Integrating the UMA and CM Models to Explain Aesthetic Judgement in Closed-Category Product Design: A Laptop Product Study

2.1 Aesthetic preferences

The study of aesthetic preferences has deep roots in psychology, dating back to ancient Greek philosophy, especially the works of Plato and Aristotle (Phillips et al., 2011; Whitfield & de Destefani, 2011). Over time, the field has evolved from a deductive philosophical approach to Fechner’s pioneering work in experimental aesthetics in 1876, marking a shift from studying aesthetic objects to a scientific methodology (Fechner, 1876). Early aesthetic research primarily centered on “high-end” art fields like painting and sculpture before gradually extending to “low-end” aesthetics like everyday product design (Suhaimi et al., 2023). Aesthetic preferences refer to how individuals assess the visual appeal of a product, often shaped by their past experiences and familiarity with design elements (Ding et al., 2025). Recent studies highlight the significant influence of aesthetics on consumer perception and decision-making. Aesthetic preferences reflect how people judge a product’s appearance based on personal experience and cultural background (Ma et al., 2025). This evaluation not only increases product appeal but also enhances user satisfaction and emotional engagement (Desmet & Hekkert, 2007). According to modern interactionism, aesthetic pleasure emerges from the dynamic interaction between individuals and objects, creating a sense of enjoyment and positive emotions (Blijlevens et al., 2014). Therefore, product design should go beyond functionality to align with consumers’ lifestyles and aesthetic expectations.

Contemporary research on aesthetic preferences focuses on two influential perspectives, Whitfield’s prototype preference theory and Paul Hekkert’s UMA model. Whitfield’s “Preference for Prototype” theory (1979) posits that people show a cognitive bias toward objects that are closely related to the prototypical form of a category. Essentially, products that resemble “classic” examples of their type (e.g., a chair with all the expected features of a “chair”) are perceived as more aesthetically pleasing, primarily due to familiarity and processing fluency (Reber et al., 2004). However, while the theory emphasizes the appeal of familiarity, it initially seemed to contradict Berlyne’s arousal theory (Berlyne & Boudewijns, 1971), which emphasizes novelty and complexity as central to aesthetic pleasure. Whitfield’s subsequent research (CM Model), which recognized that both familiarity and novelty can induce pleasure, sparked further exploration of how these opposing forces can be reconciled (Whitfield, 2000).

The UMA balance the seemingly contradictory emphasis on typicality (from Whitfield) and novelty (from Berlyne). Under UMA, too much familiarity can become boring, while too much novelty can be disorienting, so the most pleasing designs balance these two tendencies (Hekkert, 2014). This perspective not only incorporates Whitfield’s emphasis on prototypes into a larger theoretical construct, but also highlights the interplay between safety and accomplishment, two evolutionary impulses that shape how we view and appreciate designed objects (Ding et al., 2025)

In conclusion, the UMA model provides a more comprehensive and basic theoretical framework for the study of aesthetic preferences. When we process this information fluently, our positive response to aesthetics will also be stronger (Reber et al., 2004). This processing process of aesthetic pleasure experience can be regarded as a kind of happiness evolution drive.

2.2 Categorical-Motivation (CM) model

The CM model, proposed by Whitfield (2000), offers an early framework for explaining how typicality and novelty jointly shape aesthetic judgments, particularly at the cognitive level. It emerged in response to tensions between Berlyne’s collative-motivation theory which emphasized novelty, complexity, and ambiguity as sources of arousal (Berlyne, 1966), and Rosch’s (1978) prototype theory, which highlighted user preference for typical and cognitively fluent stimuli. Prior research in art and design domains supported the preference for prototypical, easily categorized forms, forming the empirical basis of the CM model (Herzog et al., 1976; O’Hare, 1976; Whitfield & Slatter, 1979; Wohlwill, 1976).

To reconcile the preference for both novelty and typicality, Whitfield (2000) proposed the CM model, which introduces three key constructs: categorical salience, motivational arousal, and social significance. In this model, Whitfield distinguishes between two types of feature salience: diagnostic and intensive. Diagnostic features help define an object’s membership within a known category and are closely tied to typicality. They represent category-valid cues and therefore foster recognition and safety. In contrast, intensive features are perceptually striking, arousing curiosity and attention. They are associated with novelty and accomplishment-related motives (Whitfield, 2009). In the CM model, the variables of typicality and novelty are presented as opposing cognitive forces: one representing fluency and safety, the other representing exploration and reward. The model assumes that aesthetic preferences depend on the category structure of the product. In closed categories, those with narrow definitions and low tolerance for deviation (e.g., teacups, pianos)—users generally favor typical, recognizable forms. In contrast, open categories (e.g., chairs, clothing) allow more variation, and users may seek novelty or expressive individuality (Whitfield, 2000, 2009; Tyagi et al., 2013).

