We considered a sample of university students. They were deemed suitable for testing whether the addition of an EEG visualization device would help improve psychological distance and user engagement, given the operational complexity of online exhibitions and evaluation of engagement in online experiences. Before we recruited the participants, we designed our experiments in accordance with the Declaration of Helsinki by the World Medical Association. The ethics review office of the Faculty of Library, Information and Media Science of the University of Tsukuba in Japan approved the study (permission number 22–4).

In the notice for recruiting participants, we marked the general content and flow of the experiment, explicitly stating that two people were required to participate in the experiment as a unit. To reduce the effect of the difference in familiarity between the two participants in each experiment on psychological distance, we selected participants who had known each other for a long time (65% of participants were mutually acquainted for more than 1 year) and were mainly from the same research laboratory. Before starting the experiment, we informed the participants that there was no compensation for participation in the experiment. After the participants received a complete explanation of the study, they agreed to participate and subsequently signed a written informed consent form. Permission of publication of any potentially identifiable images or data included in this study was obtained from pertinent individuals.

In the between-subjects design, we designed two conditions, and each participant was tested in only one of the conditions (see Figure 4). The participants were randomly assigned to two groups. These two groups were based on two conditions: the normal online exhibition experience group (NOEE group) and the group with an additional EEG signal visualization device (ESVD group). The difference between the two groups is that we added an independent variable, a media device that translates the difference in EEG signals between two participants into real-time moiré patterns, in the ESVD group. The total duration of the experiment was 5 days (10:00–18:00/day), with the NOEE group performing on Day 1–2.5 and the ESVD group on Day 2.5–5.

At the beginning of the experiment, the implementer explained the procedure to the participants and asked them to fill in their personal information. To capture the EEG signals in real time, the participants wore the EPOC X headset-based EEG equipment. Simultaneously, the experiment implementer used two other computers to record the captured data using EMOTIVPRO 3.0.

Subsequently, the participants moved on to the next task until all four tasks and questionnaires were completed within 1 h (see Figure 5). The participants learned how to operate Tasks 1–4 in online exhibitions to familiarize themselves with the specific interaction processes and used the PC’s touch area to control the interface. The PC’s built-in camera and Zoom were used to display images of the faces of the participants in real time during the experiment. Each PC was connected to an earphone that was used by the participants for communication. After visiting each exhibition, participants were required to answer a paper questionnaire (PDMD) and an electronic questionnaire (UES-SF) on an iPad.

In contrast to the NOEE group, in the ESVD group, the experimental implementer explained the EEG visualization media device to the participants before the start of the experiment. Additionally, this media device was used as a background screen (the background in front of both participants showed the same view) during the online museum experience, which participants could freely view at any time.

At the end of the experiment, we asked participants to write their feedback based on their overall experience of the experiment by describing the response requirements in the relevant field in the questionnaire. The headings in the questionnaire were described in three languages: English, Japanese, and Chinese, and the participants responded in their native language. The experiment implementer assisted the participants in this process by explaining the questionnaire titles by using neutral vocabulary and did not guide the content of the participants’ responses.

We used SASS version 25.0 for statistical analysis of the data collected during the experiment. We used descriptive statistics for the sample and divided the experimental data into three main parts:

We utilized the following analysis methods:

A total sample of 40 students from the University of Tsukuba participated in this study, including 20 males, 19 females, and one non-binary gender person. All participants were East Asian (specifically, Japanese and Chinese). The NOEE group included 20 participants (12 males and eight females) with a mean age of 24.9 ± 1.5 years. In the NOEE group, 14 participants were mutually acquainted for more than 1 year for two people involved in the same experiment (additionally, two participants were mutually acquainted for more than 6 months less than 1 year, two participants for more than 1 month less than 6 months, and two for less than 1 month). The NOEE group included 12 participants who used Zoom most frequently for online meetings, and eight participants used Microsoft Teams. The ESVD group consisted of 20 participants (eight males, 11 female, and one non-binary gender individual) with a mean age of 24.3 ± 2.3 years. In the ESVD group, 12 participants (comprising the groups of two people involved in the same experiment) were mutually acquainted for more than 1 year (in addition, four participants were mutually acquainted for more than 6 months and less than 1 year, two participants for more than 1 month and less than 6 months, and two participants for less than 1 month). The ESVD group had 15 participants; they used Zoom most frequently for online meetings, and five participants used Microsoft Teams. There were no significant differences between the groups in terms of mean age (p = 0.375), sex ratio (p = 0.343), duration of acquaintance ratio (p = 0.946), and commonly used online meeting software proportion (p = 0.501). Further details are provided in Table 1.

All participants in the NOEE and ESVD groups completed the appropriate experimental processes without encountering any technical difficulties or significant participant disturbances, which might have terminated the experience. We observed the participants’ behavior and determined that all participants were focused on and motivated from the beginning to the completion of the trial.

