Effects of Visual Perception of Building Materials on Human Emotional States and Cognitive Functioning in a Physical Learning Environment

Abstract

The influence of building materials on the physical aspects of educational spaces is significant, shaping both functionality and aesthetic appeal while directly affecting the emotional and cognitive states of students. Despite its significance, this area has not been extensively explored. This study investigated the effects of the visual perception of architectural materials on emotional states and cognitive functions in learning environments. Four materials, namely, red brick, concrete, wood, and white paint, were selected for a material substitution experiment conducted via VR simulations with 46 participants. To assess these effects, eye movement data and bioelectrical responses were methodically recorded, along with the participants’ self-reported emotional states through structured questionnaires. The results indicated that settings featuring wood and red bricks significantly enhanced emotional states, fostering relaxation and positive experiences that contributed to increased creativity. Conversely, settings with concrete and white paint improved cognitive functioning and promoted emotional stability and rational thinking, which enhanced focus and educational efficiency. These findings suggest the impact of visual perception of building materials on emotional and cognitive states, emphasizing the importance of material selection in creating learning spaces that balance cognitive demands with emotional activation. This study provides valuable insights for designing conducive physical learning environments and exploring the psychological and physiological effects of architectural materials.

1. Introduction

With the growing recognition of the role of education in promoting social progress and personal development, the demand for optimized educational spaces has increased. Inadequate physical conditions can adversely affect students’ learning processes, reduce efficiency, diminish motivation, and potentially cause emotional issues that hinder academic performance [1]. Consequently, educators and design professionals are increasingly acknowledging the critical role of the Physical Learning Environment (PLE) in enhancing learning outcomes [2].

PLE refers to various physical features that shape educational activities, including spatial layout, lighting, temperature, noise levels, the nature of learning materials and tools, and the influence of natural surroundings and social presence [3]. These physical features affect individuals’ emotions and cognitive functions through physiological processes, thereby significantly influencing their academic performance and mental engagement [3,4,5].

Based on their functions and purposes, PLEs can be classified into regular classrooms, group learning spaces, flexible learning spaces, and static learning spaces [6]. Each type is designed to support specific tasks such as education, reading, thinking, and communication. The physical attributes of these spaces should align with their functional requirements. As a fundamental component of these spaces, building materials not only shape spatial perception but also directly affect users’ emotions and cognitive functions [7,8]. Therefore, the selection of building materials extends beyond purely aesthetic considerations and has significant long-term implications for emotional states and cognitive processes [9,10].

1.1. Visual Perception of Building Materials and PLEs

In indoor learning environments and other spaces for human activity, individuals rely on visual perception to interpret the physical characteristics of architectural materials, such as color, texture, and gloss, which constitute the core elements of visual perception and can significantly affect their responses to these settings [11,12]. Research has extensively focused on the impacts of these material characteristics on emotional states and cognitive functions, examining not only the effects of color and gloss on visual experience and spatial perception but also comparing emotional responses to different material textures [13,14,15]. Studies have examined how material characteristics such as color, gloss, and texture affect visual experience, spatial perception, and emotional responses. For instance, wood promotes positive physiological responses, reducing tension and fatigue, whereas the reddish hue and rough surface of red brick convey warmth, bringing feelings of comfort and familiarity [16,17,18]. Comparative studies have shown that wood has a more positive emotional impact than white paint, and materials such as concrete [7], wood, and white paint create distinct spatial perceptions. These findings highlight the significant role of material visual perception in shaping emotional and cognitive responses.

Given the significant influence of material visual perception on indoor spaces, its application in educational settings warrants further attention. Although previous studies have examined temperature, air quality [19], sensory stimuli (e.g., noise and light) [4,20], and spatial layout [21], the effects of architectural material characteristics (color, gloss, and texture) on physiological comfort, emotional state, and cognitive performance remain underexplored. These visual features significantly shape sensory experiences and subtly regulate psychological states and learning efficiency, which can have a profound effect on educational outcomes.

Therefore, this study selected four commonly used building materials in educational architecture in China, including timber, red brick, concrete, and white paint [22,23,24,25,26]. Such materials are prevalent in classrooms, libraries, and teaching facilities. Wood and red bricks are often employed to create a warm, natural atmosphere, whereas concrete and white paint are favored in modern school buildings because of their simplicity and ease of maintenance [27,28]. Using these four materials, this study constructed virtual PLEs to investigate how they influenced learners’ emotional states and cognitive functioning.

1.2. Virtual Reality (VR) in PLEs

Virtual reality (VR) technology has emerged as a powerful tool for simulating and assessing the effects of PLEs on emotional and cognitive aspects of learning. Emotion plays a central role in cognitive processes such as perception, decision-making, creativity, memory, and social interaction, making it a key focus in human behavior research [29]. Traditional affective computing and cognitive science studies have primarily relied on non-immersive two-dimensional images or videos to evoke emotions and indirectly assess cognitive functions [30]. However, these methods lack immersion, which often results in weaker emotional responses [30]. VR technology is increasingly favored in emotion and cognition research because of its ability to provide immersive experiences that enhance emotional elicitation in laboratory settings [31].

VR technology enables immersive experiences and interactive visualizations in architectural research, allowing researchers to safely examine the effects of indoor environments in controlled settings [32]. When combined with physiological response measurement techniques from neuroscience, VR facilitates a deeper understanding of how built environments influence emotional states and cognitive functions, thereby providing more accurate and comprehensive data. This approach effectively complements the quantitative assessment of visual changes and addresses the limitations of subjective qualitative methods adopted in prior studies [33,34]. For example, VR has been used to create learning environments that enhance students’ experiential learning [35], confirm the positive effects of biophilic indoor layouts on human physiology and emotions [36], and investigate the impact of classroom width on students’ attention and memory by simulating virtual classrooms [37]. By enhancing insights into human emotional and cognitive responses, VR technology contributes innovative tools for the design and evaluation of architectural materials in PLEs.

