# Assessment of individual and dyadic workload of student and instructor pilots in real and simulated flight: An exploratory study

E. van Weelden, T.J. Wiltshire, M. Alimardani, M.M. Louwerse, R.N. Roy, F.

Dehais

► To cite this version:

E. van Weelden, T.J. Wiltshire, M. Alimardani, M.M. Louwerse, R.N. Roy, et al.. Assessment of individual and dyadic workload of student and instructor pilots in real and simulated flight: An exploratory study. Applied Ergonomics, 2025, 129, pp.104606. 10.1016/j.apergo.2025.104606. hal-05221937

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# Assessment of individual and dyadic workload of student and instructor pilots in real and simulated flight: An exploratory study

E. van Weelden<sup>a, *, †</sup>, T.J. Wiltshire<sup>a †</sup>, M. Alimardani<sup>b †</sup>, M.M. Louwerse<sup>a †</sup>, R.N. Roy<sup>c †</sup>,
F. Dehais<sup>c †</sup>

ARTICLE INFO

Handling Editor: Dr. P.R. Pennathur

Keywords:

Aviation

Mental workload

Physiological synchrony

Electrocardiography (ECG)

A B S T R A C T

Flight instructors are vital in the training of novice pilots. Just as the mental workload in student pilots fluctuates during training, so does the mental workload of the instructors, affecting their dyadic performance and judgment. This paper explored how training environments (simulated vs. real flight), flight control conditions (student pilot flying vs. monitoring), and flight phases (take-off vs. downwind vs. landing) could affect mental workload in student and instructor pilots, as well as their interpersonal coordination through electrocardiography (ECG) based measures. Student pilots performed four consecutive standard flight traffic patterns with an instructor in a simulator and in real flight, under different flight control conditions of varying workload demands, while their ECG signals were recorded. The results indicated different patterns of interpersonal coordination between simulated and real flight. The other experimental variables, i.e., flight control and flight phase, mostly had an effect on participants' heart rate and heart rate variability. In accordance with the existing literature, heart rate increased in high workload conditions and flight phases, whilst heart rate variability decreased. Additionally, we observed a linear relationship between subjective workload and physiological synchrony which indicates that student-instructor coordination increased as the student pilot experienced more mental workload in the flight tasks. The current study highlights that individual workload as well as dyadic workload, measured through cardiac signals, can be used to evaluate student-instructor coordination during flight training as an indicator of training outcomes.

# 1. Introduction

Learning to fly a plane is an intricate process that depends heavily on the dynamic interplay between a student pilot and their instructor. The student pilot must observe and practice the tasks conducted or assigned by the instructor (Marques et al., 2023), while the instructor is responsible for communicating effectively, motivating the student, and ensuring the safety and efficacy of the training (Myers et al., 2018). Effective student-instructor interactions are fundamental to successful learning outcomes, and this is particularly true in the context of aviation training, where the interaction between a student pilot and their flight instructor can significantly influence the learning process and overall safety (Myers et al., 2018; Lazure et al., 2020).

However, aviation tasks tend to be highly cognitively demanding. Student pilots experience a high level of mental workload as they must

learn new skills in high-risk situations. Similarly, the instructor's mental workload can fluctuate considerably during the training depending on the complexity of the training environment and the specific training objectives of each session (Lazure et al., 2020). Therefore, each person goes through different levels of mental workload during the training that could impact their individual performance and consequently their interaction dynamics with one another. For the student pilot, high levels of workload can diminish their ability to learn piloting skills and perform in the early stages of training (Hart, 1986), and for the instructor, it could negatively impact their ability to effectively evaluate and support their students (Kleber and Blickensderfer, 2021). To this end, monitoring and managing workload in pairs of participants, so-called dyadic workload, and interpersonal coordination between student pilots and their instructors is of utmost importance for optimizing training outcomes.

<footnote><sup>a</sup> Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands</footnote>

<footnote><sup>b</sup> Department of Computer Science, Vrije Universiteit, Amsterdam, the Netherlands</footnote>

<footnote><sup>c</sup> Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, Toulouse, France</footnote>

<footnote>* Corresponding author. Department of Cognitive Science and Artificial Intelligence, Tilburg University PO Box 90153 5000 LE Tilburg, the Netherlands. E-mail address: e.vanweelden@tilburguniversity.edu (E. van Weelden).</footnote>
One promising approach to quantify dyadic workload and interpersonal coordination in real time is through physiological measures that can be passively recorded and monitored during training (van Weelden et al., 2022). Pilots' mental workload can be passively monitored with the use of electrocardiography (ECG) signals (Charles and Nixon, 2019; Dussault et al., 2005; Hajra et al., 2020; Wang et al., 2024). Generally, heart rate (HR) and heart rate variability (HRV) derived from ECG signals are reliable predictors of a pilot's mental workload, with higher HR and lower HRV being associated with increased demand (Wang et al., 2024; Martins, 2016). This is consistent across environments, i.e., in simulated as well as real flight (Wang et al., 2024). Within flight phases, HR commonly changes as well, with take-off and landing phases being associated with higher HR (Wang et al., 2024). This corresponds to the fact that these flight phases are considered to be the most mentally demanding (van Weelden et al., 2022; Lee, 2010; Causse et al., 2012).

Next to monitoring an individual's HR(V), tracking the interpersonal coordination within dyads using their physiological measures is also highly informative. Interpersonal coordination can be defined as a multi-scale process that encapsulates the (shared) organization and covariation in behavioral, physiological, and mental states, i.e., inter-dependence, over time between people (Mayo and Gordon, 2020; Wiltshire et al., 2019, 2020a). Physiological synchrony—the temporal alignment of physiological processes such as HR and HRV between individuals—has emerged as a promising measure of interpersonal co-ordination and collective cognitive workload (Palumbo et al., 2017). Existing studies have predominantly examined physiological synchrony among co-pilots during simulated flight scenarios, revealing that increased physiological synchrony is associated with cooperative and highly demanding flight phases such as take-off and landing (Astolfi et al., 2012; Bezerianos et al., 2015; Verdière et al., 2019, 2020).

When measuring physiological synchrony, it is important to describe both linear and non-linear characteristics of interpersonal coordination because team interactions can exhibit complex and non-linear patterns that change over time (Wiltshire et al., 2024). Oftentimes, physiological synchrony is quantified with linear measures, such as cross-correlation (Palumbo et al., 2017) or the Delayed Coincidence Count introduced by Verdière et al. (2020). However, non-linear metrics such as measures derived from cross recurrence quantification analysis (CRQA) can capture patterns and similarities in the dyadic signals that otherwise would not be captured by linear metrics (Marwan et al., 2007; Wallot and Leonardi, 2018; Strang et al., 2014).

In the past, CRQA has been used to study cardiac synchrony between dyads during cooperative tasks, e.g., Verdière et al. (2020); Strang et al. (2014). However, Strang et al. (2014) argued that cardiac synchrony, defined with metrics resulting from CRQA, can be influenced by task demands as well as interpersonal coordination dynamics. Whereas Verdière et al. (2020) did not observe this effect for CRQA measures, they did find an effect of task demands on the Delayed Coincidence Count. These investigations highlight the potential of linear and non-linear measures of interpersonal coordination to capture the nuanced interplay of dyadic physiological signals. A notable gap exists in the aviation literature regarding the study of interpersonal physiological coordination within student-instructor dyads, particularly under the demanding conditions of simulated and real flight environments. Thus, there is an opportunity to investigate how varying task demands (task load)—such as flight phases, control roles, and training environments—impact these dynamics in ecological settings, affecting workload. The current study seeks to address this gap by analyzing student-instructor dyads' physiological coordination with a focus on the differences observed between simulated and real flight conditions and across varying levels of task difficulty.

To explore the effect of the aforementioned workload-inducing factors on interpersonal coordination, i.e., flight phases, flight control roles, and training environments, we designed an experiment in which student pilots were asked to perform a standard flight traffic pattern with a flight instructor, either in a flight simulator or real flight conditions. Student

pilots were requested to either primarily control the traffic pattern (Pilot Flying condition; PF) or passively monitor their flight instructor (Pilot Monitoring condition; PM).

We predicted that the real flight condition would induce higher levels of workload and an increased interpersonal coordination compared to the flight simulator, as the flight instructor and student would be more engaged, and collaborate more to ensure flight safety. We also expected linear relationships between measures of interpersonal coordination and (subjective) workload based on the findings in Verdière et al. (2020) and Strang et al. (2014) that task demands affect interpersonal dynamics and synchrony. However, gathering a sufficiently large sample for research involving a pool of limited student pilots and instructors, access and costs for real aircraft, and ensuring comparable flight conditions (i.e., weather) is notoriously challenging. Even though the sample size used in this study is not unusually low (e.g., ten dyads were used in Verdière et al. (2019), ten student pilots were used in Labedan et al. (2023), three pilots were used in Dehais et al. (2021), and six military student pilots were used in van Weelden et al. (2024)), the findings of the current study should be treated as preliminary, serving as a foundation for future research.

If we understand the relationship between physiological synchrony and demanding scenarios during flight training, as well as how individual workload fluctuations influence dyadic coordination, we may be able to develop aviation training support tools to track coordination dynamics and provide real-time guidance to enhance training effectiveness (Wiltshire et al., 2020b; Halgas, 2023). Since we know that interpersonal coordination supports cooperation and team performance (Wiltshire et al., 2020b), these tools would be valuable for improving training outcomes.