We divided the results of the participants’ psychological distance assessment into three parts:

In the NOEE group, there were statistical differences among the four tasks within the group according to the results of the one-way ANOVA (F = 7.473; p = 0.000; see Table 2). According to the results of multiple comparisons, the scores of Task3 were higher than those of the other three tasks, and there were significant differences between the scores of Task3 and Task1 (p = 0.002), Task2 (p = 0.000), and Task4 (p = 0.001; Table 3).

In the ESVD group, there were significant differences between the four tasks within the group according to the results of the one-way ANOVA (F = 4.510, p = 0.006; see Table 4). According to the results of multiple comparisons, the scores of Task3 were higher than those of the other three tasks, and there were significant differences between Task3 and Task1 (p = 0.048), Task2 (p = 0.001), and Task4 (p = 0.021; Table 5).

According to the results of statistical tests (independent samples t-test) between the four tasks in the NOEE and ESVD groups, there were no significant differences between Task1 in the NOEE group and Task1 in the ESVD group (p = 0.098), between Task2 in the NOEE group and Task2 in the ESVD group (p = 0.061), between Task3 in the NOEE group and Task3 in the ESVD group (p = 0.516), and between Task4 in the NOEE group and Task4 in the ESVD group (p = 0.162). After analyzing the two groups’ overall engagement (OE), the mean ± SD was 17.88 ± 4.379 for the NOEE group and 19.56 ± 3.478 for the ESVD group. Furthermore, a statistical test (independent samples t-test) for these two groups yielded T = −2.699, p = 0.008, and p < 0.05. This result indicated a statistical difference between the NOEE and ESVD groups (Table 6), where the ESVD group had higher scores than the NOEE group.

We evaluated user engagement in the NOEE and ESVD groups, first by detecting within-subject effects for different indicators in the two groups, followed by comparing the two groups in pairs.

Table 7 illustrates the repeated-measures ANOVA for all participants’ scores to compare the differences between the four tasks. The four tasks differed significantly in the PU indicator (F = 4.559, p = 0.034), but not in the FA (F = 0.504, p = 0.479), AE (F = 1.968, p = 0.163), and RW (F = 2.587, p = 0.11). Table 8 presents the paired comparisons of the overall engagement values for the two groups (NOEE and ESVD). Paired comparisons showed that participants’ overall engagement was marginally significantly higher in the ESVD group than in the NOEE group (p = 0.056; 0.05 < p < 0.01).

There are various ways of experiencing online exhibitions, including cell phones, tablets, VR devices, and computer screen displays (Broeck et al., 2017). Different experience methods have different degrees of impact on users’ psychological distance. During the COVID-19 pandemic, emerging technologies such as tracking apps, AI, big data, 5G, drones, and robotics are being used in China and Japan (Shaw et al., 2020). The high penetration rate of the Internet and smartphones, combined with the acceptance of new technologies, particularly among urban youth in these countries, may have contributed to the positive perception of the moiré patterns generated in this study.

By comparing the four exhibitions within the group, we found that different interaction methods had other effects on the psychological distance between the users. In Task3, two participants were required to work together to complete a pair of puzzles. In this task, compared with the other tasks, they could see the trajectory of each other’s operations in real time, and the displayed operation interface was synchronized. The synchronized interface provided an additional way for participants to understand each other’s emotions. The statistical results showed that in the NOEE and ESVD groups, the users’ psychological distance was the closest in the experience of Task3 (interactive puzzle game) than the others (see the section “Evaluation of Psychological Distance,” for a description of the within-group comparison results). Additionally, the participants had the highest number of conversations during the interaction of Task3, and more than half of them expressed a desire to continue experiencing Task3.

Synchronization is considered a joint change in psychophysiological signals when people interact intensively with each other (based on Ekman et al., 2012). Such phenomena while playing games have proven to be psychologically and physically beneficial (e.g., Motataianu, 2015; Robinson et al., 2020). Synchronization can increase socialization capabilities through shared interactions and online experiences (e.g., Sadhukhan et al., 2021). Given that art as a shared experience can also foster emotional contagion in viewers (Gernot et al., 2018), combining online galleries with visualizations of psychophysiological signals (in this case, EEG) may have reinforced empathy among our participants. This could also have influenced the reduction in psychological distance.

The intensity of user engagement may influence users’ perceived psychological distance to some extent. Higher user engagement in the ESVD group likely reduced the perceived psychological distance. Van Boven et al. (2010) showed that a reduction in objective distance and emotional intensity reduces perceived psychological distance. Emotional intensity also affects user engagement, as a good emotional design induces a sense of pleasure and security in the user, thus promoting user engagement during the experience (Lee et al., 2002).