Crucially, Whitfield also introduced the notion of social significance as a determinant of aesthetic judgement. Some stimuli carry symbolic or cultural value (e.g., luxury branding), influencing preferences beyond cognitive efficiency or sensory arousal. Although social significance is acknowledged in the CM model, it remains conceptually underdeveloped. While the CM model and associated studies (e.g., Tyagi et al., 2013; Suhaimi et al., 2023) have shown that typicality often dominates aesthetic judgments in closed categories, little is known about how users respond to perceptual coherence or social symbolism within the same categorical structure. For instance, do users tolerate perceptual variety in closed-category products, provided that cognitive typicality is preserved? Does the expectation of group conformity reduce the appeal of autonomy, while enhancing the desire for connectedness? These questions remain empirically underexplored, particularly for technological products such as laptops, which are both functionally constrained and aesthetically competitive.

2.3 Unified model of aesthetics

The UMA integrates research from various fields, including cognitive psychology, social psychology, design, sociology, cognitive neuroscience, philosophy, and art (Hekkert, 2014). This model provides a comprehensive framework for understanding product aesthetics by examining three key levels: perceptual, cognitive, and social. Perception forms the initial impression of a product, which is then processed and refined through cognitive and social evaluations based on personal experiences and environmental influences (Ding et al., 2025). Aesthetic preferences arised from a balance between two fundamental human drives: the need for safety and the desire for accomplishment. Safety-oriented factors include typicality, unity, and connectedness, while accomplishment-driven elements involve novelty, variety, and autonomy (Blijlevens & Hekkert, 2015; Hekkert et al., 2003; Post et al., 2013). UMA emphasized that aesthetic appeal is shaped by the dynamic interplay between these opposing forces at the perceptual, cognitive, and social levels, as well as explaining why people are drawn to certain designs (Suhaimi et al., 2023). This model has been widely used to analyze consumer reactions to product design and has been applied across multiple industries (Ding et al., 2025; Ma et al., 2025; Loos et al., 2022; Post et al., 2023; Suhaimi et al., 2023). The following sections will explore each of these three levels in detail, discussing their specific roles in shaping aesthetic preferences.

At the perceptual level of the UMA model, unity refers to visual consistency, order, and harmony, which help create a sense of familiarity and ease in perception. Wagemans et al. (2012) discovered that Gestalt principles, such as symmetry, repetition, continuity, and closure, allow the human brain to group visual elements into a structured whole, facilitating cognitive processing and pattern recognition. Similarly, Berghman and Hekkert (2017) found that designs with strong structural coherence are perceived as more aesthetically pleasing because they reduce cognitive effort. This smooth processing enhances aesthetic pleasure by reducing cognitive effort. However, when a design is too uniform, it can appear dull or uninteresting, lacking elements that engage the observer (Berlyne & Boudewijns, 1971; Biederman & Vessel, 2006). Variety, on the other hand, introduces complexity, contrast, and change, which stimulate curiosity and encourage exploration. While unity provides stability and comfort, variety adds excitement and engagement, making the balance between the two crucial for an aesthetically appealing design.

The “Unity in Variety” principle in UMA suggests that the most aesthetically pleasing designs achieve a dynamic balance: they are coherent enough to be readily comprehensible yet varied enough to sustain interest (Post et al., 2016; Loos et al., 2022). Scholars have long explored this balance in aesthetic perception. Dresp-Langley (2015) examined how visual processing mechanisms enable the human brain to integrate diverse elements into a unified perceptual experience, emphasizing that variety enriches perceptual complexity without disrupting coherence. Similarly, Loos et al. (2022) analyzed consumer responses to product designs and found that designs with a structured, yet varied composition tend to elicit stronger aesthetic preferences. Further reinforcing this perspective, the Gestalt approach emphasizes that our perceptual system inherently organizes stimuli into structured, meaningful patterns (Au-Yeung et al., 2023).

