# Designing resilient cities: Greenery, rectilinearity, and sociality as key factors in aesthetic preferences

Roxane Ayral, Sarah Khazaz, Adeline Lacroix, Anastasiia Sharova, Frédéric

Dutheil, Martial Mermillod

► To cite this version:

Roxane Ayral, Sarah Khazaz, Adeline Lacroix, Anastasiia Sharova, Frédéric Dutheil, et al.. Designing resilient cities: Greenery, rectilinearity, and sociality as key factors in aesthetic preferences. Acta Psychologica, 2025, 260, pp.105741. 10.1016/j.actpsy.2025.105741. hal-05324565

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# Designing resilient cities: Greenery, rectilinearity, and sociality as key factors in aesthetic preferences

Roxane Ayral<sup>a, ID</sup>, Sarah Khazaz<sup>a, ID</sup>, Adeline Lacroix<sup>b, ID</sup>, Anastasiia Sharova<sup>a, ID</sup>, Frédéric Dutheil<sup>c, ID</sup>, Martial Mermillod<sup>a, *, ID</sup>

## ARTICLE INFO

**Keywords:**

Aesthetics
Architecture
Shape
Greenery
Urbanicity
Sociality

## ABSTRACT

Urban nature-based solutions are crucial for climate change adaptation; however, their implementation also depends on their aesthetic appeal, which can influence public acceptance and engagement. This study explores the impact of shape, greenery, sociality, and semantic information on aesthetic evaluations of architecture, drawing on insights from neuroaesthetics. Forty-three participants (41 females, *Mage* = 20.93, *SDage* = 4.34, ages 18–44) were divided into three groups (positive, negative, and control) and asked to rate the aesthetic of buildings categorized by perceived shape, greenery, and sociality using a visual analogue scale. Results showed a preference for rectilinear buildings, greenery, sociability, as well as an interaction between building shape and greenery. These findings introduce sociality as a key factor in environmental aesthetics and highlight the importance of interactions between these factors in shaping aesthetic preferences. Semantic information had no significant effect on evaluations. The study discusses the methodological considerations and suggests improvements for future research in this area.

Preregistration: https://osf.io/bygaf/

## 1. Introduction

As cities seek to become more climate-resilient, nature-based solutions (NbS) have emerged as vital strategies. By harnessing ecosystem services provided by healthy natural systems, NbS contribute to regulating air and water quality, mitigating flood risks, promoting urban cooling, preserving biodiversity, and addressing climate change (Convention on Biological Diversity [CBD], 2009; Griscom et al., 2017; Lavorel et al., 2020; Maes & Jacobs, 2017; Seddon et al., 2020). Within this framework, urban greening is a prominent example offering benefits such as mitigating the urban heat island effect, enhancing soil infiltration, reducing pollution, and fostering social cohesion.

Beyond these ecological and health outcomes, the success of urban greening initiatives also depends on their aesthetic appeal, which can influence public acceptance and engagement (Zobec et al., 2020). While many studies have shown a general preference for natural over urban scenes (Kaplan et al., 1972), the focus is primarily on the cognitive and health benefits of nature and urban greenery (Ancora et al., 2022;

Marselle et al., 2020) and less is known about how specific characteristics of the built environments, such as architectural form and the surrounding context, interact to shape aesthetic judgments.

Aesthetic evaluations can be shaped by both bottom-up and top-down processes. On the one hand, research has shown that low-level visual features—such as the distribution of spatial frequencies—can influence observers' aesthetic judgments even in the absence of explicit semantic content (Vannucci et al., 2014). On the other hand, cultural and developmental influences may modulate these responses. For example, a study showed that children prefer urban over natural environments, a trend that reverses with age, suggesting a top-down influence (Meidenbauer et al., 2019). Furthermore, there is evidence of a naturalness bias, potentially reflecting a default belief that natural elements are inherently better (Meier et al., 2019).

Understanding how these visual and cognitive factors interact is particularly relevant in urban contexts, where buildings, green spaces, and social settings coexist. Disentangling their respective contributions can inform design strategies that enhance both the functional and

<footnote><sup>a</sup> Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France</footnote>

<footnote><sup>b</sup> Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada</footnote>

<footnote><sup>c</sup> Université Clermont Auvergne, CNRS, LAPSCO, CHU Clermont-Ferrand, Clermont-Ferrand, France</footnote>

<footnote>* Corresponding author.
E-mail address: martial.mermillod@univ-grenoble-alpes.fr (M. Mermillod).</footnote>
experiential qualities of urban environments.