1.3. Human Emotional States and Cognitive Functions in PLEs

Emotional states can be assessed in two dimensions: potency and arousal. Potency reflects the pleasantness of an emotion ranging from positive or pleasant to negative or unpleasant, whereas arousal indicates the degree of emotional activation [38,39]. Cognitive functioning involves the processes by which individuals acquire and apply knowledge, including sensation, perception, memory, thinking, imagination, and language, and it plays a critical role in regulating emotional states [5,40]. Research has demonstrated that the dimensions of potency (ranging from passive to active) and arousal (from calm to excited) can result in different cognitive outcomes and influence associated neural substrates [5]. Because emotional states and cognitive functions are interdependent, a positive emotional state enhances cognitive functioning, whereas cognitive functioning can moderate emotional states and contribute to learning outcomes.

Emotional states and cognitive functions can be assessed using physiological indicators such as galvanic skin responses, brain activity, and heart rate variability [41]. Common psychophysiological tools include electroencephalography (EEG), electrocardiography (ECG), and electrodermal activity (EDA), which are widely used to evaluate cognitive and emotional responses in different settings [41]. For instance, EEG records brain activity in response to visual stimuli by measuring voltage differences between different brain regions [42]; EDA can sensitively detect changes in the sympathetic nervous system during emotional activation [43]; and heart rate (HR) and heart rate variability (HRV) monitor autonomic nervous system activity and provide new perspectives on how emotions affect attention [44]. Additionally, changes in pupil diameter can reflect cognitive and emotional states [45].

Physiological measurements offer quantitative and objective tools for investigating how PLEs influence emotional state and cognitive function. For example, Cruz-Garza et al. [46] used EEG to assess the effects of a virtual classroom design on cognitive performance. Pijeira-Díaz et al. [47] employed EDA to assess students’ sympathetic nervous system arousal in classrooms. Ainara Aranberri Ruiz et al. [48] used HRV to explore the relationship between attention and academic performance. While traditional unimodal methods struggle to capture complex emotional states accurately, multimodal recognition technologies, which combine signals from different sensors (such as EEG, EDA, and HRV), yield more precise results and overcome the limitations of single-measurement approaches [49,50].

1.4. Research Objectives

In the field of research on PLEs, there remains a need for in-depth multidimensional physiological studies to explore how the visual perception of architectural materials affects emotional states and cognitive functions. To comprehensively understand the impact of architectural materials on learning outcomes, it is crucial to consider visual, psychological, and physiological responses to inform evidence-based designs. This study aimed to explore the influence of the visual perception of architectural materials on emotional states and cognitive functions within PLEs. This study employed VR technology to create virtual learning environments with four different architectural materials and applied multimodal physiological measurements to quantitatively assess their effects on physiological responses, emotional states, and cognitive functions. Additionally, subjective evaluations were incorporated to offer a comprehensive assessment of the overall impact of these materials. These findings are expected to guide the selection of architectural materials in PLEs, foster diverse spatial atmospheres, and enhance awareness of the influence of architectural environments on human psychology and physiology.

2. Materials and Methods

This section provides comprehensive details on the construction of the VR scenario for the PLE, the participants involved, the experimental setup, and the employed data collection and analysis methods. Section 2.1, Section 2.2 and Section 2.3 describe the construction of the virtual scenario, participant characteristics, and the configuration of the experimental environment and equipment. Section 2.4 outlines the experimental steps and highlights key aspects and conditions of the experimental process. Finally, Section 2.5, Section 2.6 and Section 2.7 present the collected data and the applied methods for data analysis. Figure 1 systematically illustrates the methodological framework and strategic design of this study.

2.1. Scene Construction

According to the GB 50189-2015 Code for Design of Educational Buildings [51], PLEs are categorized by area into small (20–50 m²), medium (50–100 m²), and large (over 100 m²) learning spaces. Static learning spaces are typically classified as small and are commonly found in study rooms and libraries. These spaces provide quiet, independent environments that support self-directed learning and are more prevalent in educational buildings because of their adaptability to individual or small-group learning needs and compact size.

To ensure the accuracy of the experimental results, a small independent learning space based on VR technology was created as the research environment. The virtual environment was designed in strict accordance with the GB 50189-2015 Code for Design of Educational Buildings (2015). The space was 25–30 m², with an aspect ratio of 1.5:1 and a net height of 2.8 m. Large windows were incorporated to provide ample natural light and maintain stable lighting conditions. The learning environment included desks and chairs. Walls and floors were rendered with realistic materials to closely replicate the visual experience of an actual learning space. To highlight the visual impact of architectural materials, four materials (red brick, wood, concrete, and white paint) were used for large areas, with each covering more than 60% of the total wall and floor area. The geometries of the walls and floors were kept consistent across all conditions to eliminate potential construction differences associated with material type. This design choice aimed to minimize visual interference from geometric variation, ensuring that the results primarily reflected the effects of visual perception of the materials rather than other confounding factors. All images were generated with a human eye height of 1.6 m to ensure a consistent perspective. Additionally, spatial layout, lighting conditions, and furniture arrangement were kept uniform to maintain controlled experimental conditions. Finally, panoramic images were rendered using a VR device. Figure 2 shows the constructed virtual learning environment.

2.2. Participants

To ensure the statistical power of the research, this study utilized G*Power 3 software to accurately calculate the required sample size [52]. Based on the experimental objectives, the F-test type was selected with the parameters set as follows: effect size f = 0.25, α error probability = 0.05, and power (1 − β error probability) = 0.8 [34], resulting in a minimum requirement of 24 participants. A total of 46 volunteers were recruited from the campus, all of whom had normal unaided or corrected vision (within 200°).

To ensure the accuracy of the experimental data, this study excluded participants with eye diseases or mental disorders that could affect the electroencephalogram (EEG) readings. The participants were instructed to avoid smoking, alcohol consumption, and intake of caffeinated or other stimulative beverages for 24 h prior to the experiment to eliminate any potential influence on their physiological states. On the day of the experiment, participants were required to avoid vigorous exercise and activities that might induce excitement or fatigue, ensuring a stable and relaxed state throughout the experiment. Additionally, to minimize variability in the thermal environments, the participants wore lightweight short-sleeved shirts and long pants (0.5 Clo) to ensure consistency in experimental conditions.

