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Article

Enhancing Cognitive Performance and Physiological Benefit in Workspaces Through Patterns of Biophilic Design: A Restorative Approach

by
Ping Zhang
1,2,3,
Zhengqi Yu
1,*,
Guoying Hou
1,2,3,
Ping Shu
1,2,3,
Yunque Bo
4,
Yankun Shi
1,2,3 and
Rui Nie
1,2,3
1
School of Architecture & Art Design, Hebei University of Technology, Tianjin 300130, China
2
Urban and Rural Renewal and Architectural Heritage Protection Center of Hebei University of Technology, Tianjin 300130, China
3
Hebei Key Laboratory of Healthy Living Environment, Tianjin 300130, China
4
Policy Research Department, Tianjin Institute of Medical Science and Technology Information, Tianjin 300041, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(10), 3293; https://doi.org/10.3390/buildings14103293
Submission received: 18 August 2024 / Revised: 7 October 2024 / Accepted: 16 October 2024 / Published: 18 October 2024
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
Browse Figures
Versions Notes

Abstract

Contact with nature is believed to enhance mental health through the process of human psychological restoration. However, prolonged indoor living limits individuals’ exposure to nature, potentially hindering the timely alleviation of stress and fatigue induced by work. While biophilic design is recognized as a potential solution, its impact on the restoration process has not been extensively studied, particularly in relation to its various design patterns. Therefore, it is important to determine the restorative effects of different patterns of biophilic design and their combination in order to guide the practical application of biophilic design. In this study, the effects of two typical biophilic design patterns and their combination on attention restoration were measured using subjective scales, cognitive tasks, and functional near-infrared spectroscopy (fNIRS) in a simulated real workspace. The results suggest a significant enhancement in the restorative impact on cognitive performance and physiological benefits when combining two biophilic design patterns, while a single design pattern does not yield the same effect. These findings contribute to a better understanding and improvement of workspaces, enhancing users’ experience and well-being.

1. Introduction

Urban residents spend over 90% of their daily time indoors [1]. Considering that they spend the most time working and sleeping, it becomes evident that workspaces can exert a substantial impact on their users. Environmental stressors in workspaces, along with work-related issues, can contribute to stress and fatigue [2,3], which may lead to negative consequences including a decline in productivity [4] and mental health problems in the long term. Therefore, it is crucial to create workspaces that effectively alleviate the stress and fatigue. In addition, over half of the global population currently resides in urban areas, and projections suggest that this proportion will increase to 68% with the persistent trend in urbanization by 2050 [5]. As a result, the workspaces that individuals are exposed to can have a more profound influence on their overall well-being.
Contact with nature bestows health benefits [6] and indirectly contributes to mental health through the process of psychological restoration [7,8]. Within the domain of environmental psychology, the mechanism of restoration resulting from contact with nature is elucidated through two primary theoretical frameworks: Attention Restoration Theory (ART) [9] and Stress Reduction Theory (SRT) [10]. According to ART, prolonged cognitive efforts can exhaust attentional resources [11], while natural environments can attract ‘effortless’ attention, facilitating the restoration of attention and alleviating mental fatigue. SRT posits that individuals respond to various stimuli, including environmental factors, with varying degrees of stress. Prolonged exposure to stress is deemed detrimental to health, and SRT suggests that natural environments can facilitate recovery from such stress. While the two theories diverge on the mechanism, both assert the restorative benefits of natural environments [12]. From this perspective, promoting contact with nature may help address the aforementioned issue.
However, extended periods of indoor living diminish individuals’ exposure to nature, a circumstance that may be exacerbated during public health emergencies [13]. A potential solution is biophilic design [14], derived from “biophilia”, the innate tendency to focus on life and lifelike processes [15]. Biophilic design incorporates natural elements into architectural spaces, fostering increased and frequent indoor contact with nature [16]. Its objective is to establish restorative indoor environments and create connections between people and nature in the built environment, promoting improved health and well-being. The patterns of biophilic design can be categorized into three types: nature in the space, natural analogs, and nature of the space. Nature in the space refers to the direct, physical, and ephemeral presence of nature in a space or place; natural analogs refers to organic, non-living, and indirect evocations of nature; and nature of the space refers to spatial configurations in nature [17]. It is clear that studying biophilic design will help uncover its value for psychological restoration and guide practical applications in workspaces.
In comparison to similar studies focusing on natural environments [18,19,20], the impact of biophilic design on the human psychological restorative process has been relatively underexplored. Previous studies have investigated the restorative effects of built environments with features like indoor greening, natural window views [21,22], natural smells [23], sounds [24], wood [25], curves [26], room size [27], and even the thermal realm [28]. These features have been shown to provide varying degrees of restorative benefits, with further studies examining parameters such as the dose, layout, and form for greening [29,30], and room depth for window views [31]. While indoor biophilic design has not been explicitly mentioned in some studies, the features examined can be categorized as biophilic design patterns. For instance, indoor greening and natural window views can be seen as visual connection with nature, as they establish a link between indoor space and the natural environment. Given the diversity of biophilic design patterns, the restorative effects of combining these patterns warrant further investigation.
Many studies utilized virtual reality (VR) for environmental simulations, leveraging its advantage in precisely manipulating the form and frequency of exposure [32]. While VR provides an immersive experience, there remains a disparity between VR and real-life scenes, and this holds for their impact on mood, cognitive performance, and physical response [33]. VR sickness may result from the display type, mode, field of view (FOV), and latency of VR, with symptoms resembling those of motion sickness and the potential to impair the experience for users [34]. For restorative benefits, studies focus more on stress recovery than attention restoration. Generally, physiological responses, such as skin conductance, heart rate, and blood pressure from the autonomic nervous system, are employed as assessment techniques for stress levels, while cognitive tasks are utilized to measure attention levels [35]. Rarely have studies evaluated the impact of biophilic design on restoration from the perspective of attention, utilizing both cognitive tasks and cognitive neuroscience tools.
Accordingly, the present study explored the effects of real workspaces featuring various patterns of biophilic design and their combinations on attention restoration. The investigation spans multiple dimensions, including subjective perception, cognitive performance, and physiological responses from the central nervous system. This study posited the following hypotheses: (1) the restorative effects vary among different patterns of biophilic design; and (2) the restorative effects of the workspace without biophilic design, with a single biophilic design pattern, and with a combination of biophilic design patterns increase sequentially.

