for every 30-second block, and then averaged across the eight blocks of each condition to yield a representative functional connectivity matrix for each dyad.\u003c/p\u003e \u003cp\u003eAlthough this computation generated both intra- and inter-brain correlations, our analysis focused exclusively on the inter-brain component. From each averaged 32 \u0026times; 32 matrix, we extracted the submatrix corresponding to the cross-brain correlations (rows 0\u0026ndash;15 from participant 1 vs. columns 16\u0026ndash;31 from participant 2), yielding a 16 \u0026times; 16 inter-brain correlation matrix. These 256 correlation coefficients were then flattened into a 1-dimensional vector and summed to generate a single scalar IBC score for each condition and dyad. This procedure yielded four IBC indices per dyad: Rest, Child 1, Child 2, and Duo. This scalar provides a montage-level summary of cross-participant temporal covariance.\u003c/p\u003e \u003cp\u003eA non-parametric Friedman test was used to evaluate differences in IBC across Rest, Child 1, Child 2, and Duo as each dyad contributed repeated measures across all four conditions, and the IBC distributions deviated from normality. The significance threshold was set to α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Brain-Behavior Correlation Analysis\u003c/h2\u003e \u003cp\u003eBehavioral measures, including the total number of actions on Tangram pieces and other performance indices, were imported from an external behavioral database. To examine the relationship between behavioral engagement and neural coupling, Spearman's rank correlation was computed for each condition between the IBC and the behavioral variables. Spearman correlations were selected due to the small sample size and the presence of monotonic, non-Gaussian relationships.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1. Feasibility of Dual-Child fNIRS Hyperscanning\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll dyads successfully completed the collaborative problem-solving activity. The study confirmed the feasibility of using fNIRS hyperscanning for naturalistic cooperative tasks involving motor and cognitive processing in children, as data were acquired from 30 children during collaborative Tangram problem-solving. Each participant pair completed the full experimental protocol, and no datasets required exclusion due to motion artifacts or technical failure. These observations demonstrate successful acquisition across all dyads in a movement-rich context. Descriptive statistics for performance are shown in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eDescriptive Measures of Task Performance Across Dyads\u003c/strong\u003e. Summary of task performance metrics for each dyad, including Acc: number of puzzles solved (count), N_interact: number of physical interactions/touches with pieces. Behavioral variables were used in correlational analyses with neural coupling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1777918997.png\" width=\"813\" height=\"217\"\u003e\u003c/p\u003e\n\u003cp\u003eComparisons between children within dyads showed similar patterns in the number of solved challenges (Acc) and interactions with pieces (N_interact). Overall, performance metrics indicated comparable engagement across partners. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2. ROI Correlations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis of HbO variation in ROIs between dyads during the \u003cem\u003esolo\u003c/em\u003e condition (one child solving while the other observed) revealed a significant inverse correlation between activation in the dlPFC and temporoparietal regions (Pearson\u0026apos;s r = -0.400, p = 0.029). Additionally, dlPFC activation correlated significantly with the number of solved challenges (z(P)_Acc) (Pearson\u0026apos;s r = 0.469, p = 0.009). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparing average ROI activation between the \u003cem\u003eSolo\u003c/em\u003e and \u003cem\u003eDuo\u003c/em\u003e (collaborative) conditions also showed a significant inverse correlation between the dlPFC and the temporoparietal regions (Pearson\u0026apos;s r = -0.390, p = 0.033). Similar to the solo condition, dlPFC activation correlated positively with task accuracy (z(P)_Acc) (Pearson\u0026apos;s r = 0.439, p = 0.015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3. Task-Related Neural Activation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup-level GLM analysis revealed regional activation within the posterior parietal ROI, with a peak at channel 17 (CP4\u0026ndash;CP6), during the cooperative condition relative to baseline (t(29) = 2.78, p_uncorr = 0.009). While this specific channel did not survive a highly conservative whole-probe Bonferroni correction across all 16 measured channels (p_adjusted \u0026gt; 0.05), it remained significant when correcting for our three a priori-defined theoretical regions (p \u0026lt; 0.016). For visualization, we also show the unthresholded t-map for the Duo \u0026gt; baseline contrast (Figure 3), while statistical inference is based on channel-wise beta estimates.