The aesthetics of architectural design has been a subject of interest since antiquity (Coburn et al., 2017). In the 1970s, environmental psychologists Kaplan and Kaplan (1978) studied the influence of aesthetic qualities of the environment on humans and proposed that aesthetics plays a central role in shaping human experience and behavior (Kaplan, 1987). More recently, it has been suggested that the aesthetics of both natural and built environments impact our emotions, cognition, behavior, mental health, and overall well-being (Coburn et al., 2017; Skov & Nadal, 2021). Moreover, Chatterjee and Vartanian (2016) proposed that aesthetic experiences arise from the interaction of three neural systems: sensory-motor, emotion-valuation, and meaning-knowledge. This “aesthetic triad” has been applied to architecture (Chatterjee et al., 2021; Coburn et al., 2017), where the sensory-motor system processes bottom-up sensory stimuli, linking them to motor responses that influence behavior, while the meaning-knowledge system shapes the brain's responses to sensory features through top-down processes based on prior experiences. Freedberg and Gallese (2007) further explored embodied simulation in aesthetic experiences suggesting that these experiences activate universal mechanisms for simulating actions, emotions, and sensations. This extends to architecture, where similar embodied simulations occur, activating the same brain regions in both first- and third-person experiences.

Research on aesthetic judgments shows a general preference for curvilinear over rectilinear shapes (for reviews, see Corradi & Munar, 2020; Gómez-Puerto et al., 2016), observed across cultures (Gómez-Puerto et al., 2018), in infants (Hopkins et al., 1976), and non-human primates (Munar et al., 2015). This preference appears at least partially rooted in low-level visual processes (Yue et al., 2020; Zachariou et al., 2018). However, the preference for curvilinear shapes is influenced by factors such as stimulus type, presentation time, expertise, and task (Chuquichambi et al., 2022). For instance, curvilinearity is favored in objects but less so in spatial design. Nonetheless, a preference for curvilinear interior architecture has been noted (Vartanian et al., 2013).

In the present study, we investigate whether the shape of the building—curvilinear or rectilinear—interacts with the type of surrounding environment—greenery or urbanity. If such an interaction emerges, it would suggest that aesthetic evaluations of buildings in green versus urban settings are, at least in part, modulated by low-level visual processes. Conversely, the absence of an interaction would imply that these evaluations may be primarily guided by top-down influences such as values, beliefs, or a naturalness bias.

Another important factor to take into account for the desirability of ecological transition is sociality which plays a key role in health and well-being (Fratiglioni et al., 2004; Helliwell & Putnam, 2004). People are often drawn to social gatherings, such as cultural or religious events. However, the context of social presence can affect its impact. For instance, Kim et al. (2019) observed that in natural settings, social presence increased fear, whereas in urban settings, its absence increased fear. These findings suggest that social presence can influence how we perceive and appreciate our physical environment, with the natural and urban contexts shaping its impact. However, the impact of social factors on the aesthetic experience has only been studied in museum contexts, where a negative impact of social presence on the experience of artworks has been observed (Pelowski et al., 2014).

Given that urban environments are inherently social spaces, it is essential to consider the role of perceived sociality when investigating aesthetic responses to urban greenery. However, research in this subject is scarce. This study aims to address this gap by examining how perceived sociality influences the aesthetic evaluation of buildings, and whether it interacts with other key factors such as architectural shape and the presence of greenery.

Furthermore, in line with the aesthetic triad model— which

emphasizes the interplay between sensory-motor, knowledge-meaning, and emotion-valuation systems in aesthetic experience (Chatterjee & Vartanian, 2014)—prior research has demonstrated that contextual information can significantly influence aesthetic evaluations. Semantic information modulates the neural, physiological and behavioral components related to perception, especially concerning emotional processes (Mermillod et al., 2018). For example, artworks were rated as more aesthetically pleasing when their provenance was described as a gallery rather than a computer (Kirk et al., 2009), and dance performances were preferred when attributed to human rather AI choreographers (Darda & Cross, 2023). These findings suggest that beliefs and expectations about an object modulates its perceived aesthetic value.