2. Materials and Methods

2.1. Environmental Setting

The experiment was conducted in a laboratory (simulated real workspace) at Hebei University of Technology, measuring 4.2 m (L) × 3.3 m (W) × 3.0 m (H), with an area of approximately 14 m2. Table 1 presents 14 patterns of biophilic design, offering guidance for implementation. Two of these patterns, visual connection with nature and material connection with nature, representing distinct categories, were implemented in the workspaces due to their high commonality and feasibility. Visual connection with nature refers to a view to elements of nature, living systems, and natural processes, while material connection with nature refers to minimally processed natural materials and elements that create a distinct sense of place [17].
For visual connection with nature, a small green wall, measuring 1 m2 and composed of potted plants on a freestanding bracket, was set up. A small green wall is proposed due to its superior psychological and physiological effects compared to a larger one [36]. Green walls are visually more novel compared to familiar potted plants but are more troublesome and costly to maintain, making them more suitable for public spaces, such as workspaces, where the need for natural elements aligns with the requirements of the working population. For material connection with nature, a wall predominantly covered with wood was set up with dimensions of 3.6 m (L) × 3.0 m (H). Moderate wood coverage is more likely to make individuals feel comfortable while high coverage makes individuals tired [37]. In addition, a condition combining both patterns was set up, with the green wall overlapping the wooden wall. A condition without biophilic design was used as a control group.
As shown in Figure 1, there was a table and a chair in the condition, with the chair positioned approximately 2.4 m away from the opposite wall. The interior was solely lit by artificial lighting, maintaining an illuminance of 950 lx on the tabletop. It was also kept ventilated and quiet, with the temperature and relative humidity controlled within a range of 24.8 ± 0.9 °C and 43.9 ± 6.4%, respectively. The plants in the green wall did not emit any distinct odors.

2.2. Measures

2.2.1. Subjective Perception

A visual analog scale (VAS) was used to assess participants’ subjective perception. The VAS has the advantages of simplicity and precision [38]. Its construct validity and reliability have been tested and it is suited for the clinical assessment of self-reported psychological conditions [39]. The VAS consisted of a 100 mm line with the anchors “No mental fatigue” at one extreme and “Mental fatigue as bad as it could be” at the other. Since attention is an abstract concept, the scale’s question pertains to mental fatigue rather than attention, aligning with a previous study [22]. Participants were asked to place a mark on the line corresponding to their perceived mental fatigue, which can be used to reflect the level of subjective attention.

