The field of disaster robotics has developed several robots that support first responders by reducing the physical demands of their work [e.g., heavy lifting (Zhang et al., 2025)] and alleviating cognitive load [e.g., by handling navigation, allowing responders to focus on their specific goals (Su et al., 2014)]. One particularly relevant domain, search and rescue (SAR), is a crucial part of disaster response, focused on rescuing trapped victims in a timely manner. In SAR, responders are constrained by spatial demands (e.g., unfamiliar environments and tight spaces), dynamic environments, and limited ability to complete tasks in situ, necessitating close teamwork to accomplish goals (Bearman et al., 2023). In such situations, intragroup features, such as collaboration, communication, cooperation, and shared mental models, become crucial for the safety of responders and civilians (Kleiner et al., 2005). To support these features, team members must develop a shared knowledge base, which requires coordination of each member's past activities, completed tasks, and remaining objectives (Han et al., 2024). While a large body of research exists to explain human-robot interactions in a dyadic team configuration (single robot, single human), few studies have aimed to examine how these robots integrate into a fluid team (comprising more than one human), in a dynamic environment where traditional concerns (such as trust, dependence, and safety) may be less applicable, as the majority of cognitive resources are expended on completing taskwork (Lyons et al., 2017; Cavicchi et al., 2025).

From a taskwork perspective, a reliable agent, such as a robot, can help manage cognitive load by completing lower-level tasks, thereby freeing a human teammate to focus on complex components (Chen and Barnes, 2014). Many benefits may accrue at the individual level by adding an autonomous agent, such as reducing mental workload (Parasuraman and Wickens, 2008; Kaber and Endsley, 1997) and maintaining situation awareness (O'Neil et al., 2020). For autonomous agents to be adopted into human-autonomy teams (HATs) and human-robot teams (HRTs), they must achieve higher levels of automation, enabling them to act and make decisions independently of human agents (McNeese et al., 2018). Namely, HATs/HRTs are consistently reported to be less efficient than all human teams at information processing, situation awareness, and team performance (Gromek and Lowe, 2025; Kleiner et al., 2005). In addition, the theory of Interactive Team Cognition asserts that communication and coordination among teams are the lifeblood of effective teamwork (Ho et al., 2024). Robots have been found to successfully facilitate communication and collaboration in various contexts, including classrooms (Ouyang and Xu, 2024; Taylor et al., 2024), healthcare settings (Weerarathna et al., 2023; Lim et al., 2024), and manufacturing environments (Ferrari and Secchi, 2024; Scholl, 1996). Interestingly, many of these teamwork benefits are attributed to taskwork benefits (i.e., reduced workload), which provide limited insight into how an autonomous agent benefits a team.

Many previous models of teamwork share a common theme: a focus on social interactions, including items such as team chemistry, connectedness, shared mental models, and a supportive outlook (Johnson, 2021; Schmutz et al., 2019). Social factors of teamwork have been positively associated with context-specific outcomes, including clinical performance, reduced search time in SAR tasks, and collaboration (Rahman et al., 2018; Ellwart and Antoni, 2017). Each of these concepts falls under the umbrella of team cognition, referring to how knowledge is distributed among members (Majchrzak et al., 2007). Strong team cognition may be a factor in team resilience, as it enables teams to respond more effectively to unexpected events, distribute resources, and allocate tasks (McNeese et al., 2016). Several additional studies have identified shared mental models (SMM) as a fundamental explanation of team processes (Manges et al., 2020; Andrews et al., 2023). In dynamic environments, such as SAR, shared mental models become increasingly important because they are essential for understanding and predicting team members' behaviors (Atweh et al., 2022). However, many components of dynamic teamwork remain relatively understudied in the HRT literature.

Although the previously outlined concepts remain crucial to the team's success, individual-level abilities (i.e., task work) should not be overlooked. Several cognitive capabilities (e.g., working memory, executive functions, spatial awareness, and emotion regulation) have been identified as essential for team members to collaborate and achieve goals (Los et al., 2020). A particularly important division of these skills, namely executive functioning, has numerous connections to positive team outcomes, including working memory, cognitive flexibility, cognitive control, and inhibition (Los et al., 2020; Hart and Staveland, 1988). Executive function abilities are cognitively demanding skills that are significantly affected by high cognitive load, fatigue, and stress (Manges et al., 2020).

