Introduction

The area of research on the role of sensory processing (SP) in aesthetic appreciation within the context of Autism Spectrum Disorder (ASD) is relatively new, and there is growing concern about how differences in SP—resulting in sensory over- or under-responsiveness—affect emotions and perception (Hughes et al. 2024). In recent years, studies have started to explore how sensory variations influence preferences for color and texture, particularly in visual art. These findings emphasize the need to consider sensory experiences as individuals with ASD engage with art. Art therapy is increasingly used as a therapeutic intervention that leverages sensory experience to facilitate communication and self-regulation in individuals with ASD (Malhotra et al. 2024). However, several limitations persist in the current literature. These include small sample sizes, a lack of empirical research, and the challenge of quantifying emotional and sensory comfort, which often relies on subjective perspectives.

Most previous research has focused on digital or simplified visual formats. In contrast, oil paintings—characterized by their complex textures and pigment variations—offer a more intricate sensory experience (Ullah and Khan 2024). Emerging tools such as eye tracking and augmented reality (AR) may offer deeper insights into how individuals with ASD perceive and engage with art. Moreover, growing attention is being paid to sensory integration in learning and therapeutic contexts, which can help tailor art education for individual sensory profiles. A significant limitation in existing research is the underrepresentation of participants from non-Western backgrounds and those with severe communication impairments (Prakash et al. 2024). Despite these challenges, this field holds great potential to improve creative involvement and emotional experiences for individuals with ASD by enabling more personalized and inclusive art-based practices. While atypical sensory reactions—such as hypersensitivity to bright colors or textures and hyposensitivity to subtle stimuli—are well-documented in ASD, few studies have examined how these differences affect the appreciation of art, especially complex mediums like oil painting (Tuso 2024; Marwati et al. 2021). This gap is significant, given that sensory experiences are closely tied to mood, attention, and overall life satisfaction in individuals with ASD.

Without a deep understanding of how individuals with ASD process sensory input, the potential of art as a medium for comfort, communication, and emotional regulation remains underutilized in therapy and education (Ma and Cao 2024). Research shows that modifying art activities based on sensory profiles can greatly improve the effectiveness of interventions (Wright 2023). For instance, individuals with high sensory sensitivity may benefit from art experiences that incorporate pastel colors and smooth textures, while those with lower sensitivity might respond better to bold contrasts and coarser materials (De Witte et al. 2021; Léger-Goodes et al. 2024).

This study is designed to achieve several key objectives: first, to investigate how sensory processing profiles influence preferences for color and texture in paintings among individuals with ASD; second, to generate empirical evidence that can inform art education and therapeutic practices; and third, to promote a sensory-sensitive framework for creative engagement within neurodiverse populations. The ultimate goal is to enhance emotional comfort, increase participation, and improve therapeutic outcomes through personalized artistic experiences tailored to individual sensory profiles. By addressing this important research gap, the study challenges the prevailing one-size-fits-all approach commonly seen in art instruction and therapy. Instead, it advocates for more diverse and individualized strategies that align with how neurodiverse individuals perceive and engage with their environments (De Domenico et al. 2024; Webber et al. 2024; Wiskera et al. 2024).

The paper is structured as follows: In Section “Literature review”, the literature reviews the existing research on painting therapy for children with ASD and identifies gaps. In Section “Methodology”, methodology, the study design is described concerning the experimental and control groups and the tools used for measurement. Results in Section “Results” show that the experimental group has significant cognitive and emotional improvements. In Section “Discussion”, discussion, the findings are interpreted, limitations are addressed, and recommendations are made. In conclusion, Section 6, summarizes the study and proposes future directions for incorporation of painting therapy in ASD interventions.

Literature review

This section provides an extensive overview of existing research on sensory processing in ASD and its impact on therapeutic and behavioral interventions, particularly those involving visual arts. The literature underscores the complexity and prevalence of sensory sensitivities in ASD and highlights emerging approaches to address these challenges in art-based contexts.

