In response to an increasingly competitive market, companies are placing heightened emphasis on design and innovation as means to align with evolving consumer demands.
As consumers seek furniture that embodies both aesthetic appeal and personal identity, understanding user preferences has become essential for businesses aiming to enhance market relevance.
Two basic design elements—form and colour—are vital. They stimulate human vision and physiology, impact users’ inner feelings, and help form corresponding colour associations and image recognition.
Despite the significant role of colour and form in furniture design, research on these elements has historically been constrained by limited methodologies and tools. As a result, substantial gaps remain in understanding consumer preferences for furniture designs.
To address this knowledge gap, the present study examines chairs as focal stimuli, specifically investigating how colour and form impact aesthetic evaluations.
This research employs advanced multimodal measurement techniques to provide comprehensive insights into the interaction between design factors and user aesthetic responses, thus offering valuable implications for furniture design and innovation.
Human–computer interaction (HCI) is important for the development of products. Recently, as a critical factor in evaluating product design quality, user aesthetic experience has received more attention from researchers, which plays an important role in purchase decisions.
Preference, the main part of user aesthetic experience, narrowly refers to how consumers like or dislike product designs. Designers often use several research tools to understand preferences, such as one-on-one interviews, general surveys, and focus group studies.
The data provided by these traditional methods are not sufficiently accurate and are easily affected by the respondents’ surrounding environment, false feelings about their inner state, and so on. Therefore, the conclusions of such surveys may mislead the design direction.
Owing to various levels of conscious awareness, process operations can drive people toward different behavioural responses8. Within HCI research, multimodal measurement techniques—such as eye-tracking, galvanic skin response (GSR), electrocardiography (EKG), and event-related potentials (ERP)—have gained prominence.
Eye-tracking reveals focal points and preferences, GSR reflects levels of emotional arousal, and EKG provides insights into psychological stress. Additionally, ERP, an advanced neurophysiological tool, captures brain activity responses to specific visual stimuli, allowing researchers to measure users’ subconscious reactions to design elements.
Collectively, these methods enable a more objective and scientific examination of aesthetic preferences, supporting the development of more user-centred designs in furniture.
Although furniture is a major product category, there is a significant lack of HCI-related research on furniture aesthetic preference. Therefore, the introduction of HCI, utilising research methods from applied psychology (such as eye tracking and physiological measurement techniques), was chosen to scientifically and objectively study consumer preferences for furniture.
This approach aims to provide precise design guidance by incorporating scientific and objective perspectives.
Leisure Chairs
The leisure chair generally comes in various colourful, creative, and popular shapes with young people.
Using the theory of ergonomics and following a certain principle of scale, Han designed a new type of leisure chair to alleviate and improve the compression and fatigue of the lumbar spine and spinal column during sedentary work and analysed it in comparison with the results of the finite element simulation.
Research on leisure chairs’ appearance, visual sensibility, and physiological feedback remains limited.
Form and colour play crucial roles in the design of leisure chairs, directly influencing people’s perceptions, comfort, and aesthetic experience.
In neurasthenic and computational aesthetics, sensory features like colour and shape are known to significantly impact user preferences. For example, Markus et al. found that specific colours can evoke unique emotional responses, while Wang and Hsu demonstrated that symmetry and simplicity in form enhance perceptions of beauty and emotional appeal.
These findings underscore the importance of visual elements in shaping consumer emotions and preferences, offering valuable insights for design.
Regarding the initial understanding of colours, names such as rose red and lemon yellow are disorderly and imprecise. Colour researchers worldwide have begun constructing numerical colour systems to address this issue.
Currently, three common colour systems are used internationally: the Munsell colour system, the Ostwald colour system, and the PCCS system formulated by the Japan Colour Research Institute.
The visual perception of colour is influenced by various factors, including hue, intensity, brightness, and lighting conditions. Even when sharing the same colour palette, furniture items such as sofas, chairs, and couches can elicit distinct sensory experiences.
