Physiological and Behavioral Response Differences Between Video Mediated and In-Person Interaction

1. Introduction

The growth of virtual communication has led to a sharp increase in the use of virtual meetings in both professional and personal settings [1,2,3]. The COVID-19 pandemic played a major role in this shift [4,5], accelerating development of improved virtual meeting tools, faster and more reliable internet [6], lower travel costs and in some contexts, evidence of increased productivity when travel has been replaced by online meetings [7,8,9], increasing opportunities to collaborate remotely with international teams [3]. Many employees also prefer remote work, partly due to a stronger focus on work-life balance (2025 was the first year this was rated more important than job security [10]) and partly due to a growing concern for the environment in terms of reducing carbon emissions from transportation [11]. In addition to its workplace benefits, virtual communication promotes accessibility and inclusion, especially for individuals with disabilities or neurodivergent conditions [12]. It also offers greater flexibility and convenience compared to in-person meetings [13]. Moreover, certain features are unique to virtual platforms, such as the ability to record conversations for later review.

Despite its benefits, virtual communication also presents challenges that have also been shown to limit its effectiveness compared to in-person interaction. Evidence suggests there are several disadvantages of virtual meetings including: lower group cohesion and reduced satisfaction with how interactions unfold [14,15]; a weaker sense of social presence and increased feelings of isolation due to reduced contact with colleagues [15,16,17]; and greater emotional fatigue caused by longer working hours, plus the expectation to be constantly available [18]. Trust between participants can also be harder to establish virtually, as research repeatedly has shown trust often develops more naturally in face-to-face settings [14,15]. Some users also report increased anxiety from seeing their own face presented during calls [19,20] or struggle with information overload [21]. These experiences are often referred to collectively as "Zoom fatigue", a form of exhaustion specific to video conferencing [22,23]. Technical issues can further contribute to this phenomenon, particularly in non-professional or everyday environments where users may lack specialized support. Common problems include unstable internet connections, poor audio or video quality, and persistent concerns about privacy and data security [24]. Importantly, these effects are not uniformly experienced across users, complicating the design of universally effective interfaces. While average patterns can be identified, individual responses to remote communication vary widely. For instance, employees with negative perceptions of workplace relationships may be more likely to prefer remote interaction [25].

Although improvements in video and audio technology have addressed some of these issues, current approaches still fall short of replicating the experiences of in-person communication.

1.1. Differences Between In-Person and Virtual Conversation

When two persons engage in a conversation, they do more than simply exchange words. People coordinate on multiple levels, forming a dynamic, self-organizing system influenced by interpersonal synergy, where meaning arises through complementary and context-sensitive interactions tailored to the situation [26]. During dialogues, participants often synchronize (or mirror) physical behaviors such as body and head motions [27], eye movements [28], and breathing patterns [29]. This coordination signals affiliation and rapport, and additionally enhances communication effectiveness, learning, and social connection [30]. Comparable forms of interpersonal synchrony have also been observed in physiological signals such as heart rate (HR) [31] and galvanic skin response (GSR) [32], as well as in neural activity measured through techniques like electroencephalography (EEG) [33] and functional near-infrared spectroscopy (fNIRS) [34], using a method known as hyperscanning, where brain activity from multiple individuals is recorded simultaneously.

From this overview, we can readily see that current predominantly video-mediated, virtual communication lacks several key components of in-person interaction. Perhaps the most widely explored is that, in face-to-face settings, individuals shift their gaze across the face, hands, and body of their conversation partner [35], using a wide range of non-verbal signals, such as gestures and eye contact, to enrich and guide communication [36,37]. Video calls, in contrast, narrow the visual field of view and disrupt natural eye contact due to camera placement. This narrowing reduces gesture visibility and impairs turn-taking and expression clarity. This has been shown to contribute to lower movement synchrony online [38]. Facial expressiveness is also affected, though this varies across individuals [39]. Physical constraints during virtual meetings can also reduce mobility, which may negatively affect cognitive functioning and communication quality [22,40]. Subjective reports also show that arousal levels are significantly lower during virtual communication [41]. Additionally, a recent study found that making hand gestures visible during online meetings led to more positive participant feedback [42], suggesting that enhancing nonverbal cues may help compensate for some of the limitations of virtual interaction. While both the sender and receiver are affected by the aforementioned factors, the primary burden appears to lie with the receiver, who faces reduced access to essential communicative cues [43,44,45,46].

