Virtual, but Not (Yet) Natural: The Role of Nonverbal Communication Quality, Cognitive Load, and Fatigue in Virtual Reality Compared to Videoconferencing and Face-to-Face Team Meetings

Virtual, but Not (Yet) Natural: The Role of Nonverbal Communication Quality, Cognitive Load, and Fatigue in Virtual Reality Compared to Videoconferencing and Face-to-Face Team Meetings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Virtual, but Not (Yet) Natural: The Role of Nonverbal Communication Quality, Cognitive Load, and Fatigue in Virtual Reality Compared to Videoconferencing and Face-to-Face Team Meetings Yannick Frontzkowski, Franziska Münstermann, Philip Gubernator, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8094774/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Virtual reality (VR) has been theorized as a richer and more natural alternative to two-dimensional videoconferencing (VC) that could reduce videoconference fatigue (VCF), yet empirical evidence remains limited and mixed. In a preregistered laboratory experiment, N = 209 participants were assigned to triads and randomly allocated to face-to-face (F2F), VC (Zoom), or VR (Meta Quest 3, Horizon Workrooms) meetings. In a 20-minute meeting, the groups completed the NASA Moon Survival Task. We assessed non-verbal communication quality (NCQ) and cognitive load after the meeting, and five VCF facets (general, motivational, emotional, social, visual) before and after. Multi-group structural equation models (ANCOVA approach, controlling for pre-fatigue, demographics, and media experience) showed that NCQ was lowest in VR, while VC and F2F did not differ. Contrary to predictions, post-scores of general, emotional, social, and visual fatigue were highest in VR compared with VC and F2F. Cognitive load was highest in F2F, lowest in VR, and did not mediate the link between NCQ and any fatigue facet. Nevertheless, found direct negative effects from NCQ on VCF. These findings suggest that current VR meeting systems do not yet alleviate VCF, nor do they provide a richer nonverbal communication platform. The naturalness of non-verbal cues appears more critical for communication quality and fatigue than technological richness. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The rapid normalization of remote and hybrid work means that an ever-larger share of daily interaction now unfolds through a screen rather than across a table (Barrero et al., 2021 ). Two-dimensional videoconferencing (VC) platforms such as Zoom or Teams kept organizations running during this rapid shift, yet prolonged use of VC is linked to lower social presence, increased tiredness, and Zoom fatigue (Bailenson, 2021 ; Fauville et al., 2021 ). Zoom Fatigue, more recently known as Videoconference Fatigue (VCF), can be seen as a multi-facet construct - general, motivational, emotional, social and visual. These facets capture different layers of exhaustion: general fatigue reflects overall tiredness, motivational fatigue a lack of drive, emotional fatigue difficulties in regulating affect, social fatigue exhaustion from interpersonal demands, and visual fatigue strain from constant screen exposure (Fauville et al., 2021 ). Meta-analytic evidence already ties this syndrome to lower commitment and a higher risk of burnout costs that make the quality of mediated conversation a genuine organizational concern. While previous research has focused on two-dimensional VC, immersive virtual reality (VR) has recently emerged as a candidate to mitigate some of these downsides. This raises the question of whether VR can indeed alleviate the very mechanisms thought to underlie videoconference fatigue. Clarifying these downsides requires comparing VR not only against VC, but also against Face-to-Face (F2F) interaction as the natural benchmark. But why do these downsides of VC occur? Media Richness Theory (MRT; Daft & Lengel, 1986 ) predicts that richer channels offering immediate feedback, multiple modalities, and personal focus better serve equivocal tasks. Media Naturalness Theory (MNT; Kock, 2004 ) extends this logic with an evolutionary lens: the farther a medium departs from F2F conditions, the more cognitive effort it imposes, leading to worse communicational quality and therefore raising ambiguity and fatigue. Communicating with unclear nonverbal signals (e.g. no real eye contact) in VC could therefore decrease communication quality. Face-to-face (F2F) dialogue, however, rests on a dense fabric of non-verbal cues-gaze, micro-gestures, posture shifts, proxemics-that silently orchestrate speaking behaviors and perceptions long before words appear (Knapp et al., 2014 ; Sacks et al., 1974 ). VC filters that fabric: limited fields of view, camera latency and the “hyper-gaze” of multiple faces at close range compel users to infer missing signals, thereby possibly heightening cognitive load (Bailenson, 2021 ). Empirical studies confirm that VC conversations contain longer gaps and more overlap between speakers than in F2F conversations, undermining conversational flow and perceived richness (Boland et al., 2022 ; Tian et al., 2024 ). Moreover, VCF can be explained by Cognitive Load Theory (CLT) which suggests that degraded signals in VC raise extraneous load and drain working memory. Against this backdrop, immersive virtual reality has been proposed as a medium that can offset some of these cognitive burdens. VR restores spatial cues and may reduce social and emotional fatigue, though its optics can increase visual strain (Sweller, 1988 , 2012 ). Together, MRT, MNT and CLT position immersive virtual reality (VR) between lean VC and rich F2F-potentially natural enough to ease cognitive strain, yet still prone to technical frictions. Recent studies show that immersive embodied VR meetings heighten social presence, smooth conversational flow, and support more turn-taking compared with VC (Held et al., 2024 ; Wang et al., 2024 ). Head pose, hand height, and mutual gaze in multi-user VR reliably predict who will speak next, suggesting that the medium restores actionable cues lost on a 2D video screen. Yet VR is no panacea: head-mounted displays add weight, heat, and vergence–accommodation conflicts, while lower resolution sustains the “screen-door” effect, all of which and possibly more can induce visual strain (Scarfe & Glennerster, 2019 ; Trevisani & Sisti, 2004 ). Regarding VCF, these upsides and downsides require a differentiated and detailed view of the risks and benefits of VC and VR for the user. Although first comparative studies exist, findings remain mixed: VR can enhance social presence and collaboration (Sanaei et al.; van Gent et al., 2024 ), yet results across fatigue and performance are inconsistent (Macchi & Pisapia, 2024 ). Thus, it remains both a theoretical question whether VR reliably outperforms VC, and a practical one whether its additional costs and demands are justified. Specifically, we examine if cognitive load acts a mediator linking communication quality to different facets of VCF. The present study aims to close these gaps through a controlled comparison of F2F, VC and immersive VR in an experimental setting. Groups of three participants negotiated a resource-allocation task while (a) non-verbal communication quality (NCQ) is assessed, (b) cognitive load is captured, and (c) five fatigue facets are assessed pre- and post-meeting. We aim to shed light on what communicational differences different media types appose compared to F2F, if VR improves communicational quality, reducing cognitive load and therefore fatigue. By modelling whether cognitive load explains the effect of communication quality on each fatigue dimension, the study fuses MRT, MNT and Cognitive Load Theory into a single process account. This study advances the literature on VCF in three ways. First, we clarify whether immersive VR alleviates the very communication deficits theorized to underlie fatigue in VC, by contrasting it not only with VC but also with F2F as the natural benchmark. Second, we aim to empirically establish low non-verbal communication quality as an antecedent of VCF, moving beyond prior work that has largely treated it as an assumed factor. Third, contribute to clarify the role of cognitive load as an explaining mediating pathway linking NCQ to different VCF facets and thereby integrating MRT, MNT, and CLT into a unified process account. Findings will guide organizations on when to default to low-cost VC, when to convene in person, and when VR’s hardware overhead is justified by measurable gains in conversational quality or well-being. Platform designers can leverage identified cues-gaze convergence, head-pose synchrony-to build real-time moderation tools, while ergonomics engineers can target the visual-fatigue liabilities our data exposes. Communication technologies are not neutral pipes; they shape the rhythm and emotional tone of our conversations. By testing how richly-and how naturally-a medium must render human signals before the benefits outweigh the burdens, this research clarifies both the promise and the pitfalls of meeting in the metaverse. Theory Remote Work and Mediated Communication To understand how different meeting technologies shape fatigue, it is necessary to embed the phenomenon in established frameworks of mediated communication and cognitive processing. While the introduction outlined the practical relevance, the following section develops the theoretical foundation: first summarizing the body of literature regarding antecedents of fatigue, then by situating VC and VR in models of media richness and naturalness, followed by discussing the role of non-verbal communication, and finally by linking these mechanisms to cognitive load and distinct facets of fatigue. Unlike face‑to‑face meetings, VC mediates audio‑visual information through webcams and screens, introducing several stressors. Riedl ( 2022 ) synthesizes prior research and identifies six root causes to fatigue: transmission delays that disrupt synchronicity; a lack of body language because webcams typically show only head and shoulders; a lack of eye contact; constant self‑view windows that heighten self‑monitoring; being stared at by multiple faces (hyper‑gaze) because gallery view makes everyone appear to look at the speaker simultaneously; multitasking and information overload when users check emails or chats during meetings. Each factor increases extraneous cognitive load and stress. A large cross‑sectional survey of 291 Chinese workers found that videoconferencing fatigue was significantly predicted by mirror anxiety, feeling physically trapped, hyper‑gaze, and the cognitive load associated with producing non‑verbal cues (Ma et al., 2025 ). Participants who felt trapped by the need to remain in camera view or who were more anxious about their appearance reported higher fatigue; technological complexity and a negative attitude toward videoconferencing also increased fatigue. These downsides due to technological hindrances raise concern because social aspects like social presence and effective communication are key predictors of commitment, trust, performance, and well-being in work teams (Newman et al., 2020 ). Immersive social virtual reality (VR) has been proposed as a technological shift that might overcome some causes of videoconference fatigue. By embedding users in a three-dimensional environment with spatial audio and embodied avatars, VR allows gaze to follow natural norms, eliminates constant self-view and partially restores mobility and non-verbal expressiveness. Studies indicate that VR enhances social presence and conversational flow relative to VC (Dey et al., 2024 ; Held et al., 2024 ). Machine-learning analyses show that head pose and gaze cues in VR enable accurate prediction of turn-taking, suggesting higher non-verbal communication quality (Held et al., 2024 ). Field experiments have also shown that hiding self-view or using avatars reduces fatigue (Ratan et al., 2022 ), implying that VR avatars could mitigate mirror anxiety. However, VR introduces new ergonomic and cognitive challenges: head-mounted displays add weight and heat, reduce peripheral vision and can induce eye strain and cybersickness (Scarfe & Glennerster, 2019 ). A laboratory study comparing VR, F2F and VC meetings found that first-time VR users reported higher general and visual fatigue than participants in VC or F2F conditions, suggesting that unfamiliarity and headset discomfort can offset VR’s benefits (Macchi & Pisapia, 2024 ). Therefore, whether VR can genuinely overcome the fatigue-inducing elements of VC, remains an open empirical question. Furthermore, the theorized underlying communicational mechanism which might drive the differences between VC and VR have not examined in detail yet. The present study addresses these questions by comparing VR, VC and F2F meetings on non-verbal communication quality, cognitive load and five dimensions of fatigue. Rich and natural communication: A theoretical embedding Media Richness Theory (MRT) posits that communication media vary in their ability to convey complex information. A medium’s richness is determined by four attributes: speed of feedback, the number and type of sensory channels, the degree of personalization and the ability to transmit natural language (Daft & Lengel, 1986 ). F2F interaction is seen as the golden baseline, providing immediate feedback, multimodal cues and high social presence. VC reduces richness: despite delivering synchronous audio and video, it constrains the field of view, flattens depth cues and typically forces all participants into a single gaze direction, limiting personalization. But what about VR? By tracking head and hand movements and spatializing audio, VR partially restores the richness of F2F; however, avatar representations often lack in realism and fine facial expressions as well as latency or tracking errors can disrupt the conversational flow. Hennig-Thurau et al. ( 2023 ) theorize, that more real-time multisensory social interactions (RMSIs) lead to more social presence and physical mobility, but also to more exhaustion. This contradiction raises the question: Is the richness of a medium itself a sufficient predictor for good communication, or do we need to acknowledge more aspects of sensory inputs, such as the quality of input? Media Naturalness Theory (MNT) extends MRT by grounding communication in evolutionary psychology. MNT argues that humans evolved to communicate F2F; any deviation from co-location, synchronicity, speech, facial expression or body language increases cognitive effort and decreases physiological arousal (Kock, 2004 ). Thus, F2F communication is most natural; VC retains speech and synchronicity but only a narrow window of facial and bodily cues; VR reintroduces co-location in a virtual sense, spatial audio and more, but avatars still lack micro-expressions and natural eye contact. According to MNT, less natural media elevate extraneous cognitive load because users must infer missing signals and continually monitor their self-presentation, leading to faster mental exhaustion and reduced communication clarity. We theorize that richness and naturalness both do their part to increase the quality of RMSIs. However, richness might overweigh in its impact due to possibility to track full body nonverbal behavior. Just full body nonverbal behavior may offer solutions to stated communicational problems with VC which impact all social interactions in a conversation. Non-Verbal Communication Quality Social interactions depend on a dense web of non-verbal signals that silently coordinate conversational flow. Eye gaze, head orientation, gesture, posture shifts and prosodic cues indicate who wants to speak, who is listening and how they react (Kendrick et al., 2023 ). Ethnomethodological analyses showed that conversations are organized through implicit turn-taking systems: speakers recognize transition relevance places and yield the floor smoothly using gaze and intonation (Sacks et al., 1974 ). In F2F settings, these cues are abundant and synchronous. VC disrupts this: limited fields of view and camera angles obscure gaze direction and hand gestures, while latency and jitter distort timing, resulting in more overlapping speech, longer pauses and awkward silences. Moreover, VC users must focus on a grid of faces, often including their own self-view, which increases cognitive load and fosters “mirror anxiety” (Shockley et al., 2021 ). Immersive VR could restore some of these signals: head- and hand-tracked avatars convey direction of attention; spatialized audio conveys distance and orientation; and body movement can substitute for some gesture information. Recent machine-learning analyses of multi-user VR conversations show that head pose, hand height and gaze alignment predict upcoming speech turns, suggesting that VR provides actionable cues for turn-taking (Held et al., 2024 ). Moreover, participants reported smoother interaction and stronger feelings of togetherness in VR compared with VC. However, VR still falls short of F2F: avatars lack micro-expressions such as eyebrow raises and subtle smiles; network delays and tracking errors interrupt timing; and the unnatural “cartoon” appearance of avatars can dampen social presence. Given these considerations, we conclude: H1a: Nonverbal communication quality is higher in VR than in VC. H1b: Nonverbal communication quality is lower in VR than in F2F. Cognitive Load Theory and Fatigue Cognitive Load Theory (CLT) distinguishes intrinsic load (task-related complexity), extraneous load (imposed by instructional or technical design) and germane load (resources used for processing and schema construction). Working memory has limited capacity; excessive extraneous load impedes learning and performance (Sweller, 1988 , 2012 ). In mediated communication, extraneous load derives from technological constraints. VC elevates extraneous load because users must interpolate missing depth cues, monitor multiple faces, manage the unnatural alignment of gaze and maintain self-presentation. VR reduces some extraneous load by restoring stereoscopic depth and spatial audio, but it introduces other burdens: head-mounted displays are heavy, create heat, limit peripheral vision and can cause vergence–accommodation conflicts that can lead to visual strain, headaches and nausea for some people (Souchet et al., 2023 ). F2F interaction imposes minimal extraneous load; participants can focus on the task rather than on managing the medium. Fatigue is a multifaceted construct comprising general fatigue (overall tiredness), motivational fatigue (loss of enthusiasm), emotional fatigue (feeling emotionally drained), social fatigue (desire to avoid social interaction) and visual fatigue (eye strain) (Fauville et al., 2021 ). In mediated communication, fatigue emerges when sustained cognitive and perceptual demands exceed available mental resources. Videoconferencing amplifies such demands through continuous self-monitoring, gaze misalignment, and the need to infer missing nonverbal cues. Consequently, users often report greater tiredness after videoconferences than after comparable F2F interactions (Riedl et al., 2023 ). By partially restoring spatial cues and reducing mirror anxiety, VR could attenuate general fatigue, but headset discomfort may still leave users more fatigued than in F2F. Previous research using older VR technology illustrates this trade‑off. In a within‑person study with inexperienced users wearing Meta Quest 2 headsets, Frontzkowski et al. ( 2025 ) found that immersive VR meetings induced more general and visual fatigue than Microsoft Teams meetings. The authors attribute this to the heavier hardware, lower resolution and lack of prior VR experience among participants, who were already accustomed to 2D video meetings. These findings caution that VR’s benefits depend on both ergonomic design and user familiarity. The present study employs newer hardware (Meta Quest 3) with improved optics and comfort and provides a wider range of experiences with VR technology among participants. We therefore expect VR to reduce the extraneous load associated with decoding non‑verbal cues relative to VC while keeping headset‑related strain manageable. Thus, we conclude: H2a: General fatigue is higher in VC than in VR. H2b: General fatigue is lower in F2F than in VR. Motivational fatigue reflects a lack of enthusiasm and willingness to invest effort. VR’s enhanced social presence and more natural interactions are expected to support engagement relative to VC. In VC, mirror anxiety and hyper-gaze can undermine motivation, whereas F2F interactions typically sustain it through richer non-verbal cues and greater immediacy. Consequently, we conclude: H2c: Motivational fatigue is higher in VC than in VR. H2d: Motivational fatigue is lower in F2F than in VR. Emotional fatigue involves feelings of being emotionally drained. VC can heighten emotional drain because users must consciously manage self-presentation and decode blurred social cues, leading to frustration and stress. VR’s avatars reduce self-focused attention and may thereby lessen emotional fatigue relative to VC. F2F interactions, with their full complement of expressive signals, should be least emotionally fatiguing. Hence, we conclude: H2e: Emotional fatigue is higher in VC than in VR. H2f: Emotional fatigue is lower in F2F than in VR. Social fatigue denotes a desire to avoid interaction. Constant self-monitoring and hyper-gaze in VC can make social interactions feel oppressive; VR alleviates some of this by allowing more natural eye movements and reducing the sense of being stared at. Nevertheless, avatars still lack subtle facial expressions and may limit connectedness. F2F offers the richest social cues and thus should induce the least social fatigue. We therefore conclude: H2g: Social fatigue is higher in VC than in VR. H2h: Social fatigue is lower in F2F than in VR. Finally, visual fatigue stems from ocular strain. Here the trade-offs reverse. Head-mounted VR displays introduce visual strain through vergence–accommodation conflicts and limited peripheral vision, whereas VC requires only a monitor. F2F interactions involve no screen at all. Thus, we expect: H2i: Visual fatigue is lower in F2F than in VR. H2j: Visual fatigue is lower in VC than in VR. Linking Antecedents to Fatigue: Cognitive Load as a mediator Drawing on MRT, MNT and CLT, we argue that cognitive load mediates the relationship between non-verbal communication quality and fatigue. Poor communication quality-characterized by ambiguous nonverbal cues-forces users to expend additional mental effort to interpret signals and plan their contributions. This extra effort represents extraneous cognitive load that drains limited working‑memory resources. Evidence from videoconferencing research underscores this mechanism: Riedl ( 2022 ) identifies cognitive effort as a core root of videoconference fatigue, and Ma et al. ( 2025 ) report that the cognitive load involved in producing non‑verbal cues predicts self‑reported fatigue. Recent experimental studies also show that interventions reducing cognitive demands-such as turning off self‑view-simultaneously lower perceived fatigue (Basch et al., 2025 ). While immersive VR can restore some missing cues and thereby reduce cognitive load relative to two‑dimensional videoconferencing, head‑mounted displays introduce their own strain, potentially elevating visual fatigue (Scarfe & Glennerster, 2019 ). We therefore propose a mediation model: H3: The relationship between nonverbal communication quality and fatigue is mediated by cognitive load in such a way that nonverbal communication quality is negatively related to cognitive load which, in turn, is positively related to a) general b) motivational, c) emotional, d) social and e) visual fatigue. Methods Recruitment and Sample During recruitment, participants were invited via mailing lists of different universities, direct acquisition from various companies, the online forum Reddit , and direct invitations by the investigators. Participants could choose to receive €30 or 2.5 course credit participation points (which was only applicable for students required to collect them for their study program). To register for the study, we set up an online booking website on which participants could choose a date and time on which they wanted to participate and form groups of three. We also displayed how many slots are left for every date and informed about the compensations. Participants were invited to participate in a study on group decision-making. Participants were always able to cancel a booking and to withdraw from the experiment. The total sample consisted of N = 221 participants. We excluded 12 participants because of mayor technical issues during the experiment (like internet connection loss, power cuts, or software bugs). The final sample consisted of N = 209 participants. Participants ages ranged from 18 to 65 ( M = 28.26, SD = 10.38), 58.85% were male, 41.15% female, and 0% diverse. 66.99% of participants were students, 15.31% worked in the private sector, 15.79% in the public sector and 1.91% in apprenticeship. For the participants in the VR condition, 38.36% stated they had no experience with Virtual Reality Headsets at all. For the participants in the VC condition, 4.23% stated they had no experience with videoconferences at all. All procedures performed in this study were in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Approval by the institutional ethics committee is documented in the Appendix. The study was preregistered on the Open Science Framework (OSF; Felfe & Frontzkowski) including hypotheses, design, and analysis plan. This research has received funding by the center of digitalization- and technology research of the military in Germany. Design The study employed a 3x2 (conditions x time, pre-post) between-subjects experimental design with three communication conditions: face-to-face (F2F), videoconferencing (VC), and virtual reality (VR) as predictors. Participants were organized in triads and were randomly assigned to one of the three conditions. The primary objective was to examine how communication modality affected changes in VCF (pre-post) and NCQ, CL (post) as outcomes during a structured collaborative task. The setup for the three conditions was as follows. In all conditions, participants were seated at desks, equipped with a laptop, an external monitor, webcam, mouse and a pen. In the VR and VC condition, participants were seated in separate equally furnished rooms (Fig. 2– 3 ), while in the F2F condition, participants were seated together in the same room at one table (Fig. 4). Additionally, in the F2F condition we placed a wide-angle camera, so the experimenter could always see all participants from their observation room. Also, a conference speakerphone was placed in the middle of the group table in F2F, so the participants could receive instructions by the experimenter and the experimenter could also record the conversation in the group task. Figure 2 - VR Setup In the VC condition we used Zoom as the communication platform (Fig. 3 ). For the VR condition, we used the head-mounted display Meta Quest 3 with Horizon Workrooms from Meta (Fig. 5), since it supports desktop passthrough, screen mirroring, meeting rooms with multiple people and the participation via a web browser, which was necessary for the experimenter to share a timer and instruct the participants, without having an avatar of our own in the experiment. The pre and post questionnaire was administered via the online survey platform UniPark . In the VR condition, participants completed the survey within Meta Horizon Workrooms . Additionally, the Meta Quest 3s built-in desk passthrough feature was used, allowing participants to interact with the physical ranking list placed on their desk. Microphones were muted by default outside of the group task to control communication. In the F2F condition, participants sat together in the same room (Fig. 4) but were likewise instructed to avoid verbal interaction until permitted. In the F2F room, a screen with a connection to the experimenters via Zoom was set up. Participants were not able to see the experimenters, but sound was enabled to allow the experimenters to instruct the participants and share the screen on which a timer was placed for the different phases of the experiment. The study used the NASA Moon Survival Task (Hall & Watson, 1970) to simulate structured group interaction. This task was chosen because it simulates a conversation in which participants are required to make decisions under uncertain and sometimes conflicting information, encouraging information sharing and negotiation (Hamada et al., 2020 ). An initial individual ranking phase was included to ensure that all participants had formed their own judgments before the group discussion, thereby increasing the likelihood of divergent opinions and stimulating richer deliberation during the collaborative phase. Procedure Upon arrival, participants were instructed to silence their mobile phones and use the restroom in advance to avoid disruptions. After providing informed consent, they were randomly assigned to one of the three experimental conditions. To ensure technical readiness in VR and promote embodiment, participants in the VR condition completed a brief onboarding procedure, which included two instructional videos, each approximately two minutes long. Participants first watched an instructional video on their laptops. The video explained how to properly wear and adjust the Meta Quest 3 headset, including fit and interpupillary distance. Once the headset was in place, participants entered a shared Meta Horizon Workrooms virtual meeting room with the other group members (represented as avatars) and the experimenter (who was audible via the web browser but not visible). After all participants had entered the VR environment, a second video was presented via screensharing by the experimenter. This video guided participants through the avatar customization menu and explained how to create and save an avatar. Participants were instructed to design an avatar resembling themselves as closely as possible and were given 8 minutes for this task. Horizon Workrooms’ desk passthrough allowed continuous visibility of their physical desk and mirrored laptop screen throughout the experiment. These steps are unique for the VR condition and necessary to ensure, that the participants can use the Meta Quest 3 Headset. Next, participants in all three conditions completed the pre-questionnaire. Following this, they listened to an audio instruction explaining the individual task: the NASA Moon Survival Task . Each participant had a printed task sheet that contained a list of 15 survival-related items to be ranked by importance. Two columns were provided: one for the individual ranking, and one for the joint group ranking to be completed later. Participants were given 8 minutes to complete their individual rankings without communication. Once all triad members had done so, the experimenter gave a verbal signal to begin the group phase. Participants then had 20 minutes to collaboratively agree on a group ranking using the second column of the task sheet. This interaction phase was the only one where verbal communication was allowed and was recorded for later analysis. Finally, participants completed a post-questionnaire, in which participants were asked for VCF, cognitive load and NCQ. The experimenter continuously monitored participants through one-way video feeds but was only audible when giving instructions. Participants were reminded of their right to withdraw at any time without penalty. Measures Videoconference fatigue was assessed before and after each meeting, using a German adaptation of the Zoom & Exhaustion Scale (ZEF; Fauville et al., 2021 ), composed of 15 items. General Fatigue describes an overall tiredness and lack of energy (four items); example item: “After participating in the video conference, I feel tired”). Social Fatigue describes weariness and reduced capacity for social interactions (three items); example item: “After participating in the video conference, I avoid social interactions”). Emotional Fatigue describes emotional drain and burnout (four items); example item: “I feel emotionally drained after this meeting”). Motivational Fatigue describes a decline in motivation and enthusiasm for activities or tasks (three items); example item: “After participating in the video conference, I don’t feel like doing anything”), and Visual Fatigue describes strain and discomfort related to the eyes (three items); example item: “After participating in the video conference, my eyes feel irritated”. The items were answered on a 5-point Likert scale (1 = “Not at all” to 5 = “Very much”). Cognitive Load was assessed after each meeting using the DLR-WAT scale (Grippenkoven, 2018 ). The scale consists of six bipolar items ranging from 1 = “Extreme underload” to 10 = “Extreme overload” with the example item “Information acquisition: To what extent were you challenged by searching for and acquiring information during the overall task? (Was this demand, in terms of information acquisition, in the range of underload, optimal load, or overload?)” score. Non-verbal Communication Quality was assessed with a self-developed scale. The scale was created using an expert rating and the pre-test data and uses a Likert scale from 1 = “Does not apply at all” to 5 = “Fully applies”. An example item is: “I could easily tell whether the other participants were paying attention.” or “It was difficult for me to signal my attention to other participants.