Furthermore, different countries and regions have diverse cultural backgrounds and religious beliefs, leading to varying cultural perceptions of colour.
Furniture forms fall under the category of artificial forms, referring to the shape presented by the outline of the furniture, encompassing both ‘external shape’ and ‘expression’. It represents the external manifestation of furniture materials and structures and incorporates elements such as points, lines, surfaces, and blocks.
Based on existing research, the classification of furniture forms primarily encompasses distinctions such as curvilinear vs. rectilinear, open and closed, and complex and simple designs. Compared to other furniture design elements, furniture forms convey visual perceptions more quickly and are highly infused with sensory factors, carrying elements of zeitgeist and cultural connotations.
In today’s market, furniture forms change more rapidly than new functionalities, reflecting society’s ever-changing desires and pursuits toward furniture.
Given the complexity and impact of colour and form in furniture design, several studies have explored consumer preferences in this context.
For instance, Jiang et al. studied the impact of colour preference on adolescent children’s choice of furniture. Ciritcioğlu et al. focused on the consumer preference on furniture surface colours. Kaputa and Supin dealt with the selected results of research aimed at the consumer’s preferences for furniture in the Slovak Republic.
Multimodal Measurement
Ergonomic studies primarily focus on human fit and decrease fatigue and discomfort through product design.
A collaborative study of ergonomics with HCI technologies, including eye tracking, physiological measurements, and EEG, should yield more favourable evidence for the emotional perception of furniture design.
By harnessing HCI emotion recognition technology, designers can gain insights into users’ emotional responses to furniture design.
Visual perception refers to the brain’s process of receiving, distinguishing, and recognising visual information. Human perception is the visual reception of information and visual cognition through memory and thought processes.
Vision was the first channel used to gather information. This can affect consumers’ subjective intentions of consumers. Eye tracking techniques record eye movements when consumers see a product. It is increasingly applied in aesthetic experience.
Several studies have examined how design elements affect users’ gaze behaviour; eye tracking has been widely applied in design fields, including mobile devices, packaging, web design, and advertising, thus providing references and guidance for designers. It has also been applied to evaluate the perception of sensory properties and quality factors or consumers’ willingness to purchase.
For instance, Palacios-Ibáñez et al. utilised eye-tracking metrics in virtual environments to analyse consumer gaze behaviour and decision-making processes for household products, achieving a 90 percent accuracy rate in predicting user preferences.
Their study highlights the role of eye-tracking as a powerful tool for understanding consumer behaviour and assessing aesthetic preferences in virtual settings, which provides valuable insights for our approach in evaluating furniture design.
Physiological measurements can generally more objectively report users’ emotions. Some available metrics include GSR, EKG, skin temperature (SKT), and respiratory pressure (RSP).
GSR and EKG have been widely applied in fields such as Medical Science, Psychology, and Biology, particularly for emotion evaluation. The GSR reflects the conductivity of the skin and correlates with emotional arousal, especially in the domain of user aesthetic experience design. EKG refers to the heart’s electrical activity.
EEG research found that humans continuously generate brainwaves. The electrical potential changes in the brain caused by the spontaneous activity of groups of brain cells are called spontaneous electrical potentials and form the basis for generating Event-Related Potentials (ERPs).
ERPs denote brain electrical activity changes when subjected to or removed from external stimuli. Most EEG responses and emotional ERPs are induced by complex visual stimuli.
ERPs are mainly applied in neurological research, such as the study of ERPs in patients with epilepsy and the brain mechanisms induced by face recognition. Its application in the design field is relatively limited, and research is sparse, mainly focusing on industrial products and clothing design.
As for the multimodal measurement method, Scherer pointed out that it could be utilised to measure people’s emotions because of the complex components. Additionally, Guo, Cao, Ding, Liu, and Zhang stated that this method can evaluate users’ emotional experiences.
Generally, there have been few studies on emotional measurements using multiple techniques, particularly in furniture design. This study aimed to determine the physiological mechanisms evoked by preferences and the relationship between emotional evaluation and design elements.