Table 1. Basic information statistics of participants.

Gender	N	Mean Age	BMI (kg/m2)	Clo
Male	23	23.5 (21–26)	22.95	0.5
Female	23	25.53 (22–26)	21.42	0.5
Total	46	24.04 (21–26)

provides basic information on the 46 participants.

2.3. Experimental Environment and Equipment

The experiment was conducted in a closed acoustic laboratory measuring 3800 mm × 6000 mm × 2900 mm (width × depth × height), equipped with an air-conditioning system to maintain stable sound conditions, temperature, and humidity. During the experiment, no background noise was played, and the laboratory’s acoustic environment was maintained between 35 and 45 dB, an optimal noise level for enhancing students’ learning, concentration, and emotional regulation [53]. This ensured that no external noise or interference affected the learners’ state. A temperature and humidity measuring device (model RC-4HC) was used to monitor the microclimatic conditions, and temperature and relative humidity data were recorded every 15 min.

Table 2. Average laboratory temperature and humidity.

Factor	Range	Accuracy	Mean
Air temperature	(from 0 to 50) °C	±0.5 °C	25.9 °C
Relative humidity	(from 5 to 95)%	±5%	46.39%

presents the average temperature and humidity levels in the laboratory.

In this experiment, the following equipment was used: three BIO wireless human factor physiological recorders (Beijing Joinfun Technology Co., Ltd., Beijing, China), each equipped with multiple sensors to monitor participants’ HR, EDA, HRV, and other physiological indicators in real time; a 16-electrode ErgoLAB Hydrodynamic Electroencephalograph (Beijing Joinfun Technology Co., Ltd., Beijing, China) to record brain activity; and an HTC Vive Pro Eye Virtual Reality Eye Tracker (Beijing Joinfun Technology Co., Ltd., Beijing, China) to track visual responses. All devices transmitted data wirelessly to a computer for real-time analysis, enabling the assessment of participants’ emotional and physiological responses. Two base stations presented stimulus materials, and two computers were employed: one for experimental control and the other for displaying questions. To maintain consistent conditions, two LED lights were installed to provide stable lighting and eliminate potential interference from natural light fluctuations. After the experiment, laboratory doors were opened for ventilation (Table S1). Figure 3 illustrates the layout of the laboratory environment and equipment.

2.4. Experimental Steps

The total duration of the experiment for each participant was 27 min and consisted of three steps: experimental instructions, preparation, and measurement.

Explanation of the Experiment: This phase involved providing participants with a detailed explanation of the experiment, checking for any potential influences on the study, and obtaining their consent, lasting approximately 5 min. The purpose and procedures of the experiment were thoroughly explained to ensure clarity, followed by the collection of personal information such as gender and age. Participants were then assessed to confirm that they met the eligibility criteria and adhered to all preset restrictions. Finally, informed consent was obtained, and participants completed a consent form to confirm their participation.

Experimental Preparation: During this phase, the equipment and questionnaires were examined, and an 8 min calibration test was performed. Participants were first instructed to take their seats and wear the measurement equipment properly. They then experienced a 3 min non-experimental VR scenario to assess any discomfort responses. While the participants familiarized themselves with the VR setup, the staff calibrated the eye tracker and physiological monitoring devices to ensure stable data collection and wireless transmission. After confirming that all systems functioned properly, the participants were instructed to maintain a consistent sitting posture throughout the experiment.

Experimental Measurement: This phase lasted 14 min and involved monitoring physiological responses, answering questions, and completing subjective questionnaires. To prevent order effects, VR scenarios were presented in a random order. The participants began by closing their eyes and relaxing for 3 min to record baseline physiological data. They then sequentially experienced the pre-set VR scenarios and simulated a typical learning state, while physiological data were collected in parallel. Each environment was experienced for 45 s. This duration was determined based on prior test data and experimental requirements to prevent participant fatigue while ensuring response accuracy, consistent with durations typically used in emotional response or cognitive load tasks [54]. After 45 s, the AI-generated voice prompts posed questions, and the participants verbally indicated their answers within the VR environment. The staff assisted in completing the questionnaires, and this interactive segment lasted for approximately 11 min. At the end of the experiment, the participants completed an evaluation questionnaire while the staff turned off the measurement devices and recorded their health status and feedback. Figure 4 presents a detailed overview of the experimental process, including the presentation of the stimulus materials and data collection methods.

2.5. Physiological Status and Indicators

2.5.1. Physiological Indicators

The human body exhibits various physiological responses, which are influenced by indoor architectural materials. For instance, exposure to a blue environment may increase HR, indicating heightened alertness [55], whereas stress and emotional arousal can increase EDA, reflecting increased sweating [56]. Skin electrical activity consists of two main components: the tonic component (SCL), which reflects slow physiological changes, and phasic activity (SCR), which reflects rapid physiological changes [57]. In this study, the tonic component (SCL) of skin electrical activity was selected as a key indicator to assess the sustained impact of environmental factors on an individual’s psychophysiological state, because of its stability and sensitivity [58]. Environmental psychological stimuli can also reduce peripheral blood flow and lower skin temperature (ST), indicating physiological stress [59]. HRV reflects small changes in cardiac cycle duration, with the Standard Deviation of the Normalized Differences (SDNN) measuring overall variability in inter-beat intervals and the Root Mean Square of Successive Differences (RMSSD) indicating continuous variations [60]. Both SDNN and RMSSD are critical parameters for assessing the autonomic nervous system activity. In this study, these five metrics were adopted to capture physiological changes, and the data were measured and recorded using a BIO wireless human factor physiological recorder. Signal preprocessing, including filtering and downsampling, was performed using ErgoLAB 3.0 software to enhance the signal quality and provide clearer data for analysis.