Sensory processing abnormalities in ASD

The study by Rehbein and Herrmann (2020) employed a comprehensive literature analysis to examine dysfunctions in the peripheral sensory nervous system (PNS) and their role in sensory abnormalities in ASD. Their findings suggest that nearly 90% of individuals with ASD experience atypical sensory processing—ranging from hypersensitivity to difficulties in multimodal integration. Clarified prevalence and the nature of sensory processing issues. However, this research lacked experimental validation and relied heavily on indirect evidence, emphasizing the need for more empirical data on peripheral dysfunctions such as gut-brain axis abnormalities and auditory processing.

In another study, Butera et al. (2020) conducted bivariate Pearson correlations and hierarchical multiple linear regression to assess the relationship between sensory processing and school performance. Results revealed that heightened sensory sensitivity correlated with lower school competence. Linked sensory sensitivity with functional outcome—school performance. Nonetheless, the small sample size and exclusion of low-functioning individuals limit the generalizability of these results.

Binur et al. (2022) used a two-alternative forced choice (2AFC) width discrimination task to explore perceptual differences in high-functioning individuals with autism. They found that autistic participants relied on the nonadaptive weighting of perceptual priors, unlike their typically developing peers. While perceptual resolution remained consistent, the findings were restricted to specific visual tasks and high-functioning populations. The more precise description of the visual processing mechanism studied.

Patil and Kaple (2023) offered a broad review of sensory-based interventions, identifying neural pathway alterations and sensory gating dysfunctions as key contributors to atypical sensory processing in ASD. While promising interventions were noted, the need for optimized, individualized approaches remains unmet.

Schulz et al. (2023), through data from the Ontario Neurodevelopmental Network, compared sensory processing in ASD and ADHD. They identified associations between sensory patterns and behavioral outcomes, though over-reliance on parent-reported data and lack of causal inference limits the findings. Chen et al. (2023) focused on visual binding (color and shape) in individuals with varying autistic traits. Their work indicated enhanced binding strength in those with higher Autism Quotient (AQ) scores, but findings were limited to subclinical traits and specific visual tasks.

Art therapy, sensory experience, and design implications

Hutson and Hutson (2024) examined art’s impact on well-being in ASD through the lens of embodied cognition and music therapy. Despite reporting positive emotional outcomes, their findings were restricted by small sample sizes and generalized methodologies. Separated art-specific research and highlighted therapeutic potential.

Williams et al. (2024) conducted a scoping review on sensory adaptive environments (SAEs) for autistic children. While they found some evidence supporting distress reduction and increased interaction, the heterogeneity in study design and methodological weaknesses precluded firm conclusions. In exploring autism-friendly toy design, Cañete et al. (2024) applied a multi-criteria decision-making framework using Ansys Granta Edupack. Though innovative, the heavy reliance on specialized software limited broad applicability.

Werkman (2024) explored sensory processing patterns across ASD subtypes, including individuals with intellectual disabilities. The study found that sensory difficulties affected societal participation regardless of cognitive functioning, Clearer articulation of inclusive focus and impact on societal engagement. Although it lacked stratified analysis by intellectual capacity. Rajuan, Liberman, and Bart (Wooditch et al. 2021) used observational tools to compare sensory processing in ASD and non-ASD children. They identified key differences and emphasized clinician perspectives but called for further research into the long-term impact of sensory challenges. Table 1 presents the summary of the reviewed literature.

Table 1 Summary of literature review.
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This study fills gaps in the literature by investigating how differences in sensory processing of individuals with ASD affect their color and texture preferences in art. Previous research has been focused on sensory sensitivities, but few have studied how these differences influence aesthetic preferences. Moreover, many studies have small or nonrepresentative samples and do not have empirical data on the relationship between sensory processing and artistic preferences. Furthermore, much of existing research fails to include consideration of emotional and sensory comfort, and most concentrate on one research method, rather than working with quantitative and qualitative approaches, to attain a more holistic view. A detailed description of the methodology used is presented in the subsequent sections.

