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Abstract

Dreamcore is a digital aesthetic with familiar-yet-strange and often unsettling elements linked to dreams. Despite its rapid online popularity, its psychological appeal remains unexplored. This study examined the relationship between trait nostalgia (TN) and Dreamcore image liking (DIL) in 449 adults aged 18–40. TN was assessed using the Southampton Nostalgia Scale and the Personal Inventory of Nostalgic Experiences, while DIL was measured via ratings of 18 selected Dreamcore images. Factor analyses suggested TN as a unidimensional construct, whereas DIL comprised two factors: darker/eerie (DIL-F1) and warmer/comforting (DIL-F2) imagery. Structural equation modeling showed that TN was positively associated with both dimensions of DIL, with no significant differences in effect size. The findings provide preliminary evidence that nostalgia may contribute to Dreamcore aesthetic engagement, reflecting psychological needs during specific periods and offering exploratory insights into individual differences in aesthetic preferences and implications for cultural and media research.

Keywords

aesthetic preferences core aesthetics dreamcore nostalgia structural equation modeling

Introduction

Core aesthetics, although lacking a formal academic definition, have gained rapid popularity on social media, emerging as a distinct and widely recognized digital art style in recent years (Brown et al., 2024). Broadly, core aesthetics refer to a family of digital subcultures that center around shared thematic or emotional atmospheres, often encapsulated in specific visual tropes, color palettes, and affective moods. The suffix “-core” emerged on social media platforms in the late 2010s as a way to conceptualize and communicate various visual trends by highlighting a defining characteristic or central theme. For example, according to Aesthetics Wiki (2025), Dreamcore commonly uses different “base images” such as liminal spaces, unrealistic terrain and structures, or fantasy-like lands, to depict a dream-like imagery.

As a branch of Core aesthetics, Dreamcore is a surrealist aesthetic that uses motifs commonly associated with dreams, daydreams or nightmares, portrayed through media such as low-fidelity images or videos and, on occasion, music (e.g., see community resources on Reddit: https://www.reddit.com/r/dreamcoreaesthetic/). Its origins remain unclear, but a major influence may be liminal space aesthetics, which gained attention online around early 2019, building on anthropological theories of rites of passage introduced by Arnold van Gennep and expanded by Victor Turner (Ginter-Agreda, 2024). Liminal spaces evoke mysterious, dreamlike environments that fuel the nostalgic, familiar-yet-strange, and sometime uncanny tone of Dreamcore. By the early to mid-2020s, Dreamcore has gained substantial traction on TikTok videos featuring stitched-together imagery and characters, and online communities (e.g., Reddit forums) further promoted user-generated content and creative exchange, with algorithmically mediated social platforms amplifying and circulating these nostalgic and surreal visuals (Bucher, 2018; Highfield & Leaver, 2016). Additionally, several indie games developed after 2020 also adopted Dreamcore's surreal visuals to create immersive, unsettling atmospheres reminiscent of fragmented memories.

Interestingly, despite its uncanny and unsettling features to some extent, Dreamcore has quickly captured widespread interest, a phenomenon that warrants investigation. Nevertheless, little is known about the factors shaping individuals’ preferences for Dreamcore aesthetics. Zhou (2024) notes that Dreamcore can invoke nostalgia and fear by drawing on collective memories and cultural artifacts, thereby creating a sense of connection to the past while simultaneously inducing alienation. Indeed, Dreamcore art typically contains elements from the past, such as children play centers, “dead malls,” long-lost toys, or vacant classrooms and corridors, which cultivate a nostalgic ambiance (Brown et al., 2024). Therefore, the appeal of Dreamcore art may vary significantly among individuals, influenced by their emotional responses to the visual elements and experiences of nostalgia.

The emerging Dreamcore aesthetic, blending familiar and otherworldly imagery, aligns with established theories of aesthetic experience. Based on the processing fluency model, the aesthetic appreciation of dreamcore images may partially derive from the perceptual ease and familiarity embedded within their nostalgic, yet surreal compositions (Reber et al., 2004). Berlyne's arousal theory posits that moderate novelty and complexity, as seen in Dreamcore's mysterious qualities, are particularly engaging (Berlyne, 1973). Additionally, the distancing-embracing model of Menninghaus et al., (2017) highlights that unsettling or uncanny images can be enjoyed if they create a safe psychological distance. these theories suggest that Dreamcore's aesthetic appeal may reflect multifaceted preferences, encompassing distinct comforting and eerie subdimensions.

