We chose modeling home environments from a model base,¹ which hosts numerous building environmental 3D models and serves as a primary platform for designers to share and exchange ideas. The model base features 16 different interior design styles (Chinese, European, Simple European, Mediterranean, Modern, Industrial, American, Japanese and Korean, New Chinese, New classical, European classical, Northern Europe, French, Post-modern, Light luxury, and Southeast Asian). We selected the most recommended living room and bedroom models for each style, resulting in a total of 32 models for this investigation. These models represent popular styles and home design images frequently used in China.

The experiment was divided into two parts. First, participants were asked to rate the restorative potential of each selected environment using a questionnaire comprising a set of restorative component rating scales. After measuring the degrees of environmental characteristics, we used regression analysis (Liang and Zeger, 1993) to determine the relationships between home characteristics and restorative potential. In the second part of the experiment, we selected the environments with the highest and lowest scores based on the restorative component rating results and tested neuro reactions to these two environments to show the connections between restorativeness and neuro activities. This two-part approach might afford an in-depth discussion of the relationships among home environmental characteristics, restorativeness, and neuro activities.

To measure the restorative potential, we used the questionnaire developed by Laumann et al. (2001) to evaluate the restorative components of an environment based on Kaplan and Kaplan’s (1989) restorative theory in this study. The questionnaire included 22 rating scales and was posted on a web platform² along with pictures of all modeling environments. A total of 66 participants (22 males and 44 females, M = 23 years old), all from a Chinese college, completed the questionnaire.

Regards to home characteristics, we identified potential elements related to restorativeness from the literature. Celikors and Wells (2022) found that a small portion of restorative qualities was associated with low-level visual features, but the potential and limits of this feature for restorativeness remained unknown. In a residential streetscapes study, Lindal and Hartig (2013) found that the effect of building height on restorativeness was partially mediated. Lee et al. (2012) discovered that some fabrics could extract semantic scales of nature, joyfulness, softness, etc. Combining these three potential characteristics with the features we summarized earlier, we established a set of 15 environmental characteristic scales (Table 2). Then, we invited two associate professors with 15 years of teaching and working experience in the interior design field to discuss and evaluate the degree of these characteristics as shown in each selected home design model picture (the Cohen’s kappa coefficient = 0.957). This expert evaluation helped us better understand the relationships between home characteristics and restorative potential and provided insights for the next phase of the study, which involved analyzing neuro reactions in the best and worst scoring environments.

The application of Neu-VR in this study allowed researchers to collect objective data on the nervous reactions of participants when they experienced various home environments. By monitoring cerebral hemoglobin concentration, pupil radius, attention shift frequency, blink frequency, and eye gaze position, the study was able to gain valuable insights into the participants’ neurophysiological responses to the environments. 15 participants (5 females and 10 males, M = 28 years old) participated in this phase of the study. The data collected through Neu-VR complemented the subjective feedback obtained from the questionnaire, providing a more comprehensive understanding of the relationships between home environmental characteristics, restorativeness, and neuro activities.

By combining the results from both the questionnaire and the neurophysiological data obtained through Neu-VR, the study aimed to create a more holistic view of the factors that contribute to restorative home environments. The findings can then be used to inform future interior design and architectural practices, ultimately creating more restorative and beneficial spaces for occupants.

Initially, the zero-order correlation analysis method was employed to identify the relationship between home characteristic ratings and restorative potential (Table 3). In this investigation, three types of view characteristics were considered: overall window view, natural view, and sky view. The results of the correlation analysis revealed no significant association between natural window view and restorative potential. However, sky view, overall window view, and window size were found to be related to restorative potential. Regarding the color categories assessed in each home image, both warm and cool colors were evaluated. Factors such as greenness, warm colors, room size, and access to outdoor space demonstrated significant relationships with the restorative potential of homes. In contrast, wood material, cool colors, furniture form, artwork, visual features, room height, and textile materials did not show any correlation with restorative potential.

To identify home characteristics that can predict restorative potential, we employed backward regression to eliminate characteristics that did not have a significant impact. The analysis yielded a model with a high confidence interval of p < 0.001 (R² = 0.843, F = 18.397) (Table 4). Considering the regression coefficients and significance levels, four predictors of home environment were identified as significant characteristics (p < 0.05) that can predict restorative potential: overall window view, warm colors, room size, and access to outdoor space. These characteristics were all positively associated with restorative potential, indicating that enhancements in these home characteristics are perceived to increase restorative potential.

