Autistic Traits Influence Aesthetic Judgments of Abstract Color Works

Autistic Traits Influence Aesthetic Judgments of Abstract Color Works | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Autistic Traits Influence Aesthetic Judgments of Abstract Color Works Leyi Wang, Mengdan Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7371825/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2025 Read the published version in BMC Psychology → Version 1 posted 10 You are reading this latest preprint version Abstract Autism spectrum disorders (ASD) are characterized by atypicalities in both social and non-social behaviors, yet little is known about how autism traits influence aesthetic judgments. This study investigated the impact of autism tendencies, measured by the Autism-Spectrum Quotient (AQ), on aesthetic evaluations of abstract color works. Results revealed that larger hue distances enhanced colorfulness and liking ratings. Critically, while colorfulness ratings were similar between AQ groups, liking ratings differed significantly, with high AQ individuals showing reduced sensitivity to hue specificity but increased sensitivity to category membership. Colorfulness ratings positively predicted liking ratings, but this relationship was weaker in the high AQ group. Among AQ subscales, only social skills negatively correlated with liking ratings. These findings highlight how autism traits modulate aesthetic preferences, offering new insights into the distinctive perceptual and cognitive processing of autism. Autism Spectrum Disorder Aesthetic Judgment Color Perception autistic trait Figures Figure 1 Figure 2 Figure 3 Introduction Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by atypicalities in social interaction, social communication, and restricted, repetitive patterns of behavior. Researchers have conducted substantial investigations into the atypical patterns of social and cognitive functions associated with autism (Leekam, 2016 ). A core feature of autism is the atypicality in social skills, which includes specific aspects of communication abilities and the social use of language. Though most previous research links the condition of ASD mostly with social and communication difficulties, as well as repetitive behaviors, there is also widespread reporting of differences in sensory perception, particularly in the visual domain (Baum et al., 2015 ). Autistic individuals have cognitive processing styles biased toward local and field-independent processing (Booth & Happé, 2018 ; Simmons & Todorova, 2018 ). To be more precise, they seem to be limited in their ability to derive organized wholes from individual parts (Frith, 1989 ). A lot of studies suggest that they have a preference for local detail processing over global integration (Behrmann et al., 2006 ; Hadad & Ziv, 2015 ; Happé, 1996 ). Also, they exhibit reduced cognitive flexibility than TD (typical developing) individuals (Leung & Zakzanis, 2014 ). All these characteristics result in their atypical responses at low-level sensory perception. Children diagnosed with ASD have faster automatic visual orienting compared to TD children, which may hinder their exploratory behavior and lead to difficulties in complex social environments (Kovarski et al., 2019 ). Additionally, autistic individuals exhibit distinctive visual processing characteristics, including enhanced abilities in color perception, altered visuospatial processing (e.g., challenges in body spatial awareness and locating oneself in space) and visuomotor skills (e.g., compensatory reliance on proprioception for spatial orientation) (Coulter, 2009 ). These features may be associated with their visual attention mechanisms and atypical early visual processing (Coulter, n.d.). At the same time, autistic individuals exhibits their unique strengths. As previous studies have shown, some perceptual capacities of individuals on the autism spectrum are enhanced compared to the TD group (Almeida et al. 2012 ; Brock et al. 2011 ; Milne et al. 2013 ). For example, autistic individuals showed superior processing of detailed partial information on a wide variety of perceptual tasks (Gadgil et al., 2013 ). There are two main interpretations of their atypical perception. The weak central coherence hypothesis attributed this condition to a failure in extracting the meaning or the gist of an input (Frith & Happé, 1994 ; Happé, 1999 ). On the other hand, the enhanced perceptual function model interprets this condition as an overdevelopment of low-level perceptual processing, which leads to enhanced basic perceptual abilities, such as detection and discrimination (Mottron & Burack, 2001 ; Mottron et al., 2006 ; Plaisted et al., 1998a ; Plaisted et al., 1998b ). Despite the substantial studies on individuals with autism, their aesthetic experience and its underlying mechanism have received much less attention. Human aesthetic experience is influenced by a complex interplay of genetic, cultural, objective, and subjective factors. There have been studies showing that autistic individuals have atypical aesthetic experiences, which is different from TD (Park et al., 2018 ; Mazza et al., 2020 ; Coelho et al., 2023 ; Brosnan & Ashwin, 2023 ). The difference in aesthetic perception has been linked to both cognitive (Park et al., 2018 ) and emotional abilities (Coelho et al., 2023 ). Park et al. ( 2018 ) revealed that the aesthetic judgment score of autistic individuals was significantly lower than the control group for fractal images but not for landscape images. The tendency to experience the images as less beautiful was correlated with lower scores in emotional skills, measured by empathy scales. Compared to typical individuals, autistic individuals exhibited higher activation in the posterior regions, likely responsible for the visuospatial analysis of the artwork. It suggested that more cognitive resources are recruited for autistic individuals, possibly as compensation for poorer emotional skills mobilized in an empathic experience. Notably, however, this pattern may also stem from the fact that autistic individuals possess heightened visual analytic skills, leading to increased activation in the occipital lobe and potentially resulting in boredom with the presented artwork. Using images of sculptures selected from masterpieces of classical and Renaissance as materials, Mazza et al. ( 2020 ) found there were different patterns of aesthetic perception between autistic and non-autistic individuals at the explicit level, which could be related to their reduced pleasure during social interaction. However, counterarguments exist suggesting that autistic and non-autistic individuals exhibit comparable levels of social motivation and hedonic responsiveness (Chiappini et al., 2024 ). Nevertheless, the judgment of proportionality, which was not connected to emotional capabilities, did not differ in individuals on the autism spectrum and TD. Furthermore, this study revealed a discrepancy between the explicit and implicit evaluation of the aesthetic perception task. At the implicit level (eye-tracking data), autistic and non-autistic individuals had similar implicit aesthetic perception. Based on the presumption that the ability to distinguish high-quality art from low-quality art reflects an initial, emotional intuitive process, which can then be attenuated by subsequent deliberation. Brosnan and Ashwin ( 2023 ) suggested individuals on the autism spectrum exhibit reduced intuitive processing and greater deliberative processing, consistent with predictions by the Dual Process Theory of Autism.(Ashwin & Brosnan, 2019 ; Brosnan et al., 2016a , 2017 ; Lewton et al., 2019 ) According to these studies, there might not be universal and consistent differences between autistic individuals and TD in aesthetic experience, which likely depends on various factors such as the stimulus type, task demands, and levels of processing. With regard to general aesthetic experience, both low-level and high-level features of the image can influence human preferences (Iigaya et al., 2021 ). Color, among other visual features, makes a critical contribution to aesthetic preferences (Albers et al., 2020 ; Nakauchi et al., 2022 ; Schloss & Palmer, 2011 ; Sun et al., 2023). Here in our study, we mainly focus on the relation between color, or color combination with aesthetic judgment. In addition, previous studies suggested that autistic traits exist on a continuum in the general population (Baron-Cohen et al., 2001 ), and individual differences in these traits have been shown to reliably predict perceptual and cognitive processing patterns associated with autism spectrum conditions (Ruzich et al., 2015 ; Yang et al., 2024 ). In our study, we are interested in whether the effect of color on aesthetic judgment differs for individuals along this continuum of autistic traits. As a neurodivergent processing style, do heightened autistic traits predict distinctive aesthetic experiences in response to stimuli that use color as the main visual element? There have been some attempts focusing on atypical color perception and cognition in individuals on the autism spectrum, including color discrimination, color categorization, color preference, etc (Franklin et al., 2008; Heaton et al., 2008 ). It was found that children with autism were significantly less accurate than controls at color memory, color search, and chromatic discrimination (Franklin et al., 2008; Franklin et al., 2010 ; Zachi et al., 2017 ), but the strength of categorical perception of color did not differ for the two groups (Franklin et al., 2008). The reduced chromatic discrimination in autistic individuals might be associated with less parvocellular (P) color pathway activity (Fujita et al., 2011 ). In addition, several studies have shown that adults with autism experience atypical perceptions of color ensembles (Maule et al., 2017 ) while exhibiting normal color adaptation (Maule et al., 2018 ), which supports the theory that perceptual priors are diminished in autism (Pellicano & Burr, 2012 ). The color preferences of the autistic group were significantly different from TD, with an overall preference for cooler colors compared to warmer colors (Grandgeorge & Masataka, 2016 ; Kusuma Wardani & Fefiana Mustikasari, 2023 ). Considering the atypical color perception and aesthetic experience of autistic individuals, we hypothesized that autistic traits might influence preferences toward color stimuli composed of multiple hues. Specifically, these hue combinations are distinct in perceptual distance (small or large) and categorical membership (same or different). We implemented two rating tasks where participants were asked to rate their liking and colorfulness of the color images, respectively. Method Participants We recruited 111 undergraduate and graduate students (39 males,72 females; age: 18–28 years old, M = 21.4) with normal or corrected-to-normal color vision. We conducted an a priori power analysis using G*Power 3.1 to determine the required sample size. To detect a medium effect size \(\:{f}^{2}\) of 0.15 (Selya et al., 2012 ) and achieve a power of 0.80, at least 92 participants were needed for a linear regression analysis. Our participants were all regular school attendees, none of whom reported having neurological or psychological conditions. All participants provided written informed consent before participating in the study and received their payment after. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee. In this study, all measures, manipulations and exclusions are reported here in the manuscript. Materials Autism-Spectrum Quotient In our study, we used the Autism-Spectrum Quotient (AQ; Baron-Cohen et al., 2001 ) to measure autistic traits of the participants. The AQ is a self-report measure of autistic traits, which is widely cited to quantify autistic traits of adults with at least average intelligence. The AQ has 50 items, divided into five subscales consisting of 10 items each that measure 5 domains: communication, social skills, imagination, attention to detail and attention switching. The AQ employed 4-point Likert scale as its scoring system (definitely agree; slightly agree; slightly disagree; definitely disagree) and half of the 50 items are reverse-scored. The AQ scale is scored such that each item contributes 1 point toward the total score if the response aligns with the direction of autistic traits. Specifically, for items reflecting autistic traits, a response of "slightly agree" or "definitely agree" is scored as 1. For the remaining items, which reflect the absence of autistic traits, a response of "slightly disagree" or "definitely disagree" also receives 1 point. All other responses are scored as 0. The total AQ score is the sum of all endorsed items, with higher scores indicating a greater number of autistic traits. A review has supported the validity of the AQ in indexing autistic traits in non-clinical populations (Ruzich et al., 2015 ). Stimuli Stimuli for the rating sessions are images (400 × 400 pixels) of randomly distributed color circles or rectangles (Fig. 1 A). The colors used to generate the color images were sampled from a color wheel in the CIELAB space (L = 70, a radius of 29), which only vary in hue (10°–355°, step size: 15°; Fig. 1 B). The color pool includes a total of 24 hues. Each color image is composed of three distinct hues (H1, H2 and H3) that are 15° (small-distance condition; e.g., 10°, 25°, 40°) or 30° (large-distance condition; 10°, 40°, 70°) apart in the color wheel. The three hues are evenly distributed throughout the image. Shape of circles and rectangles, which are widely applied in abstract artworks, were used to create color images. For each condition, each hue from the color pool was used twice to form the color image, once for the circle images and once for the rectangle images. By varying both perceptual distance (small, large) and shape (circle, rectangle), this experiment contained a total of 24 hues × 2 × 2 = 96 images. Procedure The study was divided into two main sessions, a measure of autistic traits and three behavioral tasks (liking rating, colorfulness rating and color naming). Both sessions were conducted online, and we used Naodao to present the experiment ( https://www.naodao.com/ ). Session 1: Autistic trait measurement In this session, participants were asked to complete a 50-item Autism-Spectrum Quotient with guidance, and after completing the quotient, they can move on to the next session. Session 2: Behavioral tasks This session includes three tasks: liking rating, colorfulness rating, and color naming. All three parts set standardized instructions and practice trials. Furthermore, we arranged the order as such (liking rating–colorfulness rating–color naming) to make sure that the liking rating task was not influenced by the other two tasks. Liking Rating In the first task, the participants were asked to evaluate the color images from the aesthetic perspective and rate how much they liked the images via key presses on a 4-point scale (1 = not at all; 2 = like a little; 3 = like; and 4 = strongly like). On each trial, a color image was displayed at the center of the screen for 1,200 ms, consistent with the validated paradigm used by Sun et al. (2023), and participants were not allowed to respond until the image disappeared. The order of the color images was assigned randomly for each participant. This session consisted of 24 × 2 × 2 = 96 trials in total. Each color image was presented only once. Each color combination was presented twice. Colorfulness Rating The procedure of the colorfulness rating task was essentially the same as the liking rating task, except that the participants were asked to evaluate the colorfulness of the color images via key presses on a 4-point scale (1 = not at all colorful; 2 = a little colorful; 3 = colorful; and 4 = very colorful). Color naming In the color naming task, the participants were asked to name the hues used in the rating tasks. On each trial, a square (200 × 200 pixels) filled with one of the 24 hues was presented at the screen center. On the bottom of the square, seven chromatic color terms were presented (1 = red; 2 = orange; 3 = yellow; 4 = green; 5 = blue; 6 = purple; 7 = pink). Participants were asked to select the color term that most closely described the present color. The color square and color terms remained on the screen until response. This task includes 48 trials in total, with each hue appearing 2 times. Data Analysis For each color image used in the rating sessions, the category membership (same, different) of the hues was assigned according to subjective judgment in the color naming task. Thus, the category membership is a factor tailored to each participant. To assess the potential difference between participants with different autistic trait levels, following the approach of Yang et al. ( 2024 ), we split the participants by the median AQ score (19) into two groups, the low AQ group (AQ ≤ 19, 54 individuals) and the high AQ group (AQ > 19, 57 individuals). For visual features such as color and orientation, stimulus-specific variation in visual perception and judgement has been reported (Taylor & Bays, 2018 ). It is highly plausible that there is stimulus-specific variation in preferences as well (e.g., Zhang et al, 2019 ), with participants favoring specific hues along the color wheel over others. Figure 2 A and 2 B plots the relationship between the hue position (H1) and ratings, revealing that the ratings are not uniformly distributed across hues along the color wheels. For colorfulness ratings, as hue H1 increases from 0 to π, the ratings follow a sine function. Conversely, as H1 increases from π to 2π, the ratings follow a negative sine function. For liking ratings, the ratings exhibit a negative sine function as H1 increases from 0 to 2π. To establish a linear relationship between the hue position (H1) and ratings for subsequent regression analyses, we applied the following transformations to the hue position data. For colorfulness ratings: $$\:x=\left\{\begin{array}{c}\text{sin}{x}_{0},\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:0<x\le\:\pi\:\\\:{\text{s}\text{i}\text{n}(x}_{0}-\pi\:),\:\:\:\:\pi\:<x\le\:2\pi\:\end{array}\right.$$ For liking ratings: $$\:x=sin{x}_{0}$$ Here \(\:{x}_{0}\) represents the initial hue position variable (H1 in radians), and \(\:x\) is the transformed hue position (sin (H1)). Then, we used the transformed hue position (Fig. 2 C and 2 D) as the stimulus-specific variable to explain the potential stimulus-specific variation in ratings. To investigate how AQ group, perceptual distance, category membership, shape, and hue position influence colorfulness and liking ratings, we analyzed the rating data with linear mixed models (LMMs) in R (R Core Team, 2014) using the lme4 package (Bates et al., 2014). We built a model with AQ group (low vs. high), perceptual distance (small vs. large), category membership (same vs. different), shape (circle vs. rectangle ) and hue position as fixed effect factors and participant as the random effect factor with a random intercept. Since our study is interested in whether the effect of color features (e.g., perceptual difference) is modulated by AQ group, we also included the following interactions into the model: AQ group \(\:\times\:\) perceptual distance, AQ group \(\:\times\:\) category membership, AQ group \(\:\times\:\) shape, AQ group \(\:\times\:\) hue position . The analyses were conducted on colorfulness and liking ratings separately. Results Colorfulness Rating For colorfulness ratings (Fig. 3 A & 3 B, Table 1 ), we found significant effects of perceptual distance ( b = 0.68, t (10546) = 32.08, p < .001), category membership ( b = 0.08, t (10578) = 3.38, p < .001), and hue position ( b = 0.12, t (10545) = -8.09, p < .001). As expected, participants perceive images with large-distance hues as more colorful than small-distance hues. Category membership influences colorfulness ratings in a similar way. Participants rated images with cross-category hues as more colorful than within-category hues. In addition, the colorfulness ratings significantly vary across different hues along the color wheel. As plotted in Fig. 2 A, the highest colorfulness ratings are associated with colors located at the vertical coordinate positions. There is no evidence that the two AQ groups differ in colorfulness ratings. The results suggested that high AQ group perceive the image colorfulness in a similar way as low AQ group. Liking Rating For liking ratings (Table 1 ), we revealed that perceptual distance ( b = 0.14, t (10546) = 6.24, p < .001), shape ( b = 0.08, t (10545) = 3.44, p < 0.001) and hue position ( b = 0.39, t (10545) = 24.81, p < .001) significantly influence liking ratings. Consistent with the findings from Sun et al. (2023), participants prefer color images composed of large-distance over small-distance hue. The effect of hue position suggested that the liking ratings significantly vary across different hues along the color wheel. Besides, the color images composed of circles are more liked by the participants than rectangles. More interestingly, we found a significant AQ group \(\:\times\:\) category membership interaction ( b = -0.12, t (10570) = -3.12, p = .002) and AQ group \(\:\times\:\) hue position interaction ( b = 0.06, t (10546) = 2.47, p = .01). To interpret the AQ group \(\:\times\:\) category membership interaction (Fig. 3 C), we compared liking ratings for same-category and different-category stimuli for the two AQ groups separately. It was found the high AQ group like the same-category color images better than those with different-category images ( z = 5.13, p < .0001), showing category effect in liking ratings. However, there was no significant category effect for the low AQ group ( z = 0.74, p = 1.00). For the AQ group \(\:\times\:\) hue position interaction, as shown in Fig. 3 D, the low AQ group shows an increased stimulus-specific variance in liking ratings compared with the high AQ group. Table 1 Linear-Mixed-Model Statistics for Colorfulness and Liking Ratings Variable b t df p Colorfulness Rating Distance (small vs. large) 0.68 32.08 10546 < .001*** Shape (circle vs. rectangle) 0.03 1.45 10545 0.160 Category membership 0.08 3.38 10578 < .001*** Hue position 0.68 19.88 10545 < .001*** AQ group -0.12 -1.39 234 0.167 AQ group × Distance 0.02 0.53 10546 0.594 AQ group × Shape -0.03 -1.03 10545 0.305 AQ group × Category membership -0.05 -1.41 10575 0.158 AQ group × Hue position 0.05 1.06 10545 0.290 Liking Rating Distance (small vs. large) 0.14 6.24 10546 < .001*** Shape (circle vs. rectangle) 0.08 3.44 10545 < .001*** Category membership -0.02 -0.74 10574 0.460 Hue position -0.39 -24.81 10545 < .001*** AQ group -0.04 -0.49 156 0.625 AQ group × Distance -0.05 -1.44 10546 0.149 AQ group × Shape -0.02 -0.61 10545 0.540 AQ group × Category membership -0.12 -3.12 10570 0.002** AQ group × Hue position 0.06 2.47 10546 0.01* *** p < .001 ** p < .01 * p < .05. Correlation between Colorfulness and Liking Ratings According to the results above, we can see that colorfulness and liking ratings are influenced by common color factors including perceptual distance and stimulus-specificity. Meanwhile, there are unique mechanisms underlying these two ratings. One piece of evidence is that the colorfulness rating patterns of high and low AQ groups are similar. In contrast, they differ in liking rating patterns. We hypothesized that perceived colorfulness partly predicts liking ratings. To quantitatively investigate the relationship between these two ratings, and how this relationship is moderated by autism tendencies, we built a linear mixed model with liking ratings as dependent variable, AQ group (low vs. high) and colorfulness ratings as fixed effect factors, and participant as the random effect factor. The results (Table 2 ) suggested that colorfulness ratings positively predict liking ratings ( b = 0.17, t (10645) = 12.64, p < .001). At the same time, however, there is an interaction between AQ group and colorfulness ratings ( b = -0.04, t (10651) = -2.24, p = .025), indicating that the relationship between two ratings is modulated by AQ group (Fig. 3 E). Specifically, colorfulness has a greater effect on liking ratings for the low AQ group (with a steeper slope) than the high AQ group. This is likely, for the high AQ group, there are other influencing color-related factors in addition to colorfulness, such as categorical membership. Correlation between Ratings and AQ dimensions In addition, we were interested in the relationship between AQ dimensions and ratings, which may provide insights into the findings that high and low AQ groups differ in liking rating patterns. We built two linear mixed models with liking ratings and colorfulness ratings as dependent variables respectively. In these two models, five AQ dimensions ( communication, social skills, imagination, attention to detail and attention switching ) are fixed effect factors, and participant is the random effect factor. We found only one AQ dimension (i.e., social skills) negatively correlates with liking ratings ( b = -0.03, t (111) = -2.04, p = .044; Table 2 , Fig. 3 F). None of the dimensions correlates with colorfulness ratings (Please see the detailed statistics in Table 2 ). Table 2 Linear-Mixed-Model Statistics Variable b t df p Colorfulness ->Liking ratings Colorfulness ratings 0.17 12.64 10645 Colorfulness Ratings Communication -0.004 -0.20 111 0.842 Social skills -0.03 -1.57 111 0.120 Imagination 0.02 0.75 111 0.456 Attention to detail -0.005 -0.32 111 0.749 Attention switching -0.008 -0.38 111 0.703 AQ Dimensions ->Liking Ratings Communication -0.002 -0.08 111 0.939 Social skills -0.03 -2.04 111 0.044* Imagination 0.025 0.89 111 0.377 Attention to detail -0.03 -1.48 111 0.142 Attention switching -0.01 -0.43 111 0.668 *** p < .001 ** p < .01 * p < .05. Discussion Our study investigated aesthetic experience in university students, focusing on the influence of autism tendencies measured by the Autism-Spectrum Quotient (AQ). Key findings revealed significant effects of perceptual distance and hue position on both colorfulness and liking ratings. Participants rated images with larger perceptual distances as more colorful and likable. Importantly, liking ratings differed significantly between high and low AQ groups, whereas their colorfulness ratings were similar. Specifically, we observed a significant AQ group × category membership interaction and AQ group × hue position interaction in the liking ratings, indicating that the category effect and stimulus-specific variance in liking ratings were modulated by AQ group. Furthermore, colorfulness ratings positively predicted liking ratings, with this relationship being weaker in the high AQ group. Additionally, analysis of AQ subscales revealed that only the social skills dimension negatively correlated with liking ratings. Our findings on perceptual distance replicate those of Sun et al. (2023), reaffirming the independent effect of hue distance on aesthetic experience, including both colorfulness and liking ratings. However, unlike previous studies, we found that shape significantly influenced liking ratings. Compared to Sun et al. (2023), we introduced the position of hues on the color wheel as a novel variable, which exhibited significant main effects on both colorfulness and liking ratings, suggesting that these ratings depend on the specific hues composing the images. One of our crucial findings is that low and high AQ groups perform similarly in colorfulness ratings but differently in liking ratings. This finding aligns with Mazza et al. ( 2020 ), who demonstrated that individuals on the autism spectrum exhibit distinctive aesthetic judgments in emotion-laden tasks but perform comparably to TD individuals in proportionality-based tasks. In our study, high and low AQ groups showed significantly different patterns in liking ratings but similar colorfulness ratings. The absence of significant differences in colorfulness ratings between high and low AQ groups can be explained by the Enhanced Perceptual Functioning Model (EPF), which posits that autistic and non-autistic individuals perform similarly in low-level perceptual tasks, such as color discrimination and brightness perception (Mottron et al., 2006 ). Since colorfulness perception relies primarily on low-level visual processing, this may account for the lack of group differences. In contrast, for liking ratings, the category effect and stimulus-specific variance were modulated by AQ group. First, we observed category effects in the high AQ group but not in the low AQ group. This group difference is unlikel y to stem from differences in subjective color categorization, as both groups demonstrated comparable consistency in color categorization. We additionally investigated whether the two groups differ in category membership of the images for rating using LMM. The model includes perceptual distance , AQ group and their interaction as fixed effect factors, participant as the random effect factor, category membership as the dependent variable. We found main effects of perceptual distance ( b = 0.69, z = -8.09, p 0.1). Instead, this effect may reflect a preference for structural predictability (Kanner, 1943 ; Baron-Cohen et al., 2009 ), where high AQ individuals prioritize categorical coherence over emotional valence when evaluating hues. The combination of hues from the same category provided higher structural consistency, aligning with the perceptual needs of high AQ individuals. Furthermore, the Dual Process Theory of Autism suggests that individuals on the autism spectrum exhibit stronger analytical processing and may rely more on explicit categorical rules for aesthetic judgments (Brosnan et al., 2016b ). In our study, color categories were determined through a self-naming task, suggesting that the high AQ group adhered more strictly to their self-defined categorical boundaries, leading to a preference for same-category hues. This result indicates that aesthetic judgments in autistic individuals may be more driven by structural rules than by overall emotional experiences. Regarding the stimulus-specific variance in liking ratings, the low AQ group exhibited a clear preference pattern across hue positions, with the lowest liking ratings for colors at 90° and the highest for colors at 270°. This pattern was weaker in the high AQ group. It may suggest that low AQ individuals rely more on dynamic emotional associations (e.g., emotional memories triggered by colors) in their aesthetic judgments. In contrast, high AQ individuals may depend more on stable perceptual attributes of the stimuli, as their reduced emotional responsiveness (Harms et al., 2010 ) makes it harder for them to integrate emotional cues into their evaluations. This pattern may suggest that individuals with high autistic traits exhibit emotion-cognition decoupling, where emotional processing plays a diminished role in shaping preferences. Alternatively, high AQ individuals tend to make more consistent decisions (Farmer et al., 2017 ), which may explain why their preference ratings show less variability. This consistency likely stems from a rule-based cognitive style that prioritizes internal coherence over emotional fluctuations, reducing the impact of stimulus variability on their aesthetic preferences. In addition, we suggested a positive correlation between the two ratings. The more colorful the image is perceived, the more it is liked. However, the correlation is weaker for the high AQ group. The weaker influence of colorfulness on liking in the high AQ group might be related with the stronger category effect. Specifically, for high AQ individuals, aesthetic judgments are influenced by multiple factors, including both colorfulness and category membership. Since high AQ individuals show a stronger reliance on categorical coherence (preferring colors from the same category), the impact of colorfulness is diluted. In contrast, the low AQ group, which does not exhibit a significant category effect, relies more heavily on colorfulness as a primary factor in their aesthetic evaluation. Finally, there is a significant negative correlation between the social skills subscale and liking ratings. This result may align with the viewpoint that autistic individuals exhibit enhanced rationality through reduced influence of contextual information in decision making (Rozenkrantz et al., 2021 ). In the context of aesthetic judgment, such reduced involvement of irrelevant information could lead to lower sensitivity to socially driven preferences, i.e. liking ratings. It is also consistent with our previous inference that the difference in liking ratings between the two groups because the liking rating task involves greater emotional and cognitive engagement. This study has several limitations. First, due to time and location constraints, we recruited university students rather than clinical samples of autistic individuals and TD controls. Thus, our findings cannot be directly generalized to the autistic population. Second, the stimuli consisted of abstract images composed of rectangular and circular color patches, which, while common in abstract art, may not encompass all types of artworks. Therefore, our findings on aesthetic preferences may require further validation when applied to other art forms. Lastly, we conducted the study online, and thus, differences and inaccuracies in color rendering could pose a concern for color or vision studies. However, we do not believe it undermines the current conclusion, as category membership of the color stimuli was treated as a subjective judgment rather than an objective physical property in our study. This is also why we used individual categorization data to determine the category membership for each stimulus. A predefined, group-level category assignment could be vulnerable to inaccuracies arising from variations in color rendering. In conclusion, our study highlights the interplay between color perception, aesthetic experience, and autistic traits. Individuals with low and high autistic traits exhibit both similarities and differences in aesthetic judgment. Perceptual differences increase preferences for both groups. However, individuals with high autistic traits exhibit less tolerance for category differences and are less influenced by variations in color stimuli when making aesthetic judgments. This unique pattern may be related with their deficit in social skills. These findings have significant theoretical and practical implications. First, our results suggest that heightened reliance on categorical rules during aesthetic preference formation could be a key characteristic in autistic individuals. And for practical applications, our findings could inform the design of visual materials in educational or therapeutic settings to better accommodate neurodivergent perceptual preferences. Declarations Ethical approval and consent to participant This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Soochow University. Informed consent was obtained from all individual participants included in the study. Consent for publication All authors approved the final manuscript and the submission to this journal. Availability of data and materials All data generated in the current study are made available at https://osf.io/dgna8/. Competing interests The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. Funding This work is supported by the General Project of Philosophy and Social Sciences Research in Jiangsu Higher Education Institutions (2023SJYB1399), the Natural Science Foundation of Jiangsu Province (BK20200867) and Social Science Foundation of Jiangsu Province (23YYC005). Authors ’ contributions L. W. : Methodology, Data Collection, Data Analysis; Writing- Reviewing and Editing; M. S. : Conceptualization, Methodology, Data Analysis, Writing, Supervision, Funding acquisition. Acknowledgements Not applicable. References Albers AM, Gegenfurtner KR, Nascimento SMC. 