Moreover, these frameworks provide a rationale for why individuals’ nostalgia might be linked to their preferences for these subdimensions. Nostalgia is a multifaceted psychological construct that blends fond memories of the past with a longing for experiences that are no longer accessible (Batcho, 1995). While traditionally viewed as a form of sentimentality or homesickness, contemporary research has redefined nostalgia as a psychologically beneficial experience, highlighting its positive effects on social connectedness and emotional regulation (Sedikides & Wildschut, 2016; Wildschut et al., 2010). This shift in perspective has led to growing scholarly interest in nostalgia's adaptive functions over the past few decades (Routledge et al., 2013; Sedikides & Wildschut, 2016; Wildschut et al., 2010; Yang et al., 2023). Current evidence suggests that nostalgia is shaped by both genetic and environmental factors, demonstrating stability over time (Luo et al., 2016; Wang et al., 2024). It is regarded as a fundamental psychological resource that functions similarly across diverse cultural contexts (Hepper et al., 2024). As an individual-difference trait involving affective processing and meaning-making, nostalgia has been linked to various perceptions and behaviors (Chua et al., 2024; Weingarten & Wei, 2023; Zhang & Tao, 2022). For instance, nostalgic feelings have been shown to influence aesthetic judgments in consumer contexts, affecting preferences for product shapes (Gong et al., 2023), and to predict aesthetic appreciation in artistic domains, such as the enjoyment of haiku poetry (Hitsuwari & Nomura, 2022). Trait nostalgia (TN) has been conceptualized as a stable tendency to find meaning and comfort in the past (Sedikides & Wildschut, 2024), suggesting that individuals high in TN may be especially receptive to the familiar, comforting cues often embedded in Dreamcore's retro-inspired visuals.

While existing theories suggest that nostalgia is primarily associated with positive, comforting memories (Sedikides & Wildschut, 2024), some aesthetic frameworks—such as the distancing-embracing model by Menninghaus et al. (2017)—indicate that even unsettling or ambiguous images may be enjoyed if they evoke a blend of familiarity and strangeness. This raises the intriguing possibility that nostalgia-prone individuals might also find aesthetic appeal in the darker or more ambiguous Dreamcore images, particularly when these images contain recognizable elements from the past. However, empirical evidence for this link remains limited and warrants further exploration.

Given the widespread popularity of Dreamcore as a subculture, particularly within social media environments that foster the rapid dissemination and remixing of digital visual content, we sought to identify factors that may explain why specific individuals are drawn to the emerging Dreamcore aesthetics. Based on the surreal and paradoxical nature of Dreamcore and theoretical frameworks in aesthetic psychology, we assumed that DIL may consist of distinct facets. Accordingly, the present study had three specific aims: (1) to identify potential dimensions of DIL; (2) to identify the structure of TN using two established measurements; (3) to explore how TN relates to these subdimensions of Dreamcore Image Liking. Because no studies have previously examined these questions in the context of Dreamcore aesthetics, we adopted an exploratory stance and did not formulate specific directional hypotheses about potential subdimensions. Nevertheless, we expected that individuals with higher levels of TN would show a stronger overall preference for Dreamcore images.

Materials and Methods

Study Participants and Procedures

The target population for this study consisted of young adults aged 18 to 40 years, reflecting the demographic that predominantly engages with social media platforms (e.g., TikTok) in China. This age group was selected to ensure ecological validity, as it includes the typical Chinese university student and young working adult populations who are most likely the primary audience and co-creators of Dreamcore aesthetics online. The questionnaire was developed using Wenjuanxing, China's largest online survey platform, and administered between May and October 2024. A total of 529 questionnaires were distributed. Participants who failed attention checks (trap questions) or who had excessively short response time (under 120 s) were excluded, resulting in a final sample of 449 valid questionnaires. The sample size was regarded sufficient for standard structural equation modeling, particularly if the model is not overly complex (Wolf et al., 2013).

All participants provided informed consent and agreed to participate in the study. This study was approved by the Institutional Review Board of Dongseo University (1041493-A-2024-005). All procedures conformed to the ethical standards of national and institutional research committees on human experimentation and adhered to the 1964 Declaration of Helsinki and its subsequent amendments.