Several measures have been developed to assess individual states and performance [i.e., for taskwork; NASA Task Load Index (TLX) (Broadbent et al., 1982), Cognitive Failures Questionnaire (Vallat-Azouvi et al., 2012), Working Memory Questionnaire (Porter, 2010)]. While they may be useful for establishing a baseline of individual workload or performance, using them to make major decisions about team composition and resource allocation may be inadequate (Saby and Marshall, 2013).

To objectively quantify taskwork, electroencephalogram (EEG) power spectral density (PSD) can be evaluated, decomposing neural activity into five distinct frequency bands (Chikhi et al., 2022): delta (0.5–4 HZ), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (30–100 Hz). With PSD, both the absolute and proportional (relative) power of each band may be attributed to various cognitive states like mental workload and social cognition (Saxe and Kanwisher, 2003). Several brain regions have been identified as important for team activities. The temporo-parietal junction (TPJ) has been implicated in aspects of social cognition, including theory of mind, cognitive flexibility, and cooperative decision-making (Tei et al., 2017, 2021; Tan et al., 2024; Wang et al., 2016). Cognitive components such as these will play a large role in the social interactions underlying teaming behavior and may be largely responsible for collective performance. Frontal regions of the brain [e.g., the prefrontal cortex (PFC)] are associated with executive functions such as working memory, cognitive control, and attention, which may be important for team members to perform their individual roles and manage dynamic task demands (Wang et al., 2016). In both the PFC and TPJ, theta PSD has been repeatedly used to study workload and social cognition (Cavanagh and Frank, 2014; Wang et al., 2016; Schiller et al., 2019). In a SAR environment, where even a few seconds can be the difference between life and death, identifying inefficiencies in team neurodynamics and developing solutions are critical.

Components of teamwork have been extensively quantified using behavioral and subjective measures (Ma et al., 2022). For example, several scales quantify teamwork using collaborative metrics [Collaborative Assessment Survey (CAS) (Vasodavan et al., 2022), Interprofessional Team Collaboration Scale (Orchard et al., 2012)], team efficiency (which consists mainly of custom scales that quantify context-dependent features of teamwork) (Schmutz et al., 2019), or affective measures [Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) (King et al., 2008), Group Environment Questionnaire (Carron et al., 1985)]. One particularly interesting scale, proposed by (Costa and Anderson, 2011), encompasses many of these factors while predicting team performance, collaboration, and team efficiency, all important features in a SAR environment. Additionally, many teamwork assessments rely on video observation [e.g., the Assessment of Obstetric Team Performance (Toyama et al., 2011) and the leadership and team behavior measurement tool (Nothhelfer et al., 2023)], which may be subject to researcher bias and error. Importantly, each of these scales, and many other custom scales not mentioned here, measures teamwork in unique ways, which may impact the generalizability of results.

Fewer studies have attempted to integrate neuroimaging data to understand the cognitive components implicated in teamwork. EEG hyperscanning appears to be a particularly promising method for this domain, given its high temporal resolution, which enables the identification and evaluation of neural markers of teamwork (Czeszumski et al., 2020). With hyperscanning, researchers can simultaneously record the neural activity of multiple subjects to compare activity during time-locked events (Léné et al., 2021). In the present work, we use EEG hyperscanning to evaluate teamwork by analyzing frequency bands at electrode sites or inter-brain synchrony measures. In the following section, we outline the components relevant to the current body of work.

Historically, popular metrics of teamwork have relied on performance outcomes and subjective measures, which, while providing insights into specific components of teamwork, may be insufficient to capture dynamic interactions over extended periods (Demir et al., 2019; Ma et al., 2022). EEG hyperscanning involves evaluating these frequency bands across multiple brains at synchronized time points. Hyperscanning provides the unique benefit of offering continuous, objective insights into team dynamics that cannot be captured by other metrics. For example, cortical synchrony between the frontal and prefrontal regions of the brain has been linked to collaboration within teams (Léné et al., 2021). Similarly, inter-brain synchrony (IBS) is significantly more prevalent between cooperating groups than between competing groups, suggesting that synchrony may begin when a partner's actions are recognized (Reinero et al., 2020). It has been proposed that IBS extends beyond shared experiences and could serve as an indicator of social cohesion among team members (Liu et al., 2021). Additionally, higher cognitive load may impair information processing, reducing team members' ability to detect social cues that contribute to IBS (Hayati et al., 2025). Finally, several studies (Astolfi et al., 2020; Pan et al., 2020) have reported that higher IBS is positively associated with team performance outcomes.