\u003c/p\u003e\n\u003cp\u003eImportantly, exploratory evaluation of the activation magnitude revealed a medium effect size (Cohen\u0026apos;s d = 0.51), and the 95% Bootstrap Confidence Interval of the beta estimates ([7.55, 36.39]) excluded zero. Together, the effect size and the focal spatial distribution support the parietal involvement during the task. In contrast, activation in the dlPFC and the TPJ did not reach statistical significance (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4. Inter-Brain Connectivity Across Task Conditions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisual inspection of IBC distributions via boxplots indicated comparable ranges across Rest, Child 1, Child 2, and Duo conditions (Figure 4). The non-parametric Friedman test did not reveal a statistically significant effect of condition on global IBC.\u003c/p\u003e\n\u003cp\u003eGlobal IBC, as measured by large-scale cross-brain correlation, did not differ significantly between rest, single-agent problem solving, observation, and collaborative interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.5. Brain-Behavior Associations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary behavioral index, the number of actions performed on the Tangram pieces, served as an indicator of task engagement and collaborative interaction. Spearman correlations revealed no significant association between behavior and IBC in the Rest, Child 1, or Child 2 conditions (all \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). In contrast, a significant positive correlation emerged in the Duo condition, such that dyads who interacted more frequently with the puzzle pieces exhibited stronger inter-brain coupling during collaboration.\u003c/p\u003e\n\u003cp\u003eThe scatterplot shows a monotonic relationship between DUO and behavioral engagement. The scatterplot shows a monotonic association in the Duo condition (Figure 5).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study investigated the neural correlates of cooperative problem solving in children using fNIRS hyperscanning, integrating whole-brain inter-brain connectivity metrics with behavioral measures of engagement during a hands-on Tangram task. To our knowledge, this is one of the first demonstrations of two-child hyperscanning during a physically embodied, cooperative visuospatial task. This naturalistic design provides insights into how developing brains coordinate neural activity during real collaboration.\u003c/p\u003e \u003cp\u003eImportantly, the present findings do not indicate that neural synchrony directly indexes learning success. Rather, they suggest that hyperscanning measures in children may be sensitive to the degree of coordinated engagement required for collaborative knowledge construction. This distinction is central for educational neuroscience, where portable neuroimaging is increasingly used to study real-world learning environments.\u003c/p\u003e \u003cp\u003eThe central findings of this study are threefold: (I) dual-child hyperscanning proved methodologically feasible even in a dynamic, movement-rich context; (II) global inter-brain connectivity did not differ significantly across rest, solo, observational, and cooperative conditions; and (III) during cooperative problem solving, neural measures were selectively sensitive to active engagement, such that children coordinating physical actions with a partner exhibited both increased right posterior parietal activation and stronger inter-brain coupling as a function of behavioral engagement. Together, these results provide novel insights into how children\u0026rsquo;s brains coordinate during naturalistic social interactions and offer important implications for developmental social neuroscience.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Feasibility of Dual-Child Hyperscanning in Naturalistic Tasks\u003c/h2\u003e \u003cp\u003eA primary contribution of this work is the demonstration that fNIRS hyperscanning can be deployed in pairs of school-aged children engaged in a cognitively demanding, physically interactive task. This extends feasibility demonstrations previously shown in adult dyads during cooperative or competitive tasks (Baker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Czeszumski et al. 2021) and aligns with emerging evidence that fNIRS is particularly well suited to naturalistic child-child paradigms (Piazza et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By preserving ecological validity while maintaining data quality, our findings support ongoing shifts in developmental neuroscience toward studying children in social contexts rather than artificial laboratory constraints. This methodological advance may facilitate broader adoption of hyperscanning in research on collaborative learning, joint attention, and peer interaction. In addition, these findings provide a step toward integrating hyperscanning approaches into classroom research frameworks, where embodied collaboration is central to learning dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Neural Activation During Cooperative Problem Solving\u003c/h2\u003e \u003cp\u003eThe strongest task-related activation was observed within the right posterior parietal ROI during cooperative problem solving. Given the spatial resolution nd coverage of the present fNIRS montage, this finding should be interpreted as regional engagement of a visuomotor integration area rather than evidence for a specific cortical locus. The peak channel (CH 17, CP4-CP6), therefore, serves to localize the spatial distribution of the regional effect, not as an independent inferential result. Posterior parietal regions are widely implicated in integrating perception and action during object manipulation and spatially guided behavior (Wilson and Knoblich \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sebanz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Glenberg \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and the parieto-frontal mirror system has been involved in executing actions, predicting, and interpreting others' physical actions in shared space (Rizzolatti and Sinigaglia \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Tangram task required participants to jointly manipulate pieces, monitor a partner\u0026rsquo;s ongoing movements, and continuously adjust their own actions in response. The observed activation is therefore consistent with increased visuomotor coordination demands during cooperative manipulation compared to baseline.