“ After conducting a confirmatory factor analysis, we decided only to use 10 out of 12 items for the final scale. All items, and the confirmatory factor analysis results are shown in the Appendix. Cronbach’s alphas for all scales are provided in Table 2 Statistical Analysis All analyses were conducted in R using the lavaan package (Rosseel, 2012 ). Following recent methodological recommendations for experimental SEM (Kwok et al., 2018 ), we specified multi-group models with the three experimental conditions (VR, VC, F2F) as grouping variable. Each model followed an ANCOVA approach in which post-fatigue scores were regressed on pre-fatigue scores while adjusting for covariates (age, gender, and media experience). This procedure allowed testing group differences in adjusted post-intercepts as well as equality of regression slopes across groups. Model fit was evaluated using the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Given our directed hypotheses, one-tailed significance tests were applied (Cho & Abe, 2013 ), with α = .05 as criterion. Results Descriptives Mean scores and standard deviations are displayed in table 1. Descriptive comparisons of study outcomes are displayed as violin plots in figure 4. Correlations and Cronbach’s alphas are displayed in Table 2. The VR condition showed a negative correlation with NCQ, indicating poorer NCQ with VR. We did not find a correlation regarding the VC condition and NCQ. NCQ is negatively related to all post facets of fatigue (General post: r = -.27, p > .001; Motivational post: r = -.27, p > .001; Emotional post: r = -.27, p > .001; Social post: r = -.29, p > .001; Visual post: r = -.28, p > .001). NCQ was also correlated negatively with all VCF pre scores (General pre: r = -.22, p > .001; Motivational pre: r = -.26, p > .001; Emotional pre: r = -.25, p > .001; Social pre: r = -.21, p > .001; Visual pre: r = -.24, p > .001). Moreover, the respective pre and post VCF facets were highly correlated (r = .65 – .77, p > .001), indicating that NCQ and post VCF have also been affected by participants pre VCF. CL is negatively related to VR, indicating that CL is lower in VR (r = -.18, p = .007). Moreover, only one significant correlation was found between CL and motivational fatigue (r = .14, p = .036). Regarding the conditions, VC negatively correlates with visual fatigue (r = -.21, p > .001) but not with the other facets, while VR positively correlated with visual fatigue (r = .40, p = > .001), but not with the other facets. Based on these observations, we conducted Welsch’s T-Test for all pre fatigue scores between conditions and found no differences except for pre visual fatigue which is higher in the VR condition. Descriptive pre-post differences are displayed in Figure 6 and reveal, that only visual fatigue increased from pre to post in the VR condition. All other facets declined, but the decline was smaller in VR compared to VC and F2F. Figure 7 displays descriptive violin plots of the outcomes Cl and NCQ and highlights significant group differences between conditions, using Welsch’s T-Test. It shows lower NCQ in VR compared to VC and F2F, as well as highest CL scores for F2F, followed by VC and least in VR. Table 2 Correlations and Reliabilities Age Gender Con VC Con VR VR_exp VC_freq VCF_pre_G VCF_pre_M VCF_pre_E VCF_pre_S VCF_pre_V VCF_post_G VCF_post_M VCF_post_E VCF_po_S VCF_p_V CL NCQ Age - Gender -.06 - Con VC -.12 .02 - Con VR .13 -.02 -.51*** - VR_exp .13 -.15 / / - VC_freq .09 -.04 -.13 .13 .29* - VCF_pre_G -.06 .00 .11 -.11 .00 -.24** (.75) VCF_pre_M -.07 -.06 .14* -.11 .13 -.22** .67*** (.74) VCF_pre_E -.10 -.07 .06 -.04 .02 -.21** .77*** .67*** (.77) VCF_pre_S .06 -.08 .12 -.05 .09 -.26** .52*** .53*** .55*** (.70) VCF_pre_V -.07 -.05 -.09 .23*** -.06 -.06 .36*** .30*** .34*** .18** (.72) VCF_post_G -.10 .02 .00 .07 .03 -.12 .65*** .55*** .58*** .44*** .41*** (.78) VCF_post_M -.05 -.12 .07 -.04 .07 -.18* .57*** .77*** .57*** .50*** .30*** .66*** (.81) VCF_post_E .00 -.07 -.06 .09 .02 -.15 .63*** .54*** .70*** .53*** .31*** .70*** .66*** (.84) VCF_post_S .02 -.04 .04 .05 .04 -.09 .47*** .52*** .54*** .65*** .18** .58*** .65*** .68*** (.76) VCF_post_V -.06 .06 -.21** .40*** -.06 -.06 .27*** .27*** .27*** .16* .76*** .46*** .28*** .37*** .26*** (.86) CL -.13 -.18** .08 -.18** .02 -.05 .16* .10 .08 .06 .03 .10 .14* .09 .09 .04 (.78) NCQ .16* .16* .07 -.30*** -.05 .11 -.22*** -.26*** -.25*** -.21** -.24*** -.27*** -.27*** -.27*** -.29*** -.28*** .02 (.80) Note. Cronbach’s Alpha is displayed in the diagonal. Significance level is displayed as * p >= .05, ** p >= .01, *** p >= .001 Con = Condition of experiment. VR_exp = Experience of participants with VR. VR_freq = Frequency of videoconference usage of participants. Pre-measures are marked with the prefix “pre”. Post measures are marked with prefix “post”. G = General Fatigue. M = Motivational Fatigue. E = Emotional Fatigue. S = Social Fatigue. V = Visual Fatigue. Hypothesis testing Hypotheses 1a and 1b addressed group differences in nonverbal communication quality. To test these hypotheses, we estimated multi-group SEM contrasts controlling for pre-scores and covariates. Results are summarized in Table 2. Table 3 Nonverbal Communication Quality Contrast χ ²(df) p All 22.88 (2) < .001 VR – VC 11.68 (1) < .001 VR – F2F 21.84 (1) < .001 VC – F2F 1.70 (1) .192 Note. χ² = Wald test from multi-group SEM controlling for pre-scores and covariates (age, gender, media experience). p-values Holm-corrected for multiple comparisons. The omnibus Wald test indicated significant group differences, χ ²(2) = 22.88, p < .001. The mediation results are displayed in Table 5. Pairwise comparisons showed that VR yielded significantly lower nonverbal communication quality than VC ( p < .001, Holm-corrected), rejecting H1a. VR also showed significantly lower values compared to F2F ( p < .001), supporting H1b. No significant differences emerged between VC and F2F ( p = .190). Hypotheses 2a–j concerned group differences in the five facets of videoconference fatigue. Adjusted post-intercepts, controlling for pre-scores and covariates, were contrasted between groups. Results are presented in Table 3. Table 4 Videoconference Fatigue Facets Contrast χ²(df) p All VR – VC – F2F 30.70 (10) < .001 General VR – VC 3.56 (1) .059 VR – F2F 2.75 (1) .098 VC – F2F 0.05 (1) .819 Motivational VR – VC 0.01 (1) .917 VR – F2F 0.18 (1) .669 VC – F2F 0.16 (1) .686 Emotional VR – VC 3.70 (1) .055 VR – F2F 2.77 (1) .096 VC – F2F 0.06 (1) .810 Social VR – VC 3.82 (1) .051 VR – F2F 0.11 (1) .736 VC – F2F 2.97 (1) .085 Visual VR – VC 20.73 (1) < .001 VR – F2F 22.04 (1) < .001 VC – F2F .09 (1) .770 Note. χ² = Wald test from multi-group SEM controlling for pre-fatigue, age, gender, and media experience. Tests were conducted separately for each facet; p-values Holm-corrected for multiple comparisons. The omnibus Wald test across all facets was significant, χ ²(10) = 30.70, p < .001. The results are displayed in table 4. For general fatigue, significant differences emerged between VC-VR ( p = .059) and between F2F-VR ( p = .098). However, the direction of the effect was contrary to the hypothesis, therefore H2a and H2b are rejected. For motivational fatigue, for the comparison of VR-VC ( p = .917) and VR-F2F ( p = .669) no significant pairwise contrasts were observed, providing no support for H2c and H2d. For emotional fatigue, significant differences emerged between VC-VR ( p = .055) and between F2F-VR ( p = .096). However, the direction of the effect was contrary to the hypothesis, therefore rejecting H2e and H2f. For social fatigue, significant differences emerged between VC-VR ( p = .051), but not between F2F-VR ( p = .736). However, the direction of the effect was contrary to the hypothesis, therefore rejecting H2g and H2h. For visual fatigue, VR participants reported significantly higher values than both VC ( p < .001) and F2F ( p < .001), supporting H2i and H2j. Table 5 Mediation Models with Cognitive Load Direct Effect (a) Indirect Effect (b) SE p (a path) p (b path) General -.245 -.008 0.034 .002 .805 Motivational -.185 -.010 0.030 .002 .749 Emotional -.180 -.012 0.034 .004 .737 Social -.285 -.012 0.036 .000 .733 Visual -.210 -.009 0.033 .003 .783 Note. Direct effects (a) and Indirect effects (b) estimated via bias-corrected bootstrap (1000 samples) in multi-group SEMs controlling for pre-fatigue, age, gender, and media experience. Hypotheses 3a–e examined whether the relationship between nonverbal communication quality and fatigue was mediated by cognitive load. The mediation results are displayed in table 5. Across all five models, the indirect effects through cognitive load were not statistically significant, as the 95% confidence intervals for all estimates included zero. Accordingly, hypotheses 3a–e are not supported. Discussion This study compared F2F, VC, and VR meetings in terms of non-verbal communication quality (NCQ), cognitive load, and five facets of videoconference fatigue (VCF). Three patterns emerged. First, NCQ was lowest in VR, whereas VC and F2F showed similarly high (H1a rejected; H1b supported; VC ≈ F2F). Second, VR showed, as hypothesized, an increase in visual fatigue pre-post scores for visual fatigue while no effect was found for VC or F2F. Contrary to our expectations, VR also showed the most general (H2a-b), emotional (H2e-f), and social fatigue (H2g). No difference was found for the comparison of social fatigue between VR and F2F. Third, cognitive load did not mediate these effects. Non-verbal communication quality Based on MRT, we expected VR to enable richer exchanges through embodied avatars, spatial audio, and gaze cues. However, participants rated NCQ lowest in VR, contradicting H1a. VC and F2F did not differ significantly. This pattern aligns better with MNT: although VR adds channels, it reduces perceived naturalness because avatars lack micro-expressions and true eye contact, and timing can be perturbed by tracking/latency. Moreover, the avatars limited facial and body animations and details likely reducing subtle social cues such as nods, smiles, or gaze-synchrony. These factors likely disrupted conversational fluency and overshadowed richness advantages. Another explanation lies in user familiarity and adaptation. While participants were highly accustomed to VC, VR remained novel to many − 70.51% of VR users had low to no prior VR experience. Such unfamiliarity likely increased attentional demand for device and software handling, diverting cognitive resources away from the interaction itself. In other words, participants were still “learning the medium” instead of fully engaging in the conversation. This suggests that the perceived potential of VR may be out weighted by the lack of user experience and potential benefits might therefore be underestimated. Beyond technological and experiential factors, individual states also played a role. We found negative correlations between NCQ and pre-fatigue measures, indicating that participants who started already more tired perceived the quality of NCQ as lower. To improve NCQ in VR, future systems should enhance avatar realism through facial tracking and more fluid body tracking and animations while minimizing latency and device discomfort. Moreover, training or repeated exposure might reduce possible novelty effects and foster intuitive use, allowing the potential social advantages of immersive meetings to unfold more fully. Newer HMDs with integrated face and eye tracking may improve naturalness, but current implementations still draw attention to device management, further lowering perceived NCQ. Fatigue differences The most consistent effect was visual fatigue peaking in VR (supporting H2i-H2j), consistent with expected ergonomic and optical burdens (e.g., vergence-accommodation conflict, restricted FOV, thermal load). Thus, VR increased exhaustion not only because it lacks cues, but because its cues are harder to interpret and are delivered through hardware that imposes additional physiological costs. Notably, T-Tests show that motivational and visual fatigue were already higher in the VR condition before the treatment, indicating that a fatiguing effect may have already taken place in the VR condition due to preparing for the VR headset and conducting the pre-questionnaire with VR, further hinting at the ergonomic and optical burdens. Moreover, post-measures were only higher in VR for visual fatigue. Only when controlling for pre-measures in the SEM, we find significant differences for all facets of VCF. Contrary to H2a, H2e and H2g, post scores of general, emotional and social fatigue remained higher in VR than in VC (direction opposite to prediction), while VR – compared to F2F - showed higher general, emotional, and visual fatigue, and VC showed higher social fatigue than F2F. Remarkably, all VCF facets decreased from pre- to post-measures, except for visual fatigue in the VR condition. The overall decline suggests that participants started the experiment already fatigued and recovered during the task. The smaller reduction of VCF in VR reflects that this group experiences less recovery and remains on higher VCF levels. However, this baseline effect does not alter the interpretation of group differences, as all analyses controlled for pre-fatigue levels. Moreover, the higher VCF in VR may have reciprocally impaired perceived communication quality. When users experience VCF, their ability to pay attention and decode social signals might decline, creating a downward spiral where VCF and lower NCQ reinforce each other. Such cross-effects are plausible given the observed correlations between NCQ and all pre VCF facets, suggesting that VCF in VR is not only a byproduct of hardware strain but could also undermine the very communicative advantages VR could offer. Cognitive load as mediator H3 proposed that lower NCQ increases cognitive load, which in turn heightens fatigue. While we found negative c-path effects from NCQ on all five facets of fatigue, we did not find a significant relationship between NCQ and Cl (a-path). Moreover, we only found one positive correlation from CL with motivational fatigue but none from CL with NCQ (b-path). Interestingly, cognitive load tended to be highest in F2F, opposite to our prediction. On a first glance, three explanations seem to be plausible: First, sitting with two strangers in the same room might be more cognitively demanding due to social inconveniences, than having a meeting with zoom or VR. This might have influenced cognitive efforts to work together. Second, building on the first argument, the DLR-WAT instrument used to capture CL might not capture all facets of cognitive burdens that play a role in a team meeting. For example, even with familiar team members, F2F interaction may impose additional cognitive demands through subtle social expectations and continuous nonverbal processing, which could be attenuated in VR (through partial anonymity) or VC (through increased interpersonal distance). Third, F2F may elicit higher cognitive load due to denser and faster social information (micro-expressions, fine-grained gaze, overlapping turns): this “social-cognitive activation” can raise perceived workload without translating into higher fatigue-especially in short tasks. Future work should separate extraneous vs. intrinsic and physiological load (e.g., pupillometry/EEG), align load facets with specific fatigue facets, and test adaptation across repeated VR exposure. Theoretical contributions This study contributes to ongoing debate and theoretical arguments from Bailensons ( 2021 ) and Riedls ( 2022 ) frameworks of the root causes of VCF in several ways. First, we confirm a core assumption, that difficulties with interpreting and sending nonverbal signals are relevant for VCF, in a controlled experimental setting. Our findings deepen the understanding what specific aspects of nonverbal communication might be relevant for VCF during meetings: attract, recognize & show attention. If it is difficult or uncertain whether others pay attention, or if it feels hard to get attention, individuals tend to experience higher levels of videoconference fatigue (VCF). Likewise, when it is unclear whether one’s nonverbal signals are perceived or ignored, VCF increases. Thus, the nonverbal regulation of attention in meetings appears to be a relevant factor in explaining VCF. Second, our findings provide deeper insights into the relative importance of two theoretical approaches to predict NCQ: Media Richness Theory (Daft & Lengel, 1986 ) and Media Naturalness Theory (Kock, 2004 ). While we initially emphasized the meaning of richness - expecting VR’s additional 3D channels and immersion to enhance NCQ- our results showed the opposite: NCQ was highest in F2F, followed by VC, and lowest in VR, indicating that the added channels of immersion cannot compensate for the lack of natural human expressiveness (e.g. real mimics), probably making naturalness the stronger driver of NCQ as compared to richness. Third, Bailenson ( 2021 ) assumed that immersive technology could solve VCF by facilitating sending and interpreting of nonverbal signals whereas Hennig-Thurau et al. ( 2023 ) argued, that higher immersiveness might result in more VCF. As we found that NCQ is lower and VCF is higher in VR than compared to VC and F2F the potential benefits of VR should be assessed more cautiously and less optimistically. This is particularly true since the assumed advantages in nonverbal communication cannot be proven. Nevertheless, immersiveness in general may not necessarily cause more VCF; rather, specific current technological shortcomings in VR - such as unnatural avatars or limited facial expressiveness—may cause this effect. Thus, we add to the literature by shifting attention to the technical improvement of these features. Finally, we tested Bailensons ( 2021 ) assumption, that CL as an explaining mediator for fatigue. We found no evidence for a mediation, leading to the assumption, that CL might not be the core reason for VCF, and other mediators might be responsible. Together, our findings indicate that recent VR technology does not solve VCF for meetings, and that the reasons behind VCF might be different than previously assumed in the literature. Practical implications Should organizations already use VR headsets for work meetings? When compared to VC, our data suggest VR should not yet replace VC as the default for remote Meetings. Current VR systems induce more fatigue-especially visual strain-than videoconferencing or F2F interaction. This is likely due to the physical burdens of headsets (weight, heat, limited field of view) and the unnatural rendering of avatars, which require more cognitive effort to interpret. However, these drawbacks are small in magnitude and may diminish with next-generation hardware offering natural eye and face tracking, lower latency, and lighter optics. VR may still be valuable for specific use cases: short, spatially rich, or socially engaging sessions where a sense of “being there” outweighs fatigue risks. Team leaders might consider VR as a tool for team building or meetings with a higher social focus. In contrast, videoconferencing remains the most efficient medium for routine or analytical meetings, while F2F continues to set the benchmark for emotionally or relationally intense exchanges. Limitations Several limitations should be acknowledged. First, the study employed a single type of VR headset (Meta Quest 3) without facial expression tracking. We theorized that VR should be a richer medium compared to VC; however, it is essential to consider that some cues are missing in VR, that are given in VC and F2F. Given that facial cues are a core component of non-verbal communication, the absence of real-time facial rendering likely constrained perceived naturalness and interaction quality. Future research should compare headsets with and without face-tracking (e.g. Apple Vision pro) to assess how this technological advancement influences NCQ and fatigue outcomes. Second, although participants prior experience with VR was statistically controlled, VR remains less familiar and routinized than videoconferencing. Thus, novelty effects and limited technical proficiency may have influenced both communication fluency and fatigue. While we controlled for experience with VR, only a small number of participants reported medium to high experience. It needs to be taken into consideration that even though no significant correlation was found from NCQ with prior VR experience, this could be because of the general lack of variance with prior VR experience, and that experience with VR does not necessarily mean, that participants have experience with this specific VR Headset and the specific software used for the meeting. Longitudinal studies or repeated-exposure designs are required to disentangle short-term adaption effects from stable medium characteristics. Lastly, pre results of motivational and visual fatigue were higher in the VR condition than the VC and VR condition. Arguably, visual strain was already induced before the questionnaire due to the VR setup video and the avatar creation. Since visual fatigue was already heightened, the difference between pre and post visual fatigue measures might have been higher than shown in our data. Future studies should take into consideration, that even only a few minutes long tutorials for VR might already have a fatiguing influence. Conclusion Immersive virtual reality aims to recreate the social richness of F2F, yet our results show that current systems fall short. Participants in VR reported lower NCQ, higher VCF-especially visual strain-and no cognitive advantage over conventional videoconferencing. Integrating MRT, MNT, and CLT, the findings demonstrate that technological richness alone does not enhance communication: when cues feel unnatural or ergonomically demanding, they add rather than reduce strain. Theoretically, this highlights naturalness as the stronger predictor of meeting outcomes and calls for models that integrate both cognitive and physiological sources of effort. Practically, VR may benefit short, spatially rich collaborations but remains no fatigue-saving substitute for everyday communication. Only lighter, more natural, and less effortful systems could fulfill VR’s promise of making people feel truly together-without wearing them out. Declarations Author Contribution All mentioned authors contributed in several ways:- Reviewing the literature (FM, PG, KB, JF)- Planing of the study design (FM, PG, KB, JF)- Conduction of Pre-Test (FM, PG, KB, JF)- Questionnaire Design (FM, PG, KB, JF)- Data acquisition (FM, PG, KB, JF)- Statistical analysis (FM, PG, KB, JF) Acknowledgement This research project was only possible thanks to the many helping hands who supported the participant recruitment and the laboratory experiments, and also mentally to handle the stress we faced during the acquisition. 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Front Virtual Real 5:1463189. https://doi.org/10.3389/frvir.2024.1463189 Wang P, Han E, Queiroz ACM, DeVeaux C, Bailenson JN (2024) [Jeremy N.]. Predicting and Understanding Turn-Taking Behavior in Open-Ended Group Activities in Virtual Reality. https://doi.org/10.48550/ARXIV.2407.02896 Additional Declarations No competing interests reported. Supplementary Files 01descriptives.r 04ttests.r 03mgsemmediation.r 05cfancq.r 02mgsem.r Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Frontzkowski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBAC+wNgKoGBXwJIMTYAuQcIaDEAEQeAWiRnkKzF4AbRWqQPH3v84U9a4ubb7c8kGHfcYeA73oBfiz1fWrrBAZ6cxG13zphJMJ55xiB5hoA1Bjw8ZhIHJCoSt93IYZP+23YY6MIEYrQYVCRunpEOdBhIy/0HxGhJyEncIJFgBtFyA78OoBa2NIkzB9KMZ9zIMbYAauGRPEPQYczHJCr+JMv2z0h/eAOoRY7v+AEC1kCBYwOUwUOceiCwJ1rlKBgFo2AUjDwAAP6BSPwyQZNIAAAAAElFTkSuQmCC","orcid":"","institution":"Helmut Schmidt University","correspondingAuthor":true,"prefix":"","firstName":"Yannick","middleName":"","lastName":"Frontzkowski","suffix":""},{"id":553234639,"identity":"c57573ef-aefc-46af-856c-5046ad65388a","order_by":1,"name":"Franziska Münstermann","email":"","orcid":"","institution":"Helmut Schmidt 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1","display":"","copyAsset":false,"role":"figure","size":122551,"visible":true,"origin":"","legend":"\u003cp\u003eHypotheses Model\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/8bb8400d087290bae016873b.jpg"},{"id":98424508,"identity":"df419d23-3c01-400e-92ef-00454b4ef22b","added_by":"auto","created_at":"2025-12-17 16:33:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34087,"visible":true,"origin":"","legend":"\u003cp\u003eVR Setup\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/ed736e547771ffcd4f91a349.jpg"},{"id":98424402,"identity":"4485923d-b509-413c-ad65-c44cca80eab6","added_by":"auto","created_at":"2025-12-17 16:33:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32678,"visible":true,"origin":"","legend":"\u003cp\u003eVC Setup\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/71a827ee492a0f4d07d2affe.jpg"},{"id":97984902,"identity":"ecb98740-7979-4920-857a-a5a21c66e71b","added_by":"auto","created_at":"2025-12-11 13:38:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33609,"visible":true,"origin":"","legend":"\u003cp\u003eF2F Setup\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/7eb6c1d16ea6504c50e27dd4.jpg"},{"id":97984910,"identity":"fd26e1a1-a70c-4e80-9136-5a46762e9c20","added_by":"auto","created_at":"2025-12-11 13:38:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33991,"visible":true,"origin":"","legend":"\u003cp\u003eHorizon Workrooms\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/f714053674ceb2f89495b1ee.jpg"},{"id":98424723,"identity":"076d84f5-3493-4d19-8635-15be79bb1a97","added_by":"auto","created_at":"2025-12-17 16:33:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":26245,"visible":true,"origin":"","legend":"\u003cp\u003ePre-Post VCF Scores\u003c/p\u003e\n\u003cp\u003eNote. Descriptive comparison between the five VCF facets pre scores with the post scores. Scales are zoomed in to scores from 1-3 instead of 1-5.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/90658426a64baa3db5f23dc6.jpg"},{"id":98423759,"identity":"32963009-428c-452d-a168-3b361813c386","added_by":"auto","created_at":"2025-12-17 16:32:34","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":21288,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive violin plots: CL \u0026amp; NCQ\u003c/p\u003e\n\u003cp\u003eNote. Descriptive comparison between study variables per condition displayed as violin plots. Inside the violin plots, boxplots are displayed. The horizontal line displays the median, the point inside the boxplot displays the mean. Group differences were calculated using Welch’s T-Test. Significant differences are displayed with * p \u0026gt; .01.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/c6152d9cdc876dccdcf37cbd.jpg"},{"id":98443952,"identity":"4e889d54-b83b-4677-b62a-223fa530be72","added_by":"auto","created_at":"2025-12-17 17:14:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/714544eb-693a-4717-a9e6-3dc6b6c6647d.pdf"},{"id":97984894,"identity":"d8429834-d442-4bcb-ade7-9a9a888f0a47","added_by":"auto","created_at":"2025-12-11 13:38:55","extension":"r","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14855,"visible":true,"origin":"","legend":"","description":"","filename":"01descriptives.r","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/32a0477c237457d9596e42d2.r"},{"id":98424560,"identity":"9a1fd70d-cc0f-4062-80d3-ff353dbf640e","added_by":"auto","created_at":"2025-12-17 16:33:29","extension":"r","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5915,"visible":true,"origin":"","legend":"","description":"","filename":"04ttests.r","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/d70ea4c3ea29c0d09e65c38b.r"},{"id":98424270,"identity":"4ee60372-1451-4110-a2cb-d136a5b4bf87","added_by":"auto","created_at":"2025-12-17 16:33:07","extension":"r","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7665,"visible":true,"origin":"","legend":"","description":"","filename":"03mgsemmediation.r","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/4d22be767041ae0950416383.r"},{"id":98424086,"identity":"e90a9916-7cdb-4940-b9f2-3c5780123aa4","added_by":"auto","created_at":"2025-12-17 16:32:55","extension":"r","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6253,"visible":true,"origin":"","legend":"","description":"","filename":"05cfancq.r","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/91cf36a2d5023ffafcbcc5a5.r"},{"id":98424733,"identity":"ec667d2a-5997-4af1-b603-19b08aadaf60","added_by":"auto","created_at":"2025-12-17 16:33:44","extension":"r","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15428,"visible":true,"origin":"","legend":"","description":"","filename":"02mgsem.r","url":"https://assets-eu.researchsquare.com/files/rs-8094774/v1/4e7d3e6ddee47d5a93717676.r"}],"financialInterests":"No competing interests reported.","formattedTitle":"Virtual, but Not (Yet) Natural: The Role of Nonverbal Communication Quality, Cognitive Load, and Fatigue in Virtual Reality Compared to Videoconferencing and Face-to-Face Team Meetings ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid normalization of remote and hybrid work means that an ever-larger share of daily interaction now unfolds through a screen rather than across a table (Barrero et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Two-dimensional videoconferencing (VC) platforms such as Zoom or Teams kept organizations running during this rapid shift, yet prolonged use of VC is linked to lower social presence, increased tiredness, and \u003cem\u003eZoom fatigue\u003c/em\u003e (Bailenson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fauville et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Zoom Fatigue, more recently known as \u003cem\u003eVideoconference Fatigue\u003c/em\u003e (VCF), can be seen as a multi-facet construct - general, motivational, emotional, social and visual. These facets capture different layers of exhaustion: general fatigue reflects overall tiredness, motivational fatigue a lack of drive, emotional fatigue difficulties in regulating affect, social fatigue exhaustion from interpersonal demands, and visual fatigue strain from constant screen exposure (Fauville et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meta-analytic evidence already ties this syndrome to lower commitment and a higher risk of burnout costs that make the quality of mediated conversation a genuine organizational concern. While previous research has focused on two-dimensional VC, immersive virtual reality (VR) has recently emerged as a candidate to mitigate some of these downsides. This raises the question of whether VR can indeed alleviate the very mechanisms thought to underlie videoconference fatigue. Clarifying these downsides requires comparing VR not only against VC, but also against Face-to-Face (F2F) interaction as the natural benchmark.\u003c/p\u003e\u003cp\u003eBut why do these downsides of VC occur? Media Richness Theory (MRT; Daft \u0026amp; Lengel, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) predicts that richer channels offering immediate feedback, multiple modalities, and personal focus better serve equivocal tasks. Media Naturalness Theory (MNT; Kock, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) extends this logic with an evolutionary lens: the farther a medium departs from F2F conditions, the more cognitive effort it imposes, leading to worse communicational quality and therefore raising ambiguity and fatigue. Communicating with unclear nonverbal signals (e.g. no real eye contact) in VC could therefore decrease communication quality.\u003c/p\u003e\u003cp\u003eFace-to-face (F2F) dialogue, however, rests on a dense fabric of non-verbal cues-gaze, micro-gestures, posture shifts, proxemics-that silently orchestrate speaking behaviors and perceptions long before words appear (Knapp et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sacks et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). VC filters that fabric: limited fields of view, camera latency and the \u0026ldquo;hyper-gaze\u0026rdquo; of multiple faces at close range compel users to infer missing signals, thereby possibly heightening cognitive load (Bailenson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Empirical studies confirm that VC conversations contain longer gaps and more overlap between speakers than in F2F conversations, undermining conversational flow and perceived richness (Boland et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, VCF can be explained by Cognitive Load Theory (CLT) which suggests that degraded signals in VC raise extraneous load and drain working memory.\u003c/p\u003e\u003cp\u003eAgainst this backdrop, immersive virtual reality has been proposed as a medium that can offset some of these cognitive burdens. VR restores spatial cues and may reduce social and emotional fatigue, though its optics can increase visual strain (Sweller, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Together, MRT, MNT and CLT position immersive virtual reality (VR) between lean VC and rich F2F-potentially natural enough to ease cognitive strain, yet still prone to technical frictions.\u003c/p\u003e\u003cp\u003eRecent studies show that immersive embodied VR meetings heighten social presence, smooth conversational flow, and support more turn-taking compared with VC (Held et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Head pose, hand height, and mutual gaze in multi-user VR reliably predict who will speak next, suggesting that the medium restores actionable cues lost on a 2D video screen. Yet VR is no panacea: head-mounted displays add weight, heat, and vergence\u0026ndash;accommodation conflicts, while lower resolution sustains the \u0026ldquo;screen-door\u0026rdquo; effect, all of which and possibly more can induce visual strain (Scarfe \u0026amp; Glennerster, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Trevisani \u0026amp; Sisti, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Regarding VCF, these upsides and downsides require a differentiated and detailed view of the risks and benefits of VC and VR for the user.