These issues also seem to be reflected in physiological differences between in person and online communication. Such physiological studies comparing in-person and virtual communication, however, remain limited. One reason for this limitation may be that the technologies available to detect biological signals, not least brain signals, are subject to motion artifacts, the unwanted signals caused by movement and muscle activity induced during natural conversation, from change in facial expression to head nodding, or body repositioning. These artifacts distort measurements making it difficult to isolate clean physiological signals particularly in naturalistic experimental settings. Nonetheless, existing evidence suggests that virtual interactions are associated with reduced brain synchrony between participants [22,33] and diminished physiological responses such as less pupil dilation when viewing faces [47]. In studies that use listening to mitigate head / muscular movement, where in person and video lectures are compared, research has shown an increase in frontal theta activity during virtual meetings that indicated fatigue and decreased levels of alpha activity in the parietal and occipital region that indicated lower levels of engagement. In addition, there is a decrease in heart rate variability (HRV), which again indicates stress or fatigue[48]. Higher HRV has been linked to better subjective conversation quality in face-to-face settings [49]. In contrast, a separate study that used text-based communication to avoid movement-related artifacts found no statistically significant differences in HR or GSR between in-person and virtual conditions, although both measures showed a trend toward higher values in the face-to-face setting [41]. Similarly, GSR responses to eye contact were found to be reduced during virtual meetings [50].

Overall, in-person communication tends to evoke stronger physiological and behavioral responses and greater interpersonal synchrony. In contrast, virtual settings tend to reduce these effects, likely due to limited access to spatial and nonverbal cues, leading to lower emotional engagement, arousal, connection, and trust.

1.2. Contributions of This Study

Despite widespread reports of qualitative differences between virtual and face-to-face interaction—such as reduced connection, awkward turn-taking, and increased fatigue, the underlying behavioral and physiological mechanisms remain poorly understood. Existing studies have examined isolated aspects of conversation, including face viewing [47,50], text-based chat [41], or lecture-based video conferencing [48]. However, these approaches typically use artificial or constrained tasks and focus on a single signal type, limiting their relevance to natural social interaction. To move beyond these limitations, we investigated how both behavioral and physiological signals differ across communication modalities during unscripted, face-to-face style conversations.

Our motivating hypothesis is that by better understanding the physiological and behavioural differences between in person and digitally mediated communication, we can design better digitally mediated interactions informed by those models to enhance the sense of co-presence—the feeling of being physically together. We anticipate that such models that can be translated into interaction designs will help virtual meetings better support natural dynamics, with potential benefits for user experience, trust, collaboration, and sustainability. Because interaction is shaped by complex and interdependent behavioral and physiological processes, we argue that modifying a single variable (e.g., heart rate) is unlikely to meaningfully improve co-presence. Instead, we hypothesise that mapping multiple biosignals can reveal patterns shaped by latent interaction constraints, offering more effective targets for future design interventions.

The key contributions of this work are:

• Integrated behavioral and physiological analysis: We simultaneously recorded diverse physiological and behavioral signals—including eye tracking, head and body movement, heart rate, respiratory rate, and EEG—enabling a comprehensive analysis of how communication modality influences embodied and neural responses.
• Unscripted, high-validity interaction: Unlike prior studies our design involved unstructured, dyadic conversation to more closely approximate real social encounters.
• Sender vs. receiver-side differentiation: We interpret physiological and behavioral changes through the lens of both sender and receiver constraints, identifying which limitations of virtual platforms most affect interaction quality—and where design interventions could be most impactful.

2. Materials and Methods

Participants were recruited from staff and students at the University of Southampton and provided written informed consent in accordance with the University of Southampton’s ethical guidelines and the Declaration of Helsinki. Ethical approval was granted by the University’s ethics committee in December 2023 (ERGO Application ID: 89571). Recruitment took place between 1.4.2024 and 1.7.2024. Each experimental session lasted roughly 90 minutes including preparation time. Eighteen participants (14 Male, Mean Age 34.8 STD 9.9) enrolled in this study and each received 20 GBP in compensation.

2.1. Experimental Task

The experimental protocol was an adapted version of a study by McFarland et al. [29] where respiratory markers during conversation were investigated. All stimuli (Texts, Videos) were selected so that they did not illicit any strong emotion to avoid contamination of unintended biomedical signals. For our version we defined the following tasks:

• Baselines, each 2 minutes: These trials were used to familiarize the participant with the new laboratory environment and the experimental task. It was also used to record signals without interaction with another person.