In addition to the influence of contextual framing, emotional valence itself directly influences aesthetic responses. Experimental studies have shown that positive affective states enhance the perceived attractiveness of stimuli, whereas negative states tend to diminish aesthetic appreciation (Cheng, 2013). This supports the idea that affective valence is a crucial determinant in aesthetic appraisal.

Based on this theoretical and empirical foundation, we hypothesized that emotionally valenced contextual information about a building—such as the use of eco-friendly (positive) versus toxic (negative) materials—would modulate its aesthetic evaluation, with positive information increasing and negative information decreasing aesthetic ratings relative to a no-information control condition.

In sum, this study examines how the building's shape, surrounding greenery, sociality, and semantic information influence the aesthetic evaluations of a building. Conforming to the abundance of evidence in literature, we hypothesized that curvilinear façades would be evaluated more favourably than rectilinear ones. Given the general preference for natural over urban scenes, we expected surrounding greenery to enhance aesthetic evaluations more than urban elements. Moreover, during aesthetic evaluation of buildings, if bottom-up visual processing supersedes top-down influences such as beliefs that nature is better, an interaction should emerge between greenery and the shape of the building. However, if it is the knowledge about the benefits of greenery that drives the preference, the shape of the building should not interact with the level of greenery. Additionally, the bidirectional tendency of the impact of social presence on environmental perception led us to hypothesize that the perceived sociality of buildings will influence aesthetic evaluations, with varying effects depending on greenery and urbanicity levels. More precisely, we predict lower preferences for social scenes in green environments and higher preferences in urban settings. Finally, we hypothesized that the knowledge of conceptual information—either positive or negative—about the building (e.g., the use of eco-friendly or toxic materials) would influence its aesthetic evaluation. Specifically, we expected positive conceptual information to increase aesthetic ratings and negative information to decrease them, relative to a control condition with no additional information.

## 2. Method

### 2.1. Participants

Forty-three undergraduate psychology students (41 biologically female and two biologically male participants, Mage = 20.93, SDage = 4.34, ages 18–44) from the University of Grenoble Alpes (France) participated for course credit. An a priori power analysis was conducted using G*Power (version 3.1.9.6; Faul et al., 2007) to estimate the required sample size. Although the actual data analysis used a linear mixed-effects model, G*Power was used to approximate power based on a repeated-measures ANOVA with a between-within interaction. The model included three groups (levels of the between-subjects factor: positive, negative, and no information) and eight measurements (from
the combination of three within-subjects factors: shape, greenery, and sociality). Assuming a medium effect size ($f = 0.25$), $\alpha = 0.05$, and power ($1 - \beta = 0.95$), with a correlation of 0.5 among repeated measures, the analysis indicated a minimum required sample size of $N = 30$. While this estimate does not account for the full complexity of the mixed-effect model, it provided a conservative and practical basis for determining a minimal sample size. All participants had normal or corrected-to-normal vision and were non-experts, as preference for curvature is typically observed in non-experts (Chuquichambi et al., 2022). Participants were fluent in French and provided written informed consent.

## 2.2. Materials

A pre-test with 27 undergraduate psychology students (24 females, Mage = 19.22, SDage = 0.93, ages 18–22) was conducted to rate a stimuli-set of 237 images (from Wikimedia, Pexels, and Archdaily) based on perceived shape of the building (curvilinear/rectilinear), surround-ing greenery (green/urban), and sociality (social/isolated), and to rate the valence of 38 sentences based on their valence (pleasant/unpleasant) and arousal (low/high) on a continuous visual analogue scale (VAS). The 10 highest-rated images and phrases for each category were selected, resulting in a stimuli-set of 80 building photographs and 20 sentences (10 positive and 10 negative). All images were standardized in dimensions (3840 × 2160 pixels) and white balance, categorized by perceived shape, greenery, and sociality. Sentences were categorized as positive or negative.

Due to copyright restrictions, the full set of stimulus materials cannot be shared publicly; however, a selection of representative, royalty-free images and phrases is provided in Table 1 to illustrate the types of stimuli used. To increase ecology, all stimuli were presented on a 16:9 OLED screen (3840 × 2160 pixels, 120 Hz refresh rate, 400 cd/m² luminance) using the PsychoPy software (Peirce et al., 2019), at a viewing distance of approximately 100 cm.

