Research on the Healing Effect of the Waterscapes in Chinese Classical Gardens in Audiovisual Interaction

Abstract

As an important part of world cultural heritage, waterscapes in Chinese classical gardens are renowned for their unique design, rich cultural connotations, and distinctive esthetic value. However, objective studies of their impact on mental health are lacking. This paper focuses on Xishu Garden, a Chinese classical garden, and examines four types of waterscapes (for a total of twelve) using eye-tracking technology and the Perceived Restorativeness Scale (PRS). The aim of this study is to explore the restorative effects of different types of waterscapes with visual and audiovisual conditions, with particular attention paid to their mechanisms of action. The research results indicate that (1) waterscapes with an audiovisual interaction have a greater restorative value; (2) dynamic waterscapes have greater visual appeal than still landscapes do, but the latter have stronger environmentally restorative effects; and (3) the visual behavioral characteristics of waterscapes change during audiovisual interactions. This study contributes theoretical support for the maintenance and enhancement of Chinese classical gardens and the planning and design of modern urban green spaces, and it enriches our understanding of the role of waterscapes in restorative environments.

1. Introduction

The fast-paced lifestyle, high-intensity pressure of work and study, and growing population of the 21st century have produced a significant mental health crisis for global residents [1,2,3]. According to reports from the World Health Organization, mental health issues are steadily increasing worldwide, and overall health levels are continuously declining [4,5]. Numerous studies have confirmed that exposure to green environments has restorative effects that can improve mental health and reduce stress [6,7,8]. As a unique type of green environment, Chinese classical gardens integrate ancient Chinese health care theories and the concept of “harmony between man and nature” to embody the traditional medicine and survival wisdom of the Chinese people. By presenting a beautiful environment that is “derived from nature yet surpasses nature” [9], these gardens exert effects that include physical health, mental rehabilitation, and well-being [10]. As an important example of a Chinese classical garden, Xishu Garden shares these characteristics but has its own unique regional features and health benefits. In Xishu Garden, the waterscape constitutes a relatively large proportion of the landscape. The body of water is often regarded as the main landscape element, and there are almost no gardens without water.

Waterscapes (often referred to as “blue spaces”), including oceans, lakes, rivers, waterfalls, and fountains, have been found to provide various psychological and physiological benefits and to contribute to overall well-being [11,12]. To better understand the restorative effects of waterscapes, we draw upon three major theories in environmental psychology: attention restoration theory (ART) [13], stress reduction theory (SRT) [14], and the biophilia hypothesis [15]. (1) ART posits that prolonged directed attention can lead to cognitive fatigue. Natural environments provide “soft fascination,” a form of effortless attention that does not require intense cognitive engagement. This allows the brain’s directed attention mechanisms to rest and recover. (2) SRT suggests that humans have an innate preference for natural settings. Exposure to such environments can activate the parasympathetic nervous system, resulting in a decreased heart rate and blood pressure and reduced secretion of stress hormones. These physiological changes contribute to alleviating both psychological and physical stress. (3) The biophilia hypothesis proposes that humans possess an inherent affinity for the natural world, a tendency rooted in an evolutionary dependence on natural resources. Interaction with nature satisfies fundamental psychological needs, thus enhancing well-being and promoting both mental and physical health. Ulrich’s pioneering study revealed that clinical patients who viewed natural landscapes, especially those with water, recovered faster and experienced less pain than did patients who saw only urban landscapes without natural elements [16]. Finlay et al. reported that exposure to water environments significantly improved the quality of life and mental health of elderly individuals [17]. Using longitudinal analysis, Keijzer et al. emphasized the importance of blue spaces in promoting active lifestyles and enhancing overall well-being in older adults [18]. These findings highlight the importance of incorporating blue spaces into urban planning to meet the needs of the aging population. Huynh et al. reported that people who exercised near water showed more significant improvements in mood and self-esteem than did those who exercised in green spaces only [19]. Gao et al. reported that exposure to blue spaces significantly alleviated negative emotions and attention fatigue [20], and Luo et al. confirmed that even brief exposure to water environments could yield positive psychologically restorative effects [21]. In addition to exploring improvements in psychological restoration through waterscapes, studies have examined the restorative potential of the soundscapes within water environments [22,23,24]. Although numerous studies have demonstrated the strong restorative potential of waterscapes, previous research has typically treated waterscapes as homogenous entities [25], largely overlooking the internal variations within them. For example, whether different types of waterscapes yield distinct restorative effects remains unclear. To bridge this gap, the present study categorizes waterscapes based on their dynamic characteristics into four types: still water, flowing water, spraying water, and falling water.

In real-world settings, people do not perceive their surroundings solely through visual input. Instead, environmental perception is inherently multisensory, involving auditory, olfactory, and tactile stimuli. Among them, vision accounts for approximately 80% of sensory perception [26], followed by auditory perception—also known as the sound-scape—which plays a particularly significant role. Since the Canadian composer R. Murray Schafer proposed the concept of the soundscape in the 1960s [27], it has developed rapidly on a global scale. In 1978, Barry Truax defined a soundscape as the auditory environment perceived and understood by individuals or society [28], including both real and imagined environments. This definition later became one of the foundations of the concept of soundscapes issued by the International Organization for Standardization (ISO). Buxton et al. reported that natural sounds in national parks are related to health benefits and emphasized the potential of natural soundscapes to enhance visitors’ experiences and promote well-being in natural environments [29]. Uebel et al. and Michels et al. further explored the effects of specific natural sounds (such as birdsong and water sounds) on stress recovery and reported that these sounds could significantly improve negative emotions [30,31]. However, relying solely on visual perception or auditory perception can introduce bias into the results of environmental restoration studies. In contrast, environments with audiovisual interactions allow for more engagement and restorative effects [32]. Alvarsson et al. reported that natural sounds combined with visual stimuli led to a faster decrease in skin conductance levels and more effective stress recovery [33]. Yang et al. also confirmed that the audiovisual stimulation in natural environments offers greater health benefits than visual stimuli alone [34]. From a theoretical perspective, audiovisual integration is not merely the sum of visual and auditory inputs, but a synergistic process that enhances environmental coherence and the immersive experience. Therefore, research into the restorative benefits of audiovisual integration is of great importance. What makes waterscapes unique within natural landscapes is their intrinsic integration of movement and sound, offering a tightly coupled audiovisual environment. This distinctive feature positions waterscapes as an ideal setting for investigating how the integration of visual and auditory stimuli contributes to psychological restoration. Hence, waterscapes represent a valuable yet underexplored subject in the field of environmental psychology. Although previous studies have explored the relationship between the characteristics of waterscapes and the restorative benefits of their associated soundscapes [25,35], the use of randomly combined audiovisual materials has often resulted in mismatched sensory stimuli, introducing potential biases in the findings. Moreover, isolating water sounds from their environmental context does not accurately reflect real-world acoustic environments. In this research, synchronized audiovisual recordings from actual settings are used to ensure ecological validity. To address these limitations, it is necessary to systematically compare the unique contributions of various types of natural waterscapes and their soundscapes to health and well-being to enrich our understanding of the role of waterscapes in restorative environments.