Methodology

In Fig. 1, this study explores systematically the effects of sensory processing differences on color and texture preferences in individuals with ASD. First, it describes what the research objectives are and how to recruit participants with a variety of sensory profiles. The paintings are created with carefully designed controlled variations in color intensity and texture complexity. Preference ratings are collected through quantitative surveys and sensory and emotional insights are collected through qualitative interviews. Key patterns are identified through statistical analysis (e.g., ANOVA) and thematic coding, allowing for a complete understanding of sensory-driven aesthetic preferences (Braun and Clarke 2023).

Fig. 1
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Methodological framework for analysing sensory processing and aesthetic preferences in ASD.

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Participants

This study was to examine the effects of differences in sensory processing on the preference for aesthetics of visual art, 46 participants with ASD were recruited. To compare the different sensory sensitivities of the participants with ASD to their reactions to color and texture, participants were selected based on their sensory profile and preferences. It was evident that the participants were at different developmental stages as they were aged between 6 and 40 years. Due to such a wide range of ages, the study may include how preferences of certain shades, touches, or artistic preferences may shift over time. By including both the elderly and youths in society, the goal of the study is to clarify how and in what ways aesthetic preferences are different at different ages by analyzing whether the specific aspects of sensory processing increase, decrease or remain stable with age. As expected, due to gender diversity in the ASD population, the participants included both males, females, and those who identified as non-binary. Since previous literature suggests that gender may affect sensory experiences and preferences, this portrayal is necessary. If one wants to create art therapy and art education programs that can be safe for every participant and can meet each of them on his or her level, one has to understand gender differences and how people of different genders can perceive art and interact with it, especially when it comes to sensory integration. The feature of participant characteristics such as age, gender, and sensory is a positive guarantee for the investigation of the different ways in which the variation in sensory processing in ASD affects aesthetics. This range of demographics is crucial for understanding how the different individuals with ASD interact with visual art, information that may inform treatment interventions and art instruction. Demographic and sensory processing profiles of participants along with their age, gender, sensory sensitivity level, and preferred color and texture as well as their emotional resonance score about the paintings are represented in Table 2.

Table 2 Sensory sensitivity levels, color and texture preferences, and emotional resonance scores.
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An interesting pattern of sensory sensitivity, color preference, and texture preference is observed in the participants. High sensory sensitive people have a different range of emotional response scores and slightly predominate “Soft” colors, which can be associated with the notion of comfort. Besides, participants preferring “Smooth” texture more often provided higher scores for the degree of emotional impact, which could indicate the link between the smoothness of the texture and the positive emotions. The bar plot enhances these distinctions, showing the way, average emotional resonance scores vary with Sensory Sensitive and Non-Sensory Sensitive, as well as, Red Blue and Green groups. This visualization and summary underpin the correlation between certain sensory attributes and feelings thus offering useful information to art-based therapeutic interventions and improving the sensory engagement of clients with ASD. Such findings are especially valuable when designing sensory-specific therapeutic and educational strategies for children with ASD.

Data collection

Quantitative surveys

In the quantitative part of the study, structured questionnaires were used to measure respondents’ preference for color and texture in visual art. A total of 120 respondents participated in the survey. The survey included a preference rating scale where respondents provided a preference rating of several paintings that differed in terms of color intensity and texture coarseness. Respondents were asked to rate using a Likert scale of 1–5, with 1 corresponding to a strong dislike and 5 to a strong preference. This scale made it possible to quantify the preferences so that the results could be analyzed statistically later. Besides, the survey collected data on the age, gender, and level of sensory sensitivity, which was very important in analyzing the results. The data gathered were analyzed using the Analysis of Variance (ANOVA) to determine the preference differences due to sensory sensitivity. These analyses aimed to discover relationships between sensory integration impairments and certain preferences in the choice of colors and textures.