\u003c/p\u003e \u003cp\u003eWithin the joint action literature, coordinated behavior has been proposed to rely on the alignment between observed and executed movements (Wilson and Knoblich \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sebanz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In this framework, posterior parietal activity may reflect the integration of visual information about a partner\u0026rsquo;s actions with one\u0026rsquo;s own motor planning processes, supporting temporally organized interaction. Importantly, the present data do not allow attribution to a specific functional network. fNIRS measures superficial hemodynamic signals with limited spatial specificity and substantial inter-channel covariance (Pinti et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Y\u0026uuml;cel et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the current findings are compatible with accounts in which social coordination emerges from domain-general sensorimotor coupling during joint behavior (Hari et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Redcay and Schilbach \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the peak channel's sensitivity to the multiple-comparisons strategy further supports a regional interpretation. The posterior parietal ROI reached significance, whereas individual channels did not survive channel-wise correction. We therefore interpret the effect as recruitment of a posterior parietal processing region, while precise localization will require higher-density coverage or image reconstruction approaches in future studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Condition-Level Stability in Inter-Brain Connectivity\u003c/h2\u003e \u003cp\u003eContrary to initial expectations, global IBC did not vary significantly across experimental conditions. Rather than contradicting prior hyperscanning findings in adults (Nozawa et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), this result suggests that inter-brain coupling is not necessarily a categorical marker of task type. Instead, it may depend on the temporal structure of interaction, consistent with evidence that IBC reflects dynamic social alignment over time (Dai et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wass et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral factors may contribute to the absence of condition-level effects. First, developmental variability may increase heterogeneity in neural coordination across dyads, making global condition contrasts less sensitive in children (Sheridan, 2014) (Sheridan et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Second, the large-scale, channel-agnostic IBC metric used here summarizes widespread cross-brain covariance, which likely includes both interaction-related and condition-invariant components. Such aggregation can reduce sensitivity to categorical contrasts while remaining responsive to moment-to-moment behavioral coupling.\u003c/p\u003e \u003cp\u003eAccordingly, cooperative interaction may not induce uniform synchrony across all dyads, but rather produce coupling contingent on interaction dynamics. This interpretation aligns with the present observation that brain\u0026ndash;behavior associations were detectable even in the absence of condition-level differences, suggesting that IBC is better understood as a continuous index of coordination rather than a binary effect of cooperation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Behaviorally Driven Inter-Brain Synchrony in Collaborative Problem Solving\u003c/h2\u003e \u003cp\u003eA central finding of the present study was that IBC during the collaborative condition was positively associated with behavioral engagement, specifically the number of actions performed on the Tangram pieces. This pattern is consistent with accounts proposing that neural synchrony reflects interactional attunement rather than mere co-presence or shared task structure (Hasson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Redcay and Schilbach \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In developmental contexts, where synchrony has been discussed in relation to scaffolding and co-regulation (Hoehl et al. 2021), the present results indicate that neural coupling increased when children actively manipulated the environment together.\u003c/p\u003e \u003cp\u003eImportantly, this association suggests that dyads engaging more actively in joint manipulation exhibited stronger temporal coordination of neural activity. Such a pattern is compatible with frameworks emphasizing embodied coordination and perception\u0026ndash;action coupling (Glenberg \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gallese \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kontra et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as well as interactive approaches to social neuroscience (Schilbach et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), without implying that identical cognitive processes occurred simultaneously in both participants.