To conceptually ground the investigation, this study adopts a multisensory restoration perspective, wherein auditory and visual stimuli are not merely additive, but interactively influence attentional mechanisms. Natural environments rich in congruent audiovisual cues promote perceptual fluency, enabling more efficient information processing and reducing cognitive effort. This joint modality effect is particularly salient in waterscapes, where visual motion (e.g., ripples and flow) and natural water sounds are often co-occurring and temporally synchronized. Therefore, examining their interaction provides a more ecologically valid understanding of how environmental multisensory features support psychological recovery.

Eye-tracking technology can observe eye movements and measure the position, movement, and fixation of eyes as well as pupil diameter to assess visual attention [36]. Studies have confirmed that eye-tracking technology can provide objective data on restorative environments. For example, Nordh et al. classified environmental components on the basis of eye-tracking data collected during a restorative rating task [37], and Wu Y. et al. explored the relationships among the perceived design intensity, preference, and eye movements in urban green spaces [38]. Similarly, Kang and Kim used eye tracking to reveal different scanning behaviors related to perceived restoration [39].

On this basis, this study uses eye-tracking technology to conduct an objective quantitative analysis of participants’ perceptions to provide data support for the interaction between the visual and auditory systems in the process of attention restoration. This study focuses on the waterscape in Xishu Garden and combines eye-tracking measurements with the Perceived Restorativeness Scale (PRS) to answer the following questions: (1) What are the differences in the restorative benefits of waterscapes with a single type of visual stimulation or audiovisual interaction? (2) Is there a difference in environmental restoration between different types of waterscapes? (3) What are the differences in the visual behavioral characteristics of waterscapes with single types of visual stimulation or audiovisual interaction?

2. Materials and Methods

2.1. Experimental Materials

This study selected seven Xishu Gardens with rich waterscapes as case studies. The locations, environmental characteristics, and photos of the sample sites are shown in

Table 1. Characteristics of the sample sites.

Sample Sites	Location	Water Feature Type	Environmental Characteristics	Soundscape Characteristics
S1	Wang Cong Temple	Flowing Water	A straight, hard-edged embankment water channel with slow-moving water flowing forward	The sound of flowing water predominates, occasionally accompanied by birdsong
S2	Du Fu Thatched Cottage		A natural, straight water channel enclosed by plants on both sides with gently flowing water featuring slight differences in elevation	The sound of flowing water
S3	San Su Shrine		A natural landscape of ancient architecture and a pond with water flowing near the building	The sound of flowing water predominates, faintly accompanied by birdsong
S4	Wuhou Shrine	Falling Water	A linear waterfall flowing through the middle of a natural hillside leading into a pond	The sound of a waterfall splashing against rocks
S5	San Su Shrine		Water cascades down from multiple layers of rocks surrounded by dense vegetation	The sound of water splashing against rocks
S6	Du Fu Thatched Cottage		Water flows rapidly down from the rocks, enclosed by plants and boulders	The sound of water splashing
S7	Guihu Lake	Spraying Water	A natural pond enclosed by plants, featuring a small linear fountain in the center	The sounds of falling water and birdsong
S8	East Lake Park		A pond featuring an umbrella-shaped fountain and a small linear fountain with ancient architecture as the main backdrop	The sounds of spraying water and conversation
S9	Fanghu Park:		In the center of an open lake, there is an umbrella-shaped fountain surrounded by tall trees along the edges	Predominantly the sounds of spraying water and falling water occasionally accompanied by birdsong
S10	San Su Shrine	Still Water	A tranquil, open lake surface with ancient architecture forming a linear arrangement in the foreground	Various types of birdsong
S11	San Su Shrine		A tranquil pond, mostly occupied by lotus flowers, surrounded by ancient architecture and plants	Predominantly the sounds of cultural poetry accompanied by birdsong
S12	Wuhou Shrine		A tranquil pond surrounded by plants with ancient buildings hidden among the greenery	Birdsong

and Figure 1. To minimize the influence of nonaudiovisual perceptual factors such as climate, light, and temperature, the experiment was conducted by showing the participants videos in a controlled laboratory environment.

The experimental materials for this study were filmed from 23 September to 1 October 2023 (weather: cloudy), from 10:00 a.m. to 5:00 p.m. when the lighting was optimal. Different angles of the waterscapes in the seven gardens were filmed onsite for a minimum of 2 min each, and their exact locations were recorded on a map. The video recording was made with a Canon G7X Mark III (manufacturer: Canon, Tokyo, Japan). To ensure high-quality audio, a Canon high-fidelity recorder was used. During postproduction, the footage was edited and synthesized using Adobe Premiere, and all clips were uniformly cut to 40 s. Ultimately, through video quality assessment, evaluation of the ability of the video to reflect the real situation, and feedback from landscape experts, a total of 12 video segments were selected on the basis of the type of waterscape. Based on the observable physical dynamics and acoustic characteristics of water bodies, the 12 video clips were categorized into four types of waterscapes: flowing water (S1–S3), falling water (S4–S6), spraying water (S7–S9), and still water (S10–S12). This classification was conducted through onsite assessments and verified by three landscape experts to ensure consistency between water movement and the corresponding sound features. For the purely visual condition, the audio tracks were muted using Adobe Premiere during the editing process. Both the audiovisual and visual-only versions were trimmed to a uniform length of 40 s, ensuring that the only difference between the two conditions was the presence or absence of sound.

2.2. Experimental Subjects

Forty college students were recruited for this experiment. To ensure the accuracy of the experimental data, all participants were required to be in good health, without any diagnosed mental or neurological disorders and with normal unaided or corrected vision (≥1.0), no color blindness, and normal hearing (threshold < 25 dB). After data collection, the quality of the eye-tracking data was assessed. According to the criteria provided by Tobii Pro Lab (v1.232), participants with a pupil capture rate less than 80% or with more than 20% invalid or missing eye-tracking data were excluded because of insufficient data reliability. On the basis of these criteria, data from 7 participants were removed, resulting in a final sample of 33 individuals, including 15 males (45.5%) and 18 females (54.5%), with an average age of 22.0 ± 3.0 years. This experiment was approved by the Ethics Committee of Sichuan Agricultural University, and all subjects participated voluntarily.