2.2.2. Cognitive Performance

Sustained attention to response task (SART) [40] was used to assess participants’ cognitive performance. The SART can evaluate the attention level in studies of restorative environments because it fits the definition of directed attention in ART [41]. The SART was run through PsychoPy v2023.1.2 [42]. In the SART, digits from 1 to 9 were presented randomly on the laptop screen every 1150 ms and remained visible for 250 ms. Each digit was presented 25 times during the task, totaling 225 occurrences for all digits. Participants were instructed to respond by pressing the spacebar of the laptop keyboard when a digit other than “3” was presented and to refrain from pressing it when “3” was presented. Additionally, they were instructed to assign equal importance to both speed and accuracy.
The following indicators were used to evaluate cognitive performance, reflecting the level of objective attention.
  • d-prime (D-P): the participant’s sensitivity in the detection of the target (digit “3”);
  • Errors of commission: when a response is made that is incorrect [43], represented here by the times the spacebar is pressed in the presence of the digit “3”;
  • Reaction time (RT): the average latency to press the spacebar;
  • Inverse efficiency score (IES) [44,45]: an integrated measure of speed and accuracy, calculated by dividing RT by the proportion of correct responses for digits other than “3”.

2.2.3. Physiological Response

Functional near-infrared spectroscopy (fNIRS) was used to assess participants’ physiological responses. fNIRS is a non-invasive optical technique and measures regional cerebral hemodynamic changes (i.e., changes in oxyhemoglobin (HbO) and reduced hemoglobin (HbR) levels) since HbO and HbR can be contrasted by their different optical absorption properties [46]. When a brain area is active and involved in a certain task, its metabolic demand for oxygen and glucose increases, resulting in an oversupply of regional cerebral blood flow, which leads to an increase in HbO and a decrease in HbR concentrations [47].
As a relatively new neuroimaging technology for investigating brain function, fNIRS offers advantages in terms of safety, portability, and movement tolerability. Compared to the more common electroencephalography (EEG), which detects the brain’s electromagnetic activity, fNIRS provides better spatial resolution and avoids causing substantial movement artifact issues [48], allowing participants a greater degree of natural movement and behavior during experiments. Additionally, fNIRS does not require the application of conductive paste, as is necessary with EEG, thereby reducing preparation time and lessening the psychological burden on participants. Consequently, fNIRS can obtain new data on psychological restoration from a hemodynamic perspective and is more suitable for experimental use.
The fNIRS device (NirSmart) used near-infrared light at wavelengths of 760 nm and 850 nm. In total, 7 light sources and 8 detectors were configured for the device, allowing channels to cover the prefrontal cortex (PFC) due to its important role in cognitive control [49]. The arrangement of channels is shown in Figure 2 and the sensors were evenly spaced at a 3 cm distance from each other.
Similar to subjective perception, mental fatigue was measured to reflect the level of objective attention. The relationship between mental fatigue and indicators acquired by fNIRS, such as HbO concentration [50], cerebral oxygen saturation [51], functional connectivity [52], and lateralization [53], was studied. Hemodynamic lateralization in PFC is a potential indicator of mental fatigue. This lateralization is defined as the difference in HbO concentration between the right and left PFC (RL HbO concentration), which increases as mental fatigue intensifies [54].

2.3. Procedure

The experiment was conducted in the time range from 2 to 6 p.m., considering the influence of biological rhythms. To investigate the restorative effects of environmental exposure to the workspaces, the experiment employed a pretest–posttest design. Firstly, a participant entered the workspace, took a seat, and was introduced to the experiment’s content by the experimenter, who deliberately refrained from disclosing the experiment’s purpose to prevent subjective bias. Following that, the participant engaged in the practice session for the cognitive tasks used in the experiment, ensuring familiarity with the mode of operation and the frequency of stimuli in the tasks. Finally, the experimenter placed the fNIRS device head cap on the participant and checked the strength of the acquired signals. The preparation was completed and the experiment began.
As shown in Figure 3, the participant rested for 3 min to allow the fNIRS to acquire resting-state physiological data as a baseline. Afterward, the participant filled out the 1st VAS and performed the 1st SART. The subsequent 2-back test, employed to induce mental fatigue in the participant [55], was set for 10 min. A fatigue induction phase is critical for studying restorative effects [19]. The participant then performed the 2nd SART and filled out the 2nd VAS. During the environmental exposure phase, the participant rested for 5 min while looking ahead. A minimum of 5 min of nature-related activities was suggested to yield positive psychological outcomes [56], while participants may feel bored [57] and results may be interfered with if longer than 5 min. In the end, the participant filled out the 3rd VAS and performed the 3rd SART. Given the potential impact of non-environmental physical parameters on cognitive performance [58], a white occlusion was placed on the table to prevent visual contact between the participant and the environment, except during the environmental exposure phase. The 1st SART, 2-back test, and the 2nd SART can be regarded as an entire mental fatigue induction phase, considering that the SART itself induces cognitive load. The whole experiment took about 50 min.