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Brief report: Cognitive flexibility in autism spectrum disorders: a quantitative review. J Autism Dev Disord. 2014;44(10):2628–45. https://doi.org/10.1007/s10803-014-2136-4 . Lewton M, Ashwin C, Brosnan M. Syllogistic reasoning reveals reduced bias in people with higher autistic-like traits from the general population. Autism. 2019;23(5):1311–21. https://doi.org/10.1177/1362361318808779 . Maule J, Stanworth K, Pellicano E, Franklin A. Ensemble perception of color in autistic adults. Autism Res. 2017;10(5):839–51. https://doi.org/10.1002/aur.1725 . Maule J, Stanworth K, Pellicano E, Franklin A. Color afterimages in autistic adults. J Autism Dev Disord. 2018;48:1409–21. https://doi.org/10.1007/s10803-016-2786-5 . Mazza M, Pino MC, Vagnetti R, Peretti S, Valenti M, Marchetti A, Di Dio C. Discrepancies between explicit and implicit evaluation of aesthetic perception ability in individuals with autism: A potential way to improve social functioning. BMC Psychol. 2020;8(1):74. https://doi.org/10.1186/s40359-020-00437-x . Milne E, Dunn SA, Freeth M, Rosas-Martinez L. Visual search performance is predicted by the degree to which selective attention to features modulates the ERP between 350 and 600 ms. Neuropsychologia. 2013;51(6):1109–18. https://doi.org/10.1016/j.neuropsychologia.2013.03.002 . Mottron L, Burack J. Enhanced perceptual functioning in the development of autism. In: Burack JA, Charman T, Yirmiya N, Zelazo P, editors. The development of autism: perspectives from theory and research. Mahwah, NJ: Erlbaum; 2001. pp. 131–48. Mottron L, Dawson M, Soulieres I, Hubert B, Burack J. Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. J Autism Dev Disord. 2006;36:27–43. https://doi.org/10.1007/s10803-005-0040-7 . Nakauchi S, Kondo T, Kinzuka Y, Taniyama Y, Tamura H, Higashi H, Hine K, Minami T, Linhares JMM, Nascimento SMC. Universality and superiority in preference for chromatic composition of art paintings. Sci Rep. 2022;12(1):42–94. https://doi.org/10.1038/s41598-022-08365-z . Park SK, Son J-W, Chung S, Lee S, Ghim H-R, Lee S-I, Shin C-J, Kim S, Ju G, Choi SC, Kim YY, Koo YJ, Kim B-N, Yoo HJ. Autism and beauty: Neural correlates of aesthetic experiences in autism spectrum disorder. J Korean Acad Child Adolesc Psychiatry. 2018;29(3):101–13. https://doi.org/10.5765/jkacap.170031 . Pellicano E, Burr D. When the world becomes too real: a Bayesian explanation of autistic perception. Trends Cogn Sci. 2012;16(10):504–10. https://doi.org/10.1016/j.tics.2012.08.009 . Plaisted K, O’Riordan M, Baron-Cohen S. Enhanced discrimination of novel, highly similar stimuli by adults with autism during a perceptual learning task. J Child Psychol Psychiatry. 1998a;39:765–75. https://doi.org/10.1017/s0021963098002601 . Plaisted K, O’Riordan M, Baron-Cohen S. Enhanced visual search for a conjunctive target in autism: A research note. J Child Psychol Psychiatry. 1998b;39:777–83. https://doi.org/10.1017/s0021963098002613 . Rozenkrantz L, D’Mello AM, Gabrieli JD. Enhanced rationality in autism spectrum disorder. Trends Cogn Sci. 2021;25(8):685–96. https://doi.org/10.1016/j.tics.2021.05.004 . Ruzich E, Allison C, Smith P, Watson P, Auyeung B, Ring H, Baron-Cohen S. Measuring autistic traits in the general population: A systematic review of the autism-spectrum quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females. Mol Autism. 2015;6(1):2. https://doi.org/10.1186/2040-2392-6-2 . Schloss KB, Palmer SE. Aesthetic response to color combinations: Preference, harmony, and similarity. Atten Percept Psychophysics. 2011;73(2):551–71. https://doi.org/10.3758/s13414-010-0027-0 . Simmons DR, Todorova GK. Local versus global processing in autism: Special section editorial. J Autism Dev Disord. 2018;48(4):1338–40. https://doi.org/10.1007/s10803-017-3452-2 . Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ. A practical guide to calculating Cohen’sf 2, a measure of local effect size, from PROC MIXED. Front Psychol. 2012;3:111. Sun M, Ying H. (2023). Color’s Perceptual Diversity and Categorical Harmony Improve Aesthetic Experience. Psychology of Aesthetics, Creativity, and the Arts . https://doi.org/10.1037/aca0000583 Taylor R, Bays PM. Efficient coding in visual working memory accounts for stimulus-specific variations in recall. J Neurosci. 2018;38(32):7132–42. https://doi.org/10.1167/18.10.692 . Yang G, Wang Y, Jiang Y. Social perception of animacy: Preferential attentional orienting to animals links with autistic traits. Cognition. 2024;251:105900. https://doi.org/10.1016/j.cognition.2024.105900 . Zachi EC, Costa TL, Barboni MTS, Ventura DF. Color vision losses in autism spectrum disorders. Front Psychol. 2017;8:1127. https://doi.org/10.3389/fpsyg.2017.01127 . Zhang Y, Liu P, Han B, Xiang Y, Li L. Hue, chroma, and lightness preference in Chinese adults: Age and gender differences. Color Res Application. 2019;44(6):967–80. https://doi.org/10.1002/col.22426 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Dec, 2025 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 12 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviewers invited by journal 22 Aug, 2025 Editor invited by journal 19 Aug, 2025 Editor assigned by journal 15 Aug, 2025 Submission checks completed at journal 15 Aug, 2025 First submitted to journal 14 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7371825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505627546,"identity":"26bc82f9-f15d-4e65-bad5-062a55a58e3d","order_by":0,"name":"Leyi Wang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Leyi","middleName":"","lastName":"Wang","suffix":""},{"id":505627547,"identity":"38350ea4-255d-412a-b135-d2db1ac3f249","order_by":1,"name":"Mengdan Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYLACHjDJfICBgQ3ESCBaC1sCyVp4DIjTotvee/jFm5o79ga3ez5/5imzY+BnzzFg+LkDtxazM+fSLOcce5a44c7ZbdI855IZJHveGDD2nsGj5UaOmTEP2+EEgxu525h525gZDG7kGDAztuHRcv8NUMu/w/ZAlY8/87bVM9gT1HKDx/gxb9thxg03chikgQwGAwlCWs7kmDHO7TucOPNGmpnknHPHeSTOPCs42ItPy/Ezxh/efDtsz3cj+fGHN2XVcvztyRsf/MSjBQjYJJB54Dg6gFcDMKF8IKBgFIyCUTAKRjoAADAAVAqHP/kUAAAAAElFTkSuQmCC","orcid":"","institution":"Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Mengdan","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-08-14 08:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7371825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7371825/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-025-03876-6","type":"published","date":"2025-12-17T15:58:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90330153,"identity":"8193e3b0-796f-4a75-8f68-205c19272cd6","added_by":"auto","created_at":"2025-09-01 13:05:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":229505,"visible":true,"origin":"","legend":"\u003cp\u003eStimuli. (A) Example color images of small- and large-distance. The numbers at the bottom of each image represent the hue degrees of the colors contained in the image. (B) The colors used to generate the color images were sampled from a color wheel in the CIELAB space (L= 70, a radius of 29), which only vary in hue (10°−355°, step size: 15°). The 24 hues are marked by “+.” Each color image is composed of three distinct hues (H1, H2, and H3) that are 15° (small-distance condition) or 30° (large-distance condition) apart in the color wheel. For example, the small-distance condition includes hue combinations such as (10°, 25°, and 40°), marked by the solid line. The large-distance condition includes hue combinations such as (10°, 40°, and 70°), marked by the dotted line. Please note that the colors are indiscriminable from each other in the print version. See the online article for the color version of this figure.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7371825/v1/67045bd161864d1c00f23742.png"},{"id":90330160,"identity":"b6083e06-c889-4c0e-bf97-67995a607e6e","added_by":"auto","created_at":"2025-09-01 13:05:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228785,"visible":true,"origin":"","legend":"\u003cp\u003eTransformation of the hue position data. Colorfulness ratings vary as a function of the initial hue position (in radians, A) and the transformed hue position (sin(H1), B). Liking ratings vary as a function of the initial hue position (in radians, C) and the transformed hue position (sin(H1), D).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7371825/v1/c718270c52393928a964d1c3.png"},{"id":90330163,"identity":"4b22eca3-42d0-478d-a2c6-116b013372b0","added_by":"auto","created_at":"2025-09-01 13:05:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264797,"visible":true,"origin":"","legend":"\u003cp\u003eLMM Results. Predicted mean colorfulness ratings in small- versus large-distance conditions (A) and within versus cross-category conditions (B). Predicted mean liking ratings in within- versus cross-category conditions (C). Predicted mean liking ratings as a function of hue position (D) and colorfulness ratings (E) for low and high AQ groups. Predicted mean liking ratings as a function of AQ dimension: social skills (F). The shaded areas surrounding the lines indicate the 95% confidence intervals. Error bar represents ± SE.