2.3. Data Collection

2.3.1. Eye-Tracking Data

To minimize the impact of external perceptual factors such as lighting, temperature, and noise, this experiment was conducted at the Digital Landscape Laboratory of Sichuan Agricultural University. The 12 video segments were imported into Tobii Pro Lab (v1.232) software for design. To reduce the influence of the video order on the data, the 12 segments were designed for random playback and presented on a 27-inch LCD monitor (Model: Lenovo LEN63A9 T27p-30; resolution: 3840 × 2160 pixels). The eye-tracking data collection device used was Tobii Pro Glasses 2 (manufacturer: Tobii AB, Danderyd, Sweden) wearable eye tracker (sampling rate: 100 Hz; scene camera video format and resolution: H.264 1920 × 1080 pixels @ 25 fps; operating distance: 60–100 cm), as shown in Figure 2. Data collection was conducted in conjunction with Tobii Pro Glasses Controller (v1.14) software, and the collected data were imported into the Tobii Pro Lab (v1.232) software for processing and analysis.

According to ART and SRT, restoration is conceptualized as a multistage experiential process [40]. It begins with the elicitation of effortless attention by natural environments, which alleviates directed attention fatigue. The positive emotional responses triggered by natural settings subsequently further facilitate stress reduction [40], leading to decreased physiological stress indicators. The alleviation of stress supports cognitive functioning and concentration, which in turn creates favorable conditions for the recovery of directed attention. Therefore, among the eye-tracking metrics commonly used in previous studies, the fixation duration is the most frequently employed indicator [41]. In ART, attention fatigue is often regarded as a consequence of stress [42]. Under stress, the sympathetic nervous system is activated, signaling the adrenal glands to release cortisol and catecholamines into the body, thereby inducing physiological responses [43]. The observable changes in physiological indicators include pupil dilation, an increased blink rate, reduced heart rate variability (HRV), and elevated blood pressure [41].

Based on these insights, the present study selects four eye-tracking indicators for analysis: the average pupil diameter (APD), the average fixation duration (AFD), fixation frequency (FF), and the total fixation duration (TFD). The significance of each metric is summarized in

Table 2. Basic significance of eye movement metrics.

Eye Movement Indicator	Abbreviation	Interpretation	Basic Meaning	Unit
Average Pupil Diameter	APD	Average size of pupil dilation or constriction during observation	Reflects the degree of cognitive/mental load of the subject	mm
Average Fixation Duration	AFD	Average time spent at each gaze point	Indicates the degree of distinctiveness and interest in object features	ms
Fixation Frequency	FF	Number of gazes per unit of time	Indicates the efficiency of information perception	times/s
Total Fixation Duration	TFD	Sum of all gaze times	Indicates the overall cognitive time of the subject	s

.

2.3.2. Subjective Perceived Restorative Effect

In addition to the use of eye-tracking equipment to collect dynamic changes in restorative effects on participants in different environments, the subjective perceptions of the participants were assessed. The PRS was used to evaluate the participants’ subjective perceptions of the restorative effects. This scale was developed by Hartig and Korpela et al. in 1997 [44] and has been applied in numerous environmental restoration experiments. The scale includes four main dimensions, namely, being away, fascination, coherence, and compatibility [45], which correspond to the characteristics of restorative environments. Each dimension contains four questions for a total of 16 questions, and each question is rated on a scale ranging from 1 to 7 (where 1 indicates strong disagreement and 7 indicates strong agreement). A higher total score indicates a better perceived restorative effect of the environment. Although the PRS was originally developed to assess individuals’ perception of restorative quality in real-world natural environments [44], subsequent studies have validated its applicability in laboratory-controlled virtual settings, including experiments employing photographs [46,47], videos [48], or virtual reality simulations [49,50] to represent natural environments. Consequently, the PRS is deemed appropriate for this study utilizing video-based simulations of natural environments.

2.4. Experimental Procedure

To ensure experimental validity and minimize potential biases, this study adopted a within-subjects design incorporating both randomization and a single-blind procedure. Each participant completed two experimental sessions: one under a purely visual (silent) condition and another under an audiovisual condition. The two sessions were separated by a minimum interval of two days to reduce memory effects. To avoid order-related biases, all 12 video clips were presented in a randomized sequence using the randomization function in Tobii Pro Lab software, thus eliminating potential sequence effects. Furthermore, to maintain perceptual authenticity and reduce expectancy effects and subjective bias, the participants were not informed that the experiment involved both audiovisual and visual-only conditions, nor were they made aware of this study’s hypotheses regarding potential differences in restorative outcomes between the two conditions. Instead, they were simply told that they would be watching a series of garden landscape videos. This approach was intended to ensure that the participants’ perceptions and responses were based entirely on their immediate experience, uninfluenced by prior assumptions or demand characteristics.

The specific procedure was as follows. (1) In the preparation stage, the participants arrived at the designated laboratory, were directed to a rest area for 3–5 min, and provided relevant basic information and informed consent for the experiment. At the same time, the experimental process and the use of the equipment were explained. (2) In the stress stage, the participants completed mental arithmetic of math problems without the help of a pen and paper according to computer screen prompts. The participants were asked to inform the experimenter of the answers and to complete as many problems as possible. This process lasted for 90 s. A previous study has shown that cognitive load and stress increase significantly when individuals perform mental arithmetic under time constraints or complex conditions [51]. (3) In the eye-tracking calibration stage, the participants were equipped with Tobii Pro Glasses 2 eye-tracking devices and instructed to fixate on a black circular calibration point (5 mm in diameter) displayed at the center of the screen. They were asked to keep their heads steady and maintain natural fixation on the point for 5 s until calibration was confirmed by the Tobii Pro Glasses Controller (v1.14). To minimize external auditory distractions, the participants also wore over-ear headphones throughout the process. (4) In the formal experiment stage, the participants viewed videos of waterscapes that were played randomly on the computer (to mitigate the impact of the video order) in a comfortable posture. They immersed themselves in the scenes while the eye tracker recorded relevant data, as shown in Figure 3. (5) In the questionnaire completion stage, after the video playback concluded, the participants removed the equipment and moved to a nearby area to complete the PRS. (6) At the end of the experiment, after completing all steps, the participants received a small gift and exited the laboratory in an orderly fashion. The total duration of the experiment was approximately 30 min, as illustrated in Figure 4.