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  Passive Baseline: The participant sat still and focused on the fixation cross on the screen.

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  Watch and Listen Baseline: The participant watched a video. We used the first 2 minutes from Huberman Lab Clip’s youtube channel with the title “How to Properly Hydrate & How Much Water to Drink Each Day | Dr. Andrew Huberman". We picked this video because the creator talks directly to the listener, imitating a one-sided conversation.

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  Read Baseline: The participant read a text aloud after familiarizing themselves with it. We have used the text “Kombat Kate" from the Cambridge English Assessment Example.

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  Free Talk Baseline: The participant talked about a topic of their choice without any restrictions.

• Dialogue, 5 minutes each: The dialogues took place between an experimenter and the participant, either in-person or virtually. The order of these two conditions alternated for each participant to avoid any temporal effects and always started with the reading task followed by free talk.

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  In-person Dialogue Reading: For this condition, we used ChatGPT to generate a simple dialogue between two people. While the text had sections of varying lengths for each speaker, the overall length was approximately the same for both, aiming to simulate a realistic conversation with balanced talking and listening parts. For this condition the participant read the dialogue off their smart phone while trying to maintain eye contact with the experimenter when listening.

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  In-person Dialogue Free Talk: For this condition, participants were asked to choose a topic they are familiar with, one that is common enough for the experimenter to ask questions and share their own opinions. The goal for the conversation was again to have approximately equal speaking time for both the participant and the experimenter.

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  Virtual Dialogue Reading: For this condition we have used the same text from above but switched the roles in the dialogue. For this task the experimenter left the room and the talk was performed online using Zoom. For this condition the participant read the dialogue off their smart phone as above.

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  Virtual Dialogue Free Talk: Same as In-person Dialogue Free Talk with a different topic and using Zoom.

• Anxiety, 5 minutes: The final task was an adapted version of the InterneT-based Stress Test for Social Anxiety Disorder (ITSSAD) by Huneke et al. [51]. Participants were told they would have five minutes to prepare for an online social interaction in which they would have to introduce themselves to a group of researchers. Simply informing participants about the upcoming interview has been shown to increase anxiety, so we did not conduct the full protocol for this pilot. After the five minutes had passed, participants were debriefed and informed that the task was designed to induce elevated levels of social anxiety without the actual interview. Then the experiment was finished.

The whole task is also visualized in Figure 1.

2.2. Experimental Procedure

Participants were sat comfortably while connected to 27-electrode EEG, 2-electrode electrocardiogram (ECG), 2-electrode galvanic skin response (GSR) and 4-electrode electrooculogram (EOG) using BrainProducts’ “actiChamp plus" system. We used a MindMedia’s “Nexus" respiration sensor with a custom connector to record respiratory rate, Pupil Lab’s “Neon" to track eye movements, pupil size, head acceleration, angular velocity and orientation, Blue Microphones’ “Blue Yeti" to record audio and used an ELP webcam model USBFHD01M-SFV to record video. EEG, ECG, EOG, GSR and respiratory rate were sampled at 500 Hz, eye tracking and pupil size at 200 Hz, head acceleration, angular velocity and orientation at 110 Hz, audio at 48 kHz, and video at 30 Hz. All signals were synchronized using LabStreamingLayer [52]. We do not describe video and audio further in the present paper since this analysis focuses on biosignals.

As part of the “ActiChamp" system, “ActiCap" slim wet electrodes were placed at positions FP1, F7, Fz, F3, FC5, T7, C3, Cz, TP9, CP1, P7, CP5, P3, O1, FP2, F8, F4, FC6, T8, C4, CP2, TP10, P8, CP6, P4, Pz and O2 according to the International 10-20 system [53]. Two EOG electrodes were placed above (FP2) and below the right eye to capture the vertical eye movement signal as well as two near the canthus of each eye for the horizontal signal. Two electrodes were placed at the insides of the elbow to get the ECG signal. Two GSR electrodes were placed on index and middle finger of the participants’ non-dominant hand to get the GSR signal.

2.2.1. Data Recording Adjustments

Data analyses were adjusted based on signal quality and participant behavior as noted below.