Measures

Trait Nostalgia

The Southampton Nostalgia Scale (SNS) is one of the most widely used tools for measuring TN (Barrett et al., 2010). It consists of seven items rated on a 7-point Likert scale, with higher scores indicating stronger nostalgic tendencies. Four items assess the propensity or frequency of nostalgic experiences (e.g., “How prone are you to feeling nostalgic?”), and the remaining three evaluate the importance and value attributed to nostalgic feelings. However, each SNS item explicitly references the word “nostalgia,” which may narrow the construct and risk confounding trait variance with item-specific variance due to repetitive wording (McCrae, 2015). Additionally, the SNS predominantly focuses on positive aspects of nostalgia, potentially overlooking its more complex or ambivalent dimensions.

To obtain a more comprehensive assessment, we also employed the Personal Inventory of Nostalgic Experiences (PINE) (Newman et al., 2020). The PINE comprises four items rated on the same 7-point Likert scale, capturing a broader spectrum of nostalgic expressions drawn from dictionaries, existing scales, and individual reports (e.g., “How much do you feel a wistful affection for the past?”). Prior research has suggested that using multiple scales provides a more nuanced measurement of TN (Wildschut et al., 2023). Accordingly, combining the SNS and PINE aligns with nostalgia's conceptualization as a sentimental longing or wistful affection for the past.

All item scores were averaged to generate overall scores for each scale. The Chinese versions of the SNS and PINE have demonstrated strong psychometric properties in previous studies (Wildschut et al., 2023).

Dreamcore Image Liking

We aimed to capture a broad range of typical motifs and atmospheric cues commonly observed in Dreamcore visuals (e.g., abandoned amusement parks, empty hallways, classrooms, toys, flowers, and clear water), as frequently discussed in Dreamcore communities. We collected a total of 20 images from publicly available, copyright-free sources on social media platforms that explicitly showcased these hallmark elements. In addition, recognizing the growing popularity of Dreamcore imagery on Chinese social media platforms, some images included culturally familiar settings and motifs from Chinese childhood environments. These culturally resonant images include No. 1 (carousel), No. 5 (classroom), No. 6 (home), No. 10 (amusement park), and No. 13 (ball pit), with No. 6 and No. 10 being the most representative. In the images, only a few characters are clear and legible, while others are blurred or distorted. This partial legibility and distortion aligns with the Dreamcore aesthetic, which blends the familiar and the uncanny, reflecting ambiguous and dreamlike feelings. All selected images are presented in the Supplementary Material.

Given the widespread exposure to Dreamcore imagery on social media, participants might have had prior exposure to widely shared visuals. To minimize this familiarity bias and ensure novelty, we created an additional set of 20 custom Dreamcore images using the AI image-generation tool DALL·E. We first trained the model on a small collection of widely circulated Dreamcore images to help capture the characteristic aesthetic. Image generation was then guided by carefully crafted prompts that specified: (1) the main subjects, (2) additional elements (e.g., pools, amusement park), (3) mood (e.g., nostalgic, eerie, calm), (4) color palette (e.g., desaturated, grainy). For example, prompts included requests to depict “a low-fidelity, nostalgic ball pit in an abandoned indoor playground with faded colors and a dim atmosphere”, “a vintage childhood bedroom submerged underwater, evoking both calmness and an uncanny sense of disquiet”, or “an abandoned teacup ride overgrown with foliage and Chinese text that complements the nostalgic and surreal atmosphere of the scene”. This approach not only reduced potential prior exposure bias but also allowed for greater control over image representativeness and adherence to Dreamcore aesthetics.

To enhance survey efficiency and minimize participant fatigue, two independent researchers with expertise in visual aesthetics and psychology evaluated all images based on their Dreamcore representativeness. Images were excluded if they exhibited poor quality, excessive element repetition, or inadequate Dreamcore characteristics, resulting in the removal of 22 images. The final set comprised 18 images that best captured the typical Dreamcore aesthetic.

Participants rated these 18 images on a 9-point Likert scale (1 = strongly dislike to 9 = strongly like), with higher scores indicating stronger preferences. To avoid response bias, participants were not informed about the images’ sources.

Background Information

Demographic information collected included gender, age, education level, annual income, and art education experience. To streamline the survey process and improve efficiency, age was categorized into four groups (18–25, 26–30, 31–35, 36–40 years), allowing participants to select the range best representing their age. Education was initially divided into six categories (junior high school or below, high school/vocational school, junior college, bachelor's degree, master's degree, doctorate), while income was categorized into six ranges (less than 30,000 CNY; 30,000–80,000 CNY; 80,000–150,000 CNY; 150,000–300,000 CNY; 300,000–1,000,000 CNY; above 1,000,000 CNY). Participants were also asked whether they had received formal art education, as this factor may influence aesthetic experiences and preferences (Grassini & Koivisto, 2024; Reymond et al., 2020).