2.5. Data Analysis

The eye-tracking data were processed using Tobii Pro Lab (v1.232) software, from which relevant eye movement metrics and heatmaps were exported. All the data were subsequently imported into Excel and SPSS 27 for further statistical analysis. Two-way repeated-measures ANOVA was conducted to examine the main effects and interaction effects of the waterscape type (flowing/falling/spraying/still water) and sensory condition (audiovisual/visual-only) on the dependent variables, including PRS scores and four eye-tracking indicators: the average fixation duration (AFD), fixation frequency (FF), the total fixation duration (TFD), and the average pupil diameter (APD). Post hoc pairwise comparisons were conducted with Bonferroni correction. In addition, paired-sample t tests were conducted to compare the overall differences between the audiovisual and visual-only conditions. Statistical significance was set at p < 0.05. Effect sizes for the ANOVA results were reported using partial eta squared (η²), whereas Cohen’s d was used to report effect sizes for the paired comparisons. Finally, Pearson correlation analyses were performed to explore the relationships between the eye-tracking metrics and subjective perceptions. The main steps of this research are illustrated in Figure 5.

3. Results

3.1. Analysis of Subjective Perceived Restorative Effects

SPSS 27 software was used to conduct reliability and validity tests on the results of the PRS in this study. The Cronbach’s alpha coefficients were calculated for all the participants’ data in the silent, sound, and overall conditions; the standardized α values were 0.956, 0.958, and 0.956, respectively. All values exceeded 0.801, indicating that the questionnaire data in this study exhibited good internal consistency and high reliability. The validity analysis of the questionnaire results was conducted using the Kaiser-Meyer-Olkin (KMO) measure, which yielded a KMO value of 0.944 (p < 0.001), indicating the high validity of the questionnaire. The score of perceived restoration for each water scene video was calculated in Excel, and the data were imported into SPSS 27 for paired-sample t tests. The results indicated that the perceived environmentally restorative benefits under silent conditions (M = 78.25, SD = 18.47) were lower than those under sound conditions (M = 80.73, SD = 17.49), with a significant difference, t (395) = −2.931, p = 0.004 < 0.01, Cohen’s d = 0.15. The results show that subjective recovery scores are significantly improved after the addition of natural sounds, which is revealing in practical application scenarios despite the small effect size.

To further explore the interaction between the type of waterscape (i.e., flowing water, falling water, spraying water, and still water) and the sound condition (i.e., silent and sound), two-way repeated-measures ANOVA was conducted to observe whether the combination of the type of water scene and the sound conditions had different effects on the perceived restoration. The types of water scenes were categorized, and the average perceived restoration scores of the three scenes in each type were used as the score for that type of water scene. The results indicated that different types of water scenes had a significant effect on the perceived restoration (F (3, 96) = 16.640, p < 0.001, η² = 0.342), and the interaction effect between the type of water scene and the sound condition was significant (F(3, 96) = 3.690, p = 0.015, η² = 0.103). Further pairwise comparison analyses with Bonferroni correction (α = 0.05) revealed that different types of water scenes and their combinations with natural sounds had varying effects on perceived restoration. Specifically, under silent conditions, the perceived restoration score for still water was significantly higher than that for flowing water and spraying water, while falling water scored significantly higher than flowing water. In contrast, under sound conditions, the perceived restoration score for still water was significantly higher than that for the other types (p < 0.05). These statistical results are visually summarized in Figure 6, which shows the main effect of the waterscape type on restoration perception. Notably, the still water scenes consistently achieved higher mean scores under both sound and silent conditions. Although the effect size (Cohen’s d = 0.15) is considered small, it reflects a meaningful trend, particularly in applied design contexts where even marginal improvements can yield substantial public health benefits.

3.2. The Effect of Sound on Visual Attention

Using Tobii Pro Lab (v1.232), the eye movement metrics for each of the 12 waterscape video scenes under both silent and sound conditions were extracted and analyzed using SPSS 27. Paired-sample t-tests were performed to examine the effects of sound on visual attention across four key indicators: APD, AFD, FF, and TFD. Three fundamental patterns emerged from the data, as presented in

Table 3. The impact of sound on various eye movement metrics (silent–sound).

Scene	Eye Movement Metrics	Mean Difference ± Standard Deviation	t	p	Cohen’s d
S1	APD	0.241 ± 0.327	4.348	0.000	0.757
	AFD	−59.729 ± 546.253	−0.624	0.537	−0.109
	FF	0.08 ± 0.651	0.693	0.493	0.121
	TFD	−0.566 ± 3.342	−1.008	0.320	−0.175
S2	APD	0.254 ± 0.332	4.548	0.000	0.792
	AFD	−351.211 ± 1309.03	−1.612	0.116	−0.281
	FF	0.095 ± 0.829	0.683	0.499	0.119
	TFD	−0.898 ± 5.947	−0.964	0.342	−0.168
S3	APD	0.235 ± 0.31	4.366	0.000	0.760
	AFD	−145.458 ± 374.523	−2.463	0.019	−0.429
	FF	0.351 ± 0.633	3.263	0.002	0.568
	TFD	−1.84 ± 4.37	−2.945	0.006	−0.513
S4	APD	0.205 ± 0.338	3.573	0.001	0.622
	AFD	−657.224 ± 1428.215	−2.682	0.011	−0.467
	FF	0.291 ± 0.683	2.641	0.012	0.460
	TFD	−1.229 ± 5.086	−1.388	0.174	−0.242
S5	APD	0.147 ± 0.245	3.366	0.002	0.586
	AFD	−213.284 ± 549.526	−2.186	0.036	−0.381
	FF	0.312 ± 0.746	2.732	0.010	0.476
	TFD	−2.63 ± 7.067	−2.178	0.036	−0.379
S6	APD	0.225 ± 0.352	3.769	0.001	0.656
	AFD	−119.793 ± 353.969	−2.008	0.052	−0.350
	FF	0.249 ± 0.568	2.870	0.007	0.500
	TFD	−0.267 ± 4.577	−0.462	0.647	−0.080
S7	APD	0.19 ± 0.29	3.451	0.002	0.601
	AFD	−87.279 ± 762.832	−0.621	0.539	−0.108
	FF	0.139 ± 0.573	1.384	0.175	0.241
	TFD	−1.845 ± 3.801	−3.207	0.003	−0.558
S8	APD	0.227 ± 0.303	4.073	0.000	0.709
	AFD	−105.17 ± 447.492	−1.417	0.165	−0.247
	FF	0.149 ± 0.554	1.451	0.156	0.253
	TFD	−3.033 ± 5.565	−3.205	0.003	−0.558
S9	APD	0.234 ± 0.271	4.712	0.000	0.820
	AFD	−3.294 ± 400.371	−0.048	0.962	−0.008
	FF	0.04 ± 0.569	0.398	0.693	0.069
	TFD	−0.695 ± 3.956	−1.177	0.247	−0.205
S10	APD	0.276 ± 0.326	4.935	0.000	0.859
	AFD	−74.311 ± 395.878	−1.077	0.289	−0.187
	FF	0.062 ± 0.408	0.527	0.601	0.092
	TFD	−0.484 ± 2.407	−1.008	0.320	−0.175
S11	APD	0.152 ± 0.251	3.366	0.002	0.586
	AFD	27.663 ± 334.225	0.467	0.643	0.081
	FF	−0.063 ± 0.577	−0.574	0.569	−0.100
	TFD	−0.483 ± 4.878	−0.832	0.411	−0.145
S12	APD	0.242 ± 0.294	4.712	0.000	0.820
	AFD	−175.189 ± 609.251	−1.563	0.127	−0.272
	FF	0.039 ± 0.575	0.374	0.711	0.065
	TFD	−0.028 ± 3.8	−0.048	0.962	−0.008

Note: APD is measured in mm, AFD is measured in ms, FF is measured in times/s, and TFD is measured in s.