• Exclusion of GSR: GSR data were discarded due to participant interference. Some individuals unintentionally interacted with the sensors during dialogue conditions, contaminating the data.
• Exclusion of EEG: EEG data were excluded due to motion artifacts caused by head movements, facial expressions, and muscle activity, which compromised signal integrity. However, minor power variations in the motor cortex suggested potential hand movements. To address this, video recordings were reviewed, and hand movements were documented. The EEG analysis is included in the Appendix A.
• Exclusion of Head Orientation: Head orientation was not analyzed because participant positioning differed between the virtual and in-person conditions.
• Inclusion of Hand Movements: Observed brain activity suggested the need to track hand movements as demonstrated in the Appendix A. Only complete hand or arm movements were considered to avoid confounds from sensor interference. Hand movements were manually annotated from video recordings at a 1 Hz resolution.
• Exclusion of Audio and Video: Audio and video data were not analyzed, as the focus remained on biosignal and behavioral processing.

2.2.2. Pre-Processing

A large dataset of physiological signals was recorded. Therefore, preprocessing and feature extraction focused on measures that either demonstrated statistical differences or were previously associated with human interaction in the literature. Given the study’s emphasis on interaction, analysis was limited to the dialogue conditions “Reading" and “Free Talk", and baselines were excluded. Results from the anxiety condition are presented in a different publication [54]. The following section describes the pre-processing steps for each of the selected signals. All filtering procedures utilized zero-phase filters to preserve temporal relationships.

• ECG: The ECG signal was derived by subtracting both ECG electrodes and applying a band-pass filter (2–30 Hz). This was used to compute HR and HRV.
• Pupil Size: The pupil size signal was low-pass filtered at 50 Hz.
• Head Movements: Data from the 9-degree of freedom (DOF) inertial measurement unit (IMU), angular velocity and acceleration in three axes each, were low-pass filtered at 50 Hz.
• Eye Tracking: Gaze position signals were low-pass filtered at 50 Hz.
• Hand Movements: The manually recorded hand movements were used without any pre-processing.
• Respiratory Signal: A band-pass filter (0.1–0.5 Hz) was applied to the respiratory signal.

Since electrophysiological, hand motion and gyroscope signals were recorded using different devices, precise synchronization had to be ensured. Instead of relying on the native timestamps from each device, we used the timestamps provided by LSL and resampled each of the signals to 100 Hz. The data were then segmented into windows based on the task. Each window had a duration of 30 seconds, updated every 5 seconds, resulting in a total of 55 windows for each of the dialogue conditions.

2.2.3. Feature Extraction

• HR: HR was extracted using a peak detection algorithm applied to the normalized negative derivative of the ECG signal of each window.
• HRV: HRV was extracted by calculating the standard deviation of all HR peak-to-peak intervals and dividing it by the root mean square of the peak-to-peak intervals.
• Pupil Size: The trace of the pupil size is corrected that the first window starts at zero. This was done to ensure that the distance between participant and experimenter for each condition was not influencing the outcome of the analysis. Each window was then featured by its mean.
• Respiratory Rate: A peak detection algorithm was used to extract the respiratory rate from the normalized respiratory signal.
• Head Movements: The power spectral density (PSD) of each signal was computed using Welch’s method with 1 Hz frequency bins.
• Gaze Position: The Gaze Position was featured by it’s variance.
• Hand Movements: The hand movements were featured by it’s percentage of time the hands were in motion.

The reading condition required participants to alternate between reading from their phones and maintaining eye contact with their conversation partners. This task-driven behavior introduces movement patterns that do not reflect natural social interaction. As a result, all movement-related measures (hand, eye, and head movements) and pupil size were excluded from analysis in this condition.

3. Results

Below, we report results for physiological and behavioral measures recorded during the two conversational tasks, "read aloud" and "free talk," under both face-to-face and virtual conditions, as described in the Experimental Task section. The recorded measures included HR, HRV, respiratory rate, pupil size, hand movements, horizontal and vertical eye gaze variance, and the PSD of head movements. While HR, HRV, and respiratory rate were analyzed across both tasks, movement-related metrics (hand, eye, and head movements) and pupil size were only assessed during the free talk condition, as the reading task involved alternating gaze between reading of their phone and watching the conversation partner. EEG and GSR data were excluded due to muscle-related artifacts and unintentional interaction with the GSR sensor. However, minor power variations over the motor cortex indicated potential hand movement, which was further examined using video recordings. A detailed discussion of the EEG analysis and exclusion rationale is provided in the Appendix A.