\u003c/p\u003e\u003cp\u003eAlthough first comparative studies exist, findings remain mixed: VR can enhance social presence and collaboration (Sanaei et al.; van Gent et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), yet results across fatigue and performance are inconsistent (Macchi \u0026amp; Pisapia, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, it remains both a theoretical question whether VR reliably outperforms VC, and a practical one whether its additional costs and demands are justified. Specifically, we examine if cognitive load acts a mediator linking communication quality to different facets of VCF.\u003c/p\u003e\u003cp\u003eThe present study aims to close these gaps through a controlled comparison of F2F, VC and immersive VR in an experimental setting. Groups of three participants negotiated a resource-allocation task while (a) non-verbal communication quality (NCQ) is assessed, (b) cognitive load is captured, and (c) five fatigue facets are assessed pre- and post-meeting. We aim to shed light on what communicational differences different media types appose compared to F2F, if VR improves communicational quality, reducing cognitive load and therefore fatigue. By modelling whether cognitive load explains the effect of communication quality on each fatigue dimension, the study fuses MRT, MNT and Cognitive Load Theory into a single process account.\u003c/p\u003e\u003cp\u003eThis study advances the literature on VCF in three ways. First, we clarify whether immersive VR alleviates the very communication deficits theorized to underlie fatigue in VC, by contrasting it not only with VC but also with F2F as the natural benchmark. Second, we aim to empirically establish low non-verbal communication quality as an antecedent of VCF, moving beyond prior work that has largely treated it as an assumed factor. Third, contribute to clarify the role of cognitive load as an explaining mediating pathway linking NCQ to different VCF facets and thereby integrating MRT, MNT, and CLT into a unified process account.\u003c/p\u003e\u003cp\u003eFindings will guide organizations on when to default to low-cost VC, when to convene in person, and when VR\u0026rsquo;s hardware overhead is justified by measurable gains in conversational quality or well-being. Platform designers can leverage identified cues-gaze convergence, head-pose synchrony-to build real-time moderation tools, while ergonomics engineers can target the visual-fatigue liabilities our data exposes. Communication technologies are not neutral pipes; they shape the rhythm and emotional tone of our conversations. By testing how richly-and how naturally-a medium must render human signals before the benefits outweigh the burdens, this research clarifies both the promise and the pitfalls of meeting in the metaverse.\u003c/p\u003e\n\u003ch3\u003eTheory\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eRemote Work and Mediated Communication\u003c/h2\u003e\u003cp\u003eTo understand how different meeting technologies shape fatigue, it is necessary to embed the phenomenon in established frameworks of mediated communication and cognitive processing. While the introduction outlined the practical relevance, the following section develops the theoretical foundation: first summarizing the body of literature regarding antecedents of fatigue, then by situating VC and VR in models of media richness and naturalness, followed by discussing the role of non-verbal communication, and finally by linking these mechanisms to cognitive load and distinct facets of fatigue.\u003c/p\u003e\u003cp\u003eUnlike face‑to‑face meetings, VC mediates audio‑visual information through webcams and screens, introducing several stressors. Riedl (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) synthesizes prior research and identifies six root causes to fatigue: transmission delays that disrupt synchronicity; a lack of body language because webcams typically show only head and shoulders; a lack of eye contact; constant self‑view windows that heighten self‑monitoring; being stared at by multiple faces (hyper‑gaze) because gallery view makes everyone appear to look at the speaker simultaneously; multitasking and information overload when users check emails or chats during meetings. Each factor increases extraneous cognitive load and stress. A large cross‑sectional survey of 291 Chinese workers found that videoconferencing fatigue was significantly predicted by mirror anxiety, feeling physically trapped, hyper‑gaze, and the cognitive load associated with producing non‑verbal cues (Ma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Participants who felt trapped by the need to remain in camera view or who were more anxious about their appearance reported higher fatigue; technological complexity and a negative attitude toward videoconferencing also increased fatigue. These downsides due to technological hindrances raise concern because social aspects like social presence and effective communication are key predictors of commitment, trust, performance, and well-being in work teams (Newman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImmersive social virtual reality (VR) has been proposed as a technological shift that might overcome some causes of videoconference fatigue. By embedding users in a three-dimensional environment with spatial audio and embodied avatars, VR allows gaze to follow natural norms, eliminates constant self-view and partially restores mobility and non-verbal expressiveness. Studies indicate that VR enhances social presence and conversational flow relative to VC (Dey et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Held et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Machine-learning analyses show that head pose and gaze cues in VR enable accurate prediction of turn-taking, suggesting higher non-verbal communication quality (Held et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Field experiments have also shown that hiding self-view or using avatars reduces fatigue (Ratan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), implying that VR avatars could mitigate mirror anxiety.\u003c/p\u003e\u003cp\u003eHowever, VR introduces new ergonomic and cognitive challenges: head-mounted displays add weight and heat, reduce peripheral vision and can induce eye strain and cybersickness (Scarfe \u0026amp; Glennerster, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A laboratory study comparing VR, F2F and VC meetings found that first-time VR users reported higher general and visual fatigue than participants in VC or F2F conditions, suggesting that unfamiliarity and headset discomfort can offset VR\u0026rsquo;s benefits (Macchi \u0026amp; Pisapia, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, whether VR can genuinely overcome the fatigue-inducing elements of VC, remains an open empirical question. Furthermore, the theorized underlying communicational mechanism which might drive the differences between VC and VR have not examined in detail yet. The present study addresses these questions by comparing VR, VC and F2F meetings on non-verbal communication quality, cognitive load and five dimensions of fatigue.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRich and natural communication: A theoretical embedding\u003c/h3\u003e\n\u003cp\u003eMedia Richness Theory (MRT) posits that communication media vary in their ability to convey complex information. A medium\u0026rsquo;s richness is determined by four attributes: speed of feedback, the number and type of sensory channels, the degree of personalization and the ability to transmit natural language (Daft \u0026amp; Lengel, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). F2F interaction is seen as the golden baseline, providing immediate feedback, multimodal cues and high social presence. VC reduces richness: despite delivering synchronous audio and video, it constrains the field of view, flattens depth cues and typically forces all participants into a single gaze direction, limiting personalization. But what about VR? By tracking head and hand movements and spatializing audio, VR partially restores the richness of F2F; however, avatar representations often lack in realism and fine facial expressions as well as latency or tracking errors can disrupt the conversational flow. Hennig-Thurau et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) theorize, that more real-time multisensory social interactions (RMSIs) lead to more social presence and physical mobility, but also to more exhaustion. This contradiction raises the question: Is the richness of a medium itself a sufficient predictor for good communication, or do we need to acknowledge more aspects of sensory inputs, such as the quality of input?\u003c/p\u003e\u003cp\u003eMedia Naturalness Theory (MNT) extends MRT by grounding communication in evolutionary psychology. MNT argues that humans evolved to communicate F2F; any deviation from co-location, synchronicity, speech, facial expression or body language increases cognitive effort and decreases physiological arousal (Kock, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Thus, F2F communication is most natural; VC retains speech and synchronicity but only a narrow window of facial and bodily cues; VR reintroduces co-location in a virtual sense, spatial audio and more, but avatars still lack micro-expressions and natural eye contact. According to MNT, less natural media elevate extraneous cognitive load because users must infer missing signals and continually monitor their self-presentation, leading to faster mental exhaustion and reduced communication clarity. We theorize that richness and naturalness both do their part to increase the quality of RMSIs. However, richness might overweigh in its impact due to possibility to track full body nonverbal behavior. Just full body nonverbal behavior may offer solutions to stated communicational problems with VC which impact all social interactions in a conversation.\u003c/p\u003e\n\u003ch3\u003eNon-Verbal Communication Quality\u003c/h3\u003e\n\u003cp\u003e Social interactions depend on a dense web of non-verbal signals that silently coordinate conversational flow. Eye gaze, head orientation, gesture, posture shifts and prosodic cues indicate who wants to speak, who is listening and how they react (Kendrick et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ethnomethodological analyses showed that conversations are organized through implicit turn-taking systems: speakers recognize transition relevance places and yield the floor smoothly using gaze and intonation (Sacks et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). In F2F settings, these cues are abundant and synchronous. VC disrupts this: limited fields of view and camera angles obscure gaze direction and hand gestures, while latency and jitter distort timing, resulting in more overlapping speech, longer pauses and awkward silences. Moreover, VC users must focus on a grid of faces, often including their own self-view, which increases cognitive load and fosters \u0026ldquo;mirror anxiety\u0026rdquo; (Shockley et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Immersive VR could restore some of these signals: head- and hand-tracked avatars convey direction of attention; spatialized audio conveys distance and orientation; and body movement can substitute for some gesture information. Recent machine-learning analyses of multi-user VR conversations show that head pose, hand height and gaze alignment predict upcoming speech turns, suggesting that VR provides actionable cues for turn-taking (Held et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, participants reported smoother interaction and stronger feelings of togetherness in VR compared with VC. However, VR still falls short of F2F: avatars lack micro-expressions such as eyebrow raises and subtle smiles; network delays and tracking errors interrupt timing; and the unnatural \u0026ldquo;cartoon\u0026rdquo; appearance of avatars can dampen social presence. Given these considerations, we conclude:\u003c/p\u003e\u003cp\u003eH1a: Nonverbal communication quality is higher in VR than in VC.\u003c/p\u003e\u003cp\u003eH1b: Nonverbal communication quality is lower in VR than in F2F.\u003c/p\u003e\n\u003ch3\u003eCognitive Load Theory and Fatigue\u003c/h3\u003e\n\u003cp\u003eCognitive Load Theory (CLT) distinguishes intrinsic load (task-related complexity), extraneous load (imposed by instructional or technical design) and germane load (resources used for processing and schema construction). Working memory has limited capacity; excessive extraneous load impedes learning and performance (Sweller, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In mediated communication, extraneous load derives from technological constraints. VC elevates extraneous load because users must interpolate missing depth cues, monitor multiple faces, manage the unnatural alignment of gaze and maintain self-presentation. VR reduces some extraneous load by restoring stereoscopic depth and spatial audio, but it introduces other burdens: head-mounted displays are heavy, create heat, limit peripheral vision and can cause vergence\u0026ndash;accommodation conflicts that can lead to visual strain, headaches and nausea for some people (Souchet et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). F2F interaction imposes minimal extraneous load; participants can focus on the task rather than on managing the medium.\u003c/p\u003e\u003cp\u003eFatigue is a multifaceted construct comprising general fatigue (overall tiredness), motivational fatigue (loss of enthusiasm), emotional fatigue (feeling emotionally drained), social fatigue (desire to avoid social interaction) and visual fatigue (eye strain) (Fauville et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In mediated communication, fatigue emerges when sustained cognitive and perceptual demands exceed available mental resources. Videoconferencing amplifies such demands through continuous self-monitoring, gaze misalignment, and the need to infer missing nonverbal cues. Consequently, users often report greater tiredness after videoconferences than after comparable F2F interactions (Riedl et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By partially restoring spatial cues and reducing mirror anxiety, VR could attenuate general fatigue, but headset discomfort may still leave users more fatigued than in F2F. Previous research using older VR technology illustrates this trade‑off. In a within‑person study with inexperienced users wearing Meta Quest 2 headsets, Frontzkowski et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that immersive VR meetings induced more general and visual fatigue than Microsoft Teams meetings. The authors attribute this to the heavier hardware, lower resolution and lack of prior VR experience among participants, who were already accustomed to 2D video meetings. These findings caution that VR\u0026rsquo;s benefits depend on both ergonomic design and user familiarity. The present study employs newer hardware (Meta Quest 3) with improved optics and comfort and provides a wider range of experiences with VR technology among participants. We therefore expect VR to reduce the extraneous load associated with decoding non‑verbal cues relative to VC while keeping headset‑related strain manageable. Thus, we conclude:\u003c/p\u003e\u003cp\u003eH2a: General fatigue is higher in VC than in VR.\u003c/p\u003e\u003cp\u003eH2b: General fatigue is lower in F2F than in VR.\u003c/p\u003e\u003cp\u003eMotivational fatigue reflects a lack of enthusiasm and willingness to invest effort. VR\u0026rsquo;s enhanced social presence and more natural interactions are expected to support engagement relative to VC. In VC, mirror anxiety and hyper-gaze can undermine motivation, whereas F2F interactions typically sustain it through richer non-verbal cues and greater immediacy. Consequently, we conclude:\u003c/p\u003e\u003cp\u003eH2c: Motivational fatigue is higher in VC than in VR.\u003c/p\u003e\u003cp\u003eH2d: Motivational fatigue is lower in F2F than in VR.\u003c/p\u003e\u003cp\u003eEmotional fatigue involves feelings of being emotionally drained. VC can heighten emotional drain because users must consciously manage self-presentation and decode blurred social cues, leading to frustration and stress. VR\u0026rsquo;s avatars reduce self-focused attention and may thereby lessen emotional fatigue relative to VC. F2F interactions, with their full complement of expressive signals, should be least emotionally fatiguing. Hence, we conclude:\u003c/p\u003e\u003cp\u003eH2e: Emotional fatigue is higher in VC than in VR.