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7371825/v1/53c6010ff9f9e1ec83cf5b72.png"},{"id":98814302,"identity":"029ac4c9-5dd6-41f3-8dc8-a0a8875bcd64","added_by":"auto","created_at":"2025-12-22 16:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321304,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7371825/v1/14cfeced-6762-45e1-97c5-7398c79841f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autistic Traits Influence Aesthetic Judgments of Abstract Color Works","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism spectrum disorders (ASD) are neurodevelopmental disorders characterized by atypicalities in social interaction, social communication, and restricted, repetitive patterns of behavior. Researchers have conducted substantial investigations into the atypical patterns of social and cognitive functions associated with autism (Leekam, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A core feature of autism is the atypicality in social skills, which includes specific aspects of communication abilities and the social use of language. Though most previous research links the condition of ASD mostly with social and communication difficulties, as well as repetitive behaviors, there is also widespread reporting of differences in sensory perception, particularly in the visual domain (Baum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Autistic individuals have cognitive processing styles biased toward local and field-independent processing (Booth \u0026amp; Happé, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Simmons \u0026amp; Todorova, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To be more precise, they seem to be limited in their ability to derive organized wholes from individual parts (Frith, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). A lot of studies suggest that they have a preference for local detail processing over global integration (Behrmann et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hadad \u0026amp; Ziv, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Happé, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Also, they exhibit reduced cognitive flexibility than TD (typical developing) individuals (Leung \u0026amp; Zakzanis, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All these characteristics result in their atypical responses at low-level sensory perception. Children diagnosed with ASD have faster automatic visual orienting compared to TD children, which may hinder their exploratory behavior and lead to difficulties in complex social environments (Kovarski et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, autistic individuals exhibit distinctive visual processing characteristics, including enhanced abilities in color perception, altered visuospatial processing (e.g., challenges in body spatial awareness and locating oneself in space) and visuomotor skills (e.g., compensatory reliance on proprioception for spatial orientation) (Coulter, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These features may be associated with their visual attention mechanisms and atypical early visual processing (Coulter, n.d.). At the same time, autistic individuals exhibits their unique strengths. As previous studies have shown, some perceptual capacities of individuals on the autism spectrum are enhanced compared to the TD group (Almeida et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brock et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Milne et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For example, autistic individuals showed superior processing of detailed partial information on a wide variety of perceptual tasks (Gadgil et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). There are two main interpretations of their atypical perception. The weak central coherence hypothesis attributed this condition to a failure in extracting the meaning or the gist of an input (Frith \u0026amp; Happé, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Happé, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). On the other hand, the enhanced perceptual function model interprets this condition as an overdevelopment of low-level perceptual processing, which leads to enhanced basic perceptual abilities, such as detection and discrimination (Mottron \u0026amp; Burack, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mottron et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Plaisted et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1998a\u003c/span\u003e; Plaisted et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the substantial studies on individuals with autism, their aesthetic experience and its underlying mechanism have received much less attention. Human aesthetic experience is influenced by a complex interplay of genetic, cultural, objective, and subjective factors. There have been studies showing that autistic individuals have atypical aesthetic experiences, which is different from TD (Park et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mazza et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Coelho et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Brosnan \u0026amp; Ashwin, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The difference in aesthetic perception has been linked to both cognitive (Park et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and emotional abilities (Coelho et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Park et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) revealed that the aesthetic judgment score of autistic individuals was significantly lower than the control group for fractal images but not for landscape images. The tendency to experience the images as less beautiful was correlated with lower scores in emotional skills, measured by empathy scales. Compared to typical individuals, autistic individuals exhibited higher activation in the posterior regions, likely responsible for the visuospatial analysis of the artwork. It suggested that more cognitive resources are recruited for autistic individuals, possibly as compensation for poorer emotional skills mobilized in an empathic experience. Notably, however, this pattern may also stem from the fact that autistic individuals possess heightened visual analytic skills, leading to increased activation in the occipital lobe and potentially resulting in boredom with the presented artwork. Using images of sculptures selected from masterpieces of classical and Renaissance as materials, Mazza et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found there were different patterns of aesthetic perception between autistic and non-autistic individuals at the explicit level, which could be related to their reduced pleasure during social interaction. However, counterarguments exist suggesting that autistic and non-autistic individuals exhibit comparable levels of social motivation and hedonic responsiveness (Chiappini et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the judgment of proportionality, which was not connected to emotional capabilities, did not differ in individuals on the autism spectrum and TD. Furthermore, this study revealed a discrepancy between the explicit and implicit evaluation of the aesthetic perception task. At the implicit level (eye-tracking data), autistic and non-autistic individuals had similar implicit aesthetic perception. Based on the presumption that the ability to distinguish high-quality art from low-quality art reflects an initial, emotional intuitive process, which can then be attenuated by subsequent deliberation. Brosnan and Ashwin (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggested individuals on the autism spectrum exhibit reduced intuitive processing and greater deliberative processing, consistent with predictions by the Dual Process Theory of Autism.(Ashwin \u0026amp; Brosnan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Brosnan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lewton et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAccording to these studies, there might not be universal and consistent differences between autistic individuals and TD in aesthetic experience, which likely depends on various factors such as the stimulus type, task demands, and levels of processing. With regard to general aesthetic experience, both low-level and high-level features of the image can influence human preferences (Iigaya et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Color, among other visual features, makes a critical contribution to aesthetic preferences (Albers et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nakauchi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schloss \u0026amp; Palmer, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sun et al., 2023). Here in our study, we mainly focus on the relation between color, or color combination with aesthetic judgment. In addition, previous studies suggested that autistic traits exist on a continuum in the general population (Baron-Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and individual differences in these traits have been shown to reliably predict perceptual and cognitive processing patterns associated with autism spectrum conditions (Ruzich et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our study, we are interested in whether the effect of color on aesthetic judgment differs for individuals along this continuum of autistic traits. As a neurodivergent processing style, do heightened autistic traits predict distinctive aesthetic experiences in response to stimuli that use color as the main visual element?\u003c/p\u003e\u003cp\u003eThere have been some attempts focusing on atypical color perception and cognition in individuals on the autism spectrum, including color discrimination, color categorization, color preference, etc (Franklin et al., 2008; Heaton et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It was found that children with autism were significantly less accurate than controls at color memory, color search, and chromatic discrimination (Franklin et al., 2008; Franklin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zachi et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), but the strength of categorical perception of color did not differ for the two groups (Franklin et al., 2008). The reduced chromatic discrimination in autistic individuals might be associated with less parvocellular (P) color pathway activity (Fujita et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, several studies have shown that adults with autism experience atypical perceptions of color ensembles (Maule et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) while exhibiting normal color adaptation (Maule et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which supports the theory that perceptual priors are diminished in autism (Pellicano \u0026amp; Burr, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The color preferences of the autistic group were significantly different from TD, with an overall preference for cooler colors compared to warmer colors (Grandgeorge \u0026amp; Masataka, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kusuma Wardani \u0026amp; Fefiana Mustikasari, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Considering the atypical color perception and aesthetic experience of autistic individuals, we hypothesized that autistic traits might influence preferences toward color stimuli composed of multiple hues. Specifically, these hue combinations are distinct in perceptual distance (small or large) and categorical membership (same or different). We implemented two rating tasks where participants were asked to rate their liking and colorfulness of the color images, respectively.