Some studies have suggested that certain forms of synchrony may be maladaptive, a phenomenon referred to here as over-synchronization. This argument has been more clearly outlined in the coordination vs. competition literature, where teams that collaborate well elicit a functional form of neural synchrony associated with peaks in performance, whereas those that compete elicit less (Kayhan et al., 2022). Namely, functional types of synchronies typically appear alongside fluid communication, collaboration, and social cohesion (Léné et al., 2021; Reinero et al., 2020; Liu et al., 2021). On the other hand, it may be possible for teams to over-synchronize on undesirable qualities, such as shared distractions, in less coordinated teams (Reinero et al., 2021). Over-synchronization is particularly relevant when discussing autonomous and robotic teaming, as operator trust in agents has been found to be associated with monitoring, compliance, and reliance with/on autonomous partners (Lee and See, 2004). Under this interpretation, a lack of trust from one team member could lead to reduced trust from the other via IBS. The distinction between functional and over-synchrony could be a promising direction for investigating why some hyperscanning studies find that neural synchrony predicts better performance, whereas others do not.

Hyperscanning studies typically quantify neural synchrony using coherence measures, such as total interdependence, correlation-based measures, such as cross-correlation and inter-brain correlation, or phase measures, such as phase-locking values (PLV) and phase lag index (PLI) (Kayhan et al., 2022; Hakim et al., 2023; Valencia and Froese, 2020). Coherence and correlation measures are widely used in hyperscanning studies for their ability to reveal relationships between two sites (Hakim et al., 2023). Both measures can be used with a high degree of success when the goal is to understand how individuals respond to the same stimulus, coordinate on tasks requiring social and motor cooperation, and (Nolte et al., 2004; Valencia and Froese, 2020). While hyperscanning appears to be a promising method, several studies have identified potential pitfalls, which should be considered when interpreting results. Namely, synchrony measures may be better suited for identifying the cognitive components that contribute to coordination rather than predicting team performance, which has been more accurately predicted using other forms of physiological data (Qin Y. et al., 2025). Additionally, coherence and correlation-based measures both suffer from the common source problem (which assumes that one source of neural activity will be detected across multiple electrodes), capturing inflated zero-lag synchrony that may be more closely related to co-activation than neural coupling (Kayhan et al., 2022; Bastos and Schoffelen, 2016; Valencia and Froese, 2020).

Phase measures, on the other hand, have been successfully used in teaming situations where stimuli reflect natural teamwork and are more likely to signify true synchrony (Pan et al., 2020). While PLV and PLI are similar in principle, measuring the similarity between the cortical oscillations of certain frequency bands between two brains, PLI is viewed as a robust measure, as it does not suffer from the common source problem. PLI has been used to identify coordinated behavior, verbal interactions, and leader-follower relationships in human teams (Sänger et al., 2012, 2013; Short et al., 2021; Lyons et al., 2017). However, PLI is sensitive to noise, and thus, the weighted phase lag index (wPLI) was developed. The main benefit of the wPLI is its ability to reduce the effects of volume conduction and noise while accounting for the lag between the 2 signals (Shafiei et al., 2024; Czeszumski et al., 2020; Abubshait and Wiese, 2022). To reduce the likelihood of capturing “false” synchrony, and given its success in similar teaming settings, wPLI was chosen as the synchrony measure for this study. For a comprehensive review of hyperscanning methodologies, see (Bastos and Schoffelen, 2016).

Despite extensive research on behavioral and subjective outcomes in human-robot teaming, relatively little is known about the neurocognitive mechanisms that drive teamwork in dynamic settings. Are they similar to those of all human teams? While prior EEG studies have quantified how individual features of taskwork (e.g., workload, social cognition) and teamwork (e.g., IBS) affect teams, few have examined how these dimensions interact to influence teams as a whole. In the present study, we aim to investigate the viability of robots as teammates in a SAR environment by (1) determining the role-specific changes in neural signatures of taskwork in all human (mH) and human-robot (mHR) teams, and (2) identifying differences in neurodynamic signatures of teamwork across all human and human-robot teams. To investigate these aims, we propose four hypotheses:

42 participants were recruited for the study, forming 21 teams. Overall, the study included 19 females and 23 males, divided into four female-female teams, seven male-male teams, and 11 male-female teams. Participants were mainly younger adults, fluent in English, with a mean age of 24.14 ± 3.05 years, who spent an average of 4.75 ± 5.55 h per week playing video games. Before the experimental session began, informed consent was obtained from all participants, and members of the research team confirmed that participants had not previously met. Subsequently, the two participants, along with a confederate who was a member of the research team (unknown to the participants), completed a brief team-familiarization task. The task, lasting ~5–10 min, required participants to introduce themselves to the team and plan a picnic together. This task was adapted from (Javaid and Estivill-Castro, 2021) and is an integral step in ensuring a baseline level of team familiarization.