For analytical simplicity, education and income were further consolidated into three broader categories. Education was grouped into Lower (basic education), Higher (undergraduate education), and Advanced (postgraduate education). Income was classified into Low (less than 80,000 CNY), Mid (80,000–300,000 CNY), and High (above 300,000 CNY).

Statistical Analysis

To examine the underlying structure of TN and DIL, an exploratory factor analysis (EFA) was conducted using maximum likelihood extraction with oblique rotation. The number of extracted components was determined based on eigenvalues and scree plot inspection. Items were excluded if they exhibited factor loadings below 0.40, or cross-loadings onto other components. Cross-loading was defined as having an alternative factor loading above 0.40 with a difference of less than 0.10 compared to the primary factor loading.

Following the EFA, a bi-factor confirmatory factor analysis (CFA) was performed to determine the dimensionality of TN and DIL. Bi-factor model indices guided the selection of the optimal data structure. A subsequent CFA was conducted to estimate the final model fit. To explore the relationships between latent constructs, structural equation modeling (SEM) was employed. SEM simultaneously estimates the overall fit of both the measurement and structural models, testing the associations between latent variables represented by observed item scores.

Robust Maximum Likelihood (MLR) was used for parameter estimation in modeling due to its robustness against non-normality. Regarding bi-factor CFA, there is no universally accepted standard for model selection; thus, we followed commonly recommended guidelines (e.g., Rodriguez et al., 2016b, 2016a). We evaluated the general factor (-G) and specific factors (-F1, F2…) by comparing key indicators: ωH > 0.8 (homogeneity), H index > 0.7 (construct replicability), and ωHS / ωS > 0.5 (homogeneity-to-reliability ratio), considering support for either structure if these thresholds were met. Additionally, Explained Common Variance (ECV) > 0.7 suggested a predominantly unidimensional structure. Model fit was evaluated using the Comparative Fit Index (CFI) and Tucker–Lewis Index (TLI), with values above 0.90 indicating acceptable fit. The Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR) were considered acceptable at values below 0.08 (Ullman, 2006). Given the chi-square statistic's sensitivity to sample size and violations of multivariate normality, model evaluation did not rely on this metric (Schermelleh-Engel et al., 2003). McDonald's Omega (ω) was calculated to assess composite reliability.

Descriptive statistics for continuous variables are reported as means with standard deviations (SD). Student's t-tests and one-way ANOVAs were conducted for two-group and multi-group comparisons, respectively. Statistical significance was set at p < 0.05. Descriptive statistics and EFA were conducted using IBM SPSS Statistics 29.0, while other statistical analyses and modeling were performed in R (version 4.4.1).

Results

Sample Characteristics

As presented in Table 1, the current study consists of a total of 449 participants (189 men and 260 women). The majority had attained a higher level of education (78.0%), and 16.3% reported advanced education and 5.8% reported lower education. Nearly half (49.4%) reported mid-level income. In terms of residence, 39.2% were from the Eastern Coastal region, 18.5% from the Central region, 26.5% from the Western region, 5.3% from the Northeastern region, and 1.1% resided overseas, while 9.4% of participants did not disclose their location. Art education was reported by 29.4% of participants.

Table 1.

Study Sample Characteristics.

Variable		N (%)
Gender	Men	189 (42.1%)
	Women	260 (57.9%)
Age group	18–25	153 (34.1%)
	26–30	101 (22.5%)
	31–35	118 (26.3%)
	36–40	77 (17.1%)
Education	Lower	26 (5.8%)
	Higher	350 (78.0%)
	Advanced	73 (16.3%)
Annual income	Low	204 (45.4%)
	Mid	222 (49.4%)
	High	23 (5.1%)
Residence	Eastern coastal	176 (39.2%)
	Central region	83 (18.5%)
	Western region	119 (26.5%)
	Northeastern region	24 (5.3%)
	Overseas	5 (1.1%)
	Unanswered	42 (9.4%)
Art education	No	317 (70.6%)
	Yes	132 (29.4%)

Structure of Trait Nostalgia (TN)

EFA identified a two-factor structure (factor loadings: 0.46–0.88), with Factor 1 reflecting positive or neutral nostalgia (all Items of SNS, and PINE Items 1 and 4) and Factor 2 capturing sentimental/wistful nostalgia (PINE Items 2 and 3). The model explained 66.2% of variance.