Each biosignal modality was analyzed separately. The figures in the results section that display the biosignals follow a consistent format. The left panel shows the time course of the relevant physiological measure from 30 to 300 seconds. Yellow shaded areas indicate time intervals where statistically significant differences between the two groups were identified using a Wilcoxon rank-sum test. Due to the limited sample size in this pilot study, no correction for multiple comparisons was applied to the time series data. These results are intended to indicate where differences may occur and should be interpreted as exploratory, serving to identify potential time windows of interest for future, larger-scale studies. The right panel of each figure presents a boxplot summarizing the average of the analyzed measure across the full time window. It also includes the p-value for the difference between the groups over this interval, calculated using the same Wilcoxon rank-sum test. For the PSD analysis, significant frequency band differences were found for head movements. P-values were displayed corrected using the Benjamini-Hochberg procedure.

Figure 2 presents HR data for the reading and free talk tasks in both the virtual and in-person conditions. On average, HR was higher in the in-person condition compared to the virtual condition for both tasks. The difference was more pronounced during the free talk task but did not reach statistical significance in either case. At the start of both tasks, HR initially declined, a common effect observed in physiological studies. This decrease is likely due to the orienting response [55], which occurs when individuals react to a change in their environment with an initial state of heightened arousal before adaptation.

Figure 3 shows HRV which remained relatively stable throughout both tasks. Only at the end of the reading task a difference can be seen where the HRV for the virtual condition increases and for the in-person decreases. However, this variation did not meaningfully impact the overall measure, and no statistically significant differences were found.

Figure 4 depicts respiratory rate during both tasks. No significant differences were observed between the conditions. Unlike HR, respiratory rate initially increased, likely due to a transient elevation in breathing rate as participants began speaking. This suggests that participants did not start conversing immediately but required a brief adjustment period before engaging in speech.

The following measures will only present results for the free talk condition because of the interference of the reading task with the signal acquisition as mentioned above.

Figure 5 shows pupil size during the free talk task. After an initial rise from the zero baseline, pupil size remained larger in the in-person condition compared to the virtual condition. A brief drop for the in-person average occurred around 180 seconds, but overall, the increase was statistically significant.

Figure 6 displays the counted hand movements of participants during the free talk task based upon the detected brain activity. It can be seen that the hand movement during the in-person condition hand movements were happening more often especially at the beginning and the end of the task. However, these results do not show any statistical significance.

Figure 7 shows the variance of horizontal and vertical eye gaze positions during the free talk task. The horizontal gaze variance was significantly higher in the in-person condition throughout the task, indicating greater eye movement variability. In contrast, vertical gaze variance remained similar across conditions, with no significant differences. This pattern is further illustrated in Figure 8, which shows a scatter plot of average eye gaze positions for each window and participant. The ellipses represent the 95% confidence interval for each condition. The in-person condition shows a wider spread in average gaze position along the horizontal axis.

Finally, Figure 9 presents the PSD results for head movements in three subplots. The first plot shows the normalized difference in PSD between the in-person and virtual conditions during the free talk task.

Head acceleration in the right/left and up/down directions, look similar as they show larger power for the in-person condition for low frequency areas (up to 7 Hz and 5 Hz respectively) and then a lower power for higher frequencies. Head Acceleration Back/Forward shows the reverse with lower Power for 0-2 Hz and higher Power for higher Frequencies. The angular velocities show a similar pattern. Roll and Pitch show higher power for the in-person condition up to 2 Hz and lower power above, while Yaw show lower the exact opposite. The second plot displays the p-values for these differences, calculated using the Wilcoxon signed-rank test with Benjamini-Hochberg correction. The third plot highlights the significant p-values for easier identification. Nearly all signals showed significant differences, except for very low-frequency components of back/forward and Yaw (0 Hz), head acceleration in the up/down direction above 10 Hz, and head acceleration in the back/forward direction between 20 and 30 Hz.

Head acceleration in the right/left and up/down directions showed higher power in the in-person condition at lower frequencies (up to 7 Hz and 5 Hz, respectively), but lower power at higher frequencies. In contrast, back/forward acceleration displays the opposite pattern, with reduced power below 2 Hz and increased power at higher frequencies. A similar trend was observed in angular velocities. Roll and pitch showed greater power during in-person interaction up to 2 Hz, while yaw displays the reverse. The second plot presents the p-values from Wilcoxon signed-rank tests with Benjamini-Hochberg correction, and the third highlights statistically significant differences. Most frequency components revealed significant differences, with exceptions primarily at very low frequencies at 0 Hz, as well as high-frequency components above 10 Hz (up/down) and 20–30 Hz (back/forward).