However, the relative importance of unity and variety is not uniform across all design contexts. Different product types may shift the perceptual weight of these variables in aesthetic evaluations. For example, variety factors significantly impact website aesthetics more than unity (Post et al., 2017). In topological optimization, unity resides at the heart of improving aesthetic appreciation (Loos et al., 2022). In the study of aesthetic preferences for fast-moving consumer goods (soft drink packaging) and emerging technology products (smart watches), unity has a greater influence than variety (Ding et al., 2025; Ma et al., 2025). Thus, the “Unity in Variety” principle should not be regarded as a static aesthetic rule, but rather as one whose balance depends on the categorical structure of the product. The introduction of product category structure not only helps to understand the mechanism of action of each variable in the UMA model, but also provides a theoretical basis for grasping the applicable strategies of “unity and diversity” balance in specific products.

Cognitive processes allow us to understand art, interpret sensory information, and engage in abstract thinking, helping us collect and analyze aesthetic experiences that shape our sensitivity to beauty ( Świątek et al., 2024). Drawing on past experiences, many design studies have explored how cognitive factors influence aesthetic appreciation, particularly focusing on the relationship between typicality and novelty (Ding et al., 2025). One of the most influential principles capturing this tension is Loewy’s Most Advanced Yet Acceptable (MAYA), which suggests that aesthetically successful designs strike a balance between innovation and familiarity (Loewy, 2002). This duality was echoed in Berlyne’s (1973) theory of arousal potential, which posits that aesthetic pleasure peaks at moderate complexity, balancing the competing drives for clarity and surprise. Expanding on this idea, Whitfield’s CM Model (2000) links the emotional response triggered by novelty with the cognitive process of categorization, offering a deeper understanding of how people perceive and evaluate popular designs.

Building on these ideas, the UMA incorporates typicality and novelty as complementary cognitive-level variables. Typicality satisfies our need for safety by providing easily recognized and comprehensible stimuli, while novelty caters to our drive for achievement by offering unexpected elements that spark curiosity (Hekkert, 2014). Because of human evolutionary tendencies, people generally favor products that are easy to recognize and categorize, often preferring designs that feel familiar or prototypical (Hekkert et al., 2003). The UMA model incorporates this idea alongside the MAYA principle, which suggests that the most appealing designs successfully balance familiarity with innovation (Hekkert et al., 2003; Thurgood et al., 2014).

However, the balance between typicality and novelty is not static. Different product types may change the perceived weights of these variables in an aesthetic assessment. For example, novelty factors have a greater impact on industrial boilers than typicality (Suhaimi et al., 2023). In daily necessities toothbrushes, typicality is the core of affecting consumer preferences (Yahaya, 2017). Typicality has a greater impact than novelty when it comes to aesthetic preferences for emerging technology products (smartwatches) (Ma et al., 2025). Therefore, incorporating category typology is essential for explaining how consumers cognitively evaluate products. It provides a theoretical anchor for understanding when and why either typicality or novelty dominates, thereby enhancing the predictive capacity of the UMA in diverse design contexts.

Social cues embedded in product design facilitate interactions between individuals and products, allowing people to feel a sense of belonging while also expressing their autonomy (Blijlevens & Hekkert, 2015). People often use design elements in products as social signals to express group identity and individuality, a balance captured by the concepts of connectedness and autonomy (Ding et al., 2025). Connectedness refers to how well a product aligns with social norms and cultural expectations, making users feel a sense of belonging (Baumeister & Leary, 2017). Research suggests that products can symbolize shared values and group identity, strengthening social bonds (Barrett & Bar, 2009; Markus & Kitayama, 1991). Designs that reflect familiar social cues tend to evoke comfort and security, increasing their aesthetic appeal (Deci & Ryan, 2000; Baumeister & Leary, 2017). For example, Bloch (1995) pointed out that social elements in product design significantly influence aesthetic appreciation by reinforcing a shared visual language. Empirical studies on everyday items such as sunglasses, staplers, backpacks (Blijlevens et al., 2014), smartwatches (Ma et al., 2025), and soft drink packaging (Ding et al., 2025) consistently indicate that higher connectedness leads to stronger aesthetic preferences. In contrast, autonomy reflects the need to stand out and express individuality. While connectedness fulfills the desire for belonging, autonomy addresses the need for personal differentiation (Blijlevens & Hekkert, 2015). Sociologically, autonomy relates to the pursuit of freedom, independence, and self-expression (Deci & Ryan, 2009; Lynn & Harris, 1997). From an aesthetic perspective, products that challenge traditional design norms and showcase uniqueness can attract positive responses, allowing users to express their individuality (Bourdieu, 2018).