2.5.2. EEG Activity Indicators

EEG signals can directly reflect brain activity and offer valuable insights into psychological states and brain-region interactions [61]. In this study, a 16-electrode ErgoLAB Hydrodynamic Electroencephalograph was used to measure activity in the frontal (Fz, F3, F4, F7, F8), parietal (Pz, P3, P4), occipital (O1, O2), and central (Cz, C3, C4) regions [62]. The collected data were processed using ErgoLAB software for artifact removal and filtering, followed by analysis of five primary frequency bands: delta (1–4 Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (>30 Hz) [63]. Previous studies have demonstrated that alpha (α) and beta (β) wavebands can significantly reflect changes in emotional states and cognitive functions [64,65]. Thus, this study focused on observing variations in these two bands and their distribution across the brain. Figure 5 shows the main brain regions (frontal, parietal, temporal, and occipital) and their functions, along with the corresponding EEG electrode locations.

In addition to comparing the mean values of α and β waves across all EEG electrode sites, the difference in α wave measurements between the right frontal F4 electrode and left frontal F3 electrode was calculated. This metric, known as the Frontal Asymmetry Activity Index (FAA) [42,66], has been commonly adopted to assess emotional states based on functional differences between the left and right frontal lobes. The left frontal lobe is associated with positive emotions and happiness, whereas the right frontal lobe is associated with negative emotions and tension [67]. Thus, evaluating the asymmetry in activity between these lobes can provide insights into an individual’s emotional state [68]. Because α waves are the dominant rhythm in the brain, particularly in the frontal region, their activity can be inversely related to the activity levels of the corresponding cortical areas [69]. An increase in FAA value indicates greater activity in the left frontal lobe, which is typically associated with higher levels of positive emotions [70]. The formula for calculating FAA is as follows:

$$\mathrm{FAA}=\mathrm{Ln}({\alpha }_{\mathrm{F4}})-\mathrm{Ln}({\alpha }_{\mathrm{F3}})$$

(1)

Formula (1) was adapted from Mavros et al. [70]. FAA represents the Frontal Asymmetry Index, and α_F3 and α_F4 represent the alpha powers at the left (F3) and right (F4) frontal electrodes, respectively.

2.5.3. Eye Movement Indicator

Eye movements serve as an intuitive reflection of the psychological processing of visual stimuli, providing insight into cognitive activities related to visual information [71]. Eye tracking technology offers a simple, noninvasive method for revealing cognitive and emotional dynamics by measuring changes in pupil size [45,72]. In this study, the HTC Vive Pro Eye VR eye tracker was used to collect highly accurate eye movement data at a stable sampling rate of 120 Hz without disrupting the VR experience. Data were primarily collected on Time to First Fixation (TTFF), Fixation Time (FT), Average Fixation Time (AFT), and Pupil Diameter (PD), with subsequent processing conducted using ErgoLAB software. These eye-tracking metrics offer valuable insights into emotional responses and cognitive processing in different architectural material environments [73].

Table 3. Responses of physiological indicators to emotional state and cognitive functioning.

Independent Variable	Implicit Variable	Indicator Name
Emotional state	Physiological Indicators	SCL, ST
	EEG Activity Indicators	Overall α Wave, FAA
	Eye Movement Indicators	TTFF, PD
Cognitive function	Physiological Indicators	HR, HRV(SDNN), HRV(RMSSD)
	EEG Activity Indicators	Overall β Wave
	Eye Movement Indicators	FT, AFT

shows the physiological indicators of responses to mood states and cognitive functions in the study.

2.6. Subjective Questionnaires

2.6.1. Presence in the VR Environment

To assess immersion in the virtual learning environment, the Igroup Presence Questionnaire (IPQ) was used to measure users’ sense of presence. The questionnaire consisted of 13 items covering various dimensions of presence, which Schubert et al. categorized into three main factors [74]: Spatial Presence (SP), involvement (INV), and realness (REAL), with each factor containing four entries. Additionally, a separate item evaluated users’ Global Presence (GP), reflecting the extent to which users could feel that they were actually in a virtual environment. The final scale comprised 14 items using a seven-point Likert scale for quantification, where −3 indicated strong disagreement, 3 indicated strong agreement, and 0 represented a neutral stance.

2.6.2. Questionnaire on Visual Perception of Architectural Materials

In this study, a subjective assessment method was employed to quantify the participants’ cognitive and emotional responses to architectural materials. Material Characteristic Variation (MCV) was used to evaluate the participants’ perceptions of intuitive features, such as color, gloss, and texture of the materials [11,75]. The Emotional Climate Rating of Valence (ECRV) was used to measure the participants’ levels of pleasure, arousal, and negative emotions [76] (Table S2). Both indicators were quantified using a five-point scale (−2 indicating strong dissatisfaction, 2 indicating strong satisfaction, and 0 indicating a neutral attitude), with the assessments conducted in real time within the VR environment.

2.7. Statistical Analyses

Statistical analysis was performed using SPSS 27. First, the physiological indicator data from the participants across four different VR conditions were preprocessed by subtracting the baseline mean values from the physiological indicators of each condition. The differences were then transformed using the natural logarithm to meet the normality assumption required for the analysis of variance (ANOVA). For the electroencephalographic activity indicators and eye-tracking metrics, the means were calculated directly for comparison across different VR conditions. Repeated measures ANOVA was performed to examine whether the changes in architectural materials across VR conditions had statistically significant effects on physiological indicators, electroencephalographic data, and eye-tracking metrics. Mauchly’s test of sphericity was conducted to assess the sphericity assumption. If met (p > 0.05), the within-subject effects test was adopted, whereas the Greenhouse-Geisser correction was applied if violated (p < 0.05). Finally, a correlation analysis was performed to assess the relationship between subjective cognition and physiological values, with the correlation coefficient ranging from −1 to 1, where values closer to 1 indicated a stronger relationship. The formula used to calculate physiological indicators was as follows:

$$Mean(\mathrm{Log}(D))={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n\mathrm{Ln}({Mean}_{\mathrm{VR},i}-{Mean}_{\mathrm{Baseline},i})}$$

(2)

Formula (2) was proposed in this study. D represents the physiological value difference, Mean_VR,i denotes the physiological value under the virtual reality condition, and Mean_Baseline,j represents the physiological value under the baseline condition.