The first step of statistical analysis was to identify and remove outliers. A threshold of z = 3 was set, and any point exceeding it was removed from the dataset. Next, the windows were collapsed across trials for each participant to the median level. For each trial, a single value was calculated for all frequency bands and for the broadband average (0.5–30 Hz). Medians were used rather than means to reduce the influence of noisy points that persisted through processing. Next, the data were tested for normality using the Shapiro-Wilk test. The subjective measures, namely the mental effort, team trust, and trust in the navigator, were normally distributed, while all other measures were non-normal. Theta PSD [Hypothesis 1.1 (frontal) and Hypothesis 1.2 (temporo-parietal)] and wPLI (Hypothesis 2.1 [at each frequency band and broadband)] values were tested for team configuration difference (mH vs. mHR) using a paired Wilcoxon signed-rank test separately for MC and SO. Performance (i.e., number of victims), trust, and workload ratings (i.e., trust in the navigator, team trust, workload) were compared across team configurations using a paired t-test. To assess whether neural synchrony and team trust predict mission performance (Hypothesis 2.2), a multiple linear regression was conducted with broadband global wPLI and team trust as predictor variables and the number of victims as the dependent variable. Lastly, the times the MC asked to follow the navigator, hazards communication counts, and total stopped communication were compared using a paired t-test. Statistical significance was assessed at an alpha = 0.05. Given that the goal of the present study is to explore potential differences, uncorrected p-values are reported in the results section.

The MC reported significantly greater trust in the navigator (W = 158, p = 0.01, d = 0.69) in the mH navigator compared to the mHR navigator (Figure 3A). However, no significant differences in trust in the navigator were observed by the SO (p = 0.10, Figure 3B). Additionally, self-reported team trust was not impacted for either the MC (p = 0.14; M_mH = 6.15 ± 0.92; M_mHR = 5.79 ± 1.41) or SO (p = 0.99 M_mH = 6 ± 1.31, M_mHR = 6 ± 1.05) across team configurations.

No significant differences were observed in self-reported mental workload measures by the SO (p = 0.38) or the MC (p = 0.67) across team configurations (Figures 3C, D). Performance, measured as the number of victims located, did not differ significantly (t(21) = 1.39, p = 0.18, d = 0.34) between mH and mHR team configurations (M_mH = 4.72 ± 1.31; M_mHR = 4.21 ± 1.38).

For MCs, the only significant team configuration difference we observed was relative theta activity at TP10, which was significantly higher (W = 200, p = 0.01, d = 0.56) during the mH condition than during the mHR condition. No other differences were significant between team configurations for the MCs (all p > 0.14) or SOs (all p > 0.27). Figure 4 illustrates the Theta PSD across roles and sites. Additionally, no other differences in absolute theta activity were significant between team configurations for the MCs (M_mH = 11.85 ± 6.31; M_mHR = 10.23 ± 6.84; all p > 0.10) or SOs (M_mH = 12.54 ± 5.18; M_mHR = 10.21 ± 4.39; all p > 0.18).

Exploratory analysis on wPLI revealed significantly higher neural synchrony between MC and SO during the mHR condition than the mH condition (Figure 5) for the following sites: alpha neural synchrony at the FT9 site (p = 0.001, M_mH = 0.379 ± 0.259;M_mHR = 0.390 ± 0.262), beta neural synchrony at Fp1 (p = 0.05, M_mH = 0.196 ±, M_mHR = 0.199 ± 0.146) and T8 (p = 0.04, M_mH = 0.197 ± 0.147 M_mHR =0.198 ± 0.147), and delta neural synchrony at the FC2 site (p = 0.02, M_mH = 0.523 ± 0.312, M_mHR = 0.527 ± 0.314). Finally, significantly greater broadband synchrony was observed during mHR than the mH condition at FT9 (p = 0.001, M_mH = 0.373 ± 0.007, M_mHR = 0.382 ± 0.009) and O1 (p = 0.023, M_mH = 0.372 ± 0.010, M_mHR = 0.380 ± 0.006).