The UMA model integrates these social dimensions by asserting that the most attractive designs strike an optimal balance between connectedness and autonomy. This balance is reflected in the “Autonomous yet Connected” principle (Blijlevens & Hekkert, 2015), which suggests that the most aesthetically appealing designs successfully combine the need for social belonging with the desire for individuality. Research has also explored how security and accomplishment influence the relationship between these two factors. For example, Blijlevens and Hekkert (2019) found that in high-social-risk situations, designs emphasizing social conformity tend to enhance aesthetic appeal, whereas in lower-risk contexts, products with greater autonomy may be more attractive. In summary, connectedness and autonomy at the social level play a crucial role in shaping aesthetic experiences. However, the balance between connectedness and autonomy is not static. The relative influence of these variables may vary according to the product environment, so a more detailed category definition is needed to explain their weights and their interactions in different design areas. Inclusion in category types can significantly improve UMA’s explanatory power and help designers better combine product aesthetics with consumer expectations.

Based on these considerations, the present study raises the following questions: How much do the three levels of perception, cognition, and social influence aesthetic preferences when tested simultaneously on laptop? How does the UMA’s aesthetic variables influence the appraisal of the aesthetics of laptop? Does Whitfield’s CM Model have a guiding role in UMA? Finally, we propose three hypotheses:

3.1 Research methodology

This study adopts a quantitative experimental methodology to explore how six aesthetic variables affect users’ aesthetic preferences for laptop designs. As shown in Figure 2, the research process follows a structured multi-stage procedure comprising stimulus and participant selection, data collection, instrumentation, and multilevel statistical analysis.

Figure 2. Overview of research methodology.

This flowchart outlines the stepwise process used in the study, including stimulus selection, participant recruitment, data collection, and multi-level statistical analyses.

3.2 Participants

The current study involved 234 Chinese participants to evaluate aesthetic preferences for laptops, a representative closed-category digital product. To minimize bias, individuals under 18 years of age and those with professional design backgrounds were excluded, as prior research indicates that design experts may respond based on specialized knowledge rather than fundamental aesthetic evaluation (Whitfield, 2007). Participants were recruited online via Google Forms following standardized guidelines, ensuring consistency across multiple study phases. The sample was stratified into four age groups: 24.2% were18–25 years (n = 57), 32.6% were 26–35 years (n = 76), 19.9% were 36–45 years (n = 47), and 23.3% were 46 years or older (n = 54), providing a broad range of perspectives that reflect different life stages and usage patterns of laptops. The gender distribution was relatively balanced, with 53.4% male (n = 125) and 46.6% (n = 109) female participants, which enhances the generalizability of the findings across genders.

While the use of a single-country sample may raise concerns about cross-cultural generalizability, this decision is theoretically and contextually justified. First, most prior empirical applications of the UMA framework have been conducted in Western and Southeast Asian contexts, notably in Australia, the Netherlands, and Malaysia (Blijlevens et al., 2014; Blijlevens et al., 2017; Yahaya, 2017; Suhaimi et al., 2023). Few studies have systematically investigated aesthetic preference from the perspective of Chinese users, especially in the context of closed-category consumer electronics. Second, China is currently the world’s largest producer and consumer of laptops, making it a critical context for evaluating laptop aesthetics. Studying this demographic not only contributes to theory enrichment by adding culturally specific insights, but also improves the practical relevance of findings for a global design market heavily influenced by Chinese consumer preferences.

This recruitment strategy aligns with previous UMA-based studies, where sample sizes typically ranged from 85 to 300 participants (Berghman & Hekkert, 2017; Post et al., 2017; Tyagi, 2017; Suhaimi et al., 2023; Ding et al., 2025; Ma et al., 2025). By focusing on a non-expert population, this study captures baseline aesthetic judgments uninfluenced by specialized design training, thereby facilitating a more accurate evaluation of perceptual, cognitive, and social factors in product aesthetics. All participants provided informed consent through an online form. Participation was voluntary, and respondents could withdraw at any time without consequences.