3. Results

The data collection methods are described in detail in Section 2. This section presents the experimental data collected from 46 participants. Section 3.1 analyzes the statistical results of participants’ sense of presence in the VR environment to assess their immersion in the constructed PLE. Section 3.2 examines participants’ subjective visual perception of the materials. Section 3.3 and Section 3.4 provide systematic significance testing and statistical analyses of responses related to emotional states and cognitive functions, respectively, and compare the results of these analyses. The correlations between participants’ subjective feedback and objective physiological data are discussed in Section 3.5.

3.1. Presence in the VR Environment

Figure 6 illustrates the distribution of participants’ scores on the Igroup Presence Questionnaire (IPQ), which assessed their sense of presence in the VR environment. The mean scores for each of the first three presence dimensions were positive: Spatial Presence (SP) with an M of 3.15, involvement (INV) with an M of 1.72, and realism (REAL) with an M of 4.30. The post-experiment interviews indicated that limited freedom of movement could affect the VR experience, possibly contributing to the lower INV scores. Regarding the question, “Did you feel like you were really ‘there’ in the computer-generated virtual world?” Global Presence (GP) had an M of 1.46, with 80% of participants rating it 1 or higher, surpassing the neutral level. The overall average score across all the 46 participants was 2.66, suggesting that the VR scenes provided a positive sense of presence.

3.2. Visual Perception of Architectural Materials

Table 4. Test statistics for subjective questionnaire dimensions.

		Sum of Squares	Df	Mean Square	F	Sig.	η2
MCV (col)	Material	108.854	2.432	44.765	30.980	0.001 ***	0.397
	Error	165.146	114.290	1.445
MCV (gloss)	Material	98.229	2.876	34.157	24.785	0.001 ***	0.345
	Error	186.271	135.163	1.378
MCV (tex)	Material	95.542	2.912	32.804	27.556	0.001 ***	0.370
	Error	162.958	136.885	1.190
ECRV	Material	94.682	2.869	32.997	7.896	0.001 ***	0.144
	Error	563.568	134.861	4.179

*** p < 0.001.

presents the results of a repeated-measures ANOVA assessing the participants’ subjective perceptions of MCV and ECRV. Separate comparative analyses were conducted for MCV, and the results indicated that the p-values for both MCV and ECRV were less than 0.05, indicating a statistically significant difference.

Figure 7 presents the differences in the MCV and ECRV variations across the four building material environments. The MCV statistics demonstrated that the participants generally perceived the red brick and wood environments as warm, roughly textured, and non-glossy. In contrast, the concrete and white paint environments were perceived as cold, with the white paint considered smooth and glossy and the concrete described as rough and lacking luster. Regarding ECRV, red brick and wood environments were associated with positive emotional arousal and greater feelings of pleasure, whereas concrete and white paint environments were associated with increased psychological stress, reflecting negative emotions.

3.3. Emotional States

Our results indicated statistically significant differences (p < 0.05) in emotional state indicators across all four building material environments (

Table 5. Test statistics for indicators of reactive emotional states.

		Sum of Squares	Df	Mean Square	F	Sig.	η2
SCL	Material	0.262	2.483	0.106	6.125	0.002 **	0.203
	Error	1.028	59.601	0.017
ST	Material	0.084	2.692	0.031	7.632	0.001 ***	0.241
	Error	0.264	64.608	0.004
α	Material	39.506	2.499	15.808	3.131	0.038 *	0.082
	Error	441.630	87.468	5.049
FAA	Material	0.082	2.266	0.036	4.064	0.016 *	0.066
	Error	0.863	97.435	0.009
TTFF	Material	101.319	2.594	39.058	3.134	0.037 *	0.095
	Error	969.991	77.822	12.464
PD	Material	6.718	3.000	4.370	16.616	0.001 ***	0.537
	Error	12.239	43.000	0.177

* p < 0.05, ** p < 0.01, and *** p < 0.001.

). The participants exhibited the most significant increases in SCL and ST in the wood environment, followed by smaller changes in the red brick and white paint environments, with the concrete environment showing the least change. Notably, a significant increase in FAA was observed, predominantly in the wood environment. Additionally, TTFF and PD exhibited marked increases in both wood and red brick environments.

The trends in SCL and ST indicated the most significant increases in the wood environment, with SCL rising from 3.21 to 3.74 μS and ST increasing from 34.0 °C to 35.1 °C. The white paint and red brick environments also elicited increases in physiological responses, whereas changes in the concrete environment were relatively minor. Notably, the participants exhibited more pronounced increases in SCL and ST in the red brick and wood environments (Figure 8).

In the analysis of α waves, the concrete environment significantly promoted brainwave activity, followed by white paint and wood environments, while the increase in the red brick environment was relatively modest. The FAA values were all positive, indicating that all the building material environments enhanced positive emotions, with the wood environment presenting the most significant increase, far exceeding those of the concrete and white paint environments, while the increase in the red brick environment was comparatively small (Figure 8).

TTFF analysis showed that the wood and red brick environments exhibited greater changes in TTFF, with more significant increases than in the other environments. Moreover, the increase in PD in the wood and red brick environments was slightly higher than that in the concrete and white paint environments (Figure 8).

3.4. Cognitive Functions

Our results indicated that among the four building material environments, all indicators, except for AFT, showed statistically significant differences (p < 0.05) (

Table 6. Test statistics for indicators of reactive cognitive function.

		Sum of Squares	Df	Mean Square	F	Sig.	η2
HR	Material	121.601	2.507	48.511	4.865	0.006 **	0.144
	Error	724.818	72.693	9.971
HRV (SDNN)	Material	0.955	2.512	0.380	3.060	0.040 *	0.069
	Error	12.800	102.922	0.124
HRV (RMSSD)	Material	1.732	2.724	0.636	6.907	0.001 ***	0.192
	Error	7.274	79.009	0.092
β	Material	39.435	2.004	19.679	6.241	0.003 **	0.163
	Error	202.214	64.124	3.153
FT	Material	336.217	2.437	137.955	4.325	0.012 *	0.143
	Error	2020.965	63.366	31.894
AFT	Material	448.784	2.349	191.088	1.780	0.172	0.064
	Error	6556.376	61.063	107.371

* p < 0.05, ** p < 0.01, and *** p < 0.001.