Some studies describe psychological distance as the degree of divergence from the direct experience of the self, here and now, along with temporal, spatial, and social viewpoints or theoretical perspectives (Trope and Liberman, 2010). It is divided into dimensions such as temporal, spatial, and social viewpoints, or theoretical (Liberman and Trope, 2014). Scerrati et al. (2022) demonstrated that the pandemic likely drives the results of individual differences in the assessment of social proximity. Our study focuses on the “social distance” dimension, such as the distance between oneself and others.

Given that our participants were East Asians, we must discuss perceptions of social distance in these regions. In China, one regional aspect influencing social distance is guanxi, which is usually applied to business relationships, where the more the trust, the shorter is the social distance (Song et al., 2012). There is also an indication that social distance is slightly greater in urban settings (Ma et al., 2015). As for minorities, while the Chinese perceive some regional and international minorities as close, others are perceived far (Fong and Spickard, 1994).

Among minorities in Japan, indigenous people were considered as closer than people from mixed nationality backgrounds (Ball, 2009). In both countries, the social distance was greater when interacting with people with mental disabilities (Haraguchi et al., 2009; Ando et al., 2013), although physical contact was more frequent among Chinese nationals.

There was an indication of social isolation and depression among Japanese youth before the COVID-19 pandemic (Takagi et al., 2013), and such phenomena increased during the pandemic, including the suicide rate, particularly among women (Sugaya et al., 2020; Osaki et al., 2021). In China, poor mental health is associated with social distancing (Goodwin et al., 2021). The existing evidence suggests that such effects can be mitigated by family support (Li and Xu, 2022).

Social distancing is usually discussed in medical terms in the literature. In contrast, our study addresses social distancing as an opportunity for university students to experience art. Many of these students move out of their homes to pursue their education, and in the case of international students, they currently face the challenge of adapting to a different country amid a global pandemic. These factors can exacerbate isolation. Thus, we propose an alternative to decreasing the perception of social distance, with the potential mitigation of isolation among East Asian students, which could aid in addressing other mental and physical health issues.

We discovered in the feedback that the participants in the ESVD group were more concerned about their partners with whom they experienced the online exhibition together than those in the NOEE group. We also collected textual feedback from participants in the study. Figure 6 compares the keywords that appeared in the final feedback messages and the number of occurrences between the participants in the NOEE and ESVD groups. The percentage in the bottom-right corner of each keyword in the figure represents the percentage of participants who mentioned that keyword. The larger the value, the larger is the area shown in the figure. Overall, 36 of the 40 participants (18 in each of the two groups) submitted feedback. We found that the high-frequency word that was mentioned by five out of 18 participants and was the highest of all words in the NOEE group was “online” (28%); in the ESVD group, that was mentioned by seven out of 18 participants and was the highest of all words it was “feeling” (39%). The comparison between the two groups revealed that the words mentioned by the participants in the ESVD group were more often related to the partners who participated in the experiment, such as “Close to” (17%), “Conversation” (17%), “Group work” (11%), “Partner” (11%), “Distance” (11%), etc. (see Figure 6A). The words mentioned by the participants in the NOEE group were more related to online experiences, such as “Museum” (22%), “Mouse” (22%), “Experience” (17%), and “Impression” (17%), “Explain” (17%), etc. (see Figure 6B).

Our study has several limitations. First, the sample size was relatively small. A more extensive sample size is necessary to provide more meaningful data to evaluate the efficacy of using an EEG signal visualization system for the user experience of an online exhibition. The results of this study might have differed if the sample included older adults or people from nonurban settings.

The second is the relatively single mode of experience. In this study, we used the GA&C website as a case study. The GA&C website is mainly experienced by users through computers, which are used more frequently than VR and AR devices in daily life for most users (Udell, 2019). With the evolution of virtual exhibitions, experience modalities have become more diverse. Using technologies such as Web/X3D, VR, and AR, visitors are offered the possibility of exploring virtual museums, interacting with virtual exhibits in real time, and visualizing these exhibits in contexts such as 3D gallery spaces (Petridis et al., 2005; Styliani et al., 2009). Additionally, the web-based form of online museums is one of the earlier developed and most popular ways to experience virtual museums; therefore, we chose this form as a way for participants to experience them in this study. Participants visited virtual museums in different modes of experience, which produced different effects. In the future, we will add to this study and continue to explore the impact of different experiential modes on participants’ psychological distance and engagement.

Additionally, we observed that participants did not significantly observe the state of EEG visualization images during the experience of focusing on the online exhibition, possibly because the online experience and EEG visualization were displayed on separate screens. In this case, the participants needed to raise their heads to move their eyes from the computer screen to the monitor. While the participants were fully engaged in viewing the artwork and reading information about it, their eyes were mainly focused on the computer screen. They spent less time observing the screen displaying the EEG signals. Therefore, if the EEG signal is displayed on the same screen as the artwork, participants are likely to spend more time focusing on the changes in the EEG signal, which may have a different effect on the results of the experiment.