\u003c/p\u003e\u003cp\u003eH2f: Emotional fatigue is lower in F2F than in VR.\u003c/p\u003e\u003cp\u003eSocial fatigue denotes a desire to avoid interaction. Constant self-monitoring and hyper-gaze in VC can make social interactions feel oppressive; VR alleviates some of this by allowing more natural eye movements and reducing the sense of being stared at. Nevertheless, avatars still lack subtle facial expressions and may limit connectedness. F2F offers the richest social cues and thus should induce the least social fatigue. We therefore conclude:\u003c/p\u003e\u003cp\u003eH2g: Social fatigue is higher in VC than in VR.\u003c/p\u003e\u003cp\u003eH2h: Social fatigue is lower in F2F than in VR.\u003c/p\u003e\u003cp\u003eFinally, visual fatigue stems from ocular strain. Here the trade-offs reverse. Head-mounted VR displays introduce visual strain through vergence\u0026ndash;accommodation conflicts and limited peripheral vision, whereas VC requires only a monitor. F2F interactions involve no screen at all. Thus, we expect:\u003c/p\u003e\u003cp\u003eH2i: Visual fatigue is lower in F2F than in VR.\u003c/p\u003e\u003cp\u003eH2j: Visual fatigue is lower in VC than in VR.\u003c/p\u003e\n\u003ch3\u003eLinking Antecedents to Fatigue: Cognitive Load as a mediator\u003c/h3\u003e\n\u003cp\u003e Drawing on MRT, MNT and CLT, we argue that cognitive load mediates the relationship between non-verbal communication quality and fatigue. Poor communication quality-characterized by ambiguous nonverbal cues-forces users to expend additional mental effort to interpret signals and plan their contributions. This extra effort represents extraneous cognitive load that drains limited working‑memory resources. Evidence from videoconferencing research underscores this mechanism: Riedl (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identifies cognitive effort as a core root of videoconference fatigue, and Ma et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) report that the cognitive load involved in producing non‑verbal cues predicts self‑reported fatigue. Recent experimental studies also show that interventions reducing cognitive demands-such as turning off self‑view-simultaneously lower perceived fatigue (Basch et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile immersive VR can restore some missing cues and thereby reduce cognitive load relative to two‑dimensional videoconferencing, head‑mounted displays introduce their own strain, potentially elevating visual fatigue (Scarfe \u0026amp; Glennerster, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We therefore propose a mediation model:\u003c/p\u003e\u003cp\u003eH3: The relationship between nonverbal communication quality and fatigue is mediated by cognitive load in such a way that nonverbal communication quality is negatively related to cognitive load which, in turn, is positively related to a) general b) motivational, c) emotional, d) social and e) visual fatigue.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eRecruitment and Sample\u003c/h2\u003e\u003cp\u003eDuring recruitment, participants were invited via mailing lists of different universities, direct acquisition from various companies, the online forum \u003cem\u003eReddit\u003c/em\u003e, and direct invitations by the investigators. Participants could choose to receive \u0026euro;30 or 2.5 course credit participation points (which was only applicable for students required to collect them for their study program). To register for the study, we set up an online booking website on which participants could choose a date and time on which they wanted to participate and form groups of three. We also displayed how many slots are left for every date and informed about the compensations. Participants were invited to participate in a study on group decision-making. Participants were always able to cancel a booking and to withdraw from the experiment.\u003c/p\u003e\u003cp\u003eThe total sample consisted of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;221 participants. We excluded 12 participants because of mayor technical issues during the experiment (like internet connection loss, power cuts, or software bugs). The final sample consisted of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;209 participants. Participants ages ranged from 18 to 65 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28.26, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.38), 58.85% were male, 41.15% female, and 0% diverse. 66.99% of participants were students, 15.31% worked in the private sector, 15.79% in the public sector and 1.91% in apprenticeship. For the participants in the VR condition, 38.36% stated they had no experience with Virtual Reality Headsets at all. For the participants in the VC condition, 4.23% stated they had no experience with videoconferences at all.\u003c/p\u003e\u003cp\u003e All procedures performed in this study were in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Approval by the institutional ethics committee is documented in the Appendix. The study was preregistered on the Open Science Framework (OSF; Felfe \u0026amp; Frontzkowski) including hypotheses, design, and analysis plan. This research has received funding by the \u003cem\u003ecenter of digitalization- and technology research of the military\u003c/em\u003e in Germany.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eDesign\u003c/h3\u003e\n\u003cp\u003eThe study employed a 3x2 (conditions x time, pre-post) between-subjects experimental design with three communication conditions: face-to-face (F2F), videoconferencing (VC), and virtual reality (VR) as predictors. Participants were organized in triads and were randomly assigned to one of the three conditions. The primary objective was to examine how communication modality affected changes in VCF (pre-post) and NCQ, CL (post) as outcomes during a structured collaborative task.\u003c/p\u003e\u003cp\u003eThe setup for the three conditions was as follows. In all conditions, participants were seated at desks, equipped with a laptop, an external monitor, webcam, mouse and a pen. In the VR and VC condition, participants were seated in separate equally furnished rooms (Fig.\u0026nbsp;2\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while in the F2F condition, participants were seated together in the same room at one table (Fig.\u0026nbsp;4). Additionally, in the F2F condition we placed a wide-angle camera, so the experimenter could always see all participants from their observation room. Also, a conference speakerphone was placed in the middle of the group table in F2F, so the participants could receive instructions by the experimenter and the experimenter could also record the conversation in the group task.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure 2 - VR Setup\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn the VC condition we used Zoom as the communication platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the VR condition, we used the head-mounted display \u003cem\u003eMeta Quest 3\u003c/em\u003e with Horizon Workrooms from Meta (Fig.\u0026nbsp;5), since it supports desktop passthrough, screen mirroring, meeting rooms with multiple people and the participation via a web browser, which was necessary for the experimenter to share a timer and instruct the participants, without having an avatar of our own in the experiment.\u003c/p\u003e\u003cp\u003eThe pre and post questionnaire was administered via the online survey platform \u003cem\u003eUniPark\u003c/em\u003e. In the VR condition, participants completed the survey within \u003cem\u003eMeta Horizon Workrooms\u003c/em\u003e. Additionally, the Meta Quest 3s built-in desk \u003cem\u003epassthrough\u003c/em\u003e feature was used, allowing participants to interact with the physical ranking list placed on their desk.\u003c/p\u003e\u003cp\u003eMicrophones were muted by default outside of the group task to control communication. In the F2F condition, participants sat together in the same room (Fig.\u0026nbsp; 4) but were likewise instructed to avoid verbal interaction until permitted. In the F2F room, a screen with a connection to the experimenters via Zoom was set up. Participants were not able to see the experimenters, but sound was enabled to allow the experimenters to instruct the participants and share the screen on which a timer was placed for the different phases of the experiment.\u003c/p\u003e\u003cp\u003eThe study used the \u003cem\u003eNASA Moon Survival Task\u003c/em\u003e (Hall \u0026amp; Watson, 1970) to simulate structured group interaction. This task was chosen because it simulates a conversation in which participants are required to make decisions under uncertain and sometimes conflicting information, encouraging information sharing and negotiation (Hamada et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). An initial individual ranking phase was included to ensure that all participants had formed their own judgments before the group discussion, thereby increasing the likelihood of divergent opinions and stimulating richer deliberation during the collaborative phase.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eUpon arrival, participants were instructed to silence their mobile phones and use the restroom in advance to avoid disruptions. After providing informed consent, they were randomly assigned to one of the three experimental conditions.\u003c/p\u003e\u003cp\u003eTo ensure technical readiness in VR and promote embodiment, participants in the VR condition completed a brief onboarding procedure, which included two instructional videos, each approximately two minutes long. Participants first watched an instructional video on their laptops. The video explained how to properly wear and adjust the Meta Quest 3 headset, including fit and interpupillary distance. Once the headset was in place, participants entered a shared Meta Horizon Workrooms virtual meeting room with the other group members (represented as avatars) and the experimenter (who was audible via the web browser but not visible).\u003c/p\u003e\u003cp\u003eAfter all participants had entered the VR environment, a second video was presented via screensharing by the experimenter. This video guided participants through the avatar customization menu and explained how to create and save an avatar. Participants were instructed to design an avatar resembling themselves as closely as possible and were given 8 minutes for this task. Horizon Workrooms\u0026rsquo; desk passthrough allowed continuous visibility of their physical desk and mirrored laptop screen throughout the experiment. These steps are unique for the VR condition and necessary to ensure, that the participants can use the Meta Quest 3 Headset.\u003c/p\u003e\u003cp\u003eNext, participants in all three conditions completed the pre-questionnaire. Following this, they listened to an audio instruction explaining the individual task: the \u003cem\u003eNASA Moon Survival Task\u003c/em\u003e. Each participant had a printed task sheet that contained a list of 15 survival-related items to be ranked by importance. Two columns were provided: one for the individual ranking, and one for the joint group ranking to be completed later.\u003c/p\u003e\u003cp\u003eParticipants were given 8 minutes to complete their individual rankings without communication. Once all triad members had done so, the experimenter gave a verbal signal to begin the group phase. Participants then had 20 minutes to collaboratively agree on a group ranking using the second column of the task sheet. This interaction phase was the only one where verbal communication was allowed and was recorded for later analysis.\u003c/p\u003e\u003cp\u003e Finally, participants completed a post-questionnaire, in which participants were asked for VCF, cognitive load and NCQ. The experimenter continuously monitored participants through one-way video feeds but was only audible when giving instructions. Participants were reminded of their right to withdraw at any time without penalty.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003e\u003cem\u003eVideoconference fatigue\u003c/em\u003e was assessed before and after each meeting, using a German adaptation of the Zoom \u0026amp; Exhaustion Scale (ZEF; Fauville et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), composed of 15 items. General Fatigue describes an overall tiredness and lack of energy (four items); example item: \u0026ldquo;After participating in the video conference, I feel tired\u0026rdquo;). Social Fatigue describes weariness and reduced capacity for social interactions (three items); example item: \u0026ldquo;After participating in the video conference, I avoid social interactions\u0026rdquo;). Emotional Fatigue describes emotional drain and burnout (four items); example item: \u0026ldquo;I feel emotionally drained after this meeting\u0026rdquo;). Motivational Fatigue describes a decline in motivation and enthusiasm for activities or tasks (three items); example item: \u0026ldquo;After participating in the video conference, I don\u0026rsquo;t feel like doing anything\u0026rdquo;), and Visual Fatigue describes strain and discomfort related to the eyes (three items); example item: \u0026ldquo;After participating in the video conference, my eyes feel irritated\u0026rdquo;. The items were answered on a 5-point Likert scale (1 = \u0026ldquo;Not at all\u0026rdquo; to 5 = \u0026ldquo;Very much\u0026rdquo;).\u003c/p\u003e\u003cp\u003e\u003cem\u003eCognitive Load\u003c/em\u003e was assessed after each meeting using the DLR-WAT scale (Grippenkoven, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The scale consists of six bipolar items ranging from 1 = \u0026ldquo;Extreme underload\u0026rdquo; to 10 = \u0026ldquo;Extreme overload\u0026rdquo; with the example item \u0026ldquo;Information acquisition: To what extent were you challenged by searching for and acquiring information during the overall task? (Was this demand, in terms of information acquisition, in the range of underload, optimal load, or overload?)\u0026rdquo; score.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNon-verbal Communication Quality\u003c/em\u003e was assessed with a self-developed scale. The scale was created using an expert rating and the pre-test data and uses a Likert scale from 1 = \u0026ldquo;Does not apply at all\u0026rdquo; to 5 = \u0026ldquo;Fully applies\u0026rdquo;. An example item is: \u0026ldquo;I could easily tell whether the other participants were paying attention.\u0026rdquo; or \u0026ldquo;It was difficult for me to signal my attention to other participants.\u0026ldquo; After conducting a confirmatory factor analysis, we decided only to use 10 out of 12 items for the final scale. All items, and the confirmatory factor analysis results are shown in the Appendix. Cronbach\u0026rsquo;s alphas for all scales are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted in R using the \u003cem\u003elavaan\u003c/em\u003e package (Rosseel, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Following recent methodological recommendations for experimental SEM (Kwok et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we specified multi-group models with the three experimental conditions (VR, VC, F2F) as grouping variable. Each model followed an ANCOVA approach in which post-fatigue scores were regressed on pre-fatigue scores while adjusting for covariates (age, gender, and media experience). This procedure allowed testing group differences in adjusted post-intercepts as well as equality of regression slopes across groups. Model fit was evaluated using the Comparative Fit Index (CFI), Tucker\u0026ndash;Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Given our directed hypotheses, one-tailed significance tests were applied (Cho \u0026amp; Abe, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with α\u0026thinsp;=\u0026thinsp;.05 as criterion.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptives\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean scores and standard deviations are displayed in table 1. Descriptive comparisons of study outcomes are displayed as violin plots in figure 4. Correlations and Cronbach\u0026rsquo;s alphas are displayed in Table 2.