: (1) APD significantly decreased under sound conditions across all 12 scenes (all p < 0.01, Cohen’s d > 0.58), suggesting a consistent reduction in the cognitive load; (2) FF decreased in 11 scenes (91.7%), except for S11, indicating more stable and sustained visual attention in the presence of natural sounds; (3) TFD increased universally under sound conditions, implying enhanced visual engagement with the waterscape scenes.

To further explore these patterns, detailed comparisons were conducted for each indicator. First, the APD for all scenes was significantly greater under silent conditions than under sound conditions (p < 0.01), suggesting that participants’ pupils dilated more in the absence of sound, potentially reflecting higher levels of attentional effort or mental load. In terms of the AFD, although the differences were not significant for most scenes (p > 0.05), there was an overall upward trend under sound conditions. In certain scenes, such as S3 (t = −2.463, p = 0.019 < 0.05, Cohen’s d = 0.429), S4 (t = −2.682, p = 0.011 < 0.05, Cohen’s d = −0.467) and S5 (t = −2.186, p = 0.036 < 0.05, Cohen’s d = −0.381), the AFD was longer under sound conditions, with the differences approaching significance. Moreover, FF exhibited significant differences in some scenes, such as S3, S4, S5, and S6, with t values ranging from 2.641 to 3.263 (p < 0.05), indicating a higher FF for the participants under silent conditions. In terms of the TFD, scenes S3, S5, S7, and S8 presented significantly shorter total fixation durations under silent conditions than under sound conditions (p < 0.05), whereas the other scenes presented an increasing trend under sound conditions, suggesting that the addition of sound may have prolonged the participants’ TFD. In summary, the experimental results demonstrate that the addition of sound significantly impacts key eye movement metrics such as the APD and FF, with variations across different scenes. This reveals the complex role of sound elements in influencing the participants’ visual attention and physiological responses.

3.3. Influence of Types of Waterscapes on Visual Attention

3.3.1. Influence of the Type of Waterscape on Eye Movement Metrics Under Silent Conditions

All eye movement metrics were imported into Excel and categorized by the type of waterscape, with the average value of three scenes for each type used as the eye movement metric for that type of waterscape. Repeated-measures ANOVA was conducted via SPSS 27 to investigate the differences in eye movement metrics among different types of waterscapes under silent conditions. The results, which are shown in

Table 4. Effects of the type of waterscape on eye movements in silent conditions.

Eye Movement Metrics	Waterscape Type	Mean ± Standard Deviation	F	p	η2
APD	Flowing Water	3.778 ± 0.481	4.216	0.009	0.182
	Falling Water	3.780 ± 0.437 d
	Spraying Water	3.789 ± 0.451 d
	Still Water	3.721 ± 0.472 bc
AFD	Flowing Water	763.529 ± 51.764	0.120	0.948	0.006
	Falling Water	778.916 ± 73.663
	Spraying Water	793.434 ± 75.44
	Still Water	769.423 ± 57.366
FF	Flowing Water	1.484 ± 0.581	0.566	0.645	0.091
	Falling Water	1.453 ± 0.532
	Spraying Water	1.46 ± 0.578
	Still Water	1.419 ± 0.516
TFD	Flowing Water	34.452 ± 2.806	1.012	0.394	0.051
	Falling Water	33.617 ± 3.602
	Spraying Water	33.454 ± 3.728
	Still Water	33.748 ± 2.915

Note: a, b, c, and d represent comparisons with flowing water, falling water, spraying water, and still water, respectively, p < 0.05; all pairwise comparisons are Bonferroni corrected.

, indicate that different types of waterscapes have a significant effect on the APD under silent conditions (F = 4.216, p = 0.009, η² = 0.182). The significant APD differences (η² > 0.14) reveal that dynamic waterscapes (spraying/falling water) demand 1.7% greater cognitive resources than still water (mean ΔAPD = 0.0635 mm). This explains their lower restorative scores in Figure 6. Further post hoc tests revealed that the APD for spraying water (3.789 mm) and falling water (3.780 mm) was significantly greater than that for still water (3.721 mm, p < 0.05). Although no significant difference was observed for flowing water, the result was still greater than that for still water, suggesting that dynamic waterscapes tend to induce a greater cognitive load or emotional responses in silent environments. In contrast, there were no significant differences among the four types of waterscapes in terms of the other eye movement metrics, namely, the AFD (F = 0.120, p = 0.948, η² = 0.006), FF (F = 0.566, p = 0.645, η² = 0.091), and the TFD (F = 1.012, p = 0.394, η² = 0.051) (p > 0.05).

3.3.2. Influence of the Type of Waterscape on Eye Movement Metrics Under Sound Conditions

Similar to the operations under silent conditions, repeated-measures ANOVA was conducted via SPSS 27 to investigate the differences in eye movement metrics among different types of waterscapes under sound conditions. The results, which are shown in

Table 5. Effects of the type of waterscape on eye movements in sound conditions.

Eye Movement Metrics	Waterscape Type	Mean ± Standard Deviation	F	p	η2
APD	Flowing Water	3.535 ± 0.345 b	8.776	<0.001	0.316
	Falling Water	3.587 ± 0.354 ad
	Spraying Water	3.572 ± 0.377 d
	Still Water	3.498 ± 0.366 bc
AFD	Flowing Water	948.994 ± 490.477	2.084	0.112	0.099
	Falling Water	1109.017 ± 626.125
	Spraying Water	858.682 ± 501.352
	Still Water	843.369 ± 432.191
FF	Flowing Water	1.308 ± 0.517	3.614	0.018	0.160
	Falling Water	1.170 ± 0.561 d
	Spraying Water	1.352 ± 0.494
	Still Water	1.407 ± 0.460 b
TFD	Flowing Water	35.553 ± 3.041 d	2.980	0.039	0.136
	Falling Water	34.992 ± 3.438
	Spraying Water	35.311 ± 2.871 d
	Still Water	34.079 ± 3.182 ac

Note: a, b, c, and d represent comparisons with flowing water, falling water, spraying water, and still water, respectively, p < 0.05; all pairwise comparisons are Bonferroni corrected.

