A Research Framework to Develop a Real-Time Synchrony Index to Monitor Team Cohesion and Performance in Long-Duration Space Exploration

Human space exploration, particularly long-distance space exploration missions (LDSEM), represents one of the most challenging frontiers for human performance and adaptation. Among the many factors influencing mission success, group performance, defined as the ability of a group to achieve its goals efficiently, and synchrony, defined as the alignment of behaviors, emotions, and physiological states, are a critical area of focus, since it has been repeatedly shown that group synchrony and performance are positively associated. These concepts, encompassing how well a team collaborates, aligns in behavior, and maintains a shared cognitive and emotional state, are essential to the sustained operational efficiency and psychological well-being of astronauts. The unique demands of space missions – including isolation, confinement, extreme environmental conditions, and prolonged stress – highlight the need to understand and optimize group performance and synchronization to ensure the safety of operations, mission success, and crew health. The importance of group synchrony in LDSEM has not gone unnoticed. Indeed, several studies have focused on group dynamics and synchrony in teams that lived and worked together for different stretches of time, sometimes for prolonged periods of time and in conditions simulating those of LDSEM (i.e., Isolated and Confined Environments, hereafter ICE). A recent review and meta-analysis [5] summarised the findings of several analogue missions of different lengths and with and without ICE conditions. Bell and colleagues found that the relationship between cohesion, defined as the members’ inclinations to forge social bonds and stick together, and performance was positive and significant for teams with the following characteristics: living and working together for shorter stretches of time, and not in ICE conditions. At variance with this result, this relationship was not as clearly observable in group living and working together for longer stretches of time, and in ICE conditions. This is probably related to the fact that cohesion, among other team dynamics, tended to fluctuate more for teams in ICE than for teams in non-ICE conditions [5], possibly masking existing associations. In line with this idea, two other major patterns were observed for ICE missions: communication between crew members and the mission control (MC) tended to sharply decrease with time and all missions reported at least one conflict among members at the mark of 90 days or by 40% of elapsed mission time [5]. The above mentioned meta-analytical results highlight the great positive impact that a continuous, real-time, unobtrusive index of group synchrony may have on LDSEM. Indeed, if cohesion, a proxy of group synergy, shows highly fluctuating levels, then tracking it at any given time will help to follow the group dynamic more closely. This could, in turn, allow for targeted interventions to restore synergy. The tracking of group cohesion might be even more critical if sensitive cooperative tasks, whose performance is associated to synchrony levels, are to be performed. When communications between crew and the MC decrease with time, the MC could benefit from a group synchrony index, since overt communication could dwindle to a level where inferring group synchrony would be impossible. Additionally, the synchrony index could help MC unmask latent or unresolved conflicts, and their after-effects, within the team and react by putting more emphasis on group synchrony during critical phases of missions. Finally, the crew itself could benefit from a measure of their current synchrony, especially because teams seem to be able to stabilize their level of synchrony when feedback about it is provided [11]. The current work details our group’s efforts to develop the research framework needed to develop a real-time, continuous and unobtrusive index of group synchrony, to be used not only during LDSEM, but also in other contexts where tracking group synchrony is of essence. The paper is structured as follows: the first part focuses on the theoretical model of group performance, the second part focuses on group synchrony, the third part proposes a theoretical framework relating group performance and synchrony, and the final part reports the proposed research framework while highlighting the questions that remain open.

Group synchronization refers to the coordinated alignment of actions, emotions, and physiological processes among individuals. It occurs at behavioral (actions, communication), cognitive (thoughts, emotions), and physiological (brain and autonomic activity) levels, all of which are linked to group performance [18, 40, 43, 49]. Research has explored how these levels interact – whether physiological synchronization stems from behavioral or affective synchronization – but there is no consensus. Although causal mechanisms remain debated, studies have identified key relationships, such as the impact of task complexity on team physiology and the link between cohesion and behavioral synchrony. The following sections will describe these levels and their empirical connections.