). Specifically, the analysis of key HR and HRV indicators revealed that participants exhibited stronger physiological responses in concrete and white paint environments. The increase in HR was significantly higher in the concrete and white paint environments than in the red brick and wood environments. The increases in the key HRV indicators SDNN and RMSSD were also slightly higher in the concrete and white paint environments than in the red brick and wood environments (Figure 9).

In addition, the increase in β waves was significantly higher in the concrete environment than in the other three environments, indicating its significant impact on brain activity. The FT indicator results showed that the increase in the wood environment far exceeded that in the red brick, concrete, and white paint environments, suggesting that the wood environment had a stronger ability to capture participants’ visual attention (Figure 9).

3.5. Correlation Between Subjective Feedback and Physiological Response

SCL, HRV (SDNN), PD, α wave, β wave, and FAA were selected as sensitive indicators of physiological responses for the correlation analysis with ECRV. The results showed a generally positive correlation between ECRV and these indicators, with SCL exhibiting particularly significant associations. Among the objective physiological responses, the correlation between SCL and FAA was significant, as was the correlation between HRV (SDNN) and both α and β waves, all of which demonstrated strong positive correlations (Figure 10).

The brain regulates various unconscious physiological functions, including heart rate, respiration, and skin conductance, through the autonomic nervous system, and these functions are closely linked to emotional states [77]. In this study, participants’ emotional experiences were assessed using ECRV, and data on their subjective emotional states were collected. Correlation analysis revealed a significant relationship between ECRV and SCL, both of which are modulated by the autonomic nervous system. Based on the ECRV data, participants’ emotional states were categorized into three groups: negative (ECRV < 0), neutral (ECRV = 0), and positive (ECRV > 0). SCL differences among these groups were then compared after resting and exposure to environmental stimuli. Approximately 75% of participants who rated the material environment positively exhibited an increase in SCL following stimulation. This phenomenon may be related to physiological arousal induced by stimuli, where increased sympathetic nervous activity leads to heightened perspiration, thereby affecting skin conductance levels [56,78]. These findings suggest a correspondence between the participants’ subjective emotional experiences and their physiological responses, indicating that perceived emotional states were partially reflected in their physiological changes. However, a small number of participants had physiological responses that contradicted their subjective feelings, as evidenced by an increase or decrease in SCL, despite providing negative or neutral evaluations. This inconsistency suggests that individuals may experience significant physiological emotional activation without subjectively perceiving the corresponding emotional arousal [79]. Therefore, this discrepancy between physiological responses and emotional states highlights the complexity of emotional experiences, emphasizing the nonlinear or diverse relationship between emotions and physiological responses (Figure 11).

4. Discussion

This study examined the effects of a PLE composed of four building materials on participants’ physiological responses. The following discussion compares the findings of this study with those of existing research. Section 4.1 and Section 4.2 provide an in-depth analysis of how indicators of emotional state and cognitive function could reflect changes in participants’ experiences across different material environments. Section 4.3 evaluates the suitability of each building material for creating effective learning environments and its impact on learning outcomes by incorporating subjective questionnaire data. Finally, Section 4.4 addresses the study’s limitations and proposes directions for future research.

4.1. Effect of Architectural Material Environment on Emotional States

4.1.1. Skin Conductance and Skin Temperature Aspects

Studies have indicated that environments with materials such as wood and red brick can significantly enhance emotional states, promote relaxation, and improve emotional experience. A detailed analysis of SCL and ST results has revealed increases of 0.49 and 0.53 μS in SCL, respectively, compared to resting states, representing activation of the sympathetic nervous system and heightened emotional arousal [80]. This activation can stimulate sweat gland activity, leading to elevated SCL [78]. Meanwhile, the ST measurements exhibited increases of 1.06 °C and 1.12 °C, further confirming the influence of visual perception on emotional arousal [80]. These findings align with Kim et al.’s research, which suggested that increased wood proportions in indoor environments created a warmer atmosphere, leading to stronger skin responses and higher emotional arousal [34]. Additionally, Demattè et al. [7] observed that wood environments typically evoked a warmer, more natural, and more comfortable feeling than white paint environments. The observed changes in SCL and ST imply that in red brick and wood environments, participants exhibited more pronounced sympathetic nervous system activity, corresponding to greater emotional activation.

Notably, the participants in the white paint environment exhibited higher SCL and ST than those in the red brick environment, which contradicted the common view in existing research that warm colors generally elicit more positive emotional arousal than cool or neutral tones [81]. In the MCV ratings, white paint was the only material perceived as smooth and glossy, suggesting that the glossiness and texture of the materials could be key factors contributing to this discrepancy. However, the current findings do not provide sufficient clarity to precisely define the relationship between these material characteristics and emotional states or cognitive functions.

4.1.2. Frontal Asymmetry Activity Index and Alpha Waves Aspects

In a PLE composed of four different building materials, the FAA results were positive, indicating that the participants generally experienced positive emotional responses [70]. Notably, the wood environment exhibited the highest FAA value at 2.68 μV, indicating the most significant emotional activation. This finding is consistent with the research of Ojala et al. [17], who demonstrated that individuals in wooden environments reported higher levels of positive emotions and exhibited increased sympathetic nervous system activity. The alpha waves associated with relaxation and creative thinking were measured at 9.38 μV in the wood environment [82], slightly lower than the value observed in the concrete environment, suggesting that the participants felt more relaxed in the wood setting. Wood color and texture are often linked to feelings of nature and relaxation [83], further supporting the idea that such environments could facilitate relaxation and promote divergent thinking.