Moreover, this study will help optimise product design, promote the development of the entire material and furniture production industry, and improve the quality of people’s lives.
Aesthetic Evaluation
Computational aesthetics is an interdisciplinary field involving the disciplines of computer science, artificial intelligence, cognitive science, and aesthetics, and aims to study computer-generated art and design, as well as human perceptions and evaluations of their aesthetic experiences.
Aesthetic computation is an important branch of computational aesthetics that emphasises the use of computers and algorithms to understand, simulate, and enhance the human process of perceiving and appreciating beauty.
In the research of aesthetic computation, designers usually adopt the method based on subjective quantification, i.e., subjective scoring based on personal aesthetic preferences or questionnaires, and then compensating quantitative assessment of the scoring results through various mathematical methods.
In order to more accurately measure the aesthetic properties of quantified products, the scientific foundations of computational aesthetics and aesthetic computing need to be further explored and developed.
This includes the construction of more accurate, scientific models of aesthetics that capture and quantify aspects of beauty more comprehensively. This may require interdisciplinary collaborations covering fields such as mathematics, psychology, and computer science to develop more reliable theoretical frameworks and assessment methods.
For products with complex or irregular image forms, new methods of measurement and quantification need to be explored. This may involve more advanced image processing techniques, machine learning algorithms, and computer vision methods to more accurately analyse and assess the aesthetic characteristics of these products.
There is also a need to strengthen research on the relationship between the objective and the subjective in order to understand the link between individual aesthetic preferences and objective aesthetic features.
Aesthetic experience involves multiple sensory inputs, such as vision, hearing and touch. Recent research in affective computing and user experience evaluation has highlighted the value of multimodal measurement in assessing aesthetic experiences.
For instance, Kim and Lee developed a multimodal framework that combines video, audio, text, and physiological data to improve emotion recognition and user satisfaction assessment, proving the value of multimodal integration for robust user experience analysis.
Similarly, Wang et al. introduced an attention-enhanced model for emotion recognition in complex environments, using Transformers and attention mechanisms to capture subtle emotional cues in multimedia settings, making it highly effective for understanding aesthetic and emotional responses.
Machine learning techniques can effectively process multimodal data, integrate different sensory inputs, and improve the model’s ability to comprehensively understand and analyse the aesthetic experience.
Breiman proposed the RF algorithm, which is an emerging and highly flexible machine learning algorithm used to effectively address classification and regression problems. The core idea of the RF algorithm is ensemble learning, where different features and a fixed number of decision trees are integrated to achieve higher predictive performance and robustness.
The advantages of RF include:
(1)Handling large-scale, high-dimensional feature datasets with powerful adaptability to disturbances in the dataset, exhibiting high robustness in various ways.
(2)The relative importance of different features can be determined by assessing the contribution of each input variable in nonlinear sensitivity analysis and decision-making processes.
(3)Adapting to application scenarios with incomplete data, performing classification or regression in situations where the given dataset is incomplete.
(4)Since the RF algorithm is a non-parametric model, it does not make assumptions about the existence of any a priori relationships in the dataset and can deal with datasets that contain non-linear relationships.
This study used Nordic-style chairs as an example to explore synchronous measurement methods of HCI applied to the furniture domain. In addition, the impact of form and colour on aesthetic preferences for chairs was investigated. The study aims to advance the integration of HCI techniques and furniture design, particularly through synchronous measurement methods for user behaviour and emotional assessment.
To achieve this, we propose a multimodal measurement method that includes questionnaires, eye tracking, physiological measures, and EEG acquisition to investigate the factors (i.e., colour and form of chairs, sex, and consumer specialty) that significantly affect subjective preference evaluation and physiological feedback.
And we also established an aesthetic model using RF algorithm based on the multimodal measurement data. This approach provides a clear and objective perspective on furniture preferences.
It benefits the research and exploration of innovative furniture design and imparts a sense of visualisation, accuracy, and scientific rigor, enhancing its overall significance.