## 2. Methods

### 2.1. Participants

Eight male subjects ($M_{age} = 27.88, SD_{age} = 11.38$) with varying levels of flight experience ($M = 1720.50, SD = 4679.72$ flight hours, range: [9, 13,300] hours) and two male instructors (age range: [49, 53], flight hours range: [112, 6500]) associated with ISAE-SUPAERO, Toulouse, France, voluntarily joined this experiment. As previously mentioned, we encountered numerous challenges in acquiring a large sample, but the recruitment of instructors was especially complicated, leading to a highly limited sample ($N = 2$). Each student formed a dyad with one of the two instructors: in the simulator, all students flew with Instructor 1 (112 real flight hours), while in real flight, they flew with Instructor 2 (6500 real flight hours). This study was approved by the ethics committee of ISAE-SUPAERO.

### 2.2. Materials

#### 2.2.1. Aircraft and simulator

Real flight experiments took place in a P-68 Vulcanair aircraft (Toulouse Lasbordes airfield, France), while the simulator experiments took place in the ISAE-SUPAERO fixed-wing simulator (Fig. 1). The student pilots were seated on the left, the instructors were seated on the right, and the experimenters were seated in the back of the simulator or aircraft.

#### 2.2.2. ECG

Two eMotion three-channel Faros ECG sensors (125 Hz; Mega Electronics Ltd, Finland) were used to record the ECG signals of student pilots and instructors. For each ECG sensor, one electrode was placed under the right clavicle, another was placed under the left clavicle, and one electrode was placed on the left side of the abdomen. The two ECG sensors were synchronized using the Lab Streaming Layer software (Kothe et al., 2014).
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** A) Set-up in the P-68 Vulcanair aircraft, B) set-up in the ISAE-SUPAERO fixed-wing simulator. Electroencephalography (EEG) data was recorded from students, but has not been used in the current study.</figcaption></figure>

### 2.2.3. Questionnaires

During experiments, student pilots' subjective workload was measured with the use of the instantaneous self-assessment of workload technique (ISA; Brennan, 1992; Jordan, 1992). The ISA collects responses in the form of Likert scale values ranging from 1 to 5, with 1 = under-utilized, 2 = relaxed, 3 = comfortable busy pace, 4 = high, and 5 = excessive (workload).

After experiments, the raw and shortened NASA Task Load Index (NASATLX; Hart et al., 1988) was acquired. The following scales were included: mental demand, temporal demand, situational stress, and task complexity. Additionally, responses were acquired from students and instructors to an adapted version of the questionnaire for social affiliation (Cacioppo et al., 2014). Perceived synchrony was derived from the item "How synchronized were you with your instructor?" embedded in the social affiliation questionnaire. Lastly, instructor evaluations and student self-evaluations of overall flight performance and progress were collected on a scale from 1 (poor) to 10 (excellent). No objective measures of performance were collected. Thus, these evaluations served as the primary measure of performance.

## 2.3. Experimental design

Each subject performed the experiment once in the simulator and once in real flight (see Fig. 2 for a schematic representation of an experimental condition). In both simulator and real flight experiments, dyads performed four consecutive standard flight traffic patterns, with touch-and-go in-between consecutive flight patterns. Special flight events and circumstances, e.g., missed touch-and-goes or adverse weather, were recorded in the experimental notes, but were not further analyzed. Fig. 3 demonstrates a schematic outline of the standard flight traffic pattern and phases of interest in this study.

Two flight control conditions were implemented in the flight patterns: 1) Pilot Monitoring (PM), in which the student pilot was observing

and copiloting the instructor, and 2) Pilot Flying (PF), in which the student pilot was flying the aircraft and the instructor served as co-pilot and instructor.

The order of the conditions was pseudo-randomized, with each condition (i.e., PF and PM) repeated twice within each experiment, resulting in a total of four traffic patterns per experiment.

After each flight traffic pattern within experiments, student pilots were asked to rate their workload according to the ISA. Each experimental condition for training environment was concluded with four NASA-TLX questionnaires: one for each of the four flight traffic patterns. The adapted questionnaire for social affiliation and perceived synchrony (The MathWorks Inc, 2023) was also acquired after the four flight traffic patterns were completed within each training environment, one for each flight control condition (PF and PM).

In this experiment, the independent variables were training environment (simulated vs. real), flight control conditions (student pilot flying vs. monitoring), and flight phases (take-off vs. downwind vs. landing). The dependent variables were subjective measures for mental workload, social affiliation, and perceived synchrony (derived from the questionnaires) and ECG-based measures of HR, HRV, and interpersonal coordination.

## 2.4. Data pre-processing

The raw ECG data was pre-processed to ensure accurate and reliable detection of cardiac features. The data was processed in MATLAB R2023b using the Signal Processing Toolbox (The MathWorks Inc, 2023; The MathWorks Inc, 2022), and EEGLAB (Delorme and Makeig, 2004). First, the data was segmented based on markers for each flight phase for the purpose of our statistical analyses which include flight phase as an independent variable (see section 2.6.2.). From these flight phases, 10-sec epochs were extracted using the EEGLab pop reepochs() function. The epochs ECG signals were bandpass filtered between 1 and 20

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Experimental design. ECG = electrocardiography, PF = Pilot Flying, PM = Pilot Monitoring, ISA = self-assessment of workload technique, NASA-TLX = NASA Task Load Index. PF and PM are performed twice, in pseudo-randomized order.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Standard flight traffic pattern, with the three flight phases of interest.</figcaption></figure>

Hz using a Butterworth filter of the fifth order in order to remove noise. R-peaks were then located using the findpeaks() function of the Signal Processing Toolbox, with fixed thresholds for a minimum time between peaks of 250 ms, and a minimum amplitude of .45 % of the maximum amplitude within a signal. For unusually large peaks (larger than 2 mV) in an epoch, likely as a result of mechanical noise, the threshold for minimum amplitude was set at .50 mV. The aforementioned parameters were selected to identify R-peaks consistently and automatically across varying signal conditions and quality.

## 2.5. Feature extraction

### 2.5.1. Heart rate and heart rate variability

The R-peaks were used to calculate heart rate (HR) and heart rate variability (HRV). Two measures of HRV were computed, including the standard deviation of the intervals between R-peaks (SDNN) and Root Mean Square of the Successive Differences (RMSSD).

### 2.5.2. Interpersonal coordination

To estimate the physiological dynamics between student and instructor pilots, several measures of interpersonal coordination were calculated with the use of the epoched and filtered ECG signals. Some of the selected measures are indicators of linear covariance, i.e., cross-correlation and the Delayed Coincidence Count, whereas other measures estimate the non-linear properties of interpersonal coordination, i. e., metrics derived from CRQA.

**Cross-correlation.** The linear metric of cross-correlation was computed using the xcorr() function of MATLAB, with a lag parameter of zero between ECG signals within epochs (Strang et al., 2014). Cross-correlation can assume any value within the range of -1 to 1, where 1 indicates perfect positive alignment, -1 indicates perfect negative alignment, and 0 indicates no linear relationship between the signals.

**Delayed Coincidence Count.** The Delayed Coincidence Count quantifies beat-to-beat synchrony by counting the number of matching R-peaks in the epoched ECG signals within a given time interval (i.e., threshold). Higher values therefore indicate tighter instantaneous alignment of partners' heartbeats. Borrowed from spike-coincidence analyses in neuroscience (Uhlhaas et al., 2009), the Delayed Coincidence Count is especially sensitive to short-lived, local synchrony episodes that frequency-domain measures such as coherence often overlook (Verdière et al., 2020). Its discrete, count-based output also makes interpretation straightforward in research involving pairs of participants (Verdière et al., 2020). The Delayed Coincidence Count requires a threshold greater than 0 ms to define a small time window that allows

R-peaks from the two signals to be considered matching. This threshold accounts for minor temporal variability that would otherwise prevent any matches if set to 0 ms. In the present study, the Delayed Coincidence Count was calculated using the methods and code previously published by Verdière et al. (2020), using a coincidence threshold of 20 ms.

CRQA. For non-linear metrics, CRQA was conducted with the use of the Cross Recurrence Plot Toolbox for MATLAB (Marwan, 2013). Four measures were selected from this analysis, namely (Marwan et al., 2007):

* Recurrence rate: the proportion (decimal form) of recurrent points in the cross-recurrence plot.

* Determinism: proportion (decimal form) of diagonally recurrent points to the total number of recurrent points. Higher values indicate greater predictability or regularity in the structure between signals.

* Average length of the diagonal lines: average period of time in which the signals remain recurrent in the cross-recurrence plot. The range is between 1 and the length of the epochs. This measure represents the sustained similarity between signals.

* Entropy: measure of complexity of the cross-recurrence plot in relation to the distribution and lengths of diagonal (recurrent) lines. It is based on the Shannon's entropy of the probability to find a diagonal line of length *l* in the cross-recurrence plot (Wallot and Leonardi, 2018). It ranges from 0 (indicating regularity) to higher values (indicating increasing variability and complexity) without an upper limit.

The parameters used for CRQA were estimated in R (R Core Team, 2024) using the tseriesChaos package (Narzo, 2019), as outlined in Wallot and Leonardi (2018). The average mutual information was quantified using the function mutual() from the package to estimate the delay $τ$. The embedding dimension $m$ was estimated using the function false.nearest(). The estimations were made using randomly selected time series from the ECG dataset as input. Then, the averaged recommended parameters from this random sampling were used for CRQA, namely: embedding dimension $m = 5$, and delay $τ = 8$. The radius was set as $r = .5$, because this resulted in low to moderate recurrence rate for the data, as defined and recommended in Wallot and Leonardi (2018) and Wallot (2017). The ECG signals were normalized in the CRQA.