Based on the relevant literature on group performance and synchrony, we propose a model of the relationship between these two concepts. This model is meant to clarify the relationship between different sub-elements of group performance and group synchrony in order to inform the experimental framework necessary to the development of a real-time measure of synchrony that is meaningful for group performance. The model is presented in figure 1.

Group performance output, including both objective and affective outcome of teamwork, is determined by how the input is modified by the mediating mechanisms taking place within the group. In turn, the output can become input again, as material production of the group and the starting point of the next iteration. Affective outcomes can feed back into the loop, or influence the mediating mechanisms, for example by modifying shared mental models or by increasing group potency. As for synchrony, the different levels at which it can be observed (i.e., cognition, neurophysiology and behavior) all interact with each other. Elements of group performance and levels of synchrony observation can influence each other. The input needed for group performance can influence cognition and neurophysiology (e.g., compositional features such as personality traits or intelligence influence cognitive synchrony; perceptual features from task-related stimuli influence neurophysiological synchrony). Additionally, cognitive and behavioral synchrony are closely associated with the mediating mechanisms that are necessary to transform the input into the output of the group performance. Finally, although in this model, neurophysiological synchrony does not interact directly with elements of group performance, it can influence the mediating mechanisms via its relationship with both cognitive and behavioral synchrony. Overall, there is a circular relationship between group synchrony and group performance. Group synchrony influences group performance and vice-versa. Note, however, that this influence is not necessarily positive: while positive outcomes (e.g., attaining team goals or increasing trust and cohesion) can increase synchrony, negative outcomes (e.g., not attaining team goals and the associated stress) can decrease synchrony. In turn, the influence of synchrony on group performance varies as a function of the task demand: as an example, tasks demanding divergent thinking can be hindered rather than facilitated by high levels of group synchrony. However, affective outcomes, such as trust or cohesion, and synchrony seem to have a more straightforward relationship: when the latter increases, the former increases too. Among the different elements of this combined model, some are more easily measurable than others. Specifically, we argue that while mediating mechanisms (the element most closely related to output) cannot be directly measured in real-time, as they are mainly cognitive in nature, two elements of synchrony, namely behavioral and neurophysiological synchrony, can be measured in real-time. They can also be leveraged as an index of important mediating mechanisms (such as cohesion) and ultimately, predict group performance. This led us to the experimental framework that will be presented in the following section. On the basis of this theoretical model, three families of study on this topic can be defined. First, “type 1” research: the group performance focused studies. These are studies that aim to better understand the relationship between the elements pertaining to group performance strictly speaking (i.e., the pink arrows within the green box in figure 1). “Type 2” research is the inter-elements studies, composed by studies aiming at better understanding the relationship between specific elements of group performance and specific observational levels of synchrony (e.g., how input influences cognitive and neurophysiological synchrony or how cognitive and behavioral synchrony influence the mediating mechanisms; see the red arrows in figure 1). Finally, “type 3” research is the performance-synchrony relationship studies. These are studies that look at the association of synchrony (measured with indices pertaining to specific levels of observation or with composite indices) and group performance, and specifically, group outcome, under different conditions (golden arrows in figure 1).

The global research framework that we propose is schematically presented in figure 2.