, indicate that different types of waterscapes have significant effects on several eye movement metrics under sound conditions. First, the type of waterscape significantly affects the APD (F = 8.776, p < 0.001, η² = 0.316). Falling water (3.587 mm) has a significantly larger APD than flowing water (3.535 mm) or still water (3.498 mm), and still water (3.498 mm) has a significantly smaller APD than spraying water (3.572 mm). In addition, FF differed significantly among the different types of waterscapes (F = 3.614, p = 0.018, η² = 0.160). Still water (1.407) had a significantly higher FF than falling water (1.17, p < 0.05), representing a 22% increase in visual dispersion. Similarly, the TFD differed significantly (F = 2.980, p = 0.039, η² = 0.136); spraying water (35.311 s) and flowing water (35.553 s) were significantly longer than still water (34.079 s, p < 0.05). However, the differences among the types of waterscapes in terms of the AFD (F = 2.084, p = 0.113, η² = 0.099) did not reach statistical significance. Therefore, under sound conditions, dynamic waterscapes (such as flowing and falling water) elicit a greater cognitive load and visual responses than still water, whereas still water enhances FF.

3.4. Relationship Between the Subjective Perceived Restorative Effect and Eye Movement

To further explore the relationship between visual attention and the subjective perception of recovery, a correlation analysis was conducted between the eye movement data and the scores for the subjective perception of recovery scores under both silent and sound conditions. The results are shown in

Table 6. Correlation analysis between the evaluation of eye movement and perceived restorative benefits.

Sound Condition	Eye Movement Metrics	Pearson’s Correlation	p
Silent	APD	0.098	0.072
	AFD	0.016	0.771
	FF	0.167 **	0.002
	TFD	−0.049	0.366
Sound	APD	0.093	0.089
	AFD	0.064	0.241
	FF	−0.013	0.809
	TFD	−0.165 **	0.002

Note: ** indicates p < 0.01.

. The findings indicate a significant positive correlation between the AFD and perceived restorative benefits under silent conditions (r = 0.167, p = 0.002 < 0.01). In contrast, a significant negative correlation existed between the APD and perceived restorative benefits under sound conditions (r = −0.165, p = 0.002 < 0.01). The other eye movement metrics, such as the TFD and FF, did not significantly correlate with the perceived restorative benefits in either condition (p > 0.05).

3.5. Eye Movement Heatmap Analysis

By overlaying the gaze data of 33 participants in Tobii Pro Lab (v1.232), eye movement heatmaps were generated (as shown in Figure 7). In the heatmaps, different colors were used to represent the participants’ level of attention to various elements of the stimuli, with colors from deep red to light green indicating a decreasing order. The analysis of 24 heatmaps revealed that the participants’ attention was focused primarily on the visual center. The main focus element was the body of water itself, followed by buildings and plants. Almost no attention was given to the sky element. This finding is consistent with the center-surround effect of visual attention in which the human visual system tends to prioritize processing information at the visual center [52,53]. A comparison of the heatmaps between different types of waterscapes revealed that in dynamic waterscapes (i.e., flowing water, falling water, and spraying water), the participants’ attention was focused on the moving water body itself, whereas in still waterscapes, their attention was focused on buildings. Additionally, a comparison of the heatmaps with and without sound revealed that sound changed the participants’ attention to some extent. In dynamic waterscapes, attention was focused more on the primary sound source, i.e., the water element, whereas in still waterscapes, attention was more dispersed.

4. Discussion

The purpose of this study was to analyze the differences in eye movement and the environmentally restorative effects among participants who watched videos of waterscapes under different sound conditions as well as the restorative effects of different types of waterscapes. Unlike earlier studies that applied generic environmental stimuli, this research incorporated ecologically valid audiovisual materials collected in situ, allowing for the contextual synchronization of sound and vision. Furthermore, by categorizing waterscapes not only by motion type, but also by acoustic properties, this study moves beyond prior binary visual–sound comparisons and provides a typologically refined understanding of a multisensory landscape perception. This methodological refinement allows for a more precise analysis of how specific combinations of stimuli influence perceptual and physiological responses.

To address the core research questions (RQs), both subjective (PRS) and objective (eye-tracking) measures were analyzed, with particular emphasis on eye movement as the primary indicator of perceptual and cognitive processes. Regarding RQ1, audiovisual integration significantly enhanced perceived restoration, with PRS scores increasing by 3.2% overall (Δ = 2.48 points, p = 0.004). The eye-tracking results revealed that audiovisual conditions led to a consistent reduction in the pupil diameter and an increase in the total fixation duration across all scenes, indicating a lower cognitive load and deeper visual engagement. For RQ2, still waterscapes demonstrated a 12.7% greater restorative value than dynamic types such as flowing water (88.67 vs. 77.96), as measured by PRS. Correspondingly, eye-tracking data showed smaller pupil diameters and more stable fixation patterns in still waterscapes, suggesting lower visual complexity and higher compatibility with attention restoration. As for RQ3, natural sounds effectively reduced the cognitive effort, evidenced by a 6.4% decrease in the pupil diameter in representative scenes (e.g., S1: 3.778 mm → 3.535 mm, p < 0.01), along with a lower fixation frequency and higher total fixation duration. These findings lay the foundation for the theoretical interpretation that follows and highlight the combined importance of multisensory input and landscape typology in shaping restorative experience.

According to the analysis of the experimental data, the perceived restorative effect of waterscapes improved significantly after the addition of natural sounds. This finding is consistent with previous research on the restorative effects of natural sounds on the environment [54,55,56]. As an essential element of restorative environments [57], the interaction of water sounds, bird calls, cicada chirps, and other natural sounds provides a stable and nondistracting background sound that enhances sensory stimulation and further increases the attractiveness and restorative characteristics of the environment [45]. In addition, the differences in the perceived restorative effects of different types of waterscapes are noteworthy, especially the significantly greater restorative effects of still water compared to other types. This finding might be due to the relatively quiet, stable, and open nature of still water environments, which can better promote psychological recovery. According to tranquillity theory, individuals experience reduced psychological stress and anxiety levels and increased psychological recovery when they perceive tranquil environments [58]. The consistent visual stimulation provided by still waterscapes in natural sound conditions aligns with tranquil environments and makes it easier for individuals to realize an experience of “coherence” and “compatibility”. Greater spatial openness can enhance visual openness and transparency to effectively increase spatial visual focus [59]. This can provide participants with a relaxed and free psychological experience, thereby alleviating the stress and anxiety caused by traditional learning environments. Thus, the still waterscape exerts a more restorative effect, which also explains why the restorative scores for still water were significantly higher than those of dynamic waterscapes (i.e., flowing water, falling water, and spraying water).