## 2.6. Statistical analyses

Statistical analyses were performed using R (R Core Team, 2024). The measures of interpersonal coordination were aggregated per flight phase prior to conducting statistical analyses.
## 2.6.1. Outlier detection

All twelve measures derived from the ECG data were checked for outliers based on the interquartile range (IQR) criteria and data visualizations. None of the data points were excluded, as there were no extreme outliers (criterion: $3 \times \text{IQR}$) and no notable values observed in the visualizations. Due to the small sample size of subjects, this conservative approach was adopted for outlier detection – focusing on preservation of data.

## 2.6.2. Analyses of variance (ANOVAs)

**Heart rate and heart rate variability.** To confirm the impact of experimental conditions on HR (i.e., beats per minute) and HRV (i.e., SDNN and RMSSD) of student pilots and instructors, we performed three-way repeated-measures ANOVAs using the aov() function from the R Stats package (R Core Team, 2024). The independent variables were flight phase (take-off, downwind, landing), training environment (simulator, real flight) and flight control condition (PF, PM).

**Interpersonal coordination.** To explore the effects of experimental conditions on interpersonal coordination, we performed three-way repeated-measures ANOVAs using the aov() function from the R Stats package (R Core Team, 2024) for each of the six measures of physiological synchrony (i.e., the four CRQA measures, cross-correlation, and the Delayed Coincidence Count). The independent variables were flight phase (take-off, downwind, landing), training environment (simulator, real flight), and flight control (PF, PM).

## 2.6.3. Correlation between subjective workload and interpersonal coordination

To explore the relationship between pilot students' subjective workload and interpersonal coordination, we conducted one-tailed Spearman's correlation tests between the ISA scores of subjective workload and all of the calculated measures of interpersonal coordination – expecting positive relationships. The Spearman's method was selected, because it is appropriate for ordinal data (Choi et al., 2010), i. e., the ISA scores of subjective workload, and is not affected by outliers (Schober et al., 2018).

## 2.6.4. Correcting for multiple testing

All *p*-values were corrected for multiple testing and inflated Type I error rate using the false discovery rate (FDR) method (Benjamini and Hochberg, 1995). This method is less susceptible to false negatives in comparison to Bonferroni-based corrections for multiple testing, while the amount of false positives remains within an acceptable limit (Pike, 2011).

For all ANOVA results with (FDR-adjusted) *p* < .05, post-hoc comparisons of estimated marginal means (EMMs) were conducted using the emmeans package (Lenth, 2024) in R (R Core Team, 2024). FDR-adjustment of *p*-values was applied to these multiple comparisons within the function.

Given the exploratory nature of this study, both unadjusted and adjusted *p*-values are reported in the Results section to identify potential trends. Additionally, to address potential Type II errors resulting from FDR corrections, we examined the direction and magnitude of effect sizes to identify possible patterns in the data, especially where FDR-corrected results approach the significance threshold.

# 3. Results

## 3.1. Subjective measures

The current subsection presents the descriptive statistics for subjective workload, social affiliation, perceived synchrony, and (self-)evaluation of students' flight performance. It should be noted, however, that these observations are solely descriptive as the data available (N = 8 per flight control condition in both real and simulated experiments) was too limited to conduct comparison tests. However, we have provided effect

sizes expressed in percentages and Cohen's *d*, based on the means and standard deviations, to present a comprehensive description of the magnitude of the findings and to enable easier comparison with other studies.

### 3.1.1. NASA-TLX

Figs. 4 and 5 present the means and standard deviations of the NASA-TLX subscales. The results highlight potential differences in perceived workload between the two environments (i.e., real flight and simulator), and conditions (i.e., PF and PM). Specifically, real flight (M = 45.74, SD = 18.25) elicited higher reports of subjective workload for the student pilots on average compared to simulated flight (M = 33.18, SD = 15.65), corresponding to a 37.83 % higher workload (Cohen's *d* = .74). The PF condition (M = 49.50, SD = 17.56) also resulted in higher reports of subjective workload for student pilots than the PM condition (M = 30.26, SD = 12.86), representing an increase of 63.58 % (Cohen's *d* = 1.25). Overall, student pilots (M = 39.88, SD = 18.08) reported higher workload than instructors (M = 25.16, SD = 10.98), showing a 58.50 % difference (Cohen's *d* = .99).

For instructors, the real flights also elicited higher reports of subjective workload (M = 33.05, SD = 8.79) than the simulated flight (M = 17.27, SD = 6.27), reflecting a 91.42 % increase (Cohen's *d* = 2.07). Lastly, instructors reported somewhat higher workload in the PF condition (M = 27.81, SD = 11.72), in which the learners were actively controlling the (simulated or real) aircraft and the instructors were observing and tutoring, compared to the PM condition (M = 22.50, SD = 9.65), which corresponds to an increase of 23.58 % (Cohen's *d* = .49).

### 3.1.2. Instantaneous self-assessment of workload technique (ISA)

Based on the available data, the mean subjective workload of the student pilots appeared higher in real flight (M = 3.47, SD = 1.16) than in simulated flight (M = 2.58, SD = .83), representing an increase of 34.50 % in reported workload (Cohen's *d* = .88).

Similarly, subjective workload was higher in the PF condition (M = 3.82, SD = .94) than the PM condition (M = 2.36, SD = .73), corresponding to a 61.86 % increase (Cohen's *d* = 1.73). See Fig. 6 for a more detailed overview.

### 3.1.3. Social affiliation

The social affiliation questionnaire revealed higher mean ratings from the student pilots (M = 6.07, SD = .61) compared to the instructors (M = 4.93, SD = .87), representing an increase of 23.12 % in perceived social affiliation (Cohen's *d* = 1.52). This suggests that students perceived greater social affiliation to their instructors than vice versa. Fig. 7A displays the means and standard deviations per condition.

### 3.1.4. Perceived synchrony

Perceived synchrony also showed higher mean ratings from students (M = 5.27, SD = .91) compared to their instructors (M = 4.66, SD = 1.54), reflecting 13.09 % higher perception among students (Cohen's *d* = .48). Regarding the training environment, perceived synchrony was rated lower by the students in real flight (M = 5.06, SD = .85) than in simulated flight (M = 5.50, SD = .94), corresponding to a decrease of 8.69 % (Cohen's *d* = .49).

In contrast, instructors reported higher perceived synchrony in real flight (M = 5.19, SD = .83) compared to simulated flight (M = 4.13, SD = 1.89), with an increase of 25.67 % (Cohen's *d* = .73). The means and standard deviations can be seen in Fig. 7B.

### 3.1.5. Subjective ratings of performance

Based on the self-evaluations of flight performance, student pilots seemed to rate their own performance higher in the simulator (M = 7.00, SD = .71) than the real flight (M = 6.31, SD = .70), reflecting an increase of 10.93 % (Cohen's *d* = .98). Contrary to this, instructors rated students' performance higher in real flight (M = 6.86, SD = 1.14) than in the simulator (M = 6.00, SD = 2.24), corresponding to an increase of
<figure><img src="image_6.png" /><figcaption>Fig. 4. Subjective measures of student workload reported for four scales of the NASA-TLX. PF = pilot flying, PM = pilot monitoring.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Subjective measures of instructor workload reported for four scales of the NASA-TLX. PF = pilot flying, PM = pilot monitoring.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Means and standard deviations of the pilot students' ISA scores for subjective workload. The scale ranged from 1 to 5. PF = pilot flying, PM = pilot monitoring.</figcaption></figure>

14.33 % (Cohen's d = .48).

## 3.2. Heart rate and heart rate variability

### 3.2.1. Heart rate

There was a main effect of training environment, $F(1,7) = 20.83, p < .0001$, FDR-adjusted $p < .0001$, $95 \% CI = [.05, 1.00]$, $\eta_p^2 = .11$, and a main effect of the flight control condition (i.e., PF vs. PM), $F(1,7) = 22.24, p < .0001$, FDR-adjusted $p < .0001$, $95 \% CI = [.05, 1.00]$, $\eta_p^2 = .11$, on students' HR.

Corresponding to the students' HR, there was also a main effect of training environment on instructors' HR, $F(1,7) = 2113.00, p < .0001$, FDR-adjusted $p < .0001$, $95 \% CI = [.91, 1.00]$, $\eta_p^2 = .92$. There was also

an interaction between training environment and flight phase on instructors' HR, $F(2,7) = 10.61, p < .0001$, FDR-adjusted $p < .0001$, $95 \% CI = [.04, 1.00]$, $\eta_p^2 = .11$.

Table 1 shows all results of the ANOVA for students' HR, and Table 2 shows the results of the ANOVA for the instructors' HR.

Post-hoc pairwise comparisons showed significant differences between real flight and simulated flight for students' (EMM = 8.09, SE = 1.77, t(173) = 4.56, $p < .0001$, 95% CI [4.59, 11.59]) and instructors' (EMM = 24.52, SE = .53, t(173) = 45.97, $p < .0001$, 95% CI [23.47, 25.58]) HRs. Students' HR showed less beats per minute in simulated flight (EMM = 78.13, SE = 4.37) in comparison to real flight (EMM = 86.22, SE = 4.37). Instructors' HR was also lower in simulated flight (EMM = 73.64, SE = .83) compared to real flight (EMM = 98.17, SE = .83).