## 2.3. Procedure

Each participant was assessed individually in a laboratory room at

the Psychology and NeuroCognition Laboratory (LPNC CNRS UMR 5105) at Grenoble Alpes University, seated at a table facing a full-screen display. Participants were randomly assigned to one of three groups: (1) positive semantic information, (2) negative semantic information, or (3) control (no semantic information). All images were presented to all groups. Participants rated the building's beauty on a VAS from “0” (“not beautiful at all”) to “100” (“very beautiful”). Each trial consisted of: (1) a 5000 ms semantic information screen (positive or negative) for the experimental groups, or a gray screen for the control group, (2) a 5000 ms architectural image, and (3) a VAS screen with the question “How beautiful do you think this building is?” (Fig. 1). Preference for curvature can be influenced by the duration of exposure (Chuquichambi et al., 2022). To address this potential confounder in our study, which aims to explore whether a preference for curvilinear forms extends to façade architecture, we controlled for this factor by limiting the presentation time to 5000 ms.

## 2.4. Analyses

We conducted a linear mixed model analysis in R (version 2024.09.0 + 375, R Core Team, 2018) using the lme4 package (Bates et al., 2015), based on an experimental design that included random factors (participants and stimuli) sampled from a larger population across which we aimed to generalize (Judd et al., 2012). We included aesthetic evaluation ratings as the dependent variable and added fixed effects for semantic information (positive, negative, and control), shape (curvilinear vs. rectilinear), greenery (green vs. urban), and sociality (social vs. isolated). Contrast coding was used for the fixed factors: contextual information (linear: negative = -1, control = 0, positive = 1; quadratic: negative = 1, control = -2, positive = 1), shape (rectilinear = -0.5, curvilinear = 0.5), greenery (urban = -0.5, green = 0.5), and sociality (isolated = -0.5, social: = 0.5). Interaction terms were computed as the product of these codes. Statistical significance was assessed using the lmerTest package (Kuznetsova et al., 2017), which applies Satterthwaite's method to estimate degrees of freedom and p-values for mixed models. Given the lack of standardized method for calculating effect sizes in mixed models, unstandardized effect sizes are reported

<caption>**Table 1**</caption>

Stimuli. Examples for each category of stimuli: (a) images are categorized according to the shape of the building, the greenery and urbanity surrounding it, and its perceived sociality; and (b) sentences are categorized according to their positive or negative affective valence.

<table><thead><tr><th rowspan="2">a)</th><th colspan="2">CURVILINEAR</th><th colspan="2">RECTILINEAR</th></tr><tr><th>GREEN</th><th>URBAN</th><th>GREEN</th><th>URBAN</th></tr></thead><tbody><tr><td>SOCIAL</td><td></td><td></td><td></td><td></td></tr><tr><td>ISOLATED</td><td></td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

b)

Positively valenced contextual information

"This building was built with natural materials"

"This positive energy building does not pollute its environment"

"A residential building that produces no waste"

"This building houses a non-profit organisation that defends equity between races and sexes"

"The inhabitants of this building are united"

Negatively valenced contextual information

"This building was built with toxic materials"

"This building produces a lot of greenhouse gases which pollute its environment"

"A residential building that produces a lot of waste"

"This building houses a non-profit organisation which broadcasts racist and sexist remarks"

"A school that has been involved in discrimination scandals"
<figure><img src="image_11.png" /><figcaption>Fig. 1. Procedure. Schematic presentation of one trial for the condition with positive semantic information. Each trial was constituted of a screen (5000 ms) with the textual stimuli (or a blank gray screen for the control group); a full screen presentation of the architectural image (3840 × 2160 pixels, 5000 ms); and a screen with the visual analogue scale slide presented with the question "How beautiful do you think this building is?"</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 2. Results. Interaction between perceived shape and greenery levels for aesthetic evaluations. The box plots represent the observed mean aesthetic evaluations of buildings, with the interquartile range and individual data points for each condition. The red crosses represent the observed means. In addition, the colored points represent the estimated marginal means with their 95 % CI. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(Rights & Sterba, 2019). These effect sizes are represented by β coefficients, which correspond to the contrast coding of dichotomous variables as -0.5 and 0.5, thereby facilitating straightforward interpretation. The model specification was as follows:

curvilinear buildings in green surroundings (M = 39.78, SD = 28.45), and curvilinear buildings in urban environments (M = 38.77, SD = 25.37). No significant main effect of semantic information (p = .06) on aesthetic evaluation was found, nor any significant interaction between sociability and greenery (Fig. 3; p = .09). Nonetheless, a simple effects

$$ \text{aesthetic evaluation} \sim \text{contextual information}^* \text{shape}^* \text{greenery}^* \text{sociability} + (1 | \text{participants}) + (1 | \text{image stimuli}). $$