The positive emotions elicited by the red brick environment were relatively weaker than those generated by the other environments, with an FAA value of 2.63 μV. According to Elliot and Maier [84], red is often associated with anxiety and agitation, which can influence emotional experiences during learning. Thus, the red hue of the bricks could affect emotional responses in a learning context, leading to a lower FAA increase compared with the responses elicited by other material environments.

4.1.3. Pupil Diameter and Time to First Fixation Aspects

Participants generally perceived VR environments constructed with wood and red brick as warmer and more exciting than those made of concrete and white paint. The eye-tracking metrics revealed that the pupil diameters increased to 3.75 and 3.61 mm in the red brick and wood environments, respectively, while TTFF was 6.94 and 6.02 ms, indicating the longer initial fixation times in these environments. These changes reflected participants’ subjective feelings of warmth and excitement, suggesting heightened emotional arousal and increased visual attention in wood and red brick environments [45]. These findings are consistent with Li et al.’s research, which highlighted the positive effect of wood on visual attention and emotional responses, creating a natural, warm, and relaxed psychological impression [85]. Additionally, studies on color and emotional arousal have indicated that images in warmer red tones, such as those in red brick, can elicit greater pupil dilation than those in cool gray tones, such as concrete, further supporting the idea that the warm hues of red brick may stimulate higher levels of emotional arousal [81].

4.1.4. Effect on Emotional State

The comprehensive analysis revealed that environments featuring wood and red brick significantly enhanced the participants’ emotional states, fostering positive emotional experiences that promoted both relaxation and creativity. The analysis of SCL and ST demonstrated that these environments activated the sympathetic nervous system, as evidenced by increases in skin conductance and temperature, indicating heightened emotional arousal. The wood environment exhibited the most pronounced emotional stimulation in the measurements of FAA and α waves, leading to greater relaxation and facilitating creative thinking. Additionally, the results for PD and TTFF suggested that the wood and red brick environments elicited stronger emotional arousal and higher levels of visual attention, further supporting their role in creating a warm and natural atmosphere.

4.2. Effect of Architectural Material Environment on Cognitive Functions

4.2.1. Heart Rate and Heart Rate Variability Aspects

Concrete and white paint environments were closely linked to enhanced cognitive function and reduced emotional fluctuations, as evidenced by HR, HRV (SDNN), and HRV (RMSSD), which reflected the influence of material environments on the participants’ autonomic nervous system activity [86]. In these environments, the participants experienced increases in HR of 2.33 and 2.54 bpm, with HRV (SDNN) rising by 4.98 and 4.96 ms, and HRV (RMSSD) increasing by 5.41 and 5.50 ms, which were higher values than those observed in other environments. These changes suggested enhanced autonomic nervous system activity, leading to improved HRV, heightened alertness, and concentration, whereas modulation by the parasympathetic system reduced emotional fluctuations, promoting emotional stability and rational cognition [87,88]. These findings align with those of Zhang et al., who demonstrated that participants in concrete environments exhibited higher HRV, HR, and systolic blood pressure than those in wood environments, along with lower skin resistance, indicating improved concentration [9]. Variations in HR, HRV (SDNN), and HRV (RMSSD) reflected increased parasympathetic activity and a better balance between the sympathetic and parasympathetic systems, enhancing emotional stability and rational cognitive states.

4.2.2. Beta Wave Aspects

Beta waves are associated with maintenance of alertness and focused attention [82]. In the concrete environment, both the alpha and beta waves were significantly higher than other environments, measuring 10.35 and 5.49 μV, respectively. This indicates greater emotional stability and heightened concentration, which could contribute to the enhanced alpha waves observed. These findings align with those of previous research, suggesting that gray concrete is often linked to calmness and emotional stability [89]. In contrast, the wood environment had a slightly lower impact on alpha waves, suggesting that although wood promoted relaxation, it could not enhance emotional stability and focus to the same extent as concrete. Furthermore, the SCL and ST results indicated that the wood environment elicited a richer emotional experience, which could potentially affect participants’ ability to maintain mental agility and concentration.

4.2.3. Fixation Time Aspects

Compared to other materials, both red brick and wood were perceived by participants as having the most intricate textures, resulting in the largest increases in FT, measuring 24.99 and 27.04 ms, respectively. This indicated that the visual texture characteristics of these materials effectively captured participants’ attention and elicited emotional responses [90]. This finding is consistent with Wan’s research, which observed that the detailed and complex textures of wood surfaces led participants to spend more time observing and processing during cognitive tasks [91]. Such an increase in cognitive load, defined as the total mental effort expended on cognitive tasks, could have a dual effect on learning efficiency [92]. On the one hand, it may enhance the perceived depth of the environment and emotional connections, promoting memory and understanding. However, excessive visual processing can consume diverse cognitive resources, potentially hindering the ability to process learning materials and affecting learning efficiency and memory retention.

4.2.4. Effect on Cognitive Functions

These analyses indicated that the concrete and white paint environments significantly enhanced participants’ cognitive functioning, promoting emotional stability and rational cognition, which contributed to improved concentration and learning efficiency. The HR, HRV (SDNN), and HRV (RMSSD) results confirmed the positive effects of these environments on autonomic nervous system activity, suggesting that participants maintained higher levels of alertness and concentration. Additionally, the observed changes in beta waves in concrete environments reflect enhanced concentration and emotional stability. The FT results indicated that the visual textures of red brick and wood effectively captured attention but may also have increased the cognitive load, potentially affecting learning efficiency.

4.3. Selection of Building Materials Suitable for a PLE

The selection of appropriate building materials is essential for creating an effective PLE. Research has shown that different materials can have a significant impact on emotions and cognitive functions, highlighting the importance of prioritizing materials that enhance learning outcomes.

Multi-faceted physiological analyses demonstrated that environments made of wood and red brick significantly enhanced participants’ emotional states, promoting relaxation, positive feelings, and increased creativity. These materials activated the sympathetic nervous system, elevated emotional arousal, and stimulated stronger emotional responses and greater visual attention, thereby reinforcing their role in creating a warm natural atmosphere. Numerous studies have confirmed that positive emotional states can improve learning outcomes. Therefore, using wood and red bricks in learning environments can foster a comfortable and conducive atmosphere that supports cognitive expansion.