Post-hoc pairwise comparisons of the interaction between training environment and flight phase on instructors' HR revealed that in real flight, the downwind phase differed from the take-off phase (EMM = 3.78, SE = .92, t(173) = 4.09, $p < .0001$, 95% CI [1.03, 6.53]) and the landing phase (EMM = -3.47, SE = .92, t(173) = -3.76, $p < .001$, 95% CI [-6.22, -.72]). The take-off and landing phases did not differ in real flight, EMM = .31, SE = .92, t(173) = .34, $p = .74$, 95% CI [-2.44, 3.06]. While, in simulated flights, the instructors' HR did not differ between phases. However, the effects on instructor's HR should be treated with caution due to the small sample size ($N = 2$) and their difference in flight experience.

### 3.2.2. Heart rate variability: SDNN

A main effect of training environment was observed for students' HRV (SDNN), $F(1,7) = 17.32, p < .0001$, FDR-adjusted $p < .0001$, $95 \% CI = [.03, 1.00]$, $\eta_p^2 = .09$. There were no further main or interaction effects in the SDNN measure of HRV for neither student nor instructor.

Post-hoc pairwise comparisons confirmed the difference of students' SDNN between simulated (EMM = 65.78, SE = 12.12) and real flight
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Student pilots' and instructors' rated social affiliation (A) and perceived synchrony (B) with each other. The scale ranged from 1 to 7. PF = pilot flying, PM = pilot monitoring.</figcaption></figure>

<table><caption>**Table 1**
ANOVA results of student heart rate.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>971.34</td><td>2</td><td>485.67</td><td>3.22</td><td>.04</td><td>.20</td><td>.04</td><td>[.00, 1.00]</td></tr><tr><td><strong>Flight control</strong></td><td><strong>3353.41</strong></td><td><strong>1</strong></td><td><strong>3353.41</strong></td><td><strong>22.24</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.11</strong></td><td><strong>[.05, 1.00]</strong></td></tr><tr><td><strong>Training environment</strong></td><td><strong>3140.92</strong></td><td><strong>1</strong></td><td><strong>3140.92</strong></td><td><strong>20.83</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.11</strong></td><td><strong>[.05, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>83.77</td><td>2</td><td>41.88</td><td>.28</td><td>.76</td><td>.92</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>443.85</td><td>2</td><td>221.93</td><td>1.47</td><td>.23</td><td>.50</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>344.41</td><td>1</td><td>344.41</td><td>2.28</td><td>.13</td><td>.40</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>104.74</td><td>2</td><td>52.37</td><td>.35</td><td>.71</td><td>.90</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>26081.72</td><td>173</td><td>150.76</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 2**
ANOVA results of instructor heart rate.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>48.34</td><td>2</td><td>24.17</td><td>1.77</td><td>.17</td><td>.46</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>4.83</td><td>1</td><td>4.83</td><td>.35</td><td>.55</td><td>.80</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>28866.41</strong></td><td><strong>1</strong></td><td><strong>28866.41</strong></td><td><strong>2113.00</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.92</strong></td><td><strong>[.91, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>21.28</td><td>2</td><td>10.64</td><td>.78</td><td>.46</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td><strong>Flight phase/Training environment</strong></td><td><strong>289.84</strong></td><td><strong>2</strong></td><td><strong>144.92</strong></td><td><strong>10.61</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.11</strong></td><td><strong>[.04, 1.00]</strong></td></tr><tr><td>Flight control/Training environment</td><td>49.02</td><td>1</td><td>49.02</td><td>3.59</td><td>.06</td><td>.24</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>12.30</td><td>2</td><td>6.15</td><td>.45</td><td>.64</td><td>.88</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>2363.42</td><td>173</td><td>13.66</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(EMM = 41.19, SE = 12.12) experiments, EMM = -24.59, SE = 5.91, t (173) = -4.16, p < .0001, 95 % CI [-36.25, -12.93].

Tables 3 and 4 show the ANOVA results of students' and instructors' HRV (SDNN), respectively. Again, the findings of the instructors' HRV should be considered carefully.

### 3.2.3. Heart rate variability: RMSSD

Similar to the previous HRV measure, there was a main effect of training environment (F(1,7) = 22.66, p < .0001, FDR-adjusted p < .0001, 95 % CI = [.05, 1.00], η<sup>2</sup><sub>p</sub> = .12) on students' HRV (RMSSD).

However, there was also a main effect of flight control condition (i.e., PF vs. PM) (F(1,7) = 10.15, p < .01, FDR-adjusted p < .05, 95 % CI = [.01, 1.00], η<sup>2</sup><sub>p</sub> = .06) on students' HRV measured in RMSSD.

For instructor HRV (RMSSD), there was a main effect of training environment (F(1,7) = 645.74, p < .0001, FDR-adjusted p < .0001, 95 % CI = [.75, 1.00], η<sup>2</sup><sub>p</sub> = .79). There was also an interaction between training environment and flight phase (F(2,7) = 5.06, p < .01, FDR-adjusted p < .05, 95 % CI = [.01, 1.00], η<sup>2</sup><sub>p</sub> = .06).

Post-hoc pairwise comparisons showed that RMSSD was lower in real flight (EMM = 728.09, SE = 39.35) than in simulated flight (EMM =

<table><caption>**Table 3**
ANOVA results of student heart rate variability (SDNN).</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>219.13</td><td>2</td><td>109.57</td><td>.07</td><td>.94</td><td>.97</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>1076.28</td><td>1</td><td>1076.28</td><td>.64</td><td>.42</td><td>.78</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>29024.11</strong></td><td><strong>1</strong></td><td><strong>29024.11</strong></td><td><strong>17.32</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.09</strong></td><td><strong>[.03, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>441.05</td><td>2</td><td>220.52</td><td>.13</td><td>.88</td><td>.94</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>1599.05</td><td>2</td><td>799.53</td><td>.48</td><td>.62</td><td>.87</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>177.85</td><td>1</td><td>177.85</td><td>.11</td><td>.75</td><td>.92</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>815.03</td><td>2</td><td>407.52</td><td>.24</td><td>.78</td><td>.92</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>289994.96</td><td>173</td><td>1676.27</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 4**
ANOVA results of instructor heart rate variability (SDNN).</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>7335.01</td><td>2</td><td>3667.50</td><td>1.42</td><td>.25</td><td>.51</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>8101.23</td><td>1</td><td>8101.23</td><td>3.13</td><td>.08</td><td>.27</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Training environment</td><td>11641.90</td><td>1</td><td>11641.90</td><td>4.50</td><td>.04</td><td>.19</td><td>.03</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control</td><td>3300.55</td><td>2</td><td>1650.28</td><td>.64</td><td>.53</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>7587.83</td><td>2</td><td>3793.91</td><td>1.47</td><td>.23</td><td>.50</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>3067.32</td><td>1</td><td>3067.32</td><td>1.18</td><td>.28</td><td>.56</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>11580.51</td><td>2</td><td>5790.26</td><td>2.24</td><td>.11</td><td>.34</td><td>.03</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>447923.24</td><td>173</td><td>2589.15</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

816.64, SE = 39.35) for students, EMM = -88.55, SE = 18.60, t(173) = -4.76, p < .0001, 95 % CI [-125.28, -51.83]. The same difference was found for instructors' RMSSD, with lower RMSSDs in real flight (EMM = 616.98, SE = 9.70) than simulated flight (EMM = 824.56, SE = 9.70), EMM = -207.58, SE = 8.17, t(173) = -25.41, p < .0001, 95 % CI [-223.71, -191.46].

The post-hoc comparisons for the interaction between training environment and flight phase on instructors' RMSSD revealed that the downwind phase significantly differed from take-off (EMM = -41.18, SE = 14.15, t(173) = -2.91, p < .01, 95 % CI [-83.29, .94]) and landing (EMM = 28.68, SE = 14.15, t(173) = 2.03, p < .0001, 95 % CI [-13.43, 70.79]) in real flight, but this was not the case in simulated flight in which the phases did not differ from each other.

Table 5 shows all results of the ANOVA of students' HRV (RMSSD), and Table 6 shows the results of the ANOVA of instructors' HRV (RMSSD). The findings for instructors' HRV should be approached carefully for reasons previously mentioned.

### 3.3. Interpersonal coordination

#### 3.3.1. Cross-correlation

The three-way repeated-measures ANOVA for cross-correlation did not result in any main effects nor interactions, see Table 7 with the ANOVA results.

#### 3.3.2. Delayed Coincidence Count

The three-way repeated-measures ANOVA for the Delayed Coincidence Count revealed a significant main effect of the training environment on this linear measure of physiological synchrony, F(1,7) = 31.04, p < .0001, FDR-adjusted p < .0001, 95 % CI [.08, 1.00], η²_p = .15. There were no other significant effects. Table 8 shows all the ANOVA results for the Delayed Coincidence Count.

The post-hoc analysis for the main effect resulted in a difference between the training environments, EMM = .26, SE = .05, t(173) = 5.57, p < .0001, 95 % CI [.169, .36], with higher counts in real flight on average (EMM = .93, SE = .06) than in simulated flight (EMM = .67, SE = .06). These findings indicate greater temporal alignment, or physiological synchrony, between ECG signals of student-instructor dyads in real flight compared to simulated flight.

#### 3.3.3. CRQA measures

All ANOVAs for the selected CRQA measures, i.e., recurrence rate, determinism, average length of the diagonal lines, and entropy, revealed a significant main effect of the training environment (simulated vs. real flight) on the non-linear measures of physiological synchrony. There were no other significant effects. These results are reported in the next sections.