Behavioural Results
Descriptive statistical analyses were conducted on the scores for each colour and form using SPSS software.
The results show that there was little difference in the average scores.
Next, we combined the results of the measured data plotted. The preference scores were ranked as warm (1.59) > cool (0.30) > neutral (−0.12), complex (1.08) > simple (−0.41).
Moreover, the results of two-way ANOVA suggested that there was a significant difference for both the colour (F (5, 35) = 11.689, p < 0.001, ηp2 = 0.040) and the form of chairs (F (5, 35) = 51.556, p < 0.001, ηp2 = 0.155), but without their interactive effects (F (25, 35) = 0.731, p = 0.829 > 0.05, ηp2 = 0.013).
The repeated-measures ANOVA for factors such as sex and specialty revealed a violation of the sphericity assumption, according to Mauchly’s test (p < 0.001). Therefore, the Greenhouse-Geisser correction was applied, indicating significant differences among subjective evaluations of each participant (F (10.62, 39) = 8.405, p < 0.001, ηp2 = 0.185).
Furthermore, the participant’s specialty exhibited a noticeable difference in subjective preference ratings (F (10.62, 39) = 2.596, p = 0.004 < 0.05, ηp2 = 0.066). However, sex did not significantly differ in subjective evaluations (F (10.62, 39) = 0.779, p = 0.657 > 0.05, ηp2 = 0.021).
Eye-Tracking Outcomes
We selected five eye-tracking parameters based on the measurement results for detailed statistical analysis.
The differences were relatively small regarding the magnitudes of the parameter values. The results of recalculating the average values according to categories, indicate that the average values for all eye-tracking parameters were consistently higher for warm tones than for cool and neutral tones.
Additionally, chairs with complex forms tended to elicit higher eye-tracking parameter values.
For average pupil diameter, the results of two-way ANOVA showed highly significant effects of the colour of chairs (F (5, 35) = 8.187, p < 0.001, ηp2 = 0.028), but without main effect of the form of chairs (F (5, 35) = 0.809, p = 0.543 > 0.05, ηp2 = 0.003), or interaction between colour and form (F (5, 5) = 0.290, p = 0.998 > 0.05, ηp2 = 0.005).
Moreover, Mauchly’s test indicated that the assumption of sphericity was violated (p < 0.05). Thus, the statistics were reported based on the Greenhouse-Geisser correction. The results of repeated measures ANOVA revealed notable differences in chairs (F (7.04, 39) = 7.506, p < 0.001, ηp2 = 0.169), but without in sex (F (7.04, 39) = 0.457, p = 0.866 > 0.05, ηp2 = 0.012) or specialty (F (7.04, 39) = 0.894, p = 0.512 > 0.05, ηp2 = 0.024).
For first fixation duration, the two-way ANOVA clearly showed that the first fixation duration was more influenced by interaction between the colour and form of chairs (F (5, 35) = 1.414, p = 0.056 > 0.05, ηp2 = 0.034) rather than the colour (F (5, 35) = 1.185, p = 0.314 > 0.05, ηp2 = 0.004) or the form (F (5, 35) = 0.607, p = 0.695 > 0.05, ηp2 = 0.002).
According to Mauchly’s test, the assumption of sphericity was violated (p < 0.05). The repeated measures ANOVA results corrected by G-G correction signified there was no clear main influence of sex (F (11.17, 39) = 0.679, p = 0.761 > 0.05, ηp2 = 0.018) or specialty (F (11.17, 39) = 0.699, p = 0.742 > 0.05, ηp2 = 0.019) of participants except for different stimuli (F (11.17, 39) = 1.019, p = 0.428 > 0.05, ηp2 = 0.027).
Furthermore, based on the ANOVA results, no significant differences (p > 0.05) were observed in the impact of colour, form, sex, and professional factors on the other three parameters, including fixation count, saccade count, and total fixation time.