## 3. Results

The analysis revealed significant main effects of shape (β = -9.15, CI [2.3, 16], t = -2.62, p = .01), greenery (β = 8.78, CI [1.9, 15.6], t = 2.51, p = .01), and sociability (β = 11.70, CI [4.8, 18.5], t = 3.35, p = .001). Aesthetic evaluations were higher for rectilinear compared with curvilinear buildings, for green compared with urban environments, and for buildings perceived as social rather than isolated. There was also a significant shape × greenery interaction (β = 15.45, CI [1.8, 29.1], t = 2.21, p = .03), with a larger difference between rectilinear and curvilinear buildings in green environments compared to urban ones. Furthermore, the difference between green and urban conditions was more pronounced for rectilinear buildings (Fig. 2). Descriptive statistics showed that rectilinear buildings with green surroundings received the highest aesthetic ratings (M = 56.77, SD = 26.15), followed by rectilinear buildings in urban environments (M = 40.16, SD = 26.15),

<caption>**Table 2**</caption>

Numbers of observations (N) for each condition (continuous ratings between 0 and 100) are presented with the corresponding mean aesthetic evaluation and standard deviation (SD). In the experimental design, the ‘Contextual Information’ was a between-subjects factor, the ‘Shape,’ ‘Greenery,’ and ‘Sociality’ were within-subject factors. Statistically significant main effects are noted with ‘*’.

<table><thead><tr><th></th><th>N</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td><strong>Semantic Information</strong></td><td></td><td></td><td></td></tr><tr><td>Positive</td><td>1192</td><td>46.18</td><td>28.36</td></tr><tr><td>Negative</td><td>1118</td><td>40.08</td><td>27.83</td></tr><tr><td>Control</td><td>1120</td><td>45.17</td><td>26.52</td></tr><tr><td><strong>Shape *</strong></td><td></td><td></td><td></td></tr><tr><td>Rectilinear</td><td>1715</td><td>48.45</td><td>27.72</td></tr><tr><td>Curvilinear</td><td>1715</td><td>39.27</td><td>26.95</td></tr><tr><td><strong>Greenery *</strong></td><td></td><td></td><td></td></tr><tr><td>Green</td><td>1711</td><td>48.28</td><td>28.89</td></tr><tr><td>Urban</td><td>1719</td><td>39.46</td><td>25.77</td></tr><tr><td><strong>Sociality *</strong></td><td></td><td></td><td></td></tr><tr><td>Social</td><td>1714</td><td>49.71</td><td>28.16</td></tr><tr><td>Isolated</td><td>1716</td><td>38.02</td><td>25.99</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Fig. 3.** Aesthetic evaluations of buildings across conditions. Box plots represent observed scores with interquartile ranges and individual data points. Red crosses indicate observed means, and colored points represent estimated marginal means with 95 % confidence intervals. While the figure illustrates a descriptive trend suggesting higher ratings for socially perceived buildings, the interaction between perceived sociality and environment was not significant and should be interpreted with caution. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
analysis with post-hoc Tukey-adjusted comparisons indicated that in green environments, isolated stimuli were rated significantly lower than social stimuli, $t(72) = -3.56, p = .001$. In contrast, in urban environments, the difference between isolated and social stimuli was not significant, $t(72) = 1.18, p = .24$. Means and standard deviations for aesthetic evaluations are shown in Table 2.

## 4. Discussion

We predicted a preference for curvilinear over rectilinear buildings, for greenery over urbanity, and an effect of sociality without specifying a direction for preferences. We also hypothesized that aesthetic ratings would increase with positive and decrease with negative semantic information. Most importantly, the study set out to explore interactions between these factors with the aim to contribute to the understanding of the aesthetic judgment of the built environment. We predicted an interaction between shape and greenery; and lower preferences for social scenes in green environments and higher preferences in urban settings.