Comprehensive physiological analysis indicated that environments made of concrete and white paint significantly enhanced participants’ cognitive functions by increasing alertness and attention while promoting emotional stability. The simple visual textures of these materials reduced cognitive load, thereby improving learning efficiency. Consequently, concrete and white paint created a minimalist, modern, and efficient learning space that was ideal for activities requiring focused attention and rational thinking.

In the subjective questionnaire, approximately 60% of the participants believed that VR-simulated virtual scenes were suitable for learning, office work, and exhibition activities requiring reading and in-depth thinking. The atmospheric design of these functional spaces aimed to promote emotional stability and cognitive enhancement, thereby improving work and learning efficiency and facilitating deeper thinking [93,94]. The emotional and cognitive effects of concrete and white paint environments effectively met the needs of offices and learning spaces as well as other functions requiring similar atmospheres.

In the investigation of materials suitable for indoor environments, approximately 71% of participants preferred red brick and wood. This phenomenon could be explained through color psychology, as warm tones such as red, orange, and yellow are linked to positive emotions such as energy, vitality, and warmth, therefore more effectively eliciting positive feelings. This was consistent with the significant increases in SCL and ST observed in these environments.

Concrete learning and working spaces may induce emotional stress [95], whereas the innate affinity for nature renders wood a preferred material [96]. Numerous studies have confirmed the positive impact of wood on physical and mental well-being, which explains the preference for wooden environments or items [97]. Although concrete and white paint environments supported deep thinking, prolonged exposure to these settings could lead to stress and discomfort.

Therefore, in PLE, it is crucial to combine various materials to create spaces that are conducive to human activities. Concrete and white paint can serve as primary building materials owing to their simplicity and brightening effects, which enhance the visual openness of a space, promote focus, and encourage deep thinking. Wood and red brick can be used as complementary materials for local decoration. Wood adds warmth and alleviates the coldness of concrete and white paint while improving the comfort and affinity of the space. Red brick, on the other hand, is ideal for areas designed for rest or interaction, as its texture and tone bring warmth and vitality, creating a relaxing atmosphere. By thoughtfully combining these materials, the functional needs of PLE can be met while also enhancing the user’s emotional stability and cognitive function, ultimately supporting improved learning outcomes.

4.4. Limitations and Future Directions

This experiment comprehensively analyzed the effects of PLE on emotional states and cognitive functions using multimodal recognition techniques. However, there were some limitations to this study.

(1)

Sample Size Limitations: The participants in this study were primarily aged between 21 and 26 years, which may limit the generalizability and applicability of our results. Future research should include a broader age range and individuals from diverse cultural backgrounds to enhance the representativeness of the findings.

(2)

Complexity of Building Materials: As a foundational study, this study primarily examined the effects of a single material variation on PLEs. However, it did not isolate the influence of individual visual characteristics (such as color, gloss, and texture), which limits the ability to fully distinguish the specific effects of color from those of surface properties on emotional states and cognitive functions. Furthermore, real-world settings typically involve combinations of materials, and their positioning and proportions play a crucial role in shaping the overall experience. This study did not explore such combinations or spatial arrangements, which may limit the ecological validity of the findings. Future research should examine the independent effects of visual features and the effects of material combinations, particularly in terms of layout and proportion, to better reflect real-world complexity and more accurately assess their influence on emotional and cognitive processes.

(3)

Limitations of VR Technology: VR technology helps control variables, and this study confirmed that virtual scenarios provide positive immersive experiences through the IPQ scale. However, this may not fully replicate the complexities of real-world contexts. Future studies should consider using portable measurement devices for field research or long-term tracking to better understand the application of material selection in real-world environments.

5. Conclusions

This study analyzed the effects of visual perception of building materials in PLE on human emotional states and cognitive functions. Four virtual learning environments were simulated using VR technology, and visual perception experiments were conducted with 46 participants. The data collected included eye movement metrics, bioelectrical responses, and participants’ subjective evaluations of pleasure and arousal levels for each environment. A comprehensive analysis and comparison of these data provided insights into their effects on emotional states and cognitive functions. The main findings were as follows.

(1)

Wood and red brick environments significantly enhanced participants’ emotional states. Specifically, the SCL increased by 0.49 μS and the ST by 1.06 °C in the wood environment, while the SCL increased by 0.53 μS and the ST by 1.12 °C in the red brick environment. The FAA in the wood environment was 2.68 μV, indicating strong emotional arousal. Additionally, the PD increased to 3.75 mm in the red brick environment and 3.61 mm in the wood environment, with TTFF measuring 6.94 and 6.02 ms, respectively. These data suggested that both materials effectively triggered emotional arousal and focus in visual attention.

(2)

Concrete and white paint environments significantly enhanced participants’ cognitive functioning, promoting emotional stability and rational cognition. HR increased by 2.33 bpm and 2.54 bpm in these environments, respectively, while the HRV metrics of SDNN and RMSSD increased by 4.98 ms and 4.96 ms, and 5.41 ms and 5.50 ms, respectively, indicating higher alertness and attention. The β-wave amplitude in the concrete environment reached 10.35 μV, indicating high concentration. Conversely, FT of 24.99 and 27.04 ms in the red brick and wood environments, respectively, drew attention but may also have elevated cognitive load, potentially affecting learning efficiency.

This study highlights the significant effects of architectural material environments on emotional states and cognitive functions, providing a scientific basis for designing PLE that enhance learning efficiency. This study indicated that wood and red brick environments significantly improved emotional comfort and creativity, whereas concrete and white paint environments significantly enhanced cognitive function and concentration. These findings underscore the importance of carefully selecting and combining materials in PLE to balance emotional activation and cognitive needs, offering valuable insights for learning space design and architectural environment research. Therefore, when designing a PLE, it is essential to consider the effects of different materials and thoughtfully combine them to optimize the learning environment.