4. Discussion

We found significantly larger pupil dilation and greater horizontal eye movement variance during in-person interactions. Head movements also differed significantly in PSDs for acceleration and angular velocity across all three axes. Head acceleration in right/left and up/down, as well as angular movement in roll and pitch, showed higher power in low-frequency components and lower power in high-frequency components for the face-to-face condition. Most of these components showed significant differences, except up/down acceleration at frequencies above 10 Hz and very low frequencies (0-1 Hz). Meanwhile, head acceleration in back/forward and angular velocity in yaw showed the opposite with lower non-significant power for very low frequencies (0–1 Hz) and higher power for frequencies above that. Although heart rate and hand movement levels were higher in-person, these differences were not statistically significant, and no effects were observed for heart rate variability or respiratory rate. Galvanic skin response and EEG spectral power data were excluded due to muscle and movement artifacts.

The physical constraints of virtual communication, such as reduced mobility and limited gesture visibility, likely contributed to the significantly lower eye movement levels observed [22,40], and may also explain the non-significant reduction in hand movements. The lack of significant vertical movement differences above 10 Hz is expected, as participants remained seated allowing only limited variation in body height. Participants in virtual settings also likely limited all larger movement to stay within the camera frame, which may have led them to compensate with smaller movements that produced more higher frequency changes.

In our study we found increased pupil dilation for the in-person condition, which aligns with prior research showing that arousal is higher during face-to-face interactions [41], and that increased arousal is associated with greater pupil size [56]. This also agrees with a study that showed increased pupil dilation when viewing faces in-person versus on screen [47]. That same study reported broader brain activation and larger event-related neural responses in the in-person condition, consistent with trends observed in our supplementary EEG data. However, due to our minimally restrictive, naturalistic setup, we cannot confidently attribute the observed effects solely to neural activity. Movements and facial expressions during conversation introduce electromyographic (EMG) artifacts: high amplitude, broadband signals that overlap with EEG frequencies and are difficult to fully remove without distorting the underlying brain signals [57]. Given that participants moved more during face-to-face interactions in our study and previous research has shown increased coordination in such settings, the resulting muscle-related artifacts, especially when movements are synchronized, could partly explain the higher EEG synchrony observed in previous studies on in-person communication [22,33]. We also observed a non-significant increase in heart rate in the in-person condition, which is consistent with previous findings [41], and might reflect both greater physical activity and heightened engagement and arousal typically associated with face-to-face interactions.

In contrast to previous findings, we did not observe significant HRV differences between in-person and virtual communication, possibly due to the short duration of our five-minute trials, compared to longer exposures in other studies (e.g., 50-minute lectures or multi-day recordings) [48,49]. Nevertheless, this result is consistent with our respiratory data, which also showed no differences between conditions, aligning with the known relationship between HRV and respiratory rate [58].

5. Conclusions

In summary, our results show mostly significant differences in the behavioral measures between in-person and virtual communication, indicating clear modality-dependent changes in how people interact. These differences cannot be primarily attributed to receiver-side limitations [43,44,45,46], such as reduced access to non-verbal cues due to restricted visual fields, limited gesture visibility, or disrupted eye contact caused by camera positioning. Instead, our findings also point to sender-side constraints: participants in virtual settings showed markedly reduced horizontal eye movement variance, head and body motion, particularly in lateral acceleration and angular momentum and a non-significant decrease in hand gestures. These patterns likely arise from the physical restrictions imposed by the virtual setup, such as the need to stay centered in the camera frame. Overall, virtual communication appears to constrain both the expression and perception of non-verbal behavior, potentially leading to a diminished sense of social connection.

Our findings emphasize that both sender and receiver constraints impact virtual communication, and addressing both is crucial for improving system interaction design. While virtual communication technologies are relatively new, these limitations—restricted sender movement and reduced receiver access to nonverbal cues—have been recognized since early telemedicine research [59]. Various strategies exist to mitigate these effects, such as removing or hiding the camera feedback to prevent distractions for the sender[39], or improving field of view to enhance gesture visibility for the receiver [60]. Revisiting and adapting these established approaches in light of current findings could help improve the quality and naturalness of virtual interactions.