The results confirm a strong preference for buildings in green over urban environments. Surprisingly, we observed higher ratings for rectilinear shapes. An earlier meta-analysis revealed that the preference for curvature might be stronger for objects than spatial designs (Chuquichambi et al., 2022), possibly because people are more familiar with curvilinear objects than curvilinear spaces because the former is more encountered than the latter in everyday life. However, while the ‘familiarity’ argument might be reasonable, it does not explain the differing results between Vartanian et al. (2013) who observed a preference for curvilinear interior architecture and our study. One possible explanation for this difference is a potential bias in our stimulus set. For example, in the database of real-world images we used, the curvilinear-green-isolated category was represented primarily by the American round barns, which limited the diversity of examples. More evidence is needed to understand this effect, especially considering the significance of curved shapes in visual processing (Yue et al., 2020; Zachariou et al., 2018). Importantly, compared to previous studies, our stimuli set included curvilinear and rectilinear architecture embedded in different environments with varying levels of greenery and sociality. Furthermore, the preference for rectilinear shapes was explained by the interaction with greenery (Fig. 2). The interaction between shape and greenery level revealed a stronger preference for rectilinear shapes in greener environments compared to urban ones. Rectilinear buildings in green environments received the highest aesthetic ratings whereas the difference between curvilinear and rectilinear shapes in the urban condition was not significant. This interaction also suggests that the positive effect of greenery is contingent on building shape, suggesting that the aesthetic preference for buildings surrounded by greenery may be more strongly influenced by low-level visual features (Kardan et al., 2015) than by top-down associations, such as the ‘natural-is-better’ bias. If aesthetic preferences for buildings surrounded by greenery were primarily driven by top-down cognitive factors then one would expect similar preference for buildings in green environments, regardless of the architectural form (i.e., a low-level visual feature). Our findings align with previous findings showing that aesthetic judgments are sensitive to basic visual properties, including the distribution of spatial frequencies (Vannucci et al., 2014) or other psychophysical factors (e.g., color, brightness, and visual complexity). Moreover, it suggests that aesthetic preferences for architectural form may be context-dependent, rather than reflecting a fixed preference for curvature or rectilinearity in isolation. More empirical testing is needed to understand how characteristics of natural scenes and rectilinearity interact. Toward this aim, it could be valuable to create an annotated stimuli set using techniques such as Generative Artificial Intelligence to generate environments with buildings of different shapes, modulating global versus local forms (Navon, 1977), and controlling factors such as familiarity and novelty (Song et al., 2021). This will also allow a larger and more balanced set of

stimuli regarding the other factors such as sociality. Future work could also benefit from incorporating three-dimensional visualizations and virtual reality, which would allow for more ecologically valid assessments of aesthetic preference in immersive settings.

To our knowledge, this is the first study to examine the impact of social presence on aesthetic evaluation of architecture. The results revealed a clear preference for buildings perceived as social rather than isolated. Even though the interaction between sociality and greenery (Fig. 3) was not significant ($p = .09$), it warrants further investigation, as previous studies suggest social presence in natural settings can induce fear (Kim et al., 2019). We will explore further this interaction in future studies with a larger and more controlled stimuli set, including factors like optimal density (Fleming et al., 1987), the distinction between positive and negative social presence (Ellaway, 2014), building function (e.g., museum, concert hall, stadium), and how sociality is perceived in the absence of people. Notably, some buildings without human presence were rated as ‘social’ in our pre-test, highlighting the need for a clearer definition of sociality. These findings pave the way for further exploration into how different types of social presence influence the aesthetic experience of architecture in a more controlled stimuli set manipulating more systematically social presence.

The analysis showed no significant effect of semantic information on aesthetic evaluation of architecture. This lack of effect may be due to several methodological limitations. First, the semantic information focused on social and ecological issues, but we did not assess participants' personal sensibilities with scales like the New Environmental Paradigm (NEP, Dunlap et al., 2000) or Social Dominance Orientation (SDO, Pratto et al., 1994). Second, aesthetic evaluations were made using an explicit, subjective rating method, and participants may have disregarded the semantic information to focus on evaluating the building itself. Future studies could benefit from implicit measures, such as mouse-tracking (Freeman et al., 2011), to better understand the impact of contextual information, independent of potential biases induced by the task.

This study revealed two novel findings: (1) an interaction between building shape and greenery, with the highest rate of preference for rectilinear buildings in green environments, and (2) the role of sociality in shaping aesthetic judgments of buildings. Our focus was on correlational observations that could generate new hypotheses about the mechanisms underlying visual aesthetic appreciation of the built environment. As noted in Chatterjee and Vartanian (2016), neuroaesthetics relies on neuroimaging studies, which must be complemented by behavioral research to progress toward causal inferences.