Qualitative interviews

The qualitative part of the study was centered on the administration of semi-structured interviews with the participants to examine their emotional feelings and sensory experiences toward their color and texture preferences. A purposive sample of 15 participants was selected from the survey pool for in-depth interviews. The interviews were semi-structured to allow the participants to express themselves while at the same time ensuring that they were asked questions related to their feelings and experience with the paintings. This approach promoted free discussion and provided the opportunity to reveal many details. The participants were asked to name the feelings they associate with the analyzed pieces of art, focusing on feelings of comfort and attraction, as well as such concepts as ‘overstimulation’. This qualitative data gave the quantitative ratings a useful background that demonstrated how aesthetics is a subjective matter. The responses were then subjected to thematic coding, where the participants’ accounts were examined for emerging patterns and themes. This analysis aspired to uncover how their sensory processing affects their affection toward art.

Table 3 presents the questionnaire used for the qualitative study.

Table 3 Interview questionnaire for qualitative study.
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Stimuli details

In this study, the paintings presented to participants varied systematically in two main sensory dimensions: hue saturation and surface gloss. For color intensity, the paintings were either low saturation colors such as pastel blue, pink, and green which were meant to have low visual stimulation, or high saturation colors such as red, yellow, and deep blue to have high stimulation (Ashburner et al. 2021). The feelings of smoothness can also be observed in some paintings that had uniform concepts of textural variation and areas of normals with little to no layers of paint that are applied thick and glossy to gain a feel of the texture that a surface of that species brings to the hands of the beholder, on the other hand, there were feelings of roughness or complicated in different paintings that had bulky complex and over layers and paint hardened textures Each painting combined one type of color intensity and one type of texture complexity, resulting in four distinct categories: soft color, matte finish, soft color, glossy finish, bright color, matte finish, and bright color, glossy finish. This structured approach enabled a systematic comparison of participants’ preferences based on the within and between variations in color and texture.

Categories of data collected

Data were collected across three primary categories: Preference Ratings, Emotional Response, and Perceived Sensory Experience. The categories provided a complete understanding of the participants’ aesthetic preferences and the sensory and emotional experience with the stimuli (Baker et al. 2019).

Preference ratings

Each painting was also given a preference score by the participants on the Likert scale ranging from 1 – least preferred to 5 – most preferred. For instance, one participant with very high sensory processing sensitivity provided high (4–5) ratings to paintings with soft color and smooth texture and demonstrated a clear preference for less intense sensory stimuli. On the other hand, the same participant placed the level of tolerance or preference for bold color, rough texture painting lower at 1–2, meaning that he or she has less tolerance or liking of paintings that are highly saturated in color and have a rough texture. These ratings provide important information about the effect of sensory aspects on the perceived aesthetics and choice of people with ASD (Crane et al. 2020).

Emotional responses

Qualitative data was gathered by asking participants to give verbal reports of their feelings about each painting. For example, one of the studies outlined getting “comfortable” when looking at a painting that has a matt finish, soft colors, and “no roughness” to the touch. In contrast, the same participant said: “I felt overwhelmed and anxious when I was given a piece of art with a bold color and a rough texture,” they showed how different sensory characteristics of art may cause diverse emotions. These descriptors provide information as to how certain visual stimuli may affect the feelings of people with ASD (Fletcher-Watson et al. 2023).

Perceived sensory experiences

Each painting was discussed in terms of how it affected the participants’ experience of the painting in terms of the color and texture they felt comfortable with. For example, one of the participants identified with hypersensitivity mentioned that bold colors and rough texture cause discomfort as they regard these stimuli as shocking and distasteful. However, the same participant preferred smooth paint texture and soft colors as these combinations felt ‘gentle’ or ‘good’. These descriptions emphasized inter-subjective variability of the perceived sensations and stressed on variability of combinations of colors and textures in terms of comfort and liking (Jones and White 2023).

The participant’s responses to paintings based on color intensity and texture and their associated preference ratings, emotional responses, and sensory experiences are represented in Table 4. It shows how differences in sensory processing affect aesthetic preferences in people with ASD by organizing the data gathered during the study.