2.4. Participants

The one-way analysis of variance (ANOVA) in G*Power v3.1.9.7 was used to estimate the necessary sample size (α = 0.05, power = 0.8) [59], resulting in a minimum sample size of 76 subjects, equivalent to 19 subjects per group. The experiment employed a between-subjects design to avoid practice effects. Participants were recruited through chat groups and comprised students from the School of Architecture and Art Design. Finally, there were 80 participants (48 males and 32 females, with an age range of 22 to 28) in the experiment, and they were randomly assigned to four groups, ensuring an equal distribution of 20 participants (12 males and 8 females) in each group.
All participants were in good health with no history of psychiatric disorders. Before the experiment, they were instructed to obey the following guidelines on the day of the experiment: (1) refrain from consuming caffeinated beverages, (2) avoid strenuous exercise, and (3) ensure adequate rest. Each participant received a gift in compensation for completing the experimental task.
They gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Hebei University of Technology (HEBUThMEC2023020).

2.5. Statistical Analysis

The perceived mental fatigue score was obtained directly from the VAS. In the SART, whether the target response was correct and its reaction time were directly obtained, and then DP, errors of commission, RT, and IES were calculated.
The fNIRS raw data were preprocessed by NirSpark software developed on the basis of MATLAB software. In the data preprocessing, the artifacts unrelated to the experimental data were removed; bandpass filtering of 0.01–0.2 Hz was used to filter the noise and interference signals (e.g., heartbeat, respiratory rate, and instrument noise); the optical density was converted into the HbO and HbR concentration based on the modified Beer–Lambert law. The last minute of the 2nd SART and the environmental exposure phase were selected to represent before and after the environmental exposure for cerebral hemodynamic changes. The HbO concentration, after subtracting the baseline HbO values, was used for calculation. RL HbO concentration was then calculated based on this adjusted HbO concentration.
The delta values of the indicators before and after environmental exposure were used to represent the restorative effects of the workspaces, with higher delta values indicating stronger restorative effects. Considering individual differences in subjective perception, the recovery ratio (i.e., Δ perceived mental fatigue score/perceived mental fatigue score before environmental exposure) was used to compare the restorative effects on perceived mental fatigue. This normalization helped reduce the influence of individual differences to a certain extent and improved comparability.
Statistical analysis was performed using IBM SPSS Statistic 27. A one-way ANOVA was used to examine differences between groups, followed by Tukey’s HSD post hoc tests for pairwise comparisons. Before the one-way ANOVA, Levene’s test was used to examine the homogeneity of variance among groups. If variance was found to be heterogeneous, Welch’s ANOVA was used to examine group differences with Games–Howell post hoc tests.
In addition, the Pearson correlation coefficient was calculated to measure the relationship between the perceived mental fatigue recovery ratio, the delta values of SART indicators, and the delta values of RL HbO concentration. A paired t-test was used to compare the participants’ perceived mental fatigue before and after the induction to test the effectiveness of mental fatigue induction. The significance level was set to 0.05 for all statistical tests.

3. Results

The experiment was conducted from September 2023 to early November 2023. A male participant in the green wall group was excluded due to dozing off during the 3rd SART. Therefore, the data of 79 participants were analyzed in total. The result of the paired t-test indicates a significant increase in the participants’ perceived mental fatigue after the induction (Table 2), suggesting that the mental fatigue induction effectively consumed attention. In addition, no significant difference was found in perceived mental fatigue score, D-P, errors of commission, RT, IES, or RL HbO concentration before the environment exposure among the four groups.

3.1. Perceived Mental Fatigue

The perceived mental fatigue recovery ratio of the control group, green wall group, wood group, and combination group are, respectively, 0.39 (0.17, SD), 0.55 (0.22), 0.46 (0.31), and 0.53 (0.17). Levene’s test of homogeneity of variance showed no heterogeneity of variance in the perceived mental fatigue recovery ratio (p = 0.058), and ANOVA was used further. As shown in Figure 4, the result of ANOVA indicated that there was no significant difference in the perceived mental fatigue recovery ratio among the four groups (p = 0.241).

3.2. SART Performance

Table 3 shows the mean (SD) of the delta values of four indicators in SART performance; there was no heterogeneity of variance in the delta values. The results of ANOVA indicated that there was a significant difference only in the ΔErrors of commission among the four groups (F (3, 75) = 3.04; p = 0.034). The ΔD-P demonstrated a noticeable trend, although it did not reach statistical significance (p = 0.088). Consequently, Tukey’s HSD post hoc tests were used for pairwise comparisons. As shown in Figure 5, the ΔErrors of commission in the combination group were significantly higher compared to the control group (2.75, 95% CI [0.299, 5.202], p = 0.022).