\u003c/p\u003e \u003cp\u003eOur results also relate to the distinction between co-action and joint action described in adult hyperscanning research. Previous studies indicate that simultaneous presence or parallel activity alone may be insufficient to produce robust inter-brain synchrony (Cui et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, the absence of significant coupling during the observational condition in the present study suggests that social presence alone did not reliably produce neural alignment in children. Instead, coupling was most evident when participants continuously adjusted their behavior to each other during shared manipulation. This observation is compatible with the proposal that social interaction can be understood in terms of coordinated action dynamics (Konvalinka and Roepstorff \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a developmental perspective, engagement-dependent neural coupling may reflect interactional processes relevant for collaborative cognition, although the present data do not directly measure shared mental representations or learning outcomes. Given that cooperation supports learning in children (Warneken \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tomasello \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), these findings may help constrain hypotheses about when alignment-like neural signatures emerge during peer interaction, potentially during hands-on, jointly coordinated activity. Accordingly, the results motivate future classroom research but should not be interpreted as direct evidence for learning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eIt should be mentioned that this study has some limitations. First, although fNIRS is relatively resistant to motion, the task's natural, hands-on nature likely led to head movements that could not be fully prevented. Second, the participant age range covers a period of significant neurocognitive development, which may have introduced variability in executive function, social cognition, and cooperative skills. Third, the difficulty levels of the Tangram puzzles varied, possibly resulting in different cognitive demands across dyads and conditions.\u003c/p\u003e \u003cp\u003eGiven the partial sensitivity of the CH17 finding to the multiple-comparisons strategy, we treat this localization as a promising but provisional result. The moderate effect size and anatomical plausibility motivate targeted follow-up studies to confirm localized posterior-parietal recruitment during cooperative embodied tasks. Furthermore, the lack of independent measures of executive function or social-cognitive skills limited our ability to connect neural coupling to individual developmental differences. Lastly, the small sample size limits statistical power and the generalizability of the findings.\u003c/p\u003e \u003cp\u003eMoreover, a recent meta-analysis suggests that children's age and brain regions are significant predictors of effect size in parent-child research (Zhao et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Future research should involve larger, developmentally focused samples and incorporate additional behavioral assessments to refine and expand these findings. It should also examine developmental trajectories by including a broader age range of children and comparing different contexts, such as cooperative versus competitive settings, to evaluate how neural response coupling varies with the task.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study suggests that the inter-brain coupling in children covaries with behavioral engagement during cooperative action. This pattern indicates that hyperscanning signals in naturalistic contexts primarily reflect the temporal organization of shared activity. Using dual-child portable fNIRS hyperscanning in an ecologically valid cooperative learning task, we show that global inter-brain connectivity does not differentiate between rest, solo, observational, and collaborative conditions. Critically, however, neural coupling during collaboration scales with children\u0026rsquo;s behavioral engagement, emerging most strongly when partners actively coordinate their actions toward a shared goal. Within this framework, coordinated action provides the context in which coordinated neural dynamics become observable, particularly during hands-on collaborative behavior. Accordingly, the present results contribute to ongoing efforts to characterize how interpersonal neural measures relate to real-world interaction, suggesting that in developmental settings, neural coupling may emerge most robustly when children actively structure behavior together. These findings inform future research on real-world social interaction and peer learning while remaining agnostic about specific cognitive or learning processes, which will require designs directly targeting representational change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author\u0026rsquo;s work cited here was funded by the State of S\u0026atilde;o Paulo Research Foundation (grant numbers 2018/21934-5, 2023/13418-5, 2023/12217-6) and by the D\u0026rsquo;Or Institute for Research and Education (IDOR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.S.S.J.: Conceptualization, Methodology, Formal analysis, Writing - original draft.