In the bi-factor CFA model (Figure 1), error covariances were specified between SNS Items 2 and 3 due to their conceptual overlap. Table 2 presents key model fit indices used for model evaluation and selection. The bi-factor model showed a good fit. The general factor exhibited strong reliability and explained the majority of variance, with high homogeneity and structural replicability. The specific factors contributed minimally and had low homogeneity-to-reliability ratios, indicating weak differentiation. These findings supported a unidimensional TN, instead of two-factor structure.

Figure 1.

Bi-factor CFA model for trait nostalgia (TN). Note: -G = general factor; -F1, F2 = specific factors. SNS: the Southampton Nostalgia Scale; PINE: the Personal Inventory of Nostalgic Experiences.

Table 2.

Bi-Factor CFA Model Indices for Trait Nostalgia (TN) and Dreamcore Image Liking (DIL).

	TN	DIL
CFI	0.984	0.949
TLI	0.973	0.920
RMSEA	0.051	0.075
SRMR	0.028	0.035

General Factor
Homogeneity (ωH)	0.813	0.409
Construct Replicability (H index)	0.918	0.729
Homogeneity-to-Reliability Ratio (ωHS / ωS)	0.869	0.476

Specific Factor
Homogeneity (ωH)	0.134, 0.467	0.574, 0.458
Construct Replicability (H index)	0.584, 0.572	0.813, 0.823
Homogeneity-to-Reliability Ratio (ωHS / ωS)	0.145, 0.560	0.666, 0.651
Explained Common Variance (ECV)	0.73	0.345

Note: Comparing general factor and specific factor: ωH > 0.8, H index > 0.7, and ωHS / ωS > 0.5, considering support for either structure if these thresholds were met.

CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual.

To validate this, a single-factor CFA was conducted with an additional error covariance specified between PINE Items 2 and 3, given their shared focus on sentimental nostalgia. The model fit remained good. All factor loadings were significant (p < .001) and ranged from 0.52 to 0.86. McDonald's Omega coefficients confirmed strong internal consistency for TN across all measured indicators (Table 3).

Table 3.

CFA Model Indices for Trait Nostalgia (TN) and Dreamcore Image Liking (DIL).

	TN	DIL
CFI	0.958	0.922
TLI	0.945	0.905
RMSEA	0.071	0.072
SRMR	0.044	0.059
McDonald's ω	0.894	0.902, 0.723

Note: CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual.

Structure of Dreamcore Image Liking (DIL)

EFA suggested the removal of Image 3 due to a low factor loading (0.36), and Images 9, 11, and 16 due to cross-loadings. A two-factor structure emerged, with DIL-F1 representing darker, eerie imagery and DIL-F2 representing warmer, comforting imagery (see the Supplementary Material for details about specific images in each factor). The final model accounted for 56.2% of the total variance, with factor loadings ranging from 0.50 to 0.87.

As shown in Table 2, bi-factor CFA for DIL demonstrated a good model fit. The general factor accounted for limited variance and had weak homogeneity-to-reliability ratios. In contrast, the specific factors exhibited stronger homogeneity-to-reliability ratios and greater structural stability), indicating meaningful subdimensions. Accordingly, specific factors contributed substantially beyond the general factor, supporting the multidimensional (two-factor) structure (Figure 2). Thus, two-factor CFA was further conducted, showing an adequate model fit (Table 3). Factor loadings ranged from 0.54 to 0.82 for DIL-F1 and 0.49 to 0.71 for DIL-F2. Omega coefficients showed good internal consistency for each factor (ω = 0.902 for DIL-F1, ω = 0.723 for DIL-F2).

Figure 2.

Bi-factor CFA model for dreamcore image liking (DIL). Note: -G = general factor; -F1, F2 = specific factors.

Group Comparisons

Violin plots demonstrate the data distribution and group comparisons for TN (Figure 3). TN scores were significantly higher in men compared to women (5.17 vs. 4.88, p = .004, Cohen's d = 0.273) and in individuals with previous art education compared to those without (5.17 vs. 4.93, p = .034, Cohen's d = 0.221).

Figure 3.

Violin plots showing the data distribution and group comparisons for trait nostalgia (TN) (*p < .05, **p < .01).