Additionally, the results for visual attention indicate that natural sounds play a crucial role in regulating the cognitive load. Under sound conditions, the significant reduction in the pupil diameter suggested that the presence of natural sounds reduced the participants’ cognitive load [60] and allowed them to immerse themselves in the scene and alleviate their mental burden. This finding is consistent with previous research showing that natural sounds can provide an additional sensory input to distract individuals from focusing excessively on the details of the visual environment, which can reduce the overall cognitive load [61]. Under silent conditions, individuals require more visual information to perceive and understand the environment, which requires more attentional resources and results in greater pupil dilation [62]. However, under sound conditions, individuals’ visual attention is guided by sound, which makes environmental comprehension easier and results in a smaller pupil diameter. Moreover, according to the cognitive load theory, human working memory has a limited capacity [51]. When participants rely solely on visual information to interpret the environment, working memory is burdened with processing a large volume of visual data. This burden can lead to an excessive depletion of attentional resources and an increase in the intrinsic cognitive load. In contrast, under audiovisual conditions, natural soundscapes provide an additional sensory input that complements visual information, thus reducing the reliance on the visual channel alone and lowering the overall cognitive load. As the cognitive load decreases, individuals are able to allocate their cognitive resources more efficiently, which facilitates psychological restoration. This interpretation is supported by the observed significant reduction in the APD under audiovisual conditions. Pupil constriction is commonly associated with lower cognitive effort and reduced emotional arousal [62], reinforcing the notion that multisensory input can ease mental demands and promote recovery. Moreover, FF was significantly greater under silent conditions than under sound conditions, particularly for dynamic waterscapes (such as flowing and falling water). This finding indicates that without the aid of sound, individuals may disperse their attention more and frequently shift their gaze among different visual targets. This phenomenon might be due to individuals’ need to observe the environment more carefully in the absence of auditory information, which leads to frequent changes in fixation targets and an increased cognitive load. Conversely, under sound conditions, the participants’ AFD and total fixation time increased significantly, especially in dynamic waterscapes (such as flowing and spraying water). The addition of natural sounds reduced environmental distractions and directed the participants’ visual attention, thereby extending their fixation time on objects of interest and enhancing their perception of environmental details. In summary, after natural sounds were added, the APD decreased, FF decreased, the TFD increased, and the AFD increased. These findings indicate an increase in the attractiveness of the scene and in the participants’ interest, suggesting that soundscapes can promote attention restoration and enhance environmentally restorative effects and attractiveness. The results above also corroborate the findings of Berto [63], Valtchanov [64], and Franěk [65].

Notably, different types of waterscapes show significant differences in the distribution of visual attention. Regardless of sound conditions, dynamic waterscapes (such as spraying and falling water) induce larger average pupil diameters than still waterscapes, indicating greater visual variability and a greater cognitive load and attention. Specifically, motion elements in dynamic waterscapes may increase the need for visual information processing, resulting in a greater cognitive load [66]. Previous scores for perception recovery further demonstrated that still waterscapes may better facilitate psychological recovery. Furthermore, this result can be better understood through the lens of the four key components of restorative environments proposed by attention restoration theory (ART). Specifically, the openness of still waterscapes, when combined with natural soundscapes, fosters a sense of “being away” from everyday stressors. The coherent integration of waterscapes with traditional architectural elements enhances the “extent”, stimulating the desire for exploration. Subtle surface dynamics, such as ripples, provide “soft fascination”, gently capturing attention without depleting cognitive resources. Finally, the low-stimulation nature of still waterscapes ensures a high degree of “compatibility” between the environment and individuals’ psychological needs. In contrast, dynamic waterscapes (e.g., waterfalls) may exceed the bounds of soft fascination owing to their strong visual stimulation, potentially dispersing attention and partially offsetting their restorative potential. However, under sound conditions, the TFD in dynamic waterscapes is significantly greater than that in still waterscapes, suggesting that the combination of dynamic waterscapes and natural sounds effectively guides and sustains individuals’ visual attention [67]. In falling water and spraying water scenes in particular, the synchronous changes in water sounds and visual dynamics generate stronger attraction and enhance the visual experience. This paradox is one of the intriguing findings of the current study: although dynamic waterscapes are more visually engaging and attract more fixations, they are perceived as less restorative than still waterscapes are. This apparent contradiction can be explained by cognitive load theory and ART. First, dynamic stimuli tend to be visually complex, requiring participants to process more intricate visual information (e.g., water flow patterns), which demands more attentional resources and may lead to working memory over-load—ultimately increasing the cognitive load rather than promoting relaxation. Second, according to ART, effective restoration arises from environments that attract attention in a way that does not deplete cognitive resources. The rapid and continuous changes in dynamic waterscapes may compel participants to maintain directed attention to track visual targets, thereby inducing cognitive fatigue. In contrast, the subtle movements in still waterscapes—such as ripples or reflections—better align with the principle of soft fascination. These features allow attention to wander in a low-effort manner, thereby enhancing restorative outcomes. In addition to the differences in these, it is important to consider the role of environmental predictability and perceptual fluency in restorative processes. According to the Predictive Coding framework [68], environments that enable accurate sensory predictions are experienced as less mentally taxing. Dynamic waterscapes, with rapid and irregular motion patterns, may challenge sensory predictability and thereby increase arousal or cognitive alertness. In contrast, still waterscapes provide higher perceptual stability, which can lead to smoother cognitive processing and a stronger sense of psychological safety. This multisensory integration effect shows that different types of sensory inputs can enhance the overall environmental experience through mutual coordination, thus improving the attention maintenance time and environmental cognition.

Correlation analysis revealed a significant positive correlation between the AFD and perceived restorative benefits under silent conditions. In contrast, there was a significant negative correlation between the APD and perceived restorative benefits under sound conditions. These findings further support the crucial role of natural sounds in promoting psychological recovery, and they indicate that natural sounds can enhance restorative effects by reducing the cognitive load and increasing environmental attractiveness. This finding also corroborates the studies of Franěk [65], Yuxi Weng [69], and Erkang Fu [70] by emphasizing the importance of eye movement characteristics in assessing the restorative benefits of environments.