3.3. Cerebral Hemodynamic Changes

Figure 6 shows the average HbO concentration in the PFC before and after environmental exposure, compared to the baseline, reflecting the activation of various PFC areas. Warm-colored areas indicate an increase in the average HbO concentration compared to the baseline, while cool-colored areas indicate a decrease. The average HbO concentration of each channel before and after environmental exposure is presented in Table A1. Channels 3, 4, 9, 12, 13, 17, 18, 21, and 22 were chosen for the right PFC, and channels 1, 2, 5, 10, 11, 14, 15, 19, and 20 were chosen correspondingly for the left PFC.
The ΔRL HbO concentration of the control group, green wall group, wood group, and combination group are, respectively, −0.22 (0.53, SD), −0.16 (0.46), −0.03 (0.54), and 0.21 (0.52). The assumption of homogeneity of variance was upheld (p = 0.631). The result of ANOVA indicated that there was a significant difference in ΔRL HbO concentration among the four groups (F (3, 75) = 2.76; p = 0.048). As shown in Figure 7, the ΔRL HbO concentration in the combination group was significantly higher compared to the control group (0.43, 95% CI [0.004, 0.858], p = 0.047).

3.4. Correlation Between the Indicators

The delta values of the difference between symmetric channels before and after the environmental exposure were also included in the correlation analysis as they were important components of the ΔRL HbO concentration. The Pearson correlation coefficient revealed significant positive correlations between the perceived mental fatigue recovery ratio and the following variables: ΔD-P (r = 0.238, p = 0.035), ΔErrors of commission (r = 0.262, p = 0.019), and ΔChannels 14–18 HbO concentration (r = 0.308, p = 0.006). Additionally, a significant positive correlation was observed between the ΔChannels 5–9 HbO concentration and ΔD-P (r = 0.223, p = 0.048). Although the correlations are statistically significant, they are considered weak (Figure 8).

4. Discussion

The results of this study showed that the patterns of indoor biophilic design and its combination used in workspaces had different restorative effects mainly on cognitive performance and physiological response.
The result of perceived mental fatigue indicated that indoor biophilic design made no significant difference in subjective attention restoration during environmental exposure, a phase in which the participants took a break from the cognitive tasks and observed the surrounding environment. The recovery ratio averages for the four groups was relatively high, as the participants reported their perceived mental fatigue at two distinct times: after completing heavy cognitive tasks and after a break spent observing the surroundings, which was an important factor.
The SART performance results indicated that the participants in the combination group had a larger difference in errors of commission (incorrect response for the target) before and after the environmental exposure compared to the control group. This suggested fewer errors of commission, indicating greater accuracy following the environmental exposure. Given the absence of a significant difference in ΔRT, it can be inferred that the combination had a more favorable effect on cognitive performance in terms of restoration. The difference in cognitive performance before and after exposure corresponded to the degree of attention restoration objectively.
The results of the lateralization of HbO concentration in PFC provided support for the SART performance results. The participants in the combination group had a larger difference in RL HbO concentration before and after the environmental exposure compared to the control group. This suggested their lower degree of lateralization in PFC after the exposure, reflecting a lower level of mental fatigue and more attention restoration. The lateralization of HbO concentration in PFC indicated asymmetric activation related to cognitive loads, and asymmetric brain activation corresponds to different types of mental fatigue [60]. Participants with a lower degree of lateralization experienced a less cognitive load, potentially leading to better performance in the SART after the exposure. This can be explained in part by the positive correlation between the change in a symmetric channel difference and the change in SART performance before and after the exposure.
Therefore, it can be inferred that the workspace featuring a combination of two biophilic design patterns exhibited greater restorative effects on cognitive performance and physiological response during environmental exposure compared to the workspace without biophilic design.

4.1. Single Biophilic Design Pattern

The experimental settings for the green wall group and the wood group, each employing a single biophilic design pattern, represented the visual connection with nature and material connection with nature patterns, respectively, from two categories of biophilic design. The results showed that these settings had no restorative effect on subjective perception, cognitive performance, or physical response compared to the control group without biophilic design, indicating that the restorative effects of a single biophilic design pattern are limited.
The green plant wall was used to represent the visual connection with nature pattern. One study reported no improvement in participants’ attention capacity before and after rest in offices, regardless of whether indoor plants were present [61]. Similarly, another study comparing study rooms with or without indoor plants found no meaningful related result of users’ fatigue and cognitive performance [62]. For the material connection with nature pattern, a wooden wall was introduced. Comparisons between wood and non-wood indoor environments, differentiated by furniture, revealed no significant differences in restorative effects on either cognitive performance or physical response [63,64]. Although the form, size, and other details of the experiment settings in this study differed from those in previous studies, the findings are consistent with some of the results reported earlier.
Another possibility is that the expected restorative effects were disrupted by other factors. Some results from the SART showed no improvement in the participants’ attention capacity after exposure to natural scenes [65], possibly due to sample differences, individual differences, insufficient attentional depletion (mental fatigue induction), or different testing environments and procedures [66]. Identifying specific cognitive tasks that are more likely to benefit from attention restoration is essential to avoid scenarios where the restorative effect is not observed.