Regarding DIL, a paired sample t-test showed significant lower scores on DIL-F1 compared to DIL-F2 (3.88 vs. 5.67, p < .001, Cohen's d = 1.150). A total of 96 individuals exhibited liking (scored greater than 5 point) for DIL-F1, while 299 liked DIL-F2. As presented in Figure 4, older participants (26 + years; p < .001, η² = 0.066), men (p < .001, Cohen's d = 0.341), individuals with lower education levels (p = .009, η² = 0.021), and those with higher incomes (p = .028, η² = 0.016) showed greater preference for DIL-F1 images. No significant group differences were observed for DIL-F2. Residence region was unrelated to both TN and DIL.

Figure 4.

Violin plots showing the data distribution and group comparisons for two dimensions of dreamcore image liking (DIL) (*p < .05, **p < .01, ***p < .001).

Relationships Between Trait Nostalgia (TN) and Dreamcore Image Liking (DIL)

To examine the influence of TN on DIL, a structural equation model (SEM) was estimated, linking the CFA models (Figure 5). The overall model showed a good fit to the data (CFI = 0.944, TLI = 0.937, RMSEA = 0.048, SRMR = 0.051). All measurement indicators loaded significantly on their respective constructs (TN: 0.52–0.86; DIL-F1: 0.54–0.83; DIL-F2: 0.50–0.71). DIL-F1 and DIL-F2 were modeled as distinct but related dimensions. Their significant covariance showed shared variance, reflecting their conceptual overlap within the broader construct of DIL.

Figure 5.

Structural equation modeling (SEM) indicating the relationship between trait nostalgia (TN) and dreamcore image liking (DIL).

The model indicated that TN was significantly associated with both DIL factors, with standardized coefficients of 0.18 for DIL-F1 (Estimate = 0.27, 95% CI = 0.11–0.44, p = .001) and 0.27 for DIL-F2 (Estimate = 0.31, 95% CI = 0.14–0.47, p < .001). The relationship remained statistically significant after controlling for demographic variables related to DIL, including age, gender, education level, and income.

To test whether these paths differed in magnitude, a constrained model was estimated, setting the paths from TN to DIL-F1 and DIL-F2 equal. Model comparison demonstrated no significant differences in fit between the constrained and unconstrained models (p = .667), suggesting the effects were statistically equivalent.

Discussion

This study investigated the structure of trait nostalgia (TN) and Dreamcore image liking (DIL) and explored their associations. Factor analyses revealed that TN is best conceptualized as a unidimensional construct. DIL emerged as a multidimensional construct, comprising two distinct dimensions: darker/eerie imagery (DIL-F1) and warmer/comforting imagery (DIL-F2). Structural equation modeling (SEM) indicated that TN scores were significantly associated with both DIL dimensions, with similar effect sizes. This finding suggests that nostalgia's influence on Dreamcore image appreciation is general rather than dimension-specific, indicating that individuals with higher nostalgia tendencies are equally inclined to appreciate both unsettling and comforting aspects of Dreamcore aesthetics.

Although the exploratory factor analysis (EFA) initially suggested a two-factor structure for trait nostalgia (TN), the bi-factor CFA demonstrated minimal contributions from any subfactor, as evidenced by low homogeneity-to-reliability ratios, thereby reinforcing the unidimensional nature of TN. The weak differentiation between subfactors identified by EFA may stem from the SNS, which predominantly emphasizes positive aspects of nostalgia. Incorporating the PINE allowed us to capture a more sentimental and wistful aspect of nostalgia, yet overall, a single general factor emerged across all items. This aligns with recent research suggesting that nostalgia can be adequately represented by a single latent factor with strong convergent validity, as shown through assessments across Chinese and U.S. samples using four widely applied nostalgia measures (Wildschut et al., 2023). Nonetheless, some researchers argue for the multidimensional potential of nostalgia. For instance, Oba et al. (2016) proposed that nostalgia comprises two distinct facets: emotional significance, referring to the affective intensity of nostalgic memories, and chronological remoteness, reflecting the perceived temporal distance of those memories (Oba et al., 2016). This distinction suggests that nostalgia involves cognitive evaluations of the past, wherein individuals assess the importance and relevance of their memories in relation to their present experiences.

While existing research supports both single-factor and multidimensional conceptualizations of nostalgia, there is a growing inclination toward a more nuanced understanding. Nostalgia may be best described as a coherent psychological phenomenon that integrates emotional and cognitive elements within a single latent factor (Newman et al., 2020). Despite its potential multidimensionality, nostalgia functions as a unified construct serving distinct psychological purposes. Regardless, using multiple measurement scales enables a more comprehensive assessment, capturing the multifaceted nature of nostalgia while acknowledging its core experience of emotional unity. Future research should further investigate these facets to elucidate how various measures of nostalgia relate to one another and their broader implications for psychological outcomes.