We asked the 15 participants to experience two home environments with the worst (X) and best (O) restorative potential scores in Neu-VR (Figure 1). They experienced both environments in a random order, with each exposure lasting 3 min. The data for cerebral hemoglobin density, pupil radius, attention shift frequency, blink frequency, and eye gaze position were recorded by the Neu-VR sensors. After conducting a paired sample t-test analysis, we obtained the results for differences in pupil radius, attention shift frequency, blink frequency, and cerebral hemoglobin density at four positions (Table 5).

The mean value of pupil radius for environments O and X were 1.82 ± 0.17 and 2.13 ± 0.21 mm, respectively. Environment O exhibited a pupil radius 0.31 less than environment X (95% Confidence Interval: −0.38 to −0.23), with the difference being statistically significant (p < 0.001). The mean value of attention shift frequency for environments O and X were 9.26 ± 2.25 and 10.86 ± 2.65 times, respectively. Environment O exhibited an attention shift frequency 1.60 less than environment X (95% Confidence Interval: −2.71 to −0.49), with the difference being statistically significant (p = 0.01). Although there were statistical differences between blink pairs (p = 0.01) and hemoglobin density at position R3 (p = 0.04), their p-values of correlation were greater than 0.05. No statistical differences were observed between pairs of hemoglobin density at positions L1, L3, and R1.

This study is among the few that explore home characteristics and restorativeness using both subjective and objective data. The results identified several home characteristics as strong predictors of restorativeness. Firstly, our findings from the regression analysis indicated that window view is statistically associated with home restorativeness, which is consistent with previous research on window views. Numerous benefits of window views have been documented, such as opportunities for restorativeness, residential satisfaction, and wellbeing (Kaplan, 2001). Additionally, window views of green spaces have been shown to have relaxing effects and reduce stress levels (Elsadek et al., 2020). Sky views have been found to have the highest restorative potential in urban contexts compared to views of people and street trees (Masoudinejad and Hartig, 2020). Other positive effects of window views include work ability and satisfaction (Lottrup et al., 2015), thermal comfort and cognitive performance (Ko et al., 2020). In contrast, office environments without window views can induce tension and anxiety, as evidenced by electromyography (EMG), electroencephalography (EEG), and blood volume pulse (BVP) data (Chang and Chen, 2005).

In addition to mental benefits, window views have been linked to potential physical recovery, such as decreased negative evaluative comments from nurses for surgery patients (Ulrich, 1984), improvement of self-reported physical health for coronary and pulmonary patients (Raanaas et al., 2012), and reduced usage of analgesics and pain relief for cesarean women (Wang et al., 2019). Moreover, windows are associated with another restorative factor, natural lighting, which is considered the best light source (Abdelaziz Mahmoud et al., 2023). As different angles and ratios can lead to various views, window design for restorativeness may involve multiple dimensions. Our correlation results showed a significant relationship between window size and restorative potential, although no significant effect was found in the regression analysis. Some researchers have discussed the effects of window size in terms of minimum window size design (Ne’eman and Hopkinson, 1970), energy balance (Persson et al., 2006), and glare of sunlight presence (Boubekri and Boyer, 1992). To determine whether larger windows are more restorative, further research is needed to investigate the correlations between specific designs. Consequently, several studies have concluded that window views are associated with psychophysiological wellbeing for residents and have proposed multi-criteria evaluation methods for window design (Ko et al., 2022; Lin et al., 2022). And the virtual window view was also discussed (Radikovic et al., 2005).

The second element was warm color, which is consistent with the design of patient ward where inpatients preferred warm colors over cold or white (Gao and Zhang, 2020). According to the extent element of restoration theory, environmental components can easily occupy an occupant’s mind (Herzog et al., 2003). Warm colors exhibited excellent attention-guiding qualities (Pal et al., 2012), which may explain their use in learning materials designed to evoke positive emotions and enhance understanding during media learning (Plass et al., 2014; Münchow et al., 2017). Color and mood are interconnected concepts (Valdez and Mehrabian, 1994), and even colors within the same scheme can elicit different emotional responses (Naz and Epps, 2004). For restorativeness, light warm colors are recommended based on findings that pale colors evoked feelings of relaxation, calmness, and pleasantness (Al-Ayash et al., 2016) and that light colors were more likely to elicit positive moods such as joy and relaxation (Jonauskaite et al., 2019).