The regression analysis revealed that broadband global wPLI significantly predicts team performance (p = 0.023, R² = 0.18; Figure 6). Teams exhibiting higher wPLI found significantly fewer victims (β = −23.50, p = 0.006, SE = 8.14). Team trust did not significantly predict team performance (β = 0.005, p = 0.886, SE = 0.004).

Lastly, analysis of team communication metrics revealed significantly more stopped communication between participants during the mHR team configuration than during mH teams (p<0.001, M_mH = 1.02s ± 1.47s, M_mHR = 6.16s ± 8.57s; Figure 7). Neither the count of MCs requesting to follow the navigator nor the hazard communication count differs significantly across team configurations (p > 0.11).

We found that neither safety officers nor mission commanders reported or showed (via neural activity) reduced mental workload when a robot teammate was introduced, thereby disproving Hypothesis 1.1. These findings of stable workload across team configurations and roles contrast with several industrial human-robot collaboration studies that report that robotic agents successfully reduce operator mental effort (Carissoli et al., 2024). Interestingly, no differences in mission performance, i.e., number of victims identified, were observed across team configurations. These findings challenge the “substitution myth”, i.e., the assumption that a robot with equal task performance can be seamlessly substituted for a human operator without altering the underlying cognitive architecture of the system to which they belong (Matthews and Greenspan, 2020). However, these results offer little insight into whether replacing a human teammate with a robotic agent affects team cognition.

Interestingly, our findings of lower TPJ theta PSD among mission commanders indicate that the inclusion of a robotic teammate indeed affects the social-cognitive abilities of highly involved team members (i.e., mission commanders) more than those of less involved members (i.e., safety officers). This result aligns with previous findings (Nguyen et al., 2020) and provides partial support for Hypothesis 1.2. To interpret the null findings among safety officers, we expand on a few possibilities: (1) limited engagement with the robotic system, and (2) role-specific task demands. Given the pre-determined roles established for each team, where the safety officers would focus on observing and reporting hazard, and the mission commanders would coordinate with the navigator to lead the team, we expect that the safety officers would have spent most of their time engaging with the mission commander rather than the robot, and thus, we cannot conclusively say the safety officer was perturbed by the inclusion of the robot.

The mission commander had more self-contained tasks that did not require them to engage with their teammates differently under either team configuration. Namely, the safety officer was responsible only for monitoring the danger levels in each room the team visited and reporting to the mission commander. While this task may have required participants to sustain vigilance for short periods, we do not believe the task demands for the safety officers would have a substantially different effect across conditions (Parasuraman et al., 2000). Despite this potential flaw, an interdependent team structure has been found to boost the performance of human-AI teams in related domains past that of a human or AI system alone (Vaccaro et al., 2024; Qin L. et al., 2025). While the safety officer's lack of engagement with the robot may have influenced our taskwork results, we believe this team configuration more accurately reflects the structure of an actual multi-human robot team, though future work may want to further investigate this trade-off. Analysis of self-reported trust in navigator scores provides additional support: the mission commander reported trusting a robot navigator less than a human navigator, whereas the safety officers did not change their trust levels across navigator types. While distrust is generally seen as problematic [see (Nguyen et al., 2020)], our results indicate no signs of decline in cognitive abilities, and that some additional monitoring of partner performance may enable mission commanders to remain more involved with team processes, collaboration, and joint attention (Zhang et al., 2025; Kelsen et al., 2022; Liu et al., 2018; Lachat et al., 2012). These results suggest that adding a robot to a team could alter each member's taskwork abilities, although the degree of impact may be modulated by the nature of the work. Although we observed a significant disparity in trust between human and robot navigators, a recent study found no significant differences in participants' trust behavior when interacting with AI vs. human partners who performed similarly (Kelsen et al., 2022).

Among the sites surveyed for neural synchrony in this study, FT9 appears particularly robust, as both broadband and alpha wPLI were significantly higher during mHR trials than during mH trials. Prior research (Lachat et al., 2012) has identified the left frontotemporal site as specifically important for governing the rules of social interaction (e.g., turn-taking) and communication. Our results suggest that incorporating a robot into a dynamic team may increase collaboration among human members, indicating that mH teams are fundamentally different from mHR teams and supporting Hypothesis 2.1. However, these differences were not detected using subjective team trust scores, indicating that neural metrics provide valuable insight into teaming demands when robots are embedded in multiparty human teams. This result may be partially explained by our analysis of stopped communication, in which human-robot teams spent significantly more time in a stopped state. The increase in communication breakdowns could indicate that a lack of trust has manifested in behavioral detriments. Specifically, a less trusting mission commander may spend more cognitive resources monitoring the robot navigator, needing to stop more often to re-orient the team and pulling more information from the safety officer. While this behavior would not support the team's effectiveness, it could increase neural synchrony at the FT9 site without the expected benefits of increased coordination. (Short et al., 2021; Konvalinka and Roepstorff, 2012).