**Recurrence rate.** The three-way repeated-measures ANOVA for the CRQA recurrence rate resulted in a main effect for training environment, with F(1,7) = 70.09, p < .0001, FDR-adjusted p < .001, 95 % CI = [.20, 1.00], η²_p = .29. There were no other main effects or interaction effects. All of the results can be found in Table 9.

Post-hoc comparisons showed that the recurrence rate was higher in simulated flight (EMM = .03, SE = .00) than in real flight (EMM = .02, SE = .00), EMM = -.01, SE = .00, t(173) = -8.37, p < .0001, 95 % CI [-.01, -.01].

**Determinism.** Again, there was a main effect for training environment, F(1,7) = 282.43, p < .0001, FDR-adjusted p < .001, 95 % CI = [.55, 1.00], η²_p = .62. There were no main effects for flight phase or flight control, nor interaction effects, as can be seen in Table 10.

Post-hoc pairwise comparisons were performed to explore the main effect of training environment, and showed significant lower means for real flight (EMM = .61, SE = .03) compared to simulated flight (EMM = .83, SE = .03), EMM = -.22, SE = .01, t(173) = -16.81, p < .0001, 95 % CI [-.24, -.19].

**Entropy.** The results for entropy are comparable to the other CRQA measures: there was an effect for training environment (F(1,7) = 198.65, p < .0001, FDR-adjusted p < .001, 95 % CI = [.45, 1.00], η²_p = .53), but no other effects. The results can be found in Table 11.

A post-hoc comparison of the main effect revealed that there was more entropy in simulated flight (EMM = 1.93, SE = .10) than in real flight (EMM = 1.51, SE = .10), EMM = -.48, SE = .03, t(173) = -14.09, p < .0001, 95 % CI [-.55, -.42].

**Average length of the diagonal lines.** There was a main effect for training environment on the cross-recurrence average length of the diagonal lines, F(1,7) = 230.88, p < .0001, FDR-adjusted p < .001, 95 % CI = [.50, 1.00], η²_p = .57. There were no other effects, but there was a trend towards an interaction between flight phase and training environment, F(2,7) = 4.17, p < .05, FDR-adjusted p < .10, 95 % CI = [.00, 1.00], η²_p = .05. See Table 12 for all ANOVA results.

The post-hoc comparison of the main effect of training environment

<table><caption>**Table 5**
ANOVA results of student heart rate variability (RMSSD).</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>77704.62</td><td>2</td><td>38852.31</td><td>2.34</td><td>.10</td><td>.32</td><td>.03</td><td>[.00, 1.00]</td></tr><tr><td><strong>Flight control</strong></td><td><strong>168555.67</strong></td><td><strong>1</strong></td><td><strong>168555.67</strong></td><td><strong>10.15</strong></td><td><strong>.00</strong></td><td><strong>.01</strong></td><td><strong>.06</strong></td><td><strong>[.01, 1.00]</strong></td></tr><tr><td><strong>Training environment</strong></td><td><strong>376410.25</strong></td><td><strong>1</strong></td><td><strong>376410.25</strong></td><td><strong>22.66</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.12</strong></td><td><strong>[.05, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>6358.82</td><td>2</td><td>3179.41</td><td>.19</td><td>.83</td><td>.94</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>63145.81</td><td>2</td><td>31572.91</td><td>1.90</td><td>.15</td><td>.42</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>77.77</td><td>1</td><td>77.77</td><td>.00</td><td>.95</td><td>.97</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>22556.01</td><td>2</td><td>11278.00</td><td>.68</td><td>.51</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>2874228.24</td><td>173</td><td>16614.04</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 6**
ANOVA results of instructor heart rate variability (RMSSD).</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>4925.97</td><td>2</td><td>2462.99</td><td>.77</td><td>.47</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>5321.04</td><td>1</td><td>5321.04</td><td>1.66</td><td>.20</td><td>.50</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>2068319.27</strong></td><td><strong>1</strong></td><td><strong>2068319.27</strong></td><td><strong>645.74</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.79</strong></td><td><strong>[.75, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>9667.21</td><td>2</td><td>4833.60</td><td>1.51</td><td>.22</td><td>.50</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>32405.55</td><td>2</td><td>16202.77</td><td>5.06</td><td>.01</td><td>.05</td><td>.06</td><td>[.01, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>3.33</td><td>1</td><td>3.33</td><td>.00</td><td>.97</td><td>.99</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>9995.31</td><td>2</td><td>4997.66</td><td>1.56</td><td>.21</td><td>.50</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>554125.28</td><td>173</td><td>3203.04</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 7**
ANOVA results for cross-correlation.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>.00</td><td>2</td><td>.00</td><td>.39</td><td>.68</td><td>.88</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>.00</td><td>1</td><td>.00</td><td>3.02</td><td>.08</td><td>.28</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Training environment</td><td>.00</td><td>1</td><td>.00</td><td>3.90</td><td>.05</td><td>.21</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control</td><td>.00</td><td>2</td><td>.00</td><td>1.94</td><td>.15</td><td>.42</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>.00</td><td>2</td><td>.00</td><td>.57</td><td>.56</td><td>.81</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>.00</td><td>1</td><td>.00</td><td>.53</td><td>.47</td><td>.78</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>.00</td><td>2</td><td>.00</td><td>.89</td><td>.41</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>.06</td><td>173</td><td>.00</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 8**
ANOVA results for Delayed Coincidence Count.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>.29</td><td>2</td><td>.15</td><td>1.38</td><td>.25</td><td>.52</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>.01</td><td>1</td><td>.01</td><td>.08</td><td>.77</td><td>.92</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>3.30</strong></td><td><strong>1</strong></td><td><strong>3.30</strong></td><td><strong>31.04</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.15</strong></td><td><strong>[.08, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>.60</td><td>2</td><td>.30</td><td>2.84</td><td>.06</td><td>.24</td><td>.03</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>.08</td><td>2</td><td>.04</td><td>.39</td><td>.67</td><td>.88</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>.42</td><td>1</td><td>.42</td><td>3.91</td><td>.05</td><td>.21</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>.15</td><td>2</td><td>.07</td><td>.70</td><td>.50</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>18.39</td><td>173</td><td>.11</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 9**
ANOVA results for recurrence rate.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>.00</td><td>2</td><td>.00</td><td>.72</td><td>.49</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>.00</td><td>1</td><td>.00</td><td>.43</td><td>.51</td><td>.78</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>.01</strong></td><td><strong>1</strong></td><td><strong>.01</strong></td><td><strong>70.09</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.29</strong></td><td><strong>[.20, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>.00</td><td>2</td><td>.00</td><td>.35</td><td>.71</td><td>.90</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>.00</td><td>2</td><td>.00</td><td>2.66</td><td>.07</td><td>.27</td><td>.03</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>.00</td><td>1</td><td>.00</td><td>.04</td><td>.84</td><td>.94</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>.00</td><td>2</td><td>.00</td><td>.64</td><td>.53</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>.01</td><td>173</td><td>.00</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 10**
ANOVA results for determinism.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>.02</td><td>2</td><td>.01</td><td>1.56</td><td>.21</td><td>.50</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>.01</td><td>1</td><td>.01</td><td>1.52</td><td>.22</td><td>.50</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>2.24</strong></td><td><strong>1</strong></td><td><strong>2.24</strong></td><td><strong>282.43</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.62</strong></td><td><strong>[.55, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>.00</td><td>2</td><td>.00</td><td>.01</td><td>.99</td><td>.99</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>.03</td><td>2</td><td>.02</td><td>1.93</td><td>.15</td><td>.42</td><td>.02</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>.00</td><td>1</td><td>.00</td><td>.02</td><td>.90</td><td>.95</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>.00</td><td>2</td><td>.00</td><td>.08</td><td>.92</td><td>.97</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>1.37</td><td>173</td><td>.01</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

showed that the average length of the diagonal lines was greater in simulated flight (EMM = 4.66, SE = .27) than in real flight (EMM = 3.37, SE = .27), EMM = -1.29, SE = .08, t(173) = -15.20, p < .0001, 95 % CI

[-1.46, -1.12].