Table 4 Participant responses to color intensity and texture in paintings: preference ratings, emotional responses, and sensory experiences.
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The results showed that participants with an increased sensibility of the receptors preferred paintings with gentle colors and smooth surfaces, higher and associated with positive emotions. On the other hand, participants with lower sensory sensitivity preferred paintings with bright colors and coarse surfaces, the description of these paintings as ‘interesting’, and ‘stimulating’ implies that the higher level of sensory stimulation corresponds to the participants’ needs. The relationship between sensory appreciation and emotional effects was supported by quantitative feedback since many participants view art that is described as ‘smooth’, ‘relaxing’, or ‘comforting’. The more complex dataset gives a richer picture of how people with ASD make decisions about aesthetic stimuli based on sensory input, specifically the color and texture of art. The implications of these findings for changing the environment of therapy and art for the sensations of comfort and psychological states of those with ASD are important.

Quantitative analysis

Analysis of variance (ANOVA)

Figure 2 shows that ANOVA (Analysis of Variance) (James et al. 2023) is a collection of statistical models used to compare the means of two independent groups by dividing the variability into systematic and random factors. It helps to determine the effect of the independent variable on the dependent variable. We conducted the primary quantitative analysis using ANOVA to determine if sensory sensitivity levels significantly influenced preference ratings of color intensity and texture complexity. ANOVA compared the mean preference ratings across three sensory sensitivity groups: sensitivity of high, medium, and low (Kinnealey et al. 2022). The statistical model for ANOVA can be represented as:

$${Y}_{{ij}}=\mu +{\alpha }_{i}+{\in }_{{ij}}$$
(1)

Where: Yij is the preference rating of the j-th participant in the i-th sensory sensitivity group, μ is the mean preference rating overall, αi is the effect of the i-th sensory sensitivity group (high, medium, or low) and ∈ij is unexplained variability, random error term.

Fig. 2
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Analysis of variance (ANOVA).

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The null hypothesis (H0) was tested by the ANOVA analysis that there were no significant differences in mean preference ratings were found across sensory sensitivity groups. A p-value of less than 0.05 (p < 0.05 indicated statistically significant differences.

Regression analysis

Linear regression (Lee et al. 2024) is an algorithm that provides a linear relationship between an independent variable and a dependent variable to predict the outcome of future events. A graphical representation of linear regression is shown in Fig. 3. Linear regression was conducted to explore the relationship between sensory profiles and aesthetic choices. This analysis examined the effect of changes in sensory sensitivity levels on preference ratings of color and texture attributes (Lee et al. 2024). The regression model is defined as:

$$Y={\beta }_{0}+{\beta }_{1}X+e$$
(2)

Where: Y is color or texture preferred prediction, β0 is baseline preference rating (baseline preference rating), β1 is a coefficient describing the influence of sensory sensitivity on preference ratings, X sensory sensitivity score, and ϵ is the error term.

Fig. 3
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Linear regression.

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Interaction terms were included in some cases to examine whether age or gender moderated the association between sensory sensitivity and preference. The extended model was:

$$Y={\beta }_{0}+{\beta }_{1}X+{\beta }_{2}Z+{\beta }_{3}\left(X\cdot Z\right)+e$$
(3)

Where Z denoted as the moderating variable (e.g. age or gender);

Qualitative analysis

Thematic coding

To analyze the qualitative data collected through interviews, thematic coding (Murray et al. 2021) was used to identify patterns and recurring themes from identification in participants’ descriptions of their sensory and emotional experiences of movement and environment. Responses were categorized into themes that included sensory comfort, overstimulation, and emotional resonance. Participants described sensory comfort as ‘gentle’, ‘soothing’, and ‘calm’, while overstimulation was indicated by ‘overwhelming’, and ‘intense’. The lines gave emotional resonance—feeling evoked using the words relaxed, and energized, to express the emotional response to the paintings. The workflow of thematic analysis is shown in Fig. 4.