The current study has several limitations. First, while the VAS is an effective tool for addressing the limitations of ordinal measures like Likert scales, it remains a subjective method, making direct comparisons between participants' responses challenging. Despite its widespread use in aesthetic evaluations, the development of more objective assessment methods would enhance the field.

Another limitation of the present study concerns the sex imbalance in the sample, with 41 of the 43 participants being biologically female. This may affect the generalizability of our findings, particularly in light of evidence suggesting that aesthetic evaluation may be modulated by sex-specific neural mechanisms (Cela-Conde et al., 2009). Notably, while previous studies have reported that female participants tend to prefer curvilinear over rectilinear shapes (Palumbo et al., 2021; Tawil et al., 2022), our predominantly female sample showed a preference for rectilinear architecture. This discrepancy suggests that contextual or design-related factors may have influenced participants' evaluations. More broadly, given the characteristics of our sample, the present findings are most appropriately generalized to Western, educated, industrialized, rich, and democratic (WEIRD) populations (Henrich et al., 2010), and in particular to women within these groups. Future research should recruit more sex-balanced samples and consider additional socio-demographic and intercultural variables to better understand how these factors modulate aesthetic evaluations of urban
architecture, particularly in relation to natural elements and social presence. While lab-based designs allow tight control for stimulus presentation (e.g., using a calibrated OLED display to ensure consistency and contrast), future studies could consider online platforms to improve sex and demographic diversity (e.g., culture and geographic area).

A further limitation is the potential confound between greenery and colorfulness, as urban and green images differ in hue, saturation, or brightness. Thus, color itself could partly account for the observed preference for greenery. While disentangling the contribution of low-level visual features was beyond the scope of the present study, we explicitly acknowledge that colorfulness was not balanced across conditions. We prioritized the ecological validity of stimuli. However, future studies using grayscale images or systematically manipulating specific visual features could help disentangle the influence of low-level properties (e.g., color, clutter, spectral features) on aesthetic judgments. Such targeted experiments will be essential for isolating these psychophysical contributors—for example, by testing how visual complexity, color, brightness, and spatial frequency shape responses to built and natural elements.

In conclusion, urban development is closely linked to climate change and the climate risks urban areas face, such as heatwaves and flooding (Croce & Vettorato, 2021). Building healthy, sustainable, and resilient cities requires understanding the interaction between human behavior and the urban environment. Proper use of built surfaces can offer environmental, social, and economic benefits (Croce & Vettorato, 2021). Studies like this help explore how aesthetics influence the acceptance of resilience solutions. Designing areas resilient to climate disruptions is vital for our future. Understanding psychological factors that enhance the appeal of resilient areas (e.g., Singapore, Chengdu, Neom project) will foster greater public engagement in the ecological transition and prevent potential resistance. Ultimately, the challenge is to promote urban sustainability and resilience for the well-being of both people and the planet.

### CRediT authorship contribution statement

**Roxane Ayral:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization.
**Sarah Khazaz:** Writing – review & editing, Supervision, Methodology, Conceptualization.
**Adeline Lacroix:** Writing – review & editing, Formal analysis.
**Anastasiia Sharova:** Writing – review & editing, Data curation.
**Frédéric Dutheil:** Writing – review & editing, Project administration, Conceptualization.
**Martial Mermillod:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

### Ethics

This research complies privacy rights of human subjects and with the Declaration of Helsinki (2023), and received approval from a local ethics board (Institutional Review Board: IRB00013040).

### Declaration of Generative AI and AI-assisted technologies in the writing process

We used ChatGPT (GPT-4) for assistance in grammar checking and editing the manuscript. The tool helped improve clarity and readability, but all content and interpretations in the article remain the responsibility of the authors.

### Funding

This research was partially supported by MIAI Grenoble Alpes (ANR-19-P3IA-0003).

### Preregistration

The hypotheses and study design were preregistered (https://osf.io/8gpwm) prior to data collection. The analysis plan was not preregistered.

### Materials

Study materials are only available upon request for scientific use because some images are copyrighted.

### Analysis scripts

All analysis scripts are publicly available (https://osf.io/8gpwm).

### Declaration of competing interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

### Data availability

All primary data are publicly available (https://osf.io/8gpwm).

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