\u003c/p\u003e\n\u003cp\u003eThe VR condition showed a negative correlation with NCQ, indicating poorer NCQ with VR. We did not find a correlation regarding the VC condition and NCQ. NCQ is negatively related to all post facets of fatigue (General post: r = -.27, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Motivational post: r = -.27, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Emotional post: r = -.27, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Social post: r = -.29, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Visual post: r = -.28, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001). NCQ was also correlated negatively with all VCF pre scores (General pre: r = -.22, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Motivational pre: r = -.26, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Emotional pre: r = -.25, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Social pre: r = -.21, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001; Visual pre: r = -.24, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001). Moreover, the respective pre and post VCF facets were highly correlated (r = .65 \u0026ndash; .77, p \u0026gt; .001), indicating that NCQ and post VCF have also been affected by participants pre VCF. CL is negatively related to VR, indicating that CL is lower in VR (r = -.18, \u003cem\u003ep\u003c/em\u003e = .007). Moreover, only one significant correlation was found between CL and motivational fatigue (r = .14, \u003cem\u003ep\u003c/em\u003e = .036). Regarding the conditions, VC negatively correlates with visual fatigue (r = -.21, \u003cem\u003ep\u003c/em\u003e \u0026gt; .001) but not with the other facets, while VR positively correlated with visual fatigue (r = .40, \u003cem\u003ep\u003c/em\u003e = \u0026gt; .001), but not with the other facets. Based on these observations, we conducted Welsch\u0026rsquo;s T-Test for all pre fatigue scores between conditions and found no differences except for pre visual fatigue which is higher in the VR condition.\u003c/p\u003e\n\u003cp\u003eDescriptive pre-post differences are displayed in Figure 6 and reveal, that only visual fatigue increased from pre to post in the VR condition. All other facets declined, but the decline was smaller in VR compared to VC and F2F.\u003c/p\u003e\n\u003cp\u003eFigure 7 displays descriptive violin plots of the outcomes Cl and NCQ and highlights significant group differences between conditions, using Welsch\u0026rsquo;s T-Test. It shows lower NCQ in VR compared to VC and F2F, as well as highest CL scores for F2F, followed by VC and least in VR.\u003c/p\u003e\n\u003cp\u003eTable 2\u003cbr\u003e\u0026nbsp;Correlations and Reliabilities\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"994\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eCon VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eCon VR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVR_exp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVC_freq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_pre_G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_pre_M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_pre_E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_pre_S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_pre_V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_post_G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_post_M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_post_E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_po_S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eVCF_p_V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eNCQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCon VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.12 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCon VR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.13 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVR_exp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.13 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.15 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVC_freq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.13 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.13 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.29* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_pre_G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.00 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.11 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.11 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.00 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.24**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_pre_M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.14* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.11 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.13 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.22**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_pre_E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.10 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.21**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_pre_S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.08 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.12 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.26**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.52***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.55***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_pre_V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.23***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.36***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.18**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_post_G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.10 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.00 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.03 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.12 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.55***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.44***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_post_M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.12 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.18* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.57***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.57***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.66***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_post_E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.00 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.15 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.70***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.70***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.66***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_post_S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.47***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.52***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.18**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eVCF_post_V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.21**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.40***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.16* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.13 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.18**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.08 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.18**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.16* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.10 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.08 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.03 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.10 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.14* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.09 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNCQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.16* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.16* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.07 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.11 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.22***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.21**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.24***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.02 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e(.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. Cronbach\u0026rsquo;s Alpha is displayed in the diagonal. Significance level is displayed as * p \u0026gt;= .05, ** p \u0026gt;= .01, *** p \u0026gt;= .001 Con = Condition of experiment. VR_exp = Experience of participants with VR. VR_freq = Frequency of videoconference usage of participants. Pre-measures are marked with the prefix \u0026ldquo;pre\u0026rdquo;. Post measures are marked with prefix \u0026ldquo;post\u0026rdquo;. G = General Fatigue. M = Motivational Fatigue. E = Emotional Fatigue. S = Social Fatigue. V = Visual Fatigue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypotheses 1a and 1b addressed group differences in nonverbal communication quality. To test these hypotheses, we estimated multi-group SEM contrasts controlling for pre-scores and covariates. Results are summarized in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3\u0026nbsp;\u003cbr\u003e\u0026nbsp;Nonverbal Communication Quality\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eContrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2;(df)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e22.88 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e11.68 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVR \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e21.84 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.70 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. \u0026chi;\u0026sup2; = Wald test from multi-group SEM controlling for pre-scores and covariates (age, gender, media experience).\u0026nbsp;p-values Holm-corrected for multiple comparisons.\u003c/p\u003e\n\u003cp\u003eThe omnibus Wald test indicated significant group differences, \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2;(2) = 22.88, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. The mediation results are displayed in Table 5. Pairwise comparisons showed that VR yielded significantly lower nonverbal communication quality than VC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, Holm-corrected), rejecting H1a. VR also showed significantly lower values compared to F2F (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001), supporting H1b. No significant differences emerged between VC and F2F (\u003cem\u003ep\u003c/em\u003e = .190).\u003c/p\u003e\n\u003cp\u003eHypotheses 2a\u0026ndash;j concerned group differences in the five facets of videoconference fatigue. Adjusted post-intercepts, controlling for pre-scores and covariates, were contrasted between groups. Results are presented in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4\u003cbr\u003e\u0026nbsp;Videoconference Fatigue Facets\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eContrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;(df)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e30.70 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.56 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.75 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.05 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMotivational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.01 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.18 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.16 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.70 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.77 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.06 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e3.82 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.11 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e2.97 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVisual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20.73 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVR \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e22.04 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eVC \u0026ndash; F2F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.09 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote.\u0026nbsp;\u0026chi;\u0026sup2; = Wald test from multi-group SEM controlling for pre-fatigue, age, gender, and media experience. Tests were conducted separately for each facet;\u0026nbsp;p-values Holm-corrected for multiple comparisons.\u003c/p\u003e\n\u003cp\u003eThe omnibus Wald test across all facets was significant, \u003cem\u003e\u0026chi;\u003c/em\u003e\u0026sup2;(10) = 30.70, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. The results are displayed in table 4. For general fatigue, significant differences emerged between VC-VR (\u003cem\u003ep\u003c/em\u003e = .059) and between F2F-VR (\u003cem\u003ep\u003c/em\u003e = .098). However, the direction of the effect was contrary to the hypothesis, therefore H2a and H2b are rejected. For motivational fatigue, for the comparison of VR-VC (\u003cem\u003ep\u003c/em\u003e = .917) and VR-F2F (\u003cem\u003ep\u003c/em\u003e = .669) no significant pairwise contrasts were observed, providing no support for H2c and H2d. For emotional fatigue, significant differences emerged between VC-VR (\u003cem\u003ep\u003c/em\u003e = .055) and between F2F-VR (\u003cem\u003ep\u003c/em\u003e = .096). However, the direction of the effect was contrary to the hypothesis, therefore rejecting H2e and H2f. For social fatigue, significant differences emerged between VC-VR (\u003cem\u003ep\u003c/em\u003e = .051), but not between F2F-VR (\u003cem\u003ep\u003c/em\u003e = .736). However, the direction of the effect was contrary to the hypothesis, therefore rejecting H2g and H2h. For visual fatigue, VR participants reported significantly higher values than both VC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and F2F (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), supporting H2i and H2j.\u003c/p\u003e\n\u003cp\u003eTable 5\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMediation Models with Cognitive Load\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eDirect Effect (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eIndirect Effect (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003ep (a path)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003ep (b path)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eMotivational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVisual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote.\u0026nbsp;Direct effects (a) and Indirect effects (b) estimated via bias-corrected bootstrap (1000 samples) in multi-group SEMs controlling for pre-fatigue, age, gender, and media experience.\u003c/p\u003e\n\u003cp\u003eHypotheses 3a\u0026ndash;e examined whether the relationship between nonverbal communication quality and fatigue was mediated by cognitive load. The mediation results are displayed in table 5. Across all five models, the indirect effects through cognitive load were not statistically significant, as the 95% confidence intervals for all estimates included zero. Accordingly, hypotheses 3a\u0026ndash;e are not supported.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e This study compared F2F, VC, and VR meetings in terms of non-verbal communication quality (NCQ), cognitive load, and five facets of videoconference fatigue (VCF).\u003c/p\u003e\u003cp\u003eThree patterns emerged. First, NCQ was lowest in VR, whereas VC and F2F showed similarly high (H1a rejected; H1b supported; VC\u0026thinsp;\u0026asymp;\u0026thinsp;F2F). Second, VR showed, as hypothesized, an increase in visual fatigue pre-post scores for visual fatigue while no effect was found for VC or F2F. Contrary to our expectations, VR also showed the most general (H2a-b), emotional (H2e-f), and social fatigue (H2g). No difference was found for the comparison of social fatigue between VR and F2F. Third, cognitive load did not mediate these effects.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eNon-verbal communication quality\u003c/h2\u003e\u003cp\u003eBased on MRT, we expected VR to enable richer exchanges through embodied avatars, spatial audio, and gaze cues. However, participants rated NCQ lowest in VR, contradicting H1a. VC and F2F did not differ significantly. This pattern aligns better with MNT: although VR adds channels, it reduces perceived naturalness because avatars lack micro-expressions and true eye contact, and timing can be perturbed by tracking/latency. Moreover, the avatars limited facial and body animations and details likely reducing subtle social cues such as nods, smiles, or gaze-synchrony. These factors likely disrupted conversational fluency and overshadowed richness advantages. Another explanation lies in user familiarity and adaptation. While participants were highly accustomed to VC, VR remained novel to many \u0026minus;\u0026thinsp;70.51% of VR users had low to no prior VR experience. Such unfamiliarity likely increased attentional demand for device and software handling, diverting cognitive resources away from the interaction itself. In other words, participants were still \u0026ldquo;learning the medium\u0026rdquo; instead of fully engaging in the conversation. This suggests that the perceived potential of VR may be out weighted by the lack of user experience and potential benefits might therefore be underestimated. Beyond technological and experiential factors, individual states also played a role. We found negative correlations between NCQ and pre-fatigue measures, indicating that participants who started already more tired perceived the quality of NCQ as lower.