Fig. 4
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Workflow of thematic analysis.

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Coding process

Thematic coding of the qualitative interview data was conducted using e.g., NVivo 12, which facilitated systematic and organized analysis (Patel and Garcia 2023). The process began with data familiarization, where all interview transcripts were carefully read multiple times to identify recurring sensory and emotional descriptors. During the initial coding phase, preliminary labels were manually assigned to meaningful data segments, such as “comfort,” “overstimulation,” or “attraction.” These initial codes were then reviewed and grouped into broader themes during the theme development phase, for example, combining codes related to gentle and soothing sensations under the theme “sensory comfort.” To ensure reliability and consistency, a validation step was undertaken where the coding framework was reviewed and applied uniformly across all transcripts (Smith et al. 2022).

Quantitative and qualitative data integration

The results of the thematic coding were integrated with the quantitative data to deepen the understanding of the findings. Strong alignments between numerical preferences and descriptive feedback were found in this cross-referencing. For example, in the quantitative surveys, participants who ranked paintings with soft colors and smooth textures high frequently described these paintings as ‘soothing’ or ‘comfortable’ in the interviews. This integration of the sensory sensitivity levels and emotional responses reinforced the sensory processing differences in aesthetic preferences perspective (Stoppelbein and Greening 2021).

Results

This section presents the findings from both the quantitative surveys and qualitative interviews, reflecting a mixed-methods approach. While the quantitative results—based on preference ratings and statistical analysis—are discussed first due to the larger sample size, they are complemented by qualitative insights that explore emotional and sensory experiences in more depth. This integrated approach provides a comprehensive understanding of how individuals with varying sensory sensitivities perceive and respond to visual art stimuli.

ANOVA showed significant differences between participants with high, medium, and low sensory sensitivity levels in preference ratings for color intensity and texture complexity (p < 0.05). Participants with high sensory sensitivity rated paintings with soft colors and smooth textures significantly higher (mean rating: 4.2) than bold colors and rough textures (mean rating: 2.0). Conversely, participants with low sensory sensitivity showed a preference for bold colors and rough textures (mean rating: 4.3), which they described as ‘stimulating’ and interesting.’ The mean preference ratings for color intensity and texture complexity are summarized for the three participant groups (high, medium, and low sensory sensitivity) in Table 5, which demonstrates distinct trends in aesthetic preferences”.

Table 5 Preference ratings by sensory sensitivity levels.
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Mean ratings for color intensity and texture complexity for three sensory sensitivity groups are presented in Fig. 5. Participants with high sensitivity preferred soft colors (4.5) and smooth textures (4.6) but rated bold colors (2.0) and rough textures (1.8) significantly lower. People with medium sensitivity had more balanced preferences, giving soft colors (3.8), bold colors (3.5), smooth textures (3.9), and rough textures (3.2) about the same ratings. Low-sensitivity participants liked bold colors (4.3) and rough textures (4.2) but rated soft colors (2.5) and smooth textures (3.0) as less attractive. The following table displays the different patterns of aesthetic preferences that are generated by different levels of sensory sensitivity.

Fig. 5
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Comparison of color and texture preferences across sensory sensitivity levels.

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Participants with higher sensory sensitivities reported lower emotional resonance scores for bold and rough stimuli (mean: 2.0). It was compared to softer and smoother stimuli (mean: 8.5). Those with lower sensitivities, however, demonstrated higher resonance with bold and rough stimuli (mean: 7.0), correlating with descriptors such as “energizing” and “engaging.” The strong correlation r = 0.72, p < 0.01 (as confirmed by regression analysis), between sensory sensitivity and preference for softer colors and smoother textures. Table 6 shows that participants who rated as having high sensory sensitivity rated soft colors and smooth textures as significantly more emotionally resonant than bold and rough stimuli.