The results of the eye movement heatmap analysis revealed significant impacts of different types of waterscapes and sound conditions on participants’ visual attention distribution. Overall, as the core visual element, the water body attracted most of the attention of the participants. Dynamic waterscapes (such as flowing water, falling water, and spraying water) consistently demonstrated stronger visual appeal in all conditions, with attention focused mainly on moving water areas. The reason is that the motion elements in dynamic waterscapes provide stronger visual stimuli and appeal, and the human visual system is highly sensitive to dynamic information [71] that effectively attracts and maintains participants’ attention. In contrast, owing to the lack of visual variability, waterscapes attract less attention, which may lead participants to explore other distinctive elements in the scene, such as buildings. Additionally, the impact of auditory stimuli on visual attention is evident in the analysis. In dynamic waterscapes, the participants’ attention was focused more on the primary sound source, i.e., the water element. The reason is that sound stimuli enhance visual processing by directing attention to consistent visual elements, creating an engaging sensory experience and maintaining participants’ attention. Conversely, the lack of dynamic auditory cues in still waterscapes results in more a widespread distribution of visual attention among various elements, leading to more exploratory visual behavior.

5. Conclusions and Prospects

5.1. Conclusions

This study examined 12 waterscapes in Xishu Garden and used eye-tracking technology and the PRS to study the restorative effects of waterscapes with audiovisual interactions. By combining mathematical and statistical logic, the influences of the type of waterscape and soundscape on restoration were explored, leading to the following conclusions.

• Waterscapes with audiovisual interactions have a greater restorative value than single visual stimuli do. The addition of natural sounds significantly enhances the environmentally restorative effects of waterscapes, as evidenced by both subjective perceptions and eye movement characteristics. Specifically, audiovisual stimuli increased restoration scores by 3.2% overall (Δ = 2.48, p = 0.004).
• Different types of waterscapes have significant impacts on environmental restoration. Dynamic waterscapes (such as falling water and spraying water) induce a greater cognitive load and psychological stress than still waterscapes (such as still water) while also exhibiting stronger visual appeal. However, overall, still waterscapes show better restorative effects.
• There are differences in visual behavioral characteristics between waterscapes with single visual stimuli and those with audiovisual interaction. Dynamic waterscapes with audiovisual interactions are more focused, whereas still waterscapes are more dispersed.

In addition to its theoretical contributions, this study offers practical implications for landscape and urban planning. These findings suggest a typological approach to designing restorative landscapes. Still waterscapes, characterized by a low sensory load and high perceptual stability, are best suited for contemplative zones, healing gardens, or rehabilitation parks. In contrast, dynamic waterscapes may be employed in interactive or transitional public spaces where short-term attention arousal is desired, but long-term exposure is minimal. Landscape planners should consider the balance between fascination and overstimulation, ensuring congruence between the environmental intent and sensory design. The synchrony of auditory and visual features also emerges as a key factor in enhancing the user experience, supporting design decisions that avoid sensory incongruence in artificial installations.

Additional, as urban planners and landscape architects increasingly prioritize evidence-based strategies for promoting health and well-being, the integration of multisensory elements—particularly those combining visual and auditory stimuli, such as waterscapes—becomes especially vital. As natural audiovisual composites, waterscapes not only serve an esthetic function, but also play a crucial role in enhancing urban psychological resilience and overall well-being. In high-density, high-stress urban environments, a multisensory-oriented design represents a promising approach to improving residents’ quality of life. Therefore, this study advocates for the adoption of a multisensory perspective by policymakers and design practitioners to foster the development of more inclusive and restorative public spaces. However, to enable broader and more effective implementation, future research should incorporate more diverse participant samples. This is essential to ensure the inclusiveness and generalizability of design strategies aimed at enhancing urban well-being.

5.2. Limitations of the Study

This study has certain limitations. (1) First, in the selection of sample sites, Xishu Garden, which is a classical garden with commemorative and historical features [72], primarily uses natural landscaping. In contrast, fountain landscapes are modern, which limits the range of options. (2) Second, the season is a factor that affects the esthetic and restorative variations of waterscapes because plant phenology and soundscape combinations change with the season. Studying waterscapes in different seasons is crucial. (3) An-other important limitation lies in the composition of the participant sample. While university students represent a convenient and controllable population and are often considered scientifically valid and broadly representative in experimental contexts [73], their relatively narrow demographic profile—young, highly educated, and generally healthy—may limit the generalizability of the findings. Individual differences in age, life experience, sociocultural background, and familiarity with environmental settings can significantly influence responses to visual and auditory stimuli in restorative environments. Previous studies have also shown that perceptions of soundscape restorativeness vary significantly across age groups [74,75]. Therefore, although the present study provides valuable preliminary insights into the restorative potential of waterscapes under audiovisual conditions, caution should be exercised when these findings are applied to broader, real-world urban and landscape design contexts involving more diverse populations. Future research should consider expanding the sample size and introducing more diverse participant groups, such as people from different age groups, cultural backgrounds, and professions, to provide more convincing evidence for the design of restorative waterscape environments. (4) In addition, although high-fidelity audiovisual materials were used and the experimental environment was tightly controlled to enhance the ecological validity, the sense of perception and emotional immersion may still differ from that of actual onsite experiences. The absence of a physical presence, atmospheric conditions, and full multisensory engagement in a lab setting may limit the extent to which the simulated environment replicates real-life interactions.

5.3. Prospects

The rich landscape elements, exquisite gardening techniques, and valuable gardening wisdom of Chinese classical gardens provide important information and a valuable model for constructing contemporary urban green spaces. Waterscapes are important garden elements within classical gardens, making research on their restorative effects important. Future research could conduct field experiments in outdoor environments or use technologies such as VR to create more realistic settings. Combining these methods with more complex environmental variables (e.g., different weather conditions, seasonal changes) would yield conclusions with greater ecological validity and provide a comprehensive understanding of the restorative mechanisms of waterscapes. Additionally, exploring the subtle differences in the restorative effects of various features of waterscapes (e.g., the water flow rate, water body shape, and transparency) could reveal how waterscapes impact humans’ physical and mental health. Revealing this impact would provide more solid theoretical support and practical guidance for research on the landscape design and environmental psychology.

In addition to technological and environmental enhancements, future research should explore the cross-cultural validity of waterscape-induced restorative effects. The cultural background can significantly shape the environmental perception, emotional responses, and esthetic preferences. For example, the symbolic and philosophical meanings of water in East Asian cultures—often associated with stillness, harmony, and spiritual balance—may influence the perceived tranquility and restorative impact of waterscapes. In contrast, Western urban populations may associate water features with vitality or recreational values. Cross-cultural comparative studies could reveal how the sociocultural context modulates multisensory processing and restoration, thereby informing culturally sensitive design strategies for public spaces worldwide.