4.2. Combined Biophilic Design Patterns

The results also suggest that the combination of the two patterns generated a synergistic effect on restoration. An indoor biophilic office featuring green walls, potted plants, and a wooden roof and floor was found to have better restorative effects from the perspective of post-stress physiological response (heart rate variability (HRV) and skin conductance level (SCL)), and facilitated greater physiological stress recovery compared to an office with only a natural window view [21]. Some studies investigating the restorative effects of indoor environments with biophilic visual stimuli, combined with auditory or olfactory stimuli, which can be seen as the pattern of non-visual connection with nature, found that combined stimuli demonstrated better effects compared to single-sensory stimuli from a physiological response perspective [23,24,67]. Although the biophilic design patterns used in the present study were both visual, the results are consistent with these studies, suggesting the restorative advantage of combining biophilic design patterns indoors. In addition, a study comparing the effects of viewing natural images with different proportions of sky, leaves, and tree trunks on attention restoration found that images with a balanced proportion of these elements were more effective in restoring attention than those with a high proportion of a single element. Similarly, compared to the green wall group and the wood group, the combination group provided a more balanced representation of these elements [68].
According to ART, the core of restorative experience includes four components: fascination, being away, extent, and compatibility. Fascination refers to stimuli that attract attention softly, while extent refers to enough richness and coherence [9]. Although the presence of green plants or the texture of wood can softly attract attention, an essential factor for restoration, this alone may be insufficient for the restoration process to occur. By enriching the elements within the space, the combination of these features seemed to enhance their harmony and the overall coherence of the environment, where the elements were organized in a relatively reasonable and orderly way, thereby improving the restoration process to some degree. An EEG study comparing restorative and non-restorative environments found that a perceived sense of coherence within the environment resulted in increased alpha/theta synchronization in the brain. This synchronization suppresses lower visual inputs, allowing the brain to focus more on self-restoration [69].
Although the perceived sense of coherence was not directly investigated in this study, the combination of elements can be regarded as objectively broadening the coherence of the environment. Some participants reported that the green plant wall appeared somewhat abrupt against the white wall, indicating poor coherence in the green plant group setting. A study on the impact of material characteristics in natural environments found that natural environments with wooden facilities were significantly higher than those with metal facilities (unnatural) in terms of perceptual coherence [70].
Furthermore, there is a connection between the coherence in natural scenes and their ‘fractal’ characteristics—a mathematical framework used to describe natural forms and processes [71]. Fractals are deeply related to the shape of natural structures [72] and these living structures can have a positive impact on human well-being [73]. The ‘fractal fluency’ model suggests that human vision has become adept at processing the visual language of nature’s fractals, making it efficient at recognizing these patterns [74]. As a result, when exposed to non-threatening urban scenes and natural environments, the human visual system processes the latter more fluently, requiring fewer cognitive resources. According to the Perceptual Fluency Account (PFA), restoration is a by-product of this fluent processing [71]. The high restorative potential of natural scenes can be attributed to their fractal characteristics, whereas built environments tend to be less restorative due to their reliance on Euclidean geometry [75]. It can be speculated that the workspace used by the combination group in this study may have an advantage in fractal dimension, thereby promoting restoration.
However, this finding is inconsistent with a study on biophilic design in classrooms which can be seen as a special type of workspace for students [76]. In that study, indoor plants on the walls and wooden floor were used in one of the experimental settings, representing a similar combination of biophilic design patterns in the present study, but no significant change in physiological reactions (including HRV and SCL) and cognitive functions was found compared to the classroom without biophilic design. The inconsistency may be attributed to differences in experimental procedures. In terms of physiological measures, although they compared pre/post physiological changes, their focus was not on restoration, so no induction phase was included in the procedure and the cognitive task was conducted only after environmental exposure. Besides the specific forms of biophilic design patterns, the VR environment or measurement methods may also play a critical role.