3.3 Stimuli

The stimuli for this study consisted of visual images of ten laptop designs, incorporating five commercially available laptops and five conceptually designed models developed by an experienced team of industrial designers, as shown in Figure 3. This hybrid construction approach was deliberately adopted to ensure sufficient variation across the six aesthetic variables defined in the UMA: unity, variety, typicality, novelty, connectedness, and autonomy. Commercially available laptops alone could not adequately represent all six variables, particularly at the higher levels of novelty, variety, or autonomy. Therefore, integrating lab-generated designs became necessary to enhance contrast and coverage across the full UMA variables.

Figure 3. Visual stimuli of ten laptop designs used in the study.

The stimuli set consisted of five commercially available laptop images (images include elements that were redrawn or adapted from copyright-free sources such as Wikimedia Commons and Pixabay, ensuring no copyrighted content is used.) and five conceptually designed models created by professional industrial designers. All images were standardized in grayscale, with brand logos removed and uniform backgrounds applied.

Although a formal taxonomy was not applied, the experimental stimuli were generated by a professional design team with prior experience in UMA-based design studies. Their design brief was to maximize perceptual contrast along specific UMA dimensions, particularly where commercial products tend to cluster around typicality and unity. The team purposefully engineered distinctive features such as non-traditional silhouettes, asymmetrical layouts, and expressive stylistic elements, which were validated by senior design experts to ensure clear differentiation and theoretical alignment with the six variables. To minimize potential confounding effects, all ten laptop images were rendered and standardized using the following procedures:

This dual approach, combining ecological validity (through real-world products) and experimental control (through designed manipulation), ensured that the aesthetic variation remained both theoretically meaningful and methodologically rigorous. For example, Stimuli 1 and 2 represent prototypical business laptops with high typicality and unity, while Stimuli 9 and 10 feature exaggerated sculptural forms that prioritize novelty and autonomy. Stimulus 7, with its rounded, user-friendly layout, enhances perceptions of connectedness, while Stimulus 6 offers high visual variety through a layered dual-screen configuration. The other stimuli were between the intermediate values of the six aesthetic independent variables.

3.4 Procedures

The study was conducted through an online questionnaire accessible via web and mobile devices. Ethical approval for this study was granted by the Ethics Committee for Research Involving Human Subjects of Universiti Putra Malaysia (Jawatankuasa Etika Universiti Penyelidikan Manusia UPM), under Approval No. JKEUPM-2023-1213. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All participants were presented with an online informed consent form prior to commencing the survey, and only those who provided written electronic consent were allowed to participate.

The questionnaire consisted of two main sections. The first section gathered demographic details such as age, gender, and other relevant background information for participant screening and later analysis. The second section focused on a visual evaluation of laptops. Participants viewed ten laptop images, each displayed individually in a randomized order to reduce potential sequence bias. For each image, participants responded to several statements using a 7-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (7). These statements were designed to evaluate the stimuli across three levels. At the perceptual level, unity and variety were assessed (e.g., “this is a unified design” and “this design conveys variety”). At the cognitive level, typicality and novelty were evaluated (e.g., “this is a typical design” and “this is a novel design”). At the social level, connectedness and autonomy were examined (e.g., “this design makes me feel connected” and “this design emphasizes my individuality”). Additionally, an overall aesthetic pleasure rating was included (e.g., “this design is pleasing to see”). These key measurements were adapted from established aesthetics scales used in prior research (Blijlevens et al., 2014; Blijlevens et al., 2017), ensuring consistency and reliability in evaluating participants’ responses.

3.5 Data analysis

Data analysis was performed using IBM SPSS Statistics (version 26.0; https://www.ibm.com/products/spss-statistics ). First, repeated-measures ANOVA was conducted to analyze variations in participants’ responses to different laptop designs. Following this, Generalized Estimating Equations (GEE) were employed to assess the impact of each independent variable on the dependent variable, aesthetic pleasure. The GEE β coefficients allowed for a comparative evaluation of the relative influence of each factor in determining aesthetic preferences for laptops. Given the repeated-measures design, in which each participant evaluated multiple laptop stimuli, this study employed linear mixed-effects modeling (LMM) as the primary analytical approach. LMM is suited for data structures involving nested observations, where ratings (Level 1) are nested within participants (Level 2), and stimuli may vary across multiple dimensions. The model included the UMA variables as fixed effects, and random intercepts for participants to account for individual-level baseline differences in aesthetic preferences. The results of the LMM analyses enabled more precise estimation of the relative influence of each aesthetic dimension on overall pleasure ratings, while also revealing heterogeneity in individual aesthetic sensitivities.