The main idea is to measure neurophysiological and behavioral synchrony in real-time within teams. Neurophysiological and behavioral indices of synchrony would then be merged using data fusion techniques in order to obtain a single multi-source group synchrony index, predictive of cohesion and, in a task-dependent manner, of performance (see below). This synchrony index may then be shared with the team itself, as feedback useful for maintaining the desired level of cohesion and for alerting when critical states (e.g., extremely low levels of cohesion) are nearing. This index would also be used by Mission Control or any other long-distance managing entity, to inform the group about managing strategies, task assignment, etc. Based on the plethora of measures used to assess neurophysiological and behavioral synchrony, and on the complex relationship between these features and affective and objective outcomes of teamwork, it is necessary to first select the best measures and to characterise their relationship with cohesion and performance during different task types. In particular, it is important to observe how synchrony measures relate to cohesion and performance during tasks where synchrony is the overt aim, during “convergent tasks” (i.e., tasks where synchrony is not the overt aim, but is expected to improve performance), and during “divergent tasks” (i.e., tasks where synchrony and cohesion can have a negative impact on performance). Within the taxonomy of studies described above, this study belongs both to the inter-elements studies and to the performance synchrony studies. Indeed, we will focus on specific levels of observation of synchrony, trying to associate them with a mediating mechanism (i.e., cohesion) but also to the overall group performance, and specifically, the objective outcome of teamwork, under different conditions (i.e., different task types). The framework that will be used to develop experimental protocols for the measures selection phase is outlined in figure 3.

In order to characterise and eventually select the best features, we propose the following: the experimental protocol will include three different tasks. For each of these tasks, cohesion and objective performance will be measured. One task will have synchrony as its overt aim: synchronous drumming (i.e., drumming in time with a metronome) has been repeatedly shown to induce behavioral and physiological synchrony and to increase cohesion among team members. This task can be conceived as a validation task, useful for testing the ability of the selected features to measure synchrony, and its association with performance and cohesion, when these latter are known to be present. Another task will be a “convergent task”, such as decision making or a simulated hiring task [33]. The third task in the protocol will be a “divergent task”, a task where an excess of synchronization and cohesion can hinder the performance, as divergent thinking among team members is necessary, such as the creative phase of brainstorming. The measure of synchrony that will be included in this first step remains an open question. On the basis of the literature, we are aiming at including a movement-based and a communication-based measure of behavioral synchrony, respectively an optical flow-based and a language style matching-based measure. As for the specific metric of synchrony to use (e.g., multivariate recurrence, cross-correlation, phase coherence), this is still open to debate. As for neurophysiological synchrony, we are aiming to record heart rate, EDA, and EEG data. For neurophysiological synchrony, we are planning to include several indices of synchrony, and specifically, at least one linear index (e.g., cross-correlation) and one frequency-based index (e.g., frequency or phase coherence), but the specific indices to use are yet to be identified.

In this paper, we have presented a literature review about group performance and group synchrony. Based on this literature review, we have presented an experimentally oriented theoretical model of the relationship between elements of group performance and of group synchrony. We then used this model to inform a research framework to develop a real-time, continuous index of group synchrony that can be used as a proxy for cohesion and, eventually, to predict group performance. We argue that such an index could be extremely useful in the context of space missions, and especially for long-distance missions. Indeed, the relevant literature suggests that cohesion is especially fluctuating for crews spending long stretches of time in isolated and confined environments. Moreover, these crews tend to communicate less with mission control as time-on-mission increases, and they tend to experience at least one conflict within 90 days of a given mission. All this evidence suggests that an index of group synchrony, that is able to track group cohesion and to predict group performance, may be equally useful for crew members and for mission control. It could serve as feedback for the crew to increase cohesion levels and as a cue for mission control to help with personnel management. Such a group synchrony index could, of course, be helpful in other contexts where crew members have to live and work in isolated and confined environments for long stretches of time. Clearly, the applicability or real-time synchrony assessments would depend on specific mission conditions. For astronauts, the index could always be available in real-time, if the necessary calculations are made within the spacecraft, but mission control would lose the ability of having proper real-time assessments with the spacecraft’s increasing distance from Earth. However, we argue that even in the absence of a real-time assessment, a synchrony index could provide valuable information to mission control and alert them about anomalies in synchrony levels. At the time of writing, the team is finalizing the experimental protocol of the first step of our research plan: the characterization and selection of behavioral and neurophysiological synchrony indices and their relationship with cohesion and group performance, under different task types. The data collection campaign is planned to start in the current year, in collaboration with the National Centre for Space Study (CNES), the Institute for Space Medicine and Physiology (MEDES) and the Toulouse University Hospital.