Given that our behavioral results suggest that mission commanders trusted the robot navigator less, these benefits may be extremely subtle, further underscoring the need for trust calibration within these teams. This conclusion was consistent with subjective ratings of team trust, which did not differ significantly across configurations, suggesting that social awareness was oriented toward collaboration rather than monitoring. The robustness of FT9 synchrony aligns with hyperscanning literature showing fronto-temporal regions as hubs for speech rhythm and turn-taking coordination (Kelsen et al., 2022). More importantly, this study prioritizes dynamic interaction, captured using neural synchrony, as the unit of analysis, as theorized within the interactive team cognition framework (Cooke et al., 2013). By doing so, the findings indicate that although team outputs (i.e., mission performance) remain comparable between mH and mHR teams, the internal processes are fundamentally transformed when a human teammate is replaced by a robotic agent.

Additionally, higher wPLI at left occipital sites during mHR configurations suggests that team members engage more in joint attention and shared visual processes while working (Cooke et al., 2013; Vicente et al., 2023). This conclusion is further supported by the observation of higher beta synchrony at left prefrontal sites, which has been linked to joint attention and collaboration in teams (Georganta and Ulfert, 2024; Czeszumski et al., 2022). The results of Hypothesis 2.2 suggest that robot teams may impose a “hidden cost” on team members, which may not be observable using behavioral or subjective metrics but could negatively affect team outcomes over the long term. Indeed, results of our regression analysis support the idea that measures of neural synchrony are demand-oriented, as we found that more neural synchrony predicts fewer victims located. This suggests that teams may experience maladaptive neural reorganization, driven by the task and teaming demands, resulting in over-coupling or rigidity (Bassett et al., 2011). Specifically, our results suggest that in mHR team configurations, greater social cognition is required, resulting in redundant and inefficient information processing and reduced team efficiency. This negative relationship, or synchrony paradox, is supported by recent simulation research identifying negative correlations between anterior alpha synchronization and team performance, suggesting that over-coupling can indicate a maladaptive cohesive typology (Park et al., 2020). These findings deviate from traditional dyadic studies, where inter-brain synchrony is generally viewed as a positive marker of collaboration success and social facilitation.

A major limitation of this study is its exploratory nature, which focused broadly on synchrony measures. By collapsing synchrony measures across dyads into a single mean or median, we cannot evaluate how wPLI changes in response to environmental events (e.g., robot guidance). An alternative explanation for our findings is that individual team members adjust their cognitive processes in ways that inadvertently align with those of their teammates. Critically evaluating how neural synchrony changes in response to navigator directions (both human and robot) is a logical future application that may shed light on the interplay between taskwork and teamwork. Additionally, with the current setup, we are unable to map some neural metrics to behavioral outcomes. For example, it would be beneficial to pair future studies with eye-tracking to determine the direction of visual attention. Future research may consider using additional measures (e.g., eye tracking) to confirm the behavioral impact of this result, which could be interpreted as members spending more time scanning the environment for victims or monitoring the robot's behavior (Viriyopase and Narajeenron, 2025; Hergeth et al., 2016). Similarly, this study exhibited an imbalanced sex distribution across teams, which may not reflect ecologically valid teamwork. This study could have benefited from additional behavioral metrics, such as communication network analyses, which may have strengthened the utility of neural synchrony in revealing how social interactions were affected by team configuration.

Finally, the extent to which participants attributed the robot's behavior to an “autonomous” system is unknown. It is possible that some participants recognized the WoZ control and inferred that a human was controlling the robot. While the researchers aimed to reduce the likelihood that participants would attribute robot actions to human researchers, we cannot guarantee that these results are due solely to the presence of a robot. Future work should continue to investigate team neurodynamics in the presence of non-WoZ-controlled robots. Additionally, despite the robot's performance being comparable to that of a human navigator, mission commanders and safety officers may have had higher expectations of the robot, which, when unmet, led human team members to develop compensatory coordination, increasing neural synchrony.