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eWe recruited 111 undergraduate and graduate students (39 males,72 females; age: 18–28 years old, M = 21.4) with normal or corrected-to-normal color vision. We conducted an a priori power analysis using G*Power 3.1 to determine the required sample size. To detect a medium effect size \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}^{2}\\)\u003c/span\u003e\u003c/span\u003eof 0.15 (Selya et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and achieve a power of 0.80, at least 92 participants were needed for a linear regression analysis. Our participants were all regular school attendees, none of whom reported having neurological or psychological conditions. All participants provided written informed consent before participating in the study and received their payment after. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee. In this study, all measures, manipulations and exclusions are reported here in the manuscript.\u003c/p\u003e\u003cp\u003eMaterials\u003c/p\u003e\n\u003ch3\u003eAutism-Spectrum Quotient\u003c/h3\u003e\n\u003cp\u003eIn our study, we used the Autism-Spectrum Quotient (AQ; Baron-Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to measure autistic traits of the participants. The AQ is a self-report measure of autistic traits, which is widely cited to quantify autistic traits of adults with at least average intelligence. The AQ has 50 items, divided into five subscales consisting of 10 items each that measure 5 domains: communication, social skills, imagination, attention to detail and attention switching. The AQ employed 4-point Likert scale as its scoring system (definitely agree; slightly agree; slightly disagree; definitely disagree) and half of the 50 items are reverse-scored. The AQ scale is scored such that each item contributes 1 point toward the total score if the response aligns with the direction of autistic traits. Specifically, for items reflecting autistic traits, a response of \"slightly agree\" or \"definitely agree\" is scored as 1. For the remaining items, which reflect the absence of autistic traits, a response of \"slightly disagree\" or \"definitely disagree\" also receives 1 point. All other responses are scored as 0. The total AQ score is the sum of all endorsed items, with higher scores indicating a greater number of autistic traits. A review has supported the validity of the AQ in indexing autistic traits in non-clinical populations (Ruzich et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStimuli\u003c/h2\u003e\u003cp\u003eStimuli for the rating sessions are images (400 \u0026times; 400 pixels) of randomly distributed color circles or rectangles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The colors used to generate the color images were sampled from a color wheel in the CIELAB space (L\u0026thinsp;=\u0026thinsp;70, a radius of 29), which only vary in hue (10\u0026deg;\u0026ndash;355\u0026deg;, step size: 15\u0026deg;; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The color pool includes a total of 24 hues. Each color image is composed of three distinct hues (H1, H2 and H3) that are 15\u0026deg; (small-distance condition; e.g., 10\u0026deg;, 25\u0026deg;, 40\u0026deg;) or 30\u0026deg; (large-distance condition; 10\u0026deg;, 40\u0026deg;, 70\u0026deg;) apart in the color wheel. The three hues are evenly distributed throughout the image. Shape of circles and rectangles, which are widely applied in abstract artworks, were used to create color images. For each condition, each hue from the color pool was used twice to form the color image, once for the circle images and once for the rectangle images. By varying both perceptual distance (small, large) and shape (circle, rectangle), this experiment contained a total of 24 hues \u0026times; 2 \u0026times; 2\u0026thinsp;=\u0026thinsp;96 images.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eProcedure\u003c/p\u003e\u003cp\u003eThe study was divided into two main sessions, a measure of autistic traits and three behavioral tasks (liking rating, colorfulness rating and color naming). Both sessions were conducted online, and we used Naodao to present the experiment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.naodao.com/\u003c/span\u003e\u003cspan address=\"https://www.naodao.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSession 1: Autistic trait measurement\u003c/p\u003e\u003cp\u003e In this session, participants were asked to complete a 50-item Autism-Spectrum Quotient with guidance, and after completing the quotient, they can move on to the next session.\u003c/p\u003e\u003cp\u003eSession 2: Behavioral tasks\u003c/p\u003e\u003cp\u003eThis session includes three tasks: liking rating, colorfulness rating, and color naming. All three parts set standardized instructions and practice trials. Furthermore, we arranged the order as such (liking rating\u0026ndash;colorfulness rating\u0026ndash;color naming) to make sure that the liking rating task was not influenced by the other two tasks.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLiking Rating\u003c/h3\u003e\n\u003cp\u003eIn the first task, the participants were asked to evaluate the color images from the aesthetic perspective and rate how much they liked the images via key presses on a 4-point scale (1\u0026thinsp;=\u0026thinsp;not at all; 2\u0026thinsp;=\u0026thinsp;like a little; 3\u0026thinsp;=\u0026thinsp;like; and 4\u0026thinsp;=\u0026thinsp;strongly like). On each trial, a color image was displayed at the center of the screen for 1,200 ms, consistent with the validated paradigm used by Sun et al. (2023), and participants were not allowed to respond until the image disappeared. The order of the color images was assigned randomly for each participant. This session consisted of 24 \u0026times; 2 \u0026times; 2\u0026thinsp;=\u0026thinsp;96 trials in total. Each color image was presented only once. Each color combination was presented twice.\u003c/p\u003e\n\u003ch3\u003eColorfulness Rating\u003c/h3\u003e\n\u003cp\u003eThe procedure of the colorfulness rating task was essentially the same as the liking rating task, except that the participants were asked to evaluate the colorfulness of the color images via key presses on a 4-point scale (1\u0026thinsp;=\u0026thinsp;not at all colorful; 2\u0026thinsp;=\u0026thinsp;a little colorful; 3\u0026thinsp;=\u0026thinsp;colorful; and 4\u0026thinsp;=\u0026thinsp;very colorful).\u003c/p\u003e\n\u003ch3\u003eColor naming\u003c/h3\u003e\n\u003cp\u003eIn the color naming task, the participants were asked to name the hues used in the rating tasks. On each trial, a square (200 \u0026times; 200 pixels) filled with one of the 24 hues was presented at the screen center. On the bottom of the square, seven chromatic color terms were presented (1\u0026thinsp;=\u0026thinsp;red; 2\u0026thinsp;=\u0026thinsp;orange; 3\u0026thinsp;=\u0026thinsp;yellow; 4\u0026thinsp;=\u0026thinsp;green; 5\u0026thinsp;=\u0026thinsp;blue; 6\u0026thinsp;=\u0026thinsp;purple; 7\u0026thinsp;=\u0026thinsp;pink). Participants were asked to select the color term that most closely described the present color. The color square and color terms remained on the screen until response. This task includes 48 trials in total, with each hue appearing 2 times.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eFor each color image used in the rating sessions, the category membership (same, different) of the hues was assigned according to subjective judgment in the color naming task. Thus, the category membership is a factor tailored to each participant. To assess the potential difference between participants with different autistic trait levels, following the approach of Yang et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we split the participants by the median AQ score (19) into two groups, the low AQ group (AQ\u0026thinsp;\u0026le;\u0026thinsp;19, 54 individuals) and the high AQ group (AQ\u0026thinsp;\u0026gt;\u0026thinsp;19, 57 individuals).\u003c/p\u003e\u003cp\u003eFor visual features such as color and orientation, stimulus-specific variation in visual perception and judgement has been reported (Taylor \u0026amp; Bays, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is highly plausible that there is stimulus-specific variation in preferences as well (e.g., Zhang et al, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with participants favoring specific hues along the color wheel over others. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB plots the relationship between the hue position (H1) and ratings, revealing that the ratings are not uniformly distributed across hues along the color wheels. For colorfulness ratings, as hue H1 increases from 0 to π, the ratings follow a sine function. Conversely, as H1 increases from π to 2π, the ratings follow a negative sine function. For liking ratings, the ratings exhibit a negative sine function as H1 increases from 0 to 2π. To establish a linear relationship between the hue position (H1) and ratings for subsequent regression analyses, we applied the following transformations to the hue position data.\u003c/p\u003e\u003cp\u003eFor colorfulness ratings:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:x=\\left\\{\\begin{array}{c}\\text{sin}{x}_{0},\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:0\u0026lt;x\\le\\:\\pi\\:\\\\\\:{\\text{s}\\text{i}\\text{n}(x}_{0}-\\pi\\:),\\:\\:\\:\\:\\pi\\:\u0026lt;x\\le\\:2\\pi\\:\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor liking ratings:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:x=sin{x}_{0}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the initial hue position variable (H1 in radians), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e is the transformed hue position (sin (H1)). Then, we used the transformed hue position (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) as the stimulus-specific variable to explain the potential stimulus-specific variation in ratings.\u003c/p\u003e\u003cp\u003eTo investigate how AQ group, perceptual distance, category membership, shape, and hue position influence colorfulness and liking ratings, we analyzed the rating data with linear mixed models (LMMs) in R (R Core Team, 2014) using the \u003cem\u003elme4\u003c/em\u003e package (Bates et al., 2014). We built a model with \u003cem\u003eAQ group\u003c/em\u003e (low vs. high), \u003cem\u003eperceptual distance\u003c/em\u003e (small vs. large), \u003cem\u003ecategory membership\u003c/em\u003e (same vs. different), \u003cem\u003eshape (circle vs. rectangle\u003c/em\u003e) and \u003cem\u003ehue position\u003c/em\u003e as fixed effect factors and \u003cem\u003eparticipant\u003c/em\u003e as the random effect factor with a random intercept. Since our study is interested in whether the effect of color features (e.g., perceptual difference) is modulated by AQ group, we also included the following interactions into the model: \u003cem\u003eAQ group\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eperceptual distance, AQ group\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003ecategory membership, AQ group\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eshape, AQ group\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003ehue position\u003c/em\u003e. The analyses were conducted on colorfulness and liking ratings separately.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eColorfulness Rating\u003c/p\u003e\u003cp\u003eFor colorfulness ratings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we found significant effects of perceptual distance (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.68, \u003cem\u003et\u003c/em\u003e (10546)\u0026thinsp;=\u0026thinsp;32.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), category membership (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003et\u003c/em\u003e (10578)\u0026thinsp;=\u0026thinsp;3.