The third characteristic was room size, which is associated with various aspects of daily life, including emotional response to sound (Tajadura-Jiménez et al., 2010), air quality (Cherrie et al., 2011), and self-reported stress (Sundstrom, 1975). And room size directly impacts physical openness and spatial quality (Franz et al., 2005). Regarding room size and restorativeness, Gao and Zhang (2020) found that larger rooms may facilitate patient recovery. Nehrke et al. (1979) suggested that room size may influence health status, as small rooms can convey a sense of limitation (Meyers-Levy and Rui, 2007) and exacerbate small-space stressors such as lighting, noise, vibration, radiation, and low air quality (Li et al., 2022). These stressors can be magnified in long-term isolation, leading to sleep disorders and other adverse effects (Meng et al., 2020; Palinkas and Suedfeld, 2021). These findings emphasize the importance of room size in designing a restorative home environment.

The final characteristic was access to outdoor space. Nejati et al. (2016) found that rooms with access to outdoor space had significantly greater restorative potential than those without. This element is linked to other restorative cases, such as cabins (Bjerke et al., 2006) and rural second homes (Pitkänen et al., 2020), where offer easy access to outdoor spaces. Additionally, private gardens at the doorstep have been shown to reduce stress (Stigsdotter and Grahn, 2004). Numerous studies highlight the importance of access to outdoor spaces for all age groups (Mapes, 2010; DEFRA, 2011). Tremblay et al. (2015) stated that outdoor spaces are crucial for children’s health, as they encourage more movement, less sitting, and longer playtimes, lowering obesity risk (Porter et al., 2018) and promoting positive mental health (Hinkley et al., 2018). Compared to indoor physical activity, exercising outdoors has been shown to alleviate tension, anger, and depression better (Thompson Coon et al., 2011). Long-term outdoor activity benefits have also been observed in Positive Youth Development programs (Armour and Sandford, 2013). Consequently, access to green outdoor spaces in the workplace has been shown to improve worker wellbeing and reduce stress (Lottrup et al., 2013).

Upon obtaining the results for each environment’s restorative potential score, both the environments with the highest and lowest restorative potential scores were input into the Neu-VR system to evaluate participants’ reactions. The difference in pupil radius between environments O and X was 0.31 mm, a statistically significant value. Pupillometry has shown its history and validity of measuring active state of neocortex (Larsen and Waters, 2018) and neurobiologists found the associations between restorative environment and brain activity (Martínez-Soto et al., 2013). Beatty (1982) observed a pupil dilation of approximately 0.3 mm when comparing relaxed and cognitively-loaded conditions. The 0.31 mm difference between the two environments surpasses the 0.3 mm threshold. Although pupil size is predominantly influenced by light luminance (De Groot and Gebhard, 1952) and visual environment (Larsen and Waters, 2018), the possibility of using pupil size to measure restorativeness should be considered, given that light is a factor in restorativeness. Further examination of this potential indicator is warranted.

A statistically significant difference was observed in recorded attention shift frequency between environments O and X, with the former exhibiting a lower frequency of 1.60 shifts per second. According to Neu-VR equipment specifications, attention shift frequency corresponds to the frequency of visual focus changes. This concept is analogous to the fixation-click count employed in visual monitoring research to evaluate visual attention in air traffic controller training (Wee et al., 2020). Fascination, a component of Attention Restoration Theory, posits that a captivating environment effortlessly holds an individual’s attention (Kaplan and Kaplan, 1989). And eye movement as effective an interference (Lawrence et al., 2004), consequently, lower attention shift frequencies may indicate a more restorative environment. Further testing with robust restorative environment designs is required to validate this predictor. Despite the statistical differences in blink and hemoglobin density measures, correlations between these metrics suggest the presence of other influencing factors. As such, they cannot be utilized as reliable indicators of environmental restorativeness.

Gaze position data were collected, and heatmaps for environments X and O were generated. Figure 2 (X) displayed dispersed gaze positions, with a significant proportion focused on the metal frame and textual artwork. One hypothesis posited that metal may convey negative effects on restorativeness due to its association with adverse outcomes [e.g., unhealthy, enclosed, and depressive imagery of white steel (Sakuragawa et al., 2005); cold, industrial, and unpleasant connotations of steel (Wastiels et al., 2013)]. Further research with objective data is needed to investigate this hypothesis. In contrast, Figure 2 (O) revealed centralized gaze positions on window views and artworks, consistent with the high perceived restorativeness of seascapes (Nang Li et al., 2012) and artwork (Nejati et al., 2016).