4.3. Limitations and Future Work

The generalizability of these findings is limited by the small sample size and the insufficient exploration of factors related to the experimental settings (e.g., details of plants and wood) and experimental procedure (e.g., duration of the experimental exposure, degree of fatigue induction). For instance, the present study focused solely on environmental settings in terms of size while the green view index is a crucial parameter for green space that can influence the restoration effect [77,78]. The plants in the green wall were not in optimal condition, which may have diminished the potential for restoration. In terms of spatial scale, the workspaces investigated in this study were small, and whether these findings are applicable in larger workspaces requires further research. Slight variations in these factors may have led to different results. Therefore, it is necessary to investigate the specific forms and characteristics of biophilic design patterns in future research, with a focus on a few key conventional features first. Future research should also explore the restorative effects of indoor environments that combine multiple biophilic design patterns as current studies commonly focus on two patterns.
On the other hand, the experimental procedures in the current research have their own unique characteristics and differ greatly in detail. Considering the possible influence of experimental process factors on the results, a multifaceted effort is needed to develop a standardized experimental process that facilitates the comparison of results across related studies. Once a standardized experimental process is established, future research can focus more specifically on configuring biophilic design elements, integrating scattered influencing factors into a cohesive system, and enabling more targeted expansion of studies in this field.
In particular, the green wall and the wooden wall appeared to contribute differently to psychological restoration, as observed from the perspective of mean values in the indicators. Subsequent studies could consider designing experiments to separate their respective contributions to recovery, thereby optimizing the configuration of the patterns combination to provide more detailed guidance for the biophilic design. The inconsistency between objective and subjective psychological restoration also deserves attention, and their association is not yet clear, although some positive correlations were revealed in this study. In terms of physiological response measurement, multimodal approaches are suggested for future work, such as combining fNIRS with autonomic nerve system measurements (e.g., SCL), which could improve the comparability of studies.

5. Conclusions

In this study, subjective scale, cognitive task, and fNIRS were employed to investigate the effects of two typical biophilic design patterns and their combination on human psychological restoration in real workspaces. The results indicated that the workspace incorporating a combination of two patterns possessed greater restorative effects on cognitive performance and physiological benefits compared to the workspace lacking biophilic design. Conversely, the restorative impact of the workspace featuring a single pattern did not show improvement. Biophilic design possesses rich connotations to explore, and the relevant data acquired by neuroscience tools also need to be assessed further. Despite some limitations, this study provides valuable insights into enhancing the restorative effects of workspaces through biophilic design patterns. Such improvements have the potential to enhance people’s work experiences and contribute to promoting mental health.

Author Contributions

Conceptualization, P.Z. and Z.Y.; methodology, Z.Y.; software, Z.Y.; validation, Y.B., Y.S. and R.N.; formal analysis, Z.Y.; investigation, Z.Y.; resources, P.Z.; data curation, Z.Y.; writing—original draft preparation, Z.Y.; writing—review and editing, P.Z.; visualization, Z.Y.; supervision, G.H. and P.S.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 51508151.

Data Availability Statement

The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to express our gratitude to each participant for their active engagement.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The average HbO concentration (μM) of each channel before and after environmental exposure.
Table A1. The average HbO concentration (μM) of each channel before and after environmental exposure.
ControlGreen WallWoodCombination
ChannelBeforeAfterBeforeAfterBeforeAfterBeforeAfter
10.0060.027−0.059−0.049−0.066−0.043−0.0230.055
20.028−0.005−0.055−0.017−0.0150.0120.0110.047
30.060.025−0.067−0.046−0.044−0.013−0.0020.027
4−0.0010.05−0.069−0.023−0.079−0.025−0.030.017
50.0410.035−0.011−0.007−0.053−0.028−0.0150.027
60.0180.029−0.063−0.034−0.04−0.033−0.033−0.009
70.0490.035−0.015−0.013−0.0130.0080.0270.059
80.0510.041−0.052−0.036−0.028−0.035−0.0070.006
90.002−0.028−0.0470.0270.0770.080.0010.065
100.016−0.0140.0260.0030.050.0710.0150.014
110.0110.07−0.026−0.02−0.117−0.1080.0150.013
12−0.028−0.032−0.031−0.004−0.053−0.0280.0280.06
130.0490.035−0.0210.029−0.017−0.0170.0720.068
140.2010.275−0.088−0.0080.0110.0290.0050.008
15−0.012−0.052−0.0510.016−0.017−0.0170.0290.001
160.0020.009−0.0050.016−0.027−0.0610.010.021
17−0.0010.021−0.0160.008−0.015−0.017−0.0040.005
18−0.0050.012−0.0230.005−0.0820.0250.0050.014
1900.0460.0090.024−0.04−0.080.0070.038
200.0060.011−0.0010.026−0.0030.010.0010.016
2100.026−0.024−0.018−0.0170.0130.0330.042
22−0.0070.046−0.0370.028−0.0080.015−0.0030.024