\u003c/p\u003e\n\u003cp\u003eJ.R.S.: Supervision, Methodology, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received approval from the local ethics committee, and written informed consent was obtained from parents/guardians and children\u0026apos;s assent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that AI-assisted tools were used solely for language editing and grammatical revision. The authors take full responsibility for the content of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlonso, A., McDorman, S. A., \u0026amp; Romeo, R. R. (2024). How parent\u0026ndash;child brain‐to‐brain synchrony can inform the study of child development. \u003cem\u003eChild development perspectives\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 26\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eAyaz, H., Shewokis, P. A., Izzetoğlu, M., \u0026Ccedil;akir, M. P., \u0026amp; Onaral, B. (2012). Tangram solved? Prefrontal cortex activation analysis during geometric problem solving. \u003cem\u003eProceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)\u003c/em\u003e, 4724\u0026ndash;4727. doi:10.1109/EMBC.2012.6347022 \u003c/li\u003e\n\u003cli\u003eBabiloni, F., \u0026amp; Astolfi, L. (2014). 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The Role of Peer Relationship on Children\u0026apos;s Creativity During Cooperative and Competitive Interactions: An fNIRS-based Hyperscanning Study. \u003cem\u003eDevelopmental Cognitive Neuroscience\u003c/em\u003e, 101592.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"brain-structure-and-function","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsaf","sideBox":"Learn more about [Brain Structure and Function](https://www.springer.com/journal/429)","snPcode":"429","submissionUrl":"https://submission.nature.com/new-submission/429/3","title":"Brain Structure and Function","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fNIRS hyperscanning, Child development, Peer collaboration, Embodied cognition, Ecological validity","lastPublishedDoi":"10.21203/rs.3.rs-9302137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9302137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCooperative problem solving plays a central role in children\u0026rsquo;s cognitive and social development, yet how neural activity relates to real-time peer coordination remains poorly understood. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we simultaneously recorded brain activity from 15 dyads of school-aged children (6\u0026ndash;11 years) during a naturalistic Tangram puzzle task, including rest, solo problem-solving, observation, and collaboration. We examined task-related neural activation and inter-brain connectivity (IBC) to assess how interpersonal neural dynamics relate to task context and behavior. Group-level analyses revealed increased activation in the right posterior parietal cortex during collaborative problem solving, consistent with increased visuomotor coordination demands. In contrast, global IBC did not differ significantly across experimental conditions, indicating that inter-brain synchrony was not determined solely by cooperation or social presence. Critically, however, IBC during the collaborative condition was positively associated with children\u0026rsquo;s behavioral engagement, as indexed by the number of actions performed on the puzzle pieces, an effect absent in the rest, solo, and observational conditions. Together, these findings indicate that interpersonal neural synchrony in childhood scales with active, embodied engagement rather than shared task structure or co-presence alone. Beyond demonstrating the feasibility of dual-child portable fNIRS hyperscanning in an ecologically valid hands-on task, the results show that inter-brain measures primarily track the dynamics of coordinated behavior in real-world social interactions, constraining how such signals should be interpreted in naturalistic collaborative contexts.\u003c/p\u003e","manuscriptTitle":"Inter-brain synchrony in children scales with coordinated action during cooperative tangram solving","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 12:59:23","doi":"10.21203/rs.3.rs-9302137/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-23T19:54:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T16:14:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T05:33:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Structure and Function","date":"2026-04-02T10:39:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-structure-and-function","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsaf","sideBox":"Learn more about [Brain Structure and Function](https://www.springer.com/journal/429)","snPcode":"429","submissionUrl":"https://submission.nature.com/new-submission/429/3","title":"Brain Structure and Function","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"76c3e6b2-01f9-4ee4-9ef3-c74e1ec2f18b","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T12:59:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 12:59:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9302137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9302137","identity":"rs-9302137","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}