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and hue position (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003et\u003c/em\u003e (10545) = -8.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). As expected, participants perceive images with large-distance hues as more colorful than small-distance hues. Category membership influences colorfulness ratings in a similar way. Participants rated images with cross-category hues as more colorful than within-category hues. In addition, the colorfulness ratings significantly vary across different hues along the color wheel. As plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, the highest colorfulness ratings are associated with colors located at the vertical coordinate positions. There is no evidence that the two AQ groups differ in colorfulness ratings. The results suggested that high AQ group perceive the image colorfulness in a similar way as low AQ group.\u003c/p\u003e\u003cp\u003eLiking Rating\u003c/p\u003e\u003cp\u003eFor liking ratings (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we revealed that perceptual distance (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003et\u003c/em\u003e (10546)\u0026thinsp;=\u0026thinsp;6.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), shape (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003et\u003c/em\u003e (10545)\u0026thinsp;=\u0026thinsp;3.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hue position (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003et\u003c/em\u003e (10545)\u0026thinsp;=\u0026thinsp;24.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) significantly influence liking ratings. Consistent with the findings from Sun et al. (2023), participants prefer color images composed of large-distance over small-distance hue. The effect of hue position suggested that the liking ratings significantly vary across different hues along the color wheel. Besides, the color images composed of circles are more liked by the participants than rectangles.\u003c/p\u003e\u003cp\u003eMore interestingly, we found a significant AQ group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e category membership interaction (\u003cem\u003eb\u003c/em\u003e = -0.12, \u003cem\u003et\u003c/em\u003e (10570) = -3.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) and AQ group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e hue position interaction (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003et\u003c/em\u003e (10546)\u0026thinsp;=\u0026thinsp;2.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01). To interpret the AQ group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e category membership interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), we compared liking ratings for same-category and different-category stimuli for the two AQ groups separately. It was found the high AQ group like the same-category color images better than those with different-category images (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001), showing category effect in liking ratings. However, there was no significant category effect for the low AQ group (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00). For the AQ group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e hue position interaction, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, the low AQ group shows an increased stimulus-specific variance in liking ratings compared with the high AQ group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear-Mixed-Model Statistics for Colorfulness and Liking Ratings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eColorfulness Rating\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance (small vs. large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShape (circle vs. rectangle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHue position\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Shape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Category membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Hue position\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLiking Rating\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance (small vs. large)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShape (circle vs. rectangle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHue position\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-24.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Shape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Category membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Hue position\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01 *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e\u003cp\u003eCorrelation between Colorfulness and Liking Ratings\u003c/p\u003e\u003cp\u003eAccording to the results above, we can see that colorfulness and liking ratings are influenced by common color factors including perceptual distance and stimulus-specificity. Meanwhile, there are unique mechanisms underlying these two ratings. One piece of evidence is that the colorfulness rating patterns of high and low AQ groups are similar. In contrast, they differ in liking rating patterns. We hypothesized that perceived colorfulness partly predicts liking ratings. To quantitatively investigate the relationship between these two ratings, and how this relationship is moderated by autism tendencies, we built a linear mixed model with \u003cem\u003eliking ratings\u003c/em\u003e as dependent variable, \u003cem\u003eAQ group\u003c/em\u003e (low vs. high) and \u003cem\u003ecolorfulness ratings\u003c/em\u003e as fixed effect factors, and \u003cem\u003eparticipant\u003c/em\u003e as the random effect factor. The results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) suggested that colorfulness ratings positively predict liking ratings (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003et\u003c/em\u003e (10645)\u0026thinsp;=\u0026thinsp;12.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). At the same time, however, there is an interaction between AQ group and colorfulness ratings (\u003cem\u003eb\u003c/em\u003e = -0.04, \u003cem\u003et\u003c/em\u003e (10651) = -2.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.025), indicating that the relationship between two ratings is modulated by AQ group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Specifically, colorfulness has a greater effect on liking ratings for the low AQ group (with a steeper slope) than the high AQ group. This is likely, for the high AQ group, there are other influencing color-related factors in addition to colorfulness, such as categorical membership.\u003c/p\u003e\u003cp\u003eCorrelation between Ratings and AQ dimensions\u003c/p\u003e\u003cp\u003eIn addition, we were interested in the relationship between AQ dimensions and ratings, which may provide insights into the findings that high and low AQ groups differ in liking rating patterns. We built two linear mixed models with \u003cem\u003eliking ratings\u003c/em\u003e and \u003cem\u003ecolorfulness ratings\u003c/em\u003e as dependent variables respectively. In these two models, five AQ dimensions (\u003cem\u003ecommunication, social skills, imagination, attention to detail and attention switching\u003c/em\u003e) are fixed effect factors, and \u003cem\u003eparticipant\u003c/em\u003e is the random effect factor. We found only one AQ dimension (i.e., social skills) negatively correlates with liking ratings (\u003cem\u003eb\u003c/em\u003e = -0.03, \u003cem\u003et\u003c/em\u003e (111) = -2.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.044; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). None of the dimensions correlates with colorfulness ratings (Please see the detailed statistics in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear-Mixed-Model Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eColorfulness -\u0026gt;Liking ratings\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eColorfulness ratings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAQ group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAQ group \u0026times; Colorfulness ratings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAQ Dimensions -\u0026gt;Colorfulness Ratings\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCommunication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSocial skills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eImagination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAttention to detail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAttention switching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAQ Dimensions -\u0026gt;Liking Ratings\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCommunication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSocial skills\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.044*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eImagination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAttention to detail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAttention switching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01 *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study investigated aesthetic experience in university students, focusing on the influence of autism tendencies measured by the Autism-Spectrum Quotient (AQ). Key findings revealed significant effects of perceptual distance and hue position on both colorfulness and liking ratings. Participants rated images with larger perceptual distances as more colorful and likable. Importantly, liking ratings differed significantly between high and low AQ groups, whereas their colorfulness ratings were similar. Specifically, we observed a significant AQ group \u0026times; category membership interaction and AQ group \u0026times; hue position interaction in the liking ratings, indicating that the category effect and stimulus-specific variance in liking ratings were modulated by AQ group. Furthermore, colorfulness ratings positively predicted liking ratings, with this relationship being weaker in the high AQ group. Additionally, analysis of AQ subscales revealed that only the social skills dimension negatively correlated with liking ratings.\u003c/p\u003e\u003cp\u003eOur findings on perceptual distance replicate those of Sun et al. (2023), reaffirming the independent effect of hue distance on aesthetic experience, including both colorfulness and liking ratings. However, unlike previous studies, we found that shape significantly influenced liking ratings. Compared to Sun et al. (2023), we introduced the position of hues on the color wheel as a novel variable, which exhibited significant main effects on both colorfulness and liking ratings, suggesting that these ratings depend on the specific hues composing the images.\u003c/p\u003e\u003cp\u003eOne of our crucial findings is that low and high AQ groups perform similarly in colorfulness ratings but differently in liking ratings. This finding aligns with Mazza et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who demonstrated that individuals on the autism spectrum exhibit distinctive aesthetic judgments in emotion-laden tasks but perform comparably to TD individuals in proportionality-based tasks. In our study, high and low AQ groups showed significantly different patterns in liking ratings but similar colorfulness ratings. The absence of significant differences in colorfulness ratings between high and low AQ groups can be explained by the Enhanced Perceptual Functioning Model (EPF), which posits that autistic and non-autistic individuals perform similarly in low-level perceptual tasks, such as color discrimination and brightness perception (Mottron et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Since colorfulness perception relies primarily on low-level visual processing, this may account for the lack of group differences.