\u003c/p\u003e\u003cp\u003eTo improve NCQ in VR, future systems should enhance avatar realism through facial tracking and more fluid body tracking and animations while minimizing latency and device discomfort. Moreover, training or repeated exposure might reduce possible novelty effects and foster intuitive use, allowing the potential social advantages of immersive meetings to unfold more fully. Newer HMDs with integrated face and eye tracking may improve naturalness, but current implementations still draw attention to device management, further lowering perceived NCQ.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eFatigue differences\u003c/h2\u003e\u003cp\u003eThe most consistent effect was visual fatigue peaking in VR (supporting H2i-H2j), consistent with expected ergonomic and optical burdens (e.g., vergence-accommodation conflict, restricted FOV, thermal load). Thus, VR increased exhaustion not only because it lacks cues, but because its cues are harder to interpret and are delivered through hardware that imposes additional physiological costs. Notably, T-Tests show that motivational and visual fatigue were already higher in the VR condition before the treatment, indicating that a fatiguing effect may have already taken place in the VR condition due to preparing for the VR headset and conducting the pre-questionnaire with VR, further hinting at the ergonomic and optical burdens. Moreover, post-measures were only higher in VR for visual fatigue. Only when controlling for pre-measures in the SEM, we find significant differences for all facets of VCF.\u003c/p\u003e\u003cp\u003eContrary to H2a, H2e and H2g, post scores of general, emotional and social fatigue remained higher in VR than in VC (direction opposite to prediction), while VR \u0026ndash; compared to F2F - showed higher general, emotional, and visual fatigue, and VC showed higher social fatigue than F2F. Remarkably, all VCF facets decreased from pre- to post-measures, except for visual fatigue in the VR condition. The overall decline suggests that participants started the experiment already fatigued and recovered during the task. The smaller reduction of VCF in VR reflects that this group experiences less recovery and remains on higher VCF levels. However, this baseline effect does not alter the interpretation of group differences, as all analyses controlled for pre-fatigue levels.\u003c/p\u003e\u003cp\u003eMoreover, the higher VCF in VR may have reciprocally impaired perceived communication quality. When users experience VCF, their ability to pay attention and decode social signals might decline, creating a downward spiral where VCF and lower NCQ reinforce each other. Such cross-effects are plausible given the observed correlations between NCQ and all pre VCF facets, suggesting that VCF in VR is not only a byproduct of hardware strain but could also undermine the very communicative advantages VR could offer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003eCognitive load as mediator\u003c/h2\u003e\u003cp\u003eH3 proposed that lower NCQ increases cognitive load, which in turn heightens fatigue. While we found negative c-path effects from NCQ on all five facets of fatigue, we did not find a significant relationship between NCQ and Cl (a-path). Moreover, we only found one positive correlation from CL with motivational fatigue but none from CL with NCQ (b-path).\u003c/p\u003e\u003cp\u003eInterestingly, cognitive load tended to be highest in F2F, opposite to our prediction. On a first glance, three explanations seem to be plausible: First, sitting with two strangers in the same room might be more cognitively demanding due to social inconveniences, than having a meeting with zoom or VR. This might have influenced cognitive efforts to work together. Second, building on the first argument, the DLR-WAT instrument used to capture CL might not capture all facets of cognitive burdens that play a role in a team meeting. For example, even with familiar team members, F2F interaction may impose additional cognitive demands through subtle social expectations and continuous nonverbal processing, which could be attenuated in VR (through partial anonymity) or VC (through increased interpersonal distance). Third, F2F may elicit higher cognitive load due to denser and faster social information (micro-expressions, fine-grained gaze, overlapping turns): this \u0026ldquo;social-cognitive activation\u0026rdquo; can raise perceived workload without translating into higher fatigue-especially in short tasks. Future work should separate extraneous vs. intrinsic and physiological load (e.g., pupillometry/EEG), align load facets with specific fatigue facets, and test adaptation across repeated VR exposure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eTheoretical contributions\u003c/h2\u003e\u003cp\u003eThis study contributes to ongoing debate and theoretical arguments from Bailensons (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Riedls (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) frameworks of the root causes of VCF in several ways. First, we confirm a core assumption, that difficulties with interpreting and sending nonverbal signals are relevant for VCF, in a controlled experimental setting. Our findings deepen the understanding what specific aspects of nonverbal communication might be relevant for VCF during meetings: attract, recognize \u0026amp; show attention. If it is difficult or uncertain whether others pay attention, or if it feels hard to get attention, individuals tend to experience higher levels of videoconference fatigue (VCF). Likewise, when it is unclear whether one\u0026rsquo;s nonverbal signals are perceived or ignored, VCF increases. Thus, the nonverbal regulation of attention in meetings appears to be a relevant factor in explaining VCF.\u003c/p\u003e\u003cp\u003eSecond, our findings provide deeper insights into the relative importance of two theoretical approaches to predict NCQ: Media Richness Theory (Daft \u0026amp; Lengel, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and Media Naturalness Theory (Kock, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While we initially emphasized the meaning of richness - expecting VR\u0026rsquo;s additional 3D channels and immersion to enhance NCQ- our results showed the opposite: NCQ was highest in F2F, followed by VC, and lowest in VR, indicating that the added channels of immersion cannot compensate for the lack of natural human expressiveness (e.g. real mimics), probably making naturalness the stronger driver of NCQ as compared to richness.\u003c/p\u003e\u003cp\u003eThird, Bailenson (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) assumed that immersive technology could solve VCF by facilitating sending and interpreting of nonverbal signals whereas Hennig-Thurau et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argued, that higher immersiveness might result in more VCF. As we found that NCQ is lower and VCF is higher in VR than compared to VC and F2F the potential benefits of VR should be assessed more cautiously and less optimistically. This is particularly true since the assumed advantages in nonverbal communication cannot be proven. Nevertheless, immersiveness in general may not necessarily cause more VCF; rather, specific current technological shortcomings in VR - such as unnatural avatars or limited facial expressiveness\u0026mdash;may cause this effect. Thus, we add to the literature by shifting attention to the technical improvement of these features. Finally, we tested Bailensons (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) assumption, that CL as an explaining mediator for fatigue. We found no evidence for a mediation, leading to the assumption, that CL might not be the core reason for VCF, and other mediators might be responsible.\u003c/p\u003e\u003cp\u003eTogether, our findings indicate that recent VR technology does not solve VCF for meetings, and that the reasons behind VCF might be different than previously assumed in the literature.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003ePractical implications\u003c/h2\u003e\u003cp\u003eShould organizations already use VR headsets for work meetings? When compared to VC, our data suggest VR should not yet replace VC as the default for remote Meetings. Current VR systems induce more fatigue-especially visual strain-than videoconferencing or F2F interaction. This is likely due to the physical burdens of headsets (weight, heat, limited field of view) and the unnatural rendering of avatars, which require more cognitive effort to interpret. However, these drawbacks are small in magnitude and may diminish with next-generation hardware offering natural eye and face tracking, lower latency, and lighter optics.\u003c/p\u003e\u003cp\u003eVR may still be valuable for specific use cases: short, spatially rich, or socially engaging sessions where a sense of \u0026ldquo;being there\u0026rdquo; outweighs fatigue risks. Team leaders might consider VR as a tool for team building or meetings with a higher social focus. In contrast, videoconferencing remains the most efficient medium for routine or analytical meetings, while F2F continues to set the benchmark for emotionally or relationally intense exchanges.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the study employed a single type of VR headset (Meta Quest 3) without facial expression tracking. We theorized that VR should be a richer medium compared to VC; however, it is essential to consider that some cues are missing in VR, that are given in VC and F2F. Given that facial cues are a core component of non-verbal communication, the absence of real-time facial rendering likely constrained perceived naturalness and interaction quality. Future research should compare headsets with and without face-tracking (e.g. Apple Vision pro) to assess how this technological advancement influences NCQ and fatigue outcomes.\u003c/p\u003e\u003cp\u003eSecond, although participants prior experience with VR was statistically controlled, VR remains less familiar and routinized than videoconferencing. Thus, novelty effects and limited technical proficiency may have influenced both communication fluency and fatigue. While we controlled for experience with VR, only a small number of participants reported medium to high experience. It needs to be taken into consideration that even though no significant correlation was found from NCQ with prior VR experience, this could be because of the general lack of variance with prior VR experience, and that experience with VR does not necessarily mean, that participants have experience with this specific VR Headset and the specific software used for the meeting. Longitudinal studies or repeated-exposure designs are required to disentangle short-term adaption effects from stable medium characteristics.\u003c/p\u003e\u003cp\u003eLastly, pre results of motivational and visual fatigue were higher in the VR condition than the VC and VR condition. Arguably, visual strain was already induced before the questionnaire due to the VR setup video and the avatar creation. Since visual fatigue was already heightened, the difference between pre and post visual fatigue measures might have been higher than shown in our data. Future studies should take into consideration, that even only a few minutes long tutorials for VR might already have a fatiguing influence.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eImmersive virtual reality aims to recreate the social richness of F2F, yet our results show that current systems fall short. Participants in VR reported lower NCQ, higher VCF-especially visual strain-and no cognitive advantage over conventional videoconferencing. Integrating MRT, MNT, and CLT, the findings demonstrate that technological richness alone does not enhance communication: when cues feel unnatural or ergonomically demanding, they add rather than reduce strain.\u003c/p\u003e\u003cp\u003eTheoretically, this highlights \u003cem\u003enaturalness\u003c/em\u003e as the stronger predictor of meeting outcomes and calls for models that integrate both cognitive and physiological sources of effort. Practically, VR may benefit short, spatially rich collaborations but remains no fatigue-saving substitute for everyday communication. Only lighter, more natural, and less effortful systems could fulfill VR\u0026rsquo;s promise of making people feel truly together-without wearing them out.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll mentioned authors contributed in several ways:- Reviewing the literature (FM, PG, KB, JF)- Planing of the study design (FM, PG, KB, JF)- Conduction of Pre-Test (FM, PG, KB, JF)- Questionnaire Design (FM, PG, KB, JF)- Data acquisition (FM, PG, KB, JF)- Statistical analysis (FM, PG, KB, JF)\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research project was only possible thanks to the many helping hands who supported the participant recruitment and the laboratory experiments, and also mentally to handle the stress we faced during the acquisition. I want to express my sincere gratitude to all the students who worked with us for months to collect the data for this study: Max Gro\u0026szlig;an, Falk D\u0026ouml;ring, Jana Kiehn, Philip Werner, Lucie Bruckhaus, Hans D\u0026ouml;ring, Fynn Marckmann, Mathias Schulz, and Lena-Sophie Grulich, as well as Johannes Groth.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sample acquired for this research is available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBailenson JN, [Jeremy N] (2021) Nonverbal overload: A theoretical argument for the causes of Zoom fatigue. 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Front Virtual Real 5:1463189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/frvir.2024.1463189\u003c/span\u003e\u003cspan address=\"10.3389/frvir.2024.1463189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang P, Han E, Queiroz ACM, DeVeaux C, Bailenson JN (2024) [Jeremy N.]. \u003cem\u003ePredicting and Understanding Turn-Taking Behavior in Open-Ended Group Activities in Virtual Reality.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/ARXIV.2407.02896\u003c/span\u003e\u003cspan address=\"10.48550/ARXIV.2407.02896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8094774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8094774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Virtual reality (VR) has been theorized as a richer and more natural alternative to two-dimensional videoconferencing (VC) that could reduce videoconference fatigue (VCF), yet empirical evidence remains limited and mixed. In a preregistered laboratory experiment, N = 209 participants were assigned to triads and randomly allocated to face-to-face (F2F), VC (Zoom), or VR (Meta Quest 3, Horizon Workrooms) meetings. In a 20-minute meeting, the groups completed the NASA Moon Survival Task. We assessed non-verbal communication quality (NCQ) and cognitive load after the meeting, and five VCF facets (general, motivational, emotional, social, visual) before and after. Multi-group structural equation models (ANCOVA approach, controlling for pre-fatigue, demographics, and media experience) showed that NCQ was lowest in VR, while VC and F2F did not differ. Contrary to predictions, post-scores of general, emotional, social, and visual fatigue were highest in VR compared with VC and F2F. Cognitive load was highest in F2F, lowest in VR, and did not mediate the link between NCQ and any fatigue facet. Nevertheless, found direct negative effects from NCQ on VCF. These findings suggest that current VR meeting systems do not yet alleviate VCF, nor do they provide a richer nonverbal communication platform. The naturalness of non-verbal cues appears more critical for communication quality and fatigue than technological richness.","manuscriptTitle":"Virtual, but Not (Yet) Natural: The Role of Nonverbal Communication Quality, Cognitive Load, and Fatigue in Virtual Reality Compared to Videoconferencing and Face-to-Face Team Meetings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 13:38:50","doi":"10.21203/rs.3.rs-8094774/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"237a3304-3cbc-4526-bef0-2152225150be","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T12:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 13:38:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8094774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8094774","identity":"rs-8094774","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}