There has been research suggesting that men can be more nostalgic than women (Davis, 1979), consistent with the current study. In contrast, several studies have reported higher nostalgic proneness in female compared to male (Hepper et al., 2021; Stephan et al., 2014). However, others have found no consistent gender effects (Routledge et al., 2008; Wildschut et al., 2006). Given the potential for cultural influences on the level of nostalgia (Batcho, 1995), our results may reflect sample-specific or cultural factors in how nostalgia is experienced and expressed. Overall, evidence about gender differences in nostalgia remains mixed, underscoring the need for future research to determine whether these patterns are robust across cultures and contexts, and to identify the social and psychological mechanisms that might contribute to them.

Regarding Dreamcore images, the CFA supported two distinct dimensions of participants’ preferences. Notably, the model did not differentiate liking based on the origin or source of the images, suggesting that stylistic elements—rather than the type of creation—primarily influenced participants’ judgments. Images in DIL-F1, characterized by eerie and unsettling features, may evoke mild discomfort, while those in DIL-F2 appeared to elicit emotional warmth and nostalgic resonance. These findings indicated that DIL was best conceptualized as a multidimensional construct, rooted in distinct affective styles underlying viewers’ emotional responses to Dreamcore aesthetics.

In this study, participants generally preferred the warmer, comforting aesthetic (DIL-F2) over the darker, eerie style (DIL-F1), as indicated by significantly lower mean scores and fewer individuals rating DIL-F1 above the midpoint. The finding aligns with the Distance-Embracing (D-E) model, which posits that negative valence art tends to appeal selectively to certain individuals, whereas others may avoid or reject such content (Menninghaus et al., 2017). Demographic differences emerged for DIL-F1, with older participants, men, individuals with lower education, and those with higher incomes showing a stronger preference for the eerie imagery, whereas no significant demographic differences were found for DIL-F2. This suggests that while certain aesthetic preferences may be shaped by individual characteristics, others—such as the appreciation of comforting Dreamcore imagery—may reflect more universal appeal and a shared cultural resonance with specific visual elements.

Interestingly, men showed a stronger preference for the darker, more uncanny Dreamcore images, aligning with prior research in art perception. For instance, Chamorro-Premuzic et al. (2010) found that women tend to prefer simple, colorful, and joyful artworks, while men favor more complex, unsettling content. Similarly, Ortlieb et al. (2016) reported that men are more drawn to the sublime and ambiguous in Bosch's Garden of Earthly Delights, reflecting a broader gender-based tendency to engage with threatening or uncanny imagery, which echoes the Burke's aesthetic theory. These gender-related differences in aesthetic preferences may reflect broader psychological tendencies to seek out complexity and ambiguity, resonating with the unsettling yet nostalgic elements of Dreamcore imagery.

The SEM indicated the relationship between TN and DIL, highlighting the role of nostalgia in shaping contemporary artistic expressions. Dreamcore's rapid rise in online popularity, particularly after 2020 amid the COVID-19 pandemic, may reflect broader socio-psychological responses to global uncertainty. Prior research suggests that the pandemic heightened nostalgic sentiments, as individuals increasingly turned to memories and past experiences to cope with emotional distress (Huang et al., 2023; Wulf et al., 2022). The intensified engagement with nostalgia-driven content was evident in the surge of #nostalgiacore aesthetic trends on TikTok during the pandemic (Conner, 2023), and the strengthened link between nostalgia and social media interactions (Niemeyer & Siebert, 2023). Reduced social contact during this period was reportedly associated with greater consumption of nostalgic media and music, which provided a sense of comfort and connection (Yeung, 2023). Thus, on the one hand, the widespread appeal of Dreamcore aesthetics in recent years can be attributed to individual nostalgic tendencies; on the other hand, Dreamcore aesthetics may also serve as a cultural response to collective trauma, amplified by the unique social and psychological challenges of the pandemic. This directly echoes the Functional Model of Kitsch and Art (Ortlieb & Carbon, 2019), which posits that aesthetic preferences shift dynamically in response to social motivations. In particular, during times of social stress or uncertainty, individuals are drawn to familiar, emotionally comforting forms of expression. Accordingly, Dreamcore's nostalgic and surreal qualities may have offered a symbolic refuge, fulfilling individuals’ deep-seated needs for connection and meaning.