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Buildings 14 03293 g001
Figure 1. Four experimental conditions and their views.
Figure 1. Four experimental conditions and their views.
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Figure 2. Arrangement of fNIRS channels.
Figure 2. Arrangement of fNIRS channels.
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Figure 3. Experimental procedure.
Figure 3. Experimental procedure.
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Figure 4. Perceived mental fatigue recovery ratio (black solid block: mean value).
Figure 4. Perceived mental fatigue recovery ratio (black solid block: mean value).
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Figure 5. The delta values of four indicators in SART performance. * p < 0.05.
Figure 5. The delta values of four indicators in SART performance. * p < 0.05.
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Figure 6. The PFC activation before and after the environmental exposure (the dark blue areas on both sides were not measuring areas).
Figure 6. The PFC activation before and after the environmental exposure (the dark blue areas on both sides were not measuring areas).
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Figure 7. ΔRL HbO concentration. * p < 0.05.
Figure 7. ΔRL HbO concentration. * p < 0.05.
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Figure 8. The correlation between the indicators.
Figure 8. The correlation between the indicators.
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Table 1. Three categories and 14 patterns of biophilic design.
Table 1. Three categories and 14 patterns of biophilic design.
Categories Patterns
Nature in the SpaceVisual Connection with NatureNon-Visual Connection with NatureNon-Rhythmic Sensory Stimuli
Thermal and Airflow VariabilityPresence of WaterDynamic and Diffuse Light
Connection with Natural Systems
Natural AnalogsBiomorphic Forms and PatternsMaterial Connection with NatureComplexity and Order
Nature of the SpaceProspectRefugeMystery
Risk/Peril
Table 2. The effect of the mental fatigue induction on the mean (standard deviation, SD) of the participants’ perceived mental fatigue.
Table 2. The effect of the mental fatigue induction on the mean (standard deviation, SD) of the participants’ perceived mental fatigue.
GroupBefore InductionAfter Inductionp-ValueEffect Size (Cohen’s d)
Control19.3 (20.1)61.5 (15.7)<0.0011.995
Green Wall22.8 (23.1)54.9 (23.0)<0.0011.346
Wood12.0 (13.6)50.7 (23.8)<0.0011.766
Combination22.6 (14.7)61.9 (19.7)<0.0011.993
Table 3. The effect of the mental fatigue induction on the mean (SD) of the participants’ perceived mental fatigue.
Table 3. The effect of the mental fatigue induction on the mean (SD) of the participants’ perceived mental fatigue.
GroupΔD-PΔErrors of CommissionΔRTΔIES
Control−0.16 (0.58)−1.35 (3.50)28.12 (50.86)30.74 (52.09)
Green Wall0.03 (0.63)0.16 (2.24)3.43 (44.65)4.02 (47.46)
Wood0.15 (0.47)0.55 (2.28)5.22 (22.40)3.81 (25.28)
Combination0.31 (0.67)1.40 (3.49)−1.22 (51.14)2.65 (51.30)
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Zhang, P.; Yu, Z.; Hou, G.; Shu, P.; Bo, Y.; Shi, Y.; Nie, R. Enhancing Cognitive Performance and Physiological Benefit in Workspaces Through Patterns of Biophilic Design: A Restorative Approach. Buildings 2024, 14, 3293. https://doi.org/10.3390/buildings14103293

AMA Style

Zhang P, Yu Z, Hou G, Shu P, Bo Y, Shi Y, Nie R. Enhancing Cognitive Performance and Physiological Benefit in Workspaces Through Patterns of Biophilic Design: A Restorative Approach. Buildings. 2024; 14(10):3293. https://doi.org/10.3390/buildings14103293

Chicago/Turabian Style

Zhang, Ping, Zhengqi Yu, Guoying Hou, Ping Shu, Yunque Bo, Yankun Shi, and Rui Nie. 2024. "Enhancing Cognitive Performance and Physiological Benefit in Workspaces Through Patterns of Biophilic Design: A Restorative Approach" Buildings 14, no. 10: 3293. https://doi.org/10.3390/buildings14103293

APA Style

Zhang, P., Yu, Z., Hou, G., Shu, P., Bo, Y., Shi, Y., & Nie, R. (2024). Enhancing Cognitive Performance and Physiological Benefit in Workspaces Through Patterns of Biophilic Design: A Restorative Approach. Buildings, 14(10), 3293. https://doi.org/10.3390/buildings14103293

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