Table 6 Emotional resonance scores by sensory sensitivity levels.
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Figure 6 shows the means emotional resonance scores for Soft Colors & Smooth Textures and Bold Colors & Rough Textures in moderate, high, and very high sensitivity users. Participants with high sensitivity rated soft and smooth stimuli as highest (8.5), and bold and rough stimuli as lowest (2.0). Medium-sensitive subjects had balanced scores, rating soft and smooth stimuli at 6.0 and bold and rough at 5.5. The opposite trend was seen in participants with low sensitivity who preferred bold and rough stimuli (7.0) over soft and smooth (4.0). This data represents the relationship between sensory sensitivity and emotional engagement with art stimuli.

Fig. 6
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Emotional resonance for soft/smooth and bold/rough stimuli across sensory sensitivity levels.

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Thematic coding revealed three key themes: sensory comfort, overstimulation, and emotional resonance. Participants who were high in sensory sensitivity paired soft colors and smooth textures with feelings of ‘calm’ and ‘soothing’, and said these were emotionally comforting. While bold colors and rough textures were frequently assigned “overwhelming” or “harsh” tags — signs of overstimulation and discomfort — awe was reserved for complex, unobtrusive spaces that impelled orderly thinking. At the same time, participants with low sensitivity reported bold and rough stimuli as ‘exciting’ and ‘energizing,’ consistent with a preference for high-intensity sensory input. The themes in these papers emphasize the need to match sensory stimuli to individuals’ sensitivities to achieve emotional engagement. Table 7 shows the frequency of recurring themes as identified through thematic coding, indicating that sensory comfort was mentioned most frequently by participants with high sensitivity, and emotional resonance was most often cited by low sensitivity participants.

Table 7 Frequency of themes from qualitative data.
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Figure 7 illustrates the frequency of common themes in sensory sensitivity.s. Sensory comfort (85%) and overstimulation (80%) were mentioned by high-sensitivity participants more often, and emotional resonance (20%) less often. Medium sensitivity references to sensory comfort (50%), overstimulation (40%), and emotional resonance (55%) were found among those with medium sensitivity. The most frequently mentioned emotional resonance (75%) was reported by participants with low sensitivity, with sensory comfort (25%) and overstimulation (10%) mentioned minimally. These results show how sensitivity levels relate to different emotional and sensory experiences.

Fig. 7
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Prevalence of sensory comfort, overstimulation, and emotional resonance by sensory sensitivity level.

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Qualitative responses were very close to quantitative ratings. For instance, those who liked smooth textures gave high ratings and said they had positive emotional reactions to interviews, using terms like “relaxing” and “pleasant.” It added to the link between sensory profiles and aesthetic preferences. The correlation coefficients between sensory sensitivity levels and aesthetic preferences are illustrated in Table 8, in which significant positive correlations exist with soft colors and smooth textures and significant negative correlations with bold colors and rough textures.

Table 8 Correlation between sensory sensitivity and aesthetic preferences.
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The strength and significance of relationships between sensory sensitivity and different aesthetic preferences are shown in Fig. 8. Higher sensory sensitivity is associated with a greater preference for soft colors (r = 0.72, p < 0.01) and smooth texture (r = 0.68, p < 0.01) as these are correlated with soft colors preference (r = 0.72, p < 0.01) and smooth texture preference (r = 0.68, p < 0.01). On the contrary, there is a strong negative relationship between preference for bold colors (r = −0.65, p < 0.01) and rough texture (r = −0.60, p < 0.05), indicating that lower sensory sensitivity is associated with greater liking for these more intense stimuli. This shows the obvious effect of sensory sensitivity on aesthetic choice.

Fig. 8
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Correlation of sensory sensitivity with aesthetic preferences for colors and textures.