Additionally, a trend was observed pointing toward an interaction between the flight phase and the training environment for average
<table><caption>**Table 11**
ANOVA results for entropy.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>.08</td><td>2</td><td>.04</td><td>.69</td><td>.50</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>.00</td><td>1</td><td>.00</td><td>.08</td><td>.78</td><td>.92</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>11.23</strong></td><td><strong>1</strong></td><td><strong>11.23</strong></td><td><strong>198.65</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.53</strong></td><td><strong>[.45, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>.02</td><td>2</td><td>.01</td><td>.15</td><td>.86</td><td>.94</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>.38</td><td>2</td><td>.19</td><td>3.34</td><td>.04</td><td>.19</td><td>.04</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>.03</td><td>1</td><td>.03</td><td>.48</td><td>.49</td><td>.78</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>.02</td><td>2</td><td>.01</td><td>.14</td><td>.87</td><td>.94</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>9.78</td><td>173</td><td>.06</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 12**
ANOVA results for average length of the diagonal lines.</caption><thead><tr><th>Predictor</th><th>Sum of Squares</th><th>df</th><th>Mean Square</th><th>F</th><th>p</th><th>FDR-adjusted p</th><th>η<sup>2</sup><sub>p</sub></th><th>95 % CI</th></tr></thead><tbody><tr><td>Flight phase</td><td>.48</td><td>2</td><td>.24</td><td>.69</td><td>.50</td><td>.78</td><td>.01</td><td>[.00, 1.00]</td></tr><tr><td>Flight control</td><td>.01</td><td>1</td><td>.01</td><td>.04</td><td>.85</td><td>.94</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td><strong>Training environment</strong></td><td><strong>79.78</strong></td><td><strong>1</strong></td><td><strong>79.78</strong></td><td><strong>230.88</strong></td><td><strong>.00</strong></td><td><strong>.00</strong></td><td><strong>.57</strong></td><td><strong>[.50, 1.00]</strong></td></tr><tr><td>Flight phase/Flight control</td><td>.27</td><td>2</td><td>.14</td><td>.40</td><td>.67</td><td>.88</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Training environment</td><td>2.88</td><td>2</td><td>1.44</td><td>4.17</td><td>.02</td><td>.10</td><td>.05</td><td>[.00, 1.00]</td></tr><tr><td>Flight control/Training environment</td><td>.03</td><td>1</td><td>.03</td><td>.10</td><td>.75</td><td>.92</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Flight phase/Flight control/Training environment</td><td>.28</td><td>2</td><td>.14</td><td>.41</td><td>.66</td><td>.88</td><td>.00</td><td>[.00, 1.00]</td></tr><tr><td>Residuals</td><td>59.78</td><td>173</td><td>.35</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

length of the diagonal lines, $F(2,7) = 4.17, p < .05$, FDR-adjusted $p < .10, 95 \% CI = [.00, 1.00]$, $\eta_p^2 = .05$. This trend can be observed in Fig. 8:

an inverted U-shape can be seen in real flights along the flight phases, whilst a slight linear decrease is observed across the flight phases in simulated flights. Although the differences across phases are relatively small, these findings suggest that, in real flights, the instructors and students shared physiological patterns for longer periods during cruising than during take-off and landing. In simulated flights, however, the decreasing pattern may reflect a gradual reduction in synchrony

duration as the simulated flight progressed.

### 3.4. Correlation between subjective workload and interpersonal coordination

We observed a moderate positive correlation between subjective workload (i.e., ISA scores collected from student pilots after each simulated and real flight traffic pattern) and the Delayed Coincidence Count, Spearman's $\rho = .34, p < .01$, FDR-adjusted $p < .05$. The relationship is displayed in Fig. 9. However, the Spearman's correlation tests

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Boxplots showing a trend for an interaction between flight phase and training environment for the average length of the diagonal lines of the cross-recurrence plots calculated using ECG signals from student pilots and their instructors.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Scatterplot of the student pilots' subjective workload (ISA scores) and the Delayed Coincidence Count (indicative of physiological synchrony in student-instructor dyad). The data points have been jittered along the X-axis to enhance visibility of each individual point. Each color represents a number associated with a category in the ISA scale, 1 = under-utilized, 2 = relaxed, 3 = comfortable busy pace, 4 = high, 5 = excessive (workload). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

between ISA scores and the other measures of interpersonal coordination revealed no statistically significant results, i.e., none of the FDR-adjusted *p*-values were lower than .05, see Table 1 in the Supplementary Materials.

## 4. Discussion

The primary objective of this study was to investigate how varying training environments (simulated vs. real flight), flight control roles (PF vs. PM), and flight phases influence the interpersonal coordination and mental workload of student-instructor dyads during flight training. By exploring these dynamics, the study aimed to enhance understanding of interpersonal coordination under different workload-inducing conditions. To examine interpersonal coordination, the current study employed several linear and non-linear measures of physiological synchrony, namely cross-correlation, the Delayed Coincidence Count, and CRQA. Such variety of linear and non-linear metrics could give us new insights into the different characteristics of physiological synchrony occurring during flight training and how these were affected by several workload-inducing factors.

First, subjective measures of workload, social affiliation, synchrony, and students' flight performance were examined to explore the effect of the experimental conditions on the student pilots' and instructors' subjective experience. We reported observations of differences in subjective workload as measured with the NASA-TLX across the two training environments and flight control conditions, as well as between the student pilots and instructors. While we only provided descriptive analyses, the patterns observed in the ISA scores were consistent with the NASA-TLX scores and previous literature (e.g. Verdière et al. (2020)): student pilots reported higher ratings of subjective workload during real flight compared to simulated flight. Regarding perceived synchrony, students rated their synchrony with instructors lower in real flight than in simulated flight, whereas the instructors showed the opposite pattern. The size of these differences was modest for students (8.69 % decrease), but notably larger for instructors (25.67 % increase). However, the fact that there were different instructors between the two environments introduces a potential confound.

To confirm that the experimental conditions were effectively

manipulating mental workload, we also examined students' and instructors' heart rate (HR) and heart rate variability (HRV). HR and HRV are generally regarded as reliable physiological indicators of an operator's mental state, with higher HR and lower HRV associated with increased mental demand (Wang et al., 2024; Martins, 2016). In our study, HR was higher in real flight when compared to simulated flight for both student pilots and instructors. We also observed an interaction between the training environment and flight phase on instructors' HR, a finding that is consistent with the study of Labedan et al. (2023). In Labedan et al. (2023), HR and HRV of student pilots were compared between Virtual Reality simulated flights and real flights. They found that student's HR was on average 20 beats per minute higher in real flights than in Virtual Reality flights. In our study, the differences in beats per minute between the two training environments were present but smaller for the student pilots (difference of ≈8 beats per minute). Also in line with Labedan et al. (2023), both HRV measures that were obtained in the current study, SDNN and RMSSD, were lower during real flight than during simulated flights. Only for the RMSSD measure, we found an effect of flight control on students' HRV. This suggests that student pilots' parasympathetic regulation (as indexed by RMSSD; Järvelin-Pasanen et al., 2018) was sensitive to task demands, possibly due to (a lack of) experience and expertise. However, instructors' HRV was not affected by the flight control conditions, and was affected by the flight phase instead: there was an interaction between the training environment and the flight phase for instructors' HRV for RMSSD. Specifically, in real flight experiments, instructors' exhibited reduced parasympathetic activation (i.e., lower RMSSD values) during the take-off and landing phases, which is generally considered to be difficult, in comparison to the downwind (i.e., cruising) phase (van Weelden et al., 2022; Lee, 2010; Causse et al., 2012). Taken together, the findings from HR and HRV measures corroborate the results of subjective measures and support that real flight elicits a higher workload in comparison to simulated flights. The HRV results also indicate that the PF condition elicited higher workload than the PM condition for the student pilots, and that the instructors' parasympathetic activation decreased during real flight when the dyad went through difficult phases such as take-off and landing; although, the latter must be approached with care, due to the limited sample size of instructors in this exploratory study.

While HR(V) provided valuable insights into the individual physiological responses and mental workload of student pilots and instructors, the measures of physiological synchrony offer a greater understanding of the interaction and interpersonal coordination within the student-instructor dyads. We found that the training environment affected interpersonal coordination, as indicated by the results of all four CRQA measures and the Delayed Coincidence Count. Specifically, there were increased recurrence rates, determinism, average length of the diagonal lines, and entropy in simulated flight in comparison to real flight. In other words, the coordination between the ECG signals of pilot students and instructors was more consistent, structured, predictable, and diverse (or complex) in simulated flight. This could be explained by the difference in mental demands between simulated and real flight (Wang et al., 2024; Labedan et al., 2023), which was also confirmed by the observed differences in HR and HRV between the two training environments. Verdière et al. (2020) proposed that a higher workload may reduce cooperation, causing it to become sporadic instead. This hypothesis would be in line with our finding of decreased patterns in interpersonal coordination in real flight compared to simulated flight.

Our finding that real flights was associated with higher Delayed Coincidence Count values than simulated flights appears to contradict the aforementioned findings associating increased workload with decreased dyadic synchrony. This apparent contradiction stems from a methodological consideration: Delayed Coincidence Count fundamentally measures coincidental cardiac events, and its values increase with heart rate independently of true physiological coupling. Because participants experienced elevated heart rates during real flight (reflecting both psychological and physical demands), the mathematical
probability of R-peaks occurring within the threshold window naturally increased. This represents a measurement artifact rather than enhanced interpersonal synchrony. Future investigations should implement normalization procedures that account for baseline heart rate differences or employ alternative synchrony metrics designed to be rate-invariant, thereby distinguishing genuine physiological coupling from artifacts of increased cardiac frequency.

We also observed a trend for an interaction between the training environment and flight phase for the average length of the diagonal lines of the cross-recurrence plots resulting from the CRQA. The means of these interactions suggested an inverted U-curve in real flight, and a linear decrease in simulated flight. As mentioned previously, the take-off and landing phases are associated with increased workload as opposed to the downwind (i.e., cruising) phase (van Weelden et al., 2022; Lee, 2010; Causse et al., 2012), thus the patterns of the CRQA measure along flight phases align with the common patterns of workload along flight phases. It is, however, unclear why the patterns differ (i.e., U-shape vs. linear decrease) based on the training environment. One possible explanation is that instructors were less affected in terms of mental workload by the training environment than students because of their experience (Bunce et al. et al., 2011; Johannes et al., 2017). This is also visible in our subjective measures of workload; therefore, we argue that student pilots and instructors could possibly stay "in sync" for longer (i. e., have shared physiological patterns of longer duration) when a student pilot experienced low(er) workload during real flight, and was generally experiencing less workload in simulated flight. This trend and hypothesis should be explored further in larger samples.