Physiological Responses
‘AVHR’ was chosen as the analysis indicator for EKG, which refers to the average number of heart beats per minute. The heart rate is the number of heartbeats per minute and is typically expressed as beats per minute (BPM).
The ‘SC signal’ refers to the skin conductance signal obtained by measuring the changes in skin resistance. This signal is associated with sweat secretion and provides physiological information about emotional and arousal states.
The participants’ physiological responses were compared between viewing the images and the rest. According to the paired t-test, the results indicated that the physiological responses when watching chair pictures were significantly higher than when they were at rest.
GSR and EKG represent the increments in skin conductance and electrocardiogram relative to the baseline after receiving picture stimuli.
The results indicate that both △GSR and △EKG showed a trend of warm colours > cool colours > neutral colours and complex forms > simple forms.
For EKG, the ANOVA results suggested no obvious differences in each variable (p = 0.794 > 0.05).
The results of the two-way ANOVA revealed that the colour factor (F (5, 35) = 0.194, p = 0.965 > 0.05, ηp2 = 0.001) and form (F (5, 35) = 0.534, p = 0.751 > 0.05, ηp2 = 0.002) did not exhibit a significant difference on △EKG.
Additionally, the interaction between colour and form for chairs showed no significant difference in △EKG (F (25, 35) = 0.972, p = 0.503 > 0.05, ηp2 = 0.017). In the repeated-measures ANOVA, the assumption of sphericity was not met (p < 0.001).
Similarly, after G-G correction, the data results indicated significant differences among different participants (F (7.60, 39) = 4.658, p < 0.001, ηp2 = 0.112). However, the sex and specialty of the participants showed no significant impact on △EKG (F (7.60, 39) = 1.028, p = 0.414 > 0.05, ηp2 = 0.027; F (7.60, 39) = 1.055, p = 0.394 > 0.05, ηp2 = 0.028).
For △GSR, the results of two-way ANOVA showed unusual effects of different chairs (p < 0.001). Meanwhile, there were uncommon main effects of the form of chair (F (5, 35) = 2.293, p = 0.043 < 0.05, ηp2 = 0.008) and interaction of form and colour (F (25, 35) = 5.199, p < 0.001, ηp2 = 0.085), without the colour of chair (F (5, 35) = 0.989, p = 0.423 > 0.05, ηp2 = 0.004).
The results of repeated measures ANOVA corrected by G-G correction revealed significant effects of varied chairs (F (2.61, 39) = 9.234, p < 0.001, ηp2 = 0.200), but without sex (F (2.61, 39) = 0.809, p = 0.477 > 0.05, ηp2 = 0.021) or specialty (F (2.61, 39) = 0.473, p = 0.675 > 0.05, ηp2 = 0.013).
ERP Results
The P300 amplitudes of the red, yellow, green, blue, white, and black chairs were 0.406, 0.317, 0.178, 0.134, 0.054, and 0.083 eV, respectively.
The P300 amplitudes of form 1–6 chairs were 0.211, 0.253, 0.364, 0.078, 0.080, and 0.111 eV, respectively.
The amplitude of the P300 fluctuation changed in this order: warm colours (−0.36 eV) > cool colours (−0.16 eV) > neutral colours (−0.05 eV), complex forms (−0.39 eV) > simple forms (−0.17 eV).
The ANOVA analysis results suggested that there was no significant effect on P300 amplitude, regardless of sex (F (3.01, 32) = 2.410, p = 0.126 > 0.05, ηp2 = 0.188) or specialty (F (2.67, 32) = 1.207, p = 0.645 > 0.05, ηp2 = 0.537).
Colour had a significant effect on P300 amplitude (F (5, 35) = 1.105, p = 0.035 < 0.05, ηp2 = 1.227), whereas form (p = 0.225 > 0.05) and interaction (p = 0.217 > 0.05) had no significant effect.
Correlation Analysis
Based on the above results, we chose only different metrics to determine whether eye movement, physiological responses, or EEG signals could reflect furniture aesthetics.