\u003c/p\u003e\u003cp\u003eIn contrast, for liking ratings, the category effect and stimulus-specific variance were modulated by AQ group. First, we observed category effects in the high AQ group but not in the low AQ group. This group difference is unlikel y to stem from differences in subjective color categorization, as both groups demonstrated comparable consistency in color categorization. We additionally investigated whether the two groups differ in category membership of the images for rating using LMM. The model includes \u003cem\u003eperceptual distance\u003c/em\u003e, \u003cem\u003eAQ group\u003c/em\u003e and \u003cem\u003etheir interaction\u003c/em\u003e as fixed effect factors, \u003cem\u003eparticipant\u003c/em\u003e as the random effect factor, \u003cem\u003ecategory membership\u003c/em\u003e as the dependent variable. We found main effects of perceptual distance (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003ez\u003c/em\u003e = -8.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) but not effect of AQ group nor their interaction (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1). Instead, this effect may reflect a preference for structural predictability (Kanner, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1943\u003c/span\u003e; Baron-Cohen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), where high AQ individuals prioritize categorical coherence over emotional valence when evaluating hues. The combination of hues from the same category provided higher structural consistency, aligning with the perceptual needs of high AQ individuals. Furthermore, the Dual Process Theory of Autism suggests that individuals on the autism spectrum exhibit stronger analytical processing and may rely more on explicit categorical rules for aesthetic judgments (Brosnan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e). In our study, color categories were determined through a self-naming task, suggesting that the high AQ group adhered more strictly to their self-defined categorical boundaries, leading to a preference for same-category hues. This result indicates that aesthetic judgments in autistic individuals may be more driven by structural rules than by overall emotional experiences.\u003c/p\u003e\u003cp\u003eRegarding the stimulus-specific variance in liking ratings, the low AQ group exhibited a clear preference pattern across hue positions, with the lowest liking ratings for colors at 90\u0026deg; and the highest for colors at 270\u0026deg;. This pattern was weaker in the high AQ group. It may suggest that low AQ individuals rely more on dynamic emotional associations (e.g., emotional memories triggered by colors) in their aesthetic judgments. In contrast, high AQ individuals may depend more on stable perceptual attributes of the stimuli, as their reduced emotional responsiveness (Harms et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) makes it harder for them to integrate emotional cues into their evaluations. This pattern may suggest that individuals with high autistic traits exhibit emotion-cognition decoupling, where emotional processing plays a diminished role in shaping preferences. Alternatively, high AQ individuals tend to make more consistent decisions (Farmer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which may explain why their preference ratings show less variability. This consistency likely stems from a rule-based cognitive style that prioritizes internal coherence over emotional fluctuations, reducing the impact of stimulus variability on their aesthetic preferences.\u003c/p\u003e\u003cp\u003eIn addition, we suggested a positive correlation between the two ratings. The more colorful the image is perceived, the more it is liked. However, the correlation is weaker for the high AQ group. The weaker influence of colorfulness on liking in the high AQ group might be related with the stronger category effect. Specifically, for high AQ individuals, aesthetic judgments are influenced by multiple factors, including both colorfulness and category membership. Since high AQ individuals show a stronger reliance on categorical coherence (preferring colors from the same category), the impact of colorfulness is diluted. In contrast, the low AQ group, which does not exhibit a significant category effect, relies more heavily on colorfulness as a primary factor in their aesthetic evaluation.\u003c/p\u003e\u003cp\u003eFinally, there is a significant negative correlation between the social skills subscale and liking ratings. This result may align with the viewpoint that autistic individuals exhibit enhanced rationality through reduced influence of contextual information in decision making (Rozenkrantz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of aesthetic judgment, such reduced involvement of irrelevant information could lead to lower sensitivity to socially driven preferences, i.e. liking ratings. It is also consistent with our previous inference that the difference in liking ratings between the two groups because the liking rating task involves greater emotional and cognitive engagement.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, due to time and location constraints, we recruited university students rather than clinical samples of autistic individuals and TD controls. Thus, our findings cannot be directly generalized to the autistic population. Second, the stimuli consisted of abstract images composed of rectangular and circular color patches, which, while common in abstract art, may not encompass all types of artworks. Therefore, our findings on aesthetic preferences may require further validation when applied to other art forms. Lastly, we conducted the study online, and thus, differences and inaccuracies in color rendering could pose a concern for color or vision studies. However, we do not believe it undermines the current conclusion, as category membership of the color stimuli was treated as a subjective judgment rather than an objective physical property in our study. This is also why we used individual categorization data to determine the category membership for each stimulus. A predefined, group-level category assignment could be vulnerable to inaccuracies arising from variations in color rendering.\u003c/p\u003e\u003cp\u003eIn conclusion, our study highlights the interplay between color perception, aesthetic experience, and autistic traits. Individuals with low and high autistic traits exhibit both similarities and differences in aesthetic judgment. Perceptual differences increase preferences for both groups. However, individuals with high autistic traits exhibit less tolerance for category differences and are less influenced by variations in color stimuli when making aesthetic judgments. This unique pattern may be related with their deficit in social skills. These findings have significant theoretical and practical implications. First, our results suggest that heightened reliance on categorical rules during aesthetic preference formation could be a key characteristic in autistic individuals. And for practical applications, our findings could inform the design of visual materials in educational or therapeutic settings to better accommodate neurodivergent perceptual preferences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Soochow University. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated in the current study are made available at https://osf.io/dgna8/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the General Project of Philosophy and Social Sciences Research in Jiangsu Higher Education Institutions (2023SJYB1399), the Natural Science Foundation of Jiangsu Province (BK20200867) and Social Science Foundation of Jiangsu Province (23YYC005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL. W. : Methodology, \u0026nbsp;Data Collection, Data Analysis; Writing- Reviewing and Editing; M. S. : Conceptualization, Methodology, Data \u0026nbsp;Analysis, Writing, Supervision, Funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbers AM, Gegenfurtner KR, Nascimento SMC. An independent contribution of colour to the aesthetic preference for paintings. Vision Res. 2020;177:109\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.visres.2020.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.visres.2020.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmeida RA, Dickinson JE, Maybery MT, Badcock JC, Badcock DR. (2012). 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This study investigated the impact of autism tendencies, measured by the Autism-Spectrum Quotient (AQ), on aesthetic evaluations of abstract color works. Results revealed that larger hue distances enhanced colorfulness and liking ratings. Critically, while colorfulness ratings were similar between AQ groups, liking ratings differed significantly, with high AQ individuals showing reduced sensitivity to hue specificity but increased sensitivity to category membership. Colorfulness ratings positively predicted liking ratings, but this relationship was weaker in the high AQ group. Among AQ subscales, only social skills negatively correlated with liking ratings. These findings highlight how autism traits modulate aesthetic preferences, offering new insights into the distinctive perceptual and cognitive processing of autism.\u003c/p\u003e","manuscriptTitle":"Autistic Traits Influence Aesthetic Judgments of Abstract Color Works","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 13:05:44","doi":"10.21203/rs.3.rs-7371825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T02:43:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T11:28:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303780513739599334261030381337283082746","date":"2025-09-30T07:18:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T10:08:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253621623761557760140393811544593138096","date":"2025-09-07T05:36:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-22T13:02:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-19T10:41:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-16T03:57:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-16T03:56:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-08-14T08:41:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c03fcab9-7451-40a5-ab12-23f40b8a4858","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:07:00+00:00","versionOfRecord":{"articleIdentity":"rs-7371825","link":"https://doi.org/10.1186/s40359-025-03876-6","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2025-12-17 15:58:33","publishedOnDateReadable":"December 17th, 2025"},"versionCreatedAt":"2025-09-01 13:05:44","video":"","vorDoi":"10.1186/s40359-025-03876-6","vorDoiUrl":"https://doi.org/10.1186/s40359-025-03876-6","workflowStages":[]},"version":"v1","identity":"rs-7371825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7371825","identity":"rs-7371825","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}