Lastly, we observed a positive relationship between subjective workload and one linear measure of physiological synchrony. Specifically, higher scores on the ISA for subjective workload were positively correlated with increased Delayed Coincidence Counts. To our knowledge, this is the first report of such a positive relationship between subjective workload and interpersonal coordination. In contrast, Braun et al. (2024) found that highly synchronized teams experienced significantly less workload compared to poorly synchronized teams, using five-person teams in an emergency response task within a Virtual Reality environment. However, the difference in findings may be attributed to several factors. While Braun et al. (2024) used a controlled Virtual Reality environment, our study involved a high-stress real-world environment, which could have increased the subjective workload experienced by our subjects, possibly making the relationship between subjective workload and physiological synchrony more pronounced. Additionally, differences in task complexity, group size, and sample size may have contributed to the contrasting results.

Although we report the first positive association between subjective workload and Delayed Coincidence Count, our correlational approach cannot establish whether workload drives physiological synchrony or vice versa. Braun et al. (2024) demonstrated experimentally that enhancing team synchrony reduces perceived workload—evidence of a unidirectional effect from coordination to workload. To clarify the causal ordering between workload and synchrony in dyadic settings, future studies should combine time-series modeling (e.g., Granger causality) with controlled synchrony manipulations.

## 4.1. Limitations

Despite these new insights into the dynamics between student pilots and instructors in different training environments, it is important to note that this study had limitations. First, only two instructors participated: one of them joined the simulated flights and the other joined the real flights. Although the instructors were similar in gender and age, their flight experience levels were vastly different. This potential confounding factor could have contributed to the differences in the measures of interpersonal coordination between simulated and real flight experiments, and makes it difficult to draw conclusions regarding instructor-specific results. Ideally, baseline corrections would be made to

account for individual differences as mentioned earlier. However, such baseline would not be a valid estimate; an argument explained in Järvelä et al. (2014) states that it is impossible to measure baseline dyadic physiological dynamics as it requires the participants to “do nothing”, which would automatically synchronize their physiological signals without real interaction. Whether the effect of the training environment was due to the difference in workload associated with these two training environments, or the two different instructors divided over the training environments, or perhaps both, warrants further research.

Secondly, the chosen time range (10-sec) for epochs in our study was selected to minimize data loss while ensuring reliable HRV measurements. This decision was informed by the varying lengths of flight phases within experiments, which allowed for at least one or more 10-s epochs per phase across the dataset. According to Shaffer and Ginsberg (2017), longer time ranges are considered more reliable and accurate for SDNN and RMSSD measures of HRV, with a minimum of 5 min recommended. However, the same review also proposes that shorter epochs, with durations ranging from 10 to 240 s, can be applicable. Other research on operator's mental workload estimation has used epochs of shorter lengths, i.e., 6 s, to extract HRV (Singh et al., 2021). Future research could further investigate the trade-offs between longer and shorter epoch lengths to optimize HRV measurements in ecological environments.

Third, the Delayed Coincidence Count is challenging to interpret and use as a measure of interpersonal coordination. As argued previously, increased HRs occur as a function of increased mental demands (Wang et al., 2024; Martins, 2016), and this consequently increases the likelihood of the operators' R-peaks (i.e., heart beats) occurring within given time intervals set for the calculation of our interpersonal coordination metrics. Thus, our findings could suggest increased physiological synchrony as well as false positives due to increased probability. While it is worth noting that Verdière et al. (2020) also observed increased Delayed Coincidence Counts during highly demanding tasks, which would validate our finding, it is possible that Verdière et al. (2020) also measured false positives as an innate characteristic of the metric. Future research could investigate multi-modal physiological measurements, e.g., adding EEG measures of mental workload and interpersonal synchrony (Hajra et al., 2020; Astolfi et al., 2012), to cross-validate and explore the reliability of different measures of interpersonal coordination.

Fourth, the chosen lag for cross-correlation (i.e., zero) may have been too rigorous. A lag of zero ensures that the correlation between the two signals will be calculated without any time delays. There is a possibility that student pilots and instructors matched with each other with a delay as opposed to aligning their signals with exact timing. Future research could explore the effect of varying delays on the cross-correlation metric, or employ a windowed multiscale method as explored in Likens and Wiltshire (2021).

Fifth, we encountered challenges in recruiting specialized subjects and managing the high costs involved in these experiments, which limited our sample size. This small sample size introduced variability, particularly in students and instructors' flight experience. In other aspects, the sample lacked variability, e.g., all subjects including instructors were of the same gender. Additionally, it was difficult to fully plan and control the real-world experiments, due to weather and aerological factors.

Lastly, physical demand of the different flight phases has not been accounted for. Even though it is known that physical demand can actually affect HR, HRV and ECG-based RQA measures (Veerabhadrappa et al., 2021; González et al., 2014), this study excluded the physical demand scale within the NASA-TLX due to the focus on mental workload.

Despite these limitations, it is important to emphasize that this exploratory work is pioneering in the field of neuroergonomics. No previous studies of this kind have been conducted before, collecting physiological signals simultaneously from two pilots (i.e., student pilot and instructor) during both real and simulated flights.
## 4.2. Conclusion

In conclusion, our findings demonstrate that interpersonal coordination between student pilots and instructors during flight training is influenced by the fidelity of the training environment. To be more specific, real and simulated flights affected this coordination differently, likely as a function of their differences in mental demands. This is corroborated by the observed relationship between subjective workload and physiological synchrony within the student-instructor dyads. Our findings highlight that individuals' workload as well as dyadic workload, measured through ECG signals, can be used to evaluate student-instructor coordination during flight training with the ultimate goal to guide interventions that promote greater training success.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank Thomas Raby for his assistance in the data collection, and Kevin Verdière for his help and insights in data structuring and the computation of the Delayed Coincidence Count. We offer special thanks to Stéphane Juaneda for his time and effort to serve as safety pilot and instructor.

This research project was funded by the Dr. Catharine van Tussenbroek Fund (grant no. A-2023-20) and by the AXA research fund (Chair "Neuroergonomics for flight safety"). The research reported in this study is also funded by the MasterMinds project, part of the RegionDeal Mid- and West-Brabant, and co-funded by the Ministry of Economic Affairs and Municipality of Tilburg.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apergo.2025.104606.

## References

Astolfi, L., et al., 2012. Cortical activity and functional hyperconnectivity by simultaneous EEG recordings from interacting couples of professional pilots. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). IEEE, pp. 4752-4755. https://doi.org/10.1109/EMBC.2012.6347029.

Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B 57 (1), 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

Bezerianos, A., et al., 2015. Cooperation driven coherence: brains working hard together. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). IEEE, pp. 4696-4699. https://doi.org/10.1109/EMBC.2015.7319442.

Braun, J., Hogh, N., Kubowitsch, S., 2024. Evaluating team workload through physiological synchrony: an exploratory study using MdRQA data to assess teams in action. In: Proceedings of the 7th European Conference on Industrial Engineering and Operations Management (IEOM). IEOM Society International. https://doi.org/10.46254/EU07.20240173.

Brennan, S.D., 1992. An experimental report on rating scale descriptor sets for the instantaneous self-assessment (ISA) recorder. Tech. Rep. DRA Technical Memorandum (CAD5) 92017. DRA Maritime Command and Control Division.

Bunce, S.C., et al., 2011. Implementation of fNIRS for monitoring levels of expertise and mental workload. In: Schmorow, D.D., Fidopiastis, C.M. (Eds.), Foundations of Augmented Cognition. Directing the Future of Adaptive Systems: 6Th International Conference, FAC 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011. Proceedings 6. Springer Berlin Heidelberg, pp. 13-22. https://doi.org/10.1007/978-3-642-21852-1_2.

Cacioppo, S., et al., 2014. You are in sync with me: neural correlates of interpersonal synchrony with a partner. Neuroscience 277, 842-858. https://doi.org/10.1016/j.neuroscience.2014.07.051.

Causse, M., et al., 2012. An analysis of mental workload and psychological stress in pilots during actual flight using heart rate and subjective measurements. In: 5th International Conference on Research in Air Transportation (ICRAT 2012). Berkeley, United States.

Charles, R.L., Nixon, J., 2019. Measuring mental workload using physiological measures: a systematic review. Appl. Ergon. 74, 221-232. https://doi.org/10.1016/j.apergo.2018.08.028.

Choi, J., Peters, M., Mueller, R.O., 2010. Correlational analysis of ordinal data: from Pearson's r to Bayesian polychoric correlation. Asia Pac. Educ. Rev. 11, 459-466. https://doi.org/10.1007/s12564010906-y.

Dehais, F., et al., 2021. A neuroergonomics approach to measure pilot's cognitive incapacitation in the real world with EEG. Adv. Intell. Syst. Comput. 1201, 111-117. https://doi.org/10.1007/978-3-030-51041-1_16.

Delorme, A., Makeig, S., 2004. EEGAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134 (1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009.

Dussault, C., et al., 2005. EEG and ECG changes during simulator operation reflect mental workload and vigilance. Aviat Space Environ. Med. 76 (4), 344-351.

González, H., Infante, O., Lerma, C., 2014. Response to active standing of heart beat interval, systolic blood volume and systolic blood pressure: recurrence plot analysis. In: Recurrences, Translational (Ed.), Springer Proceedings in Mathematics & Statistics 103. Springer, pp. 109-123. https://doi.org/10.1007/978-3-319-09531-8_7.