This study conducted a correlation analysis between subjective and objective data. Defining the nine-level evaluation scores as Ordinal Data, all data were processed using Spearman’s correlation inspection and the Kendall correlation coefficient test.
The results suggest a weak positive correlation between the subjective evaluation score, average pupil diameter (Kendall’s tau-b = 0.044, p = 0.020 < 0.05; Spearman’s rho = 0.060, p = 0.023 < 0.05), and first fixation duration (Kendall’s tau-b = 0.045, p = 0.022 < 0.05; Spearman’s rho = 0.060, p = 0.023 < 0.05). However, there was no correlation between the subjective evaluation score and the SC signal (Kendall’s tau-b = 0.016, p = 0.404 > 0.05; Spearman’s rho = 0.022, p = 0.411 > 0.05) or the P300 amplitude (Kendall’s tau-b = 0.038, p = 0.327 > 0.05; Spearman’s rho = 0.057, p = 0.334 > 0.05).
The results were consistent, although the correlation coefficients differed.
Behavioural Result Discussion
Regarding chair colour and form, each element significantly affected the subjective evaluation scores; however, the interaction between the two was not significantly different for the behavioural results.
For the overall chair colour, the trend for all subjective behavioural data means was warm > cool > neutral. These results support the notion that long-wavelength colours (red and yellow) are more arousing than other colours, leading to a linear association between affective tone and wavelength.
In addition, the participants preferred complex to simple forms, which agrees with the literature. Regarding participants’ sex and specialty, only educational majors showed a significant difference in subjective evaluation scores, suggesting whether participants had a design-related education affected their preference for chairs.
No significant sex-based differences were found, consistent with previous studies that reported no significant differences between men and women in terms of preferences.
Multimodal Measurement Results
Visual fixation is active when assessing internal mental representations, and eye movement indicators can be used as measures of choice and preference.
Based on data analysis, it was concluded that among the five eye movement parameters, only the average pupil diameter and first fixation duration were significant and effective. The chair colour significantly affected the average pupil diameter, whereas the interaction between chair colour and form significantly affected the first fixation duration.
In previous studies, there was little research on the form and colour of furniture. However, there are references in other design fields. Bradley et al. found that pupil sizes evoked by International Affective Picture System images differed significantly.
Guo, Ding, Liu, Liu, and Zhang reported that first fixation duration and average pupil diameter could reflect the user experience of products when participants browse products freely.
It is possible that emotional responses observed through pupil size changes are also influenced by visual characteristics such as colour and contrast.
Research suggests that these visual factors can affect physiological responses, including pupil dilation, which may not solely reflect emotional or aesthetic reactions but also the inherent sensory impact of the stimuli themselves.
Thus, the emotional effects we interpret might be partially mediated by the colour and contrast of the stimuli, which engage the visual system and can modulate physiological responses.
Moreover, statistical analysis revealed that the values of these two effective eye movement parameters evoked by chairs with complex and warm colours were significantly higher than those evoked by chairs with cool/neutral colours and simple forms, suggesting that warm colours and complex forms are more likely to stimulate visual responses.
The results of the correlation analysis showed that the values of these two effective eye movement indices had a weak positive correlation with the subjective behavioural data.
This implies that changes in the visual eye movement parameters affected the participants’ preferences for different colours and forms of chairs.
Visual attention and stimulation would determine how much people like or dislike chair products, which, in turn, affect their desire to consume.
People pay more attention to things they like and give more visual attention to chairs with higher subjective ratings, thus inducing longer first fixation duration and larger pupil diameters.
Conversely, less visual attention is given to chairs with lower ratings that they dislike less, thus inducing smaller pupil diameters in the first fixation duration.
Guo et al. proved that saccade and fixation counts relate to users’ emotional experiences when shopping online. Other studies have shown that the type of traditional furniture has a significant effect on total fixation time and average fixation count.
Our study results showed no significant effects on total fixation time, fixation count, or saccade count, differing from findings in online and interactive environments, where visual richness tends to increase engagement.