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Discussion

Results from this study demonstrate a strong and nuanced relationship between sensory processing differences and aesthetic preferences in individuals with ASD. Specifically, participants with high sensory sensitivities consistently preferred softer colors and smoother textures, reflecting their need to avoid sensory overstimulation. In contrast, those with lower sensory sensitivities gravitated towards bolder colors and rougher textures, suggesting a desire for increased sensory engagement. These findings align with recent research demonstrating that sensory profiles in ASD significantly influence perceptual and emotional responses to visual stimuli (Crane et al. 2020; Ashburner et al. 2021). Such evidence supports the growing theoretical perspective that sensory sensitivity is a critical factor shaping individual experiences of art and aesthetics (Baker et al. 2019), reinforcing models of sensory integration that emphasize personalized sensory processing patterns.

The practical implications of these findings are considerable, particularly for art therapy and educational practices. Therapists can tailor art materials and environments according to individual sensory profiles, thereby enhancing engagement while minimizing discomfort. For example, using pastel colors with smooth textures may reduce overstimulation for highly sensitive individuals, whereas bright colors and rough textures can provide stimulating challenges for those with lower sensitivity. This approach echoes contemporary sensory-adapted intervention models aimed at improving emotional regulation and participation in ASD populations (Stoppelbein and Greening 2021; Kinnealey et al. 2022).

In educational settings, the results emphasize the necessity of sensory-aware teaching methods. Incorporating sensory profiles into curriculum design allows educators to create individualized, inclusive learning experiences, thereby fostering motivation and comfort for students with ASD as they explore creative expression. This is consistent with current pedagogical research advocating personalized, neurodiversity-informed educational frameworks that support sensory inclusivity and holistic development (Murray et al. 2021; Fletcher-Watson et al. 2023).

A key strength of this study lies in the integration of quantitative and qualitative data, which strengthens the validity of its conclusions. The alignment between high preference ratings and positive emotional feedback validates the connection between sensory preferences and emotional experiences. This mixed methods approach aligns with a growing consensus that combining statistical rigor with qualitative insights is essential to comprehensively understand sensory and emotional dynamics in ASD (Smith et al. 2022; Jones and White 2023).

Nonetheless, several limitations should be acknowledged. First, the study’s theoretical framework was not deeply developed, limiting its ability to fully contextualize findings within existing sensory processing models. Additionally, the relatively small sample size and lack of cultural diversity restrict the generalizability of results. Future research should address these gaps by including larger, more diverse populations to examine cross-cultural differences in sensory-aesthetic preferences. Further, expanding investigation into other sensory modalities—such as auditory or tactile feedback—would offer a more holistic understanding of sensory integration and aesthetic experience in ASD (Lee et al. 2024; Patel and Garcia 2023). Addressing these limitations will enhance the theoretical and practical significance of future studies.

Conclusion

The conclusions from this study offer important knowledge of the link between sensory processing differences and aesthetic preferences in people with ASD. The results show that sensory sensitivity has strong effects on preferences for color intensity and texture complexity and on both emotional and sensory experience. Participants who were generally sensitive about these stimuli were drawn to soft colors and smooth textures that they associated with sensory comfort and emotional calmness. By contrast, people with low sensitivity tended to gravitate toward the use of bold colors, and rough textures, and these were reported as energizing and engaging. Emotional resonance scores further reinforced these trends, with soft and smooth stimuli rated significantly higher by highly sensitive individuals and inversely by low sensitive ones, and bold and rough stimuli rated higher by low sensitive individuals and inversely by highly sensitive ones. These findings were reinforced by thematic analysis, which showed recurring sensory comfort themes, overstimulation themes, and emotional resonance themes across sensitivity levels. Quantitative analysis corroborated a strong positive correlation between sensory sensitivity and preferences for soft colors and smooth textures and a strong negative correlation with bold colors and rough textures. These results emphasize the need for tailored art-based interventions and environments that match individuals’ sensory profiles to increase emotional engagement and comfort in individuals with ASD. These findings provide a basis for future research on additional sensory dimensions (e.g., auditory, tactile) and more diverse populations to increase applicability. The findings of this study form a basis for the development of sensory-aware art therapy, education, and creative engagement for neurodiverse individuals.