Hajra, S.G., Xi, P., Law, A., 2020. A comparison of EEG and EEG metrics for in-flight monitoring of helicopter pilot workload. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 4012-4019. https://doi.org/10.1109/SMC42975.2020.9283499.

Halgas, E.A., et al., 2023. A review of using wearable technology to assess team functioning and performance. Small Group Res. 54 (1), 41-76. https://doi.org/10.1177/10464964221125717.

Hart, S.G., 1986. The relationship between workload and training: an introduction. In: Proceedings of the Human Factors Society 30th Annual Meeting, 30. Sage, Los Angeles, CA, pp. 1116-1120. https://doi.org/10.1177/154193128603001118, 11.

Hart, S.G., Staveland, L.E., 1988. Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (Eds.), Human Mental Workload. https://doi.org/10.1016/S0166-4115(08)62386-9.

Järvelä, S., Kivikangas, J.M., Kätsyri, J., Ravaja, N., 2014. Physiological linkage of dyadic gaming experience. Simulat. Gaming 45 (1), 24-40. https://doi.org/10.1177/1046878113513080.

Järvelin-Pasanen, S., Sinikallio, S., Tarvainen, M.P., 2018. Heart rate variability and occupational stress—systematic review. Ind. Health 56 (6), 500-511. https://doi.org/10.2486/indhealth.2017-0190.

Johannes, B., et al., 2017. Psychophysiological assessment in pilots performing challenging simulated and real flight maneuvers. Aerospace medicine and human performance 88 (9), 834-840. https://doi.org/10.3357/AMHP.4782.2017.

Jordan, C., 1992. Experimental study of the effect of an instantaneous self assessment workload recorder on task performance. Tech. Rep. DRA Technical Memorandum Memorandum (CAD5) 92011. DRA Maritime Command Control Division.

Kleber, J., Blickensderfer, B., 2021. Aviation human-in-the-loop simulation: best practices for subjective performance measurement. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65, pp. 129-133. https://doi.org/10.1177/1071181321651107, 1.

Kothe, C., et al., 2014. Lab streaming layer. https://github.com/sccn/labstreaminglayer.

Labedan, P., Dehais, F., Peysakhovich, V., 2023. Comparison of cardiac activity and subjective measures during virtual reality and real aircraft flight. In: A, A., Sousa, de, et al. (Eds.), VISIGRAPP 2021. Springer, pp. 1-20. https://doi.org/10.1007/978-3-031-25477-2_6.

Lazure, C., et al., 2020. Certified flight instructors' performance-review of the literature and exploration of future steps. The International Journal of Aerospace Psychology 30 (3-4), 152-170. https://doi.org/10.1080/24721840.2020.179648.

Lee, K., 2010. Effects of flight factors on pilot performance, workload, and stress at final approach to landing phase of flight. Electronic Theses and Dissertations. https://star library.ucf.edu/etd/1628.

Lenth, R., 2024. Emmeans: estimated marginal means, aka least-squares means. https://rvlenth.github.io/emmeans/.Rpackageversion11.0.3.

Likens, A.D., Wiltshire, T.J., 2021. Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics. Soc. Cognit. Affect Neurosci. 16 (1-2), 232-245. https://doi.org/10.1093/scan/nsaa130.

Marques, E., et al., 2023. Factors that facilitate and hinder adopting a new pedagogy: a case study of flight observations in an ab initio pilot training. International Journal of Training Research 1-18. https://doi.org/10.1080/14480220.2023.2247587.

Martins, A.P., 2016. A review of important cognitive concepts in aviation. Aviation 20 (2), 65-84. https://doi.org/10.3846/1648778.2016.1196559.

Marwan, N., 2013. Cross recurrence plot toolbox for MATLAB, version 5.28 (R37b). https://tscy.pik.potsdam.de/CRPtoolbox/.

Marwan, N., et al., 2007. Recurrence plots for the analysis of complex systems. Phys. Rep. 438, 237-329. https://doi.org/10.1016/j.physrep.2006.11.001, 5-6.

Mayo, O., Gordon, I., 2020. In and out of synchrony—Behavioral and physiological dynamics of dyadic interpersonal coordination. Psychophysiology 57.6, e13574. https://doi.org/10.1111/psyp.13574.

Myers, P.L., Starr, A.W., Mullins, K., 2018. Flight simulator fidelity, training transfer, and the role of instructors in optimizing learning. International Journal of Aviation, Aeronautics, and Aerospace 5, 1. https://doi.org/10.15394/ijaaa.2018.1203.

Narzo, F. D. Antonio, 2019. tseriesChaos: Analysis of Nonlinear Time Series. R package version 0.1-13.1. https://doi.org/10.32614/CRAN package tseriesChaos.

Palumbo, R.V., et al., 2017. Interpersonal autonomic physiology: a systematic review of the literature. Pers. Soc. Psychol. Rev. 21 (2), 99-141. https://doi.org/10.1177/1088868316628405.
Pike, N., 2011. Using false discovery rates for multiple comparisons in ecology and evolution. Methods Ecol. Evol. 2 (3), 278–282. https://doi.org/10.1111/j.2041-210X.2010.00061.x.

R Core Team, 2024. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Schober, P., Boer, C., Schwarte, L.A., 2018. Correlation coefficients: appropriate use and interpretation. Anesth. Analg. 126 (5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864.

Shaffer, F., Ginsberg, J.P., 2017. An overview of heart rate variability metrics and norms. Front. Public Health 5, 258. https://doi.org/10.3389/fpubh.2017.00258.

Singh, G., Chanel, C.P., Roy, R.N., 2021. Mental workload estimation based on physiological features for pilot-UAV teaming applications. Front. Hum. Neurosci. 15, 692878. https://doi.org/10.3389/fnhum.2021.692878.

Strang, A.J., et al., 2014. Physio-behavioral coupling in a cooperative team task: contributors and relations. J. Exp. Psychol. Hum. Percept. Perform. 40 (1), 145.

The MathWorks Inc., 2022. Signal Processing Toolbox Version: 23.2 (R2023B). The MathWorks Inc., Natick, Massachusetts. https://www.mathworks.com.

The MathWorks Inc., 2023. MATLAB Version: 23.2.0.2485118 (R2023B). The MathWorks Inc., Natick, Massachusetts. https://www.mathworks.com.

Uhlhaas, P.J., Pipa, G., Lima, B., Melloni, L., Neuenschwander, S., Nikolić, D., Singer, W., 2009. Neural synchrony in cortical networks: history, concept and current status. Front. Integr. Neurosci. 3, 543. https://doi.org/10.3389/neuro.07.017.2009.

van Weelden, E., et al., 2022. Aviation and neurophysiology: a systematic review. Appl. Ergon. 105, 103838. https://doi.org/10.1016/j.apergo.2022.103838.

van Weelden, E., et al., 2024. A passive brain-computer interface for predicting pilot workload in virtual reality flight training. 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS), pp. 1–6. https://doi.org/10.1109/ICHMS59971.2024.10555679.

Veerabhadrappa, R., Hettiarachchi, I.T., Bhatti, A., 2021. Using recurrence quantification analysis to quantify the physiological synchrony in dyadic ECG data. In: 2021 IEEE International Systems Conference (SysCon), pp. 1–8. https://doi.org/10.1109/SysCon48628.2021.9447059. Vancouver, BC, Canada.

Verdière, K.J., Dehais, F., Roy, R.N., 2019. Spectral EEG-based classification for operator dyads' workload and cooperation level estimation. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, pp. 3919–3924. https://doi.org/10.1109/SMC.2019.8913848.

Verdière, K.J., et al., 2020. Physiological synchrony revealed by delayed coincidence count: application to a cooperative complex environment. IEEE Transactions on Human-Machine Systems 50 (5), 395–404. https://doi.org/10.1109/THMS.2020.2986417.

Wallot, S., 2017. Recurrence quantification analysis of processes and products of discourse: a tutorial in R. Discourse Process. 54, 382–405. https://doi.org/10.1080/0163853X.2017.1297921, 5-6.

Wallot, S., Leonardi, G., 2018. Analyzing multivariate dynamics using cross-recurrence quantification analysis (CRQA), Diagonal-CrossRecurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MDRQA) - a tutorial in R. Front. Psychol. 9, 2232. https://doi.org/10.3389/fpsyg.2018.02232.

Wang, P., Houghton, R., Majumdar, A., 2024. Detecting and predicting pilot mental workload using heart rate variability: a systematic review. Sensors 24 (12), 3723. https://doi.org/10.3390/s24123723.

Wiltshire, T.J., Steffensen, S.V., Fiore, S.M., 2019. Multiscale movement coordination dynamics in collaborative team problem solving. Appl. Ergon. 79, 143–151. https://doi.org/10.1016/j.apergo.2018.07.007.

Wiltshire, T.J., et al., 2020a. Interpersonal coordination dynamics in psychotherapy: a systematic review. Cognit. Ther. Res. 44, 752–773. https://doi.org/10.1007/s10608-020-10106-3.

Wiltshire, T.J., Steffensen, S.V., Likens, A.D., 2020b. Challenges for using coordination-based measures to augment collaborative social interactions. In: Viol, K., Schöller, H., Aichhorn, W. (Eds.), Selbstorganisation – Ein Paradigma Für Die Humanwissenschaften. Springer, p. 13. https://doi.org/10.1007/978-3-658-29906-4_13.

Wiltshire, T.J., et al., 2024. Prospects for augmenting team interactions with real-time coordination-based measures in human-autonomy teams. Top. Cogn. Sci. 16.3, 391–429. https://doi.org/10.1111/tops.12606.