Studies indicate that in more dynamic contexts, elements like colour contrast, varied layouts, and interactive content significantly draw users’ attention and influence eye-tracking metrics.
In contrast, our experimental setup was highly controlled, with standardised lighting, angle, and background to reduce extraneous influences. Additionally, traditional furniture typically incorporates intricate patterns and complex textures, which may naturally draw more attention and increase fixation counts, unlike the simpler, modern designs used in this study.
This likely minimised attention shifts typically seen in more complex visual environments, suggesting that the lack of significant effects in our study may reflect the simplified, controlled stimuli, where variations in fixation metrics were less pronounced.
Furthermore, most single-eye movement measurement studies have set flexible and controllable observation times, whereas our study set a fixed observation time of 10s.
Fixation of the observation time ensures the integrity of data recording during synchronised measurements of other physiological indices and avoids confounding preferences and decision-making processes.
This led to an inability to develop significant gaze or blinking behaviours, and the partial eye movement data in the time dimension did not show significant differences, as previously mentioned in the literature.
The t-test results revealed that the physiological signal values of the EKG and GSR in the resting state were significantly different from those while viewing the stimulus material.
The results of the physiological measurement showed that the interaction between form and colour and the chair form factor had significant effects on the SC signal of the GSR without the AVHR of the EKG.
Higher GSR values were observed for warm-coloured chairs in the complex form than for cool/neutral-coloured chairs in the simple form, consistent with previous research. Wilson and Angela Sasse conducted five studies on the impact of audio and video degradation and found a remarkable increase in SC and AVHR.
Mandryk and Atkins found evidence of different physiological responses in the body when playing computer games and provided a method for evaluating user experiences with entertainment technology using physiological responses.
Nevertheless, our results suggest a significant effect on GSR (SC signal) but not EKG (AVHR). The difference in colour and form might not be enough to influence the EKG because viewing the stimuli was relatively quiet, not as active as watching videos, playing games, or listening to music. However, the t-test results also illustrated that the visual stimulation of pictures still had some effect on the EKG relative to the calm state.
The P300 wave is associated with an individual’s attentional and memory processing of stimuli and can, therefore, be used to study several psychological processes, such as cognition and decision-making.
The P300 wave has also been associated with an individual’s favour or disfavour of a particular stimulus in contexts involving preferences. The results revealed that the amplitude of the P300 component was significantly affected only by chair colour, which demonstrated that participants focused their attention on stimuli of various colours.
Participants had the highest P300 amplitude when viewing warm-toned chairs, followed by cool-toned chairs, and the lowest for neutral-toned ones, which aligns with the results of previous studies.
Higher arousal in the P300 component was observed when viewing complex-formed chairs, suggesting that complex morphology can stimulate more P300 component activity. Nevertheless, the experimental results showed that morphology did not significantly affect the P300 amplitude.
Regarding the relationship between subjective response and GSR or EEG, the correlation was not significant, which is similar to the findings of other studies and may be due to small differences in signal fluctuations.
The participants’ sex had no significant impact on the degree of preference, including subjective and objective measurements in our study.
This means the degree of preference for the different chairs was similar, regardless of the participant’s sex. Previous studies reached similar conclusions in other design fields.
Djamasbi et al. examined whether sex preferences could influence the recognition of specific information provided by specific items on a webpage by collecting subjective data and users’ fixation information.
Their study did not show any notable differences between the sexes. Additionally, Guo et al. found similar conclusions that sex did not affect eye movement indices that could reflect the product’s user experience.
Regarding the effect of the participants’ majors (design and non-design) on the preference level, the results of the data analysis showed that the major influence of the specialty on the subjective evaluation was significantly greater than that of numerous indicators, such as EKG, GSR, and EEG.
Regardless of specialty, people have similar eye movements and physiological and neural feedback when viewing pictures of chairs.
However, their behavioural data (preference decisions made) vary significantly owing to differences in educational backgrounds that lead to varying knowledge about furniture aesthetics and design.