Dreamcore is primarily shared and co-created on social media platforms. Thus, the digital space likely amplify its nostalgic and surrealist qualities, fostering broader cultural resonance. Social media dynamics—characterized by algorithmic curation and user-driven remixing—play a crucial role in shaping and diffusing distinct aesthetics like Dreamcore (Leaver et al., 2020). They influence both the content of Dreamcore imagery (e.g., nostalgic and surreal elements) and how audiences engage with and appreciate these visuals, underscoring the importance of the social media dimension in understanding Dreamcore's popularity.

Despite the broad social-cultural backdrop, our empirical findings focused on a more nuanced perspective regarding how trait nostalgia relates to Dreamcore preferences at the individual level. Nostalgia did not appear to affect individuals’ preferences for the two Dreamcore image styles identified in the current study. Individuals with stronger nostalgic tendencies responded more positively to both eerie and comforting images, indicating that nostalgia is broadly associated with overall engagement with Dreamcore aesthetics rather than tied to a specific style. This may be attributed to nostalgia's inherent blend of positive and bittersweet emotions, which can enhance receptivity to a range of aesthetic experiences, encompassing both unsettling and comforting stimuli (Sedikides & Wildschut, 2016). According to prior research, the preference differences may be explained by personality traits. For example, individuals high in openness typically favor complex, abstract, and highbrow art forms (Furnham & Bunyan, 1988; Rawlings, 2003), whereas those with elevated sensation-seeking tendencies often exhibit a broader appreciation for negative valence content (e.g., horror films and unpleasant art) driven by the arousal such stimuli evoke (Aluja-Fabregat, 2000; Rawlings, 2003; Tamborini & Stiff, 1987). Therefore, to deepen understanding of specific style preferences in Dreamcore imagery, future research should consider exploring these additional psychological determinants.

Limitations

There were several limitations that need to be acknowledged. First, the cross-sectional design precludes conclusions about causality; longitudinal or experimental work would better clarify temporal dynamics. Second, while our sample was diverse in educational and regional backgrounds, it did not fully represent wider populations or cultural contexts, potentially limiting the generalizability of findings. Third, although we employed two recognized nostalgia measures (SNS and PINE), the complexity of nostalgia suggests that integrating a wider array of scales could yield richer insights. Fourth, focusing on TN and Dreamcore image liking allowed for an in-depth analysis, but other factors including personality traits might further explain individual differences. Our findings also showed a link between demographic variables, such as gender, and specific Dreamcore imagery preferences, hinting at possible interaction or moderating effects with trait nostalgia. Moreover, although we attempted to control for potential familiarity bias by generating custom Dreamcore images, we did not directly measure participants’ familiarity with Dreamcore aesthetics or assess whether their nostalgic reactions reflected direct nostalgia (rooted in personal experiences) or indirect nostalgia (based on secondhand exposure through media) (Srivastava et al., 2023). Future studies including measures of prior exposure to Dreamcore imagery are clearly warranted to better account for these potential moderators, as they may significantly influence aesthetic preferences and engagement (Ortlieb et al., 2020). Fifth, the Dreamcore image selection was not systematically designed to comprehensively span all potential Dreamcore features, which limits the generalizability of the identified dimensions to the broader universe of Dreamcore imagery. As the first empirical investigation of Dreamcore image preferences, our study provides an initial framework that should be refined in future research through more rigorous, feature-driven sampling strategies, such as computational clustering of visual features or expert-coded semantic attributes. Sixth, our focus on static images overlooks the multimedia nature of Dreamcore, which frequently involves video and audio and may elicit distinct emotional responses (Baumgartner et al., 2006). Further research should incorporate these multi-sensory elements for a more comprehensive understanding.

Despite these limitations, this study makes several preliminary yet valuable contributions to the current understanding of the popularity of this emerging digital subculture. First, the present study identified potential comforting and eerie subdimensions within DIL, providing an empirical basis for future work to more systematically validate and refine these dimensions. Second, this study established a starting point for linking nostalgia, a well-established psychological construct, with preferences for a distinctly digital and surrealist visual genre (Dreamcore). This opens avenues for future cross-disciplinary research, integrating aesthetic psychology, digital visual cultures, and social media studies. Third, the exploratory nature of our findings underscores the need for continued investigation into how broader social and cultural factors interplay with individual psychological traits to shape aesthetic experiences. Overall, our research offers a foundational perspective that can inform future empirical and theoretical developments in the rapidly evolving landscape of digital aesthetic preferences.

Conclusions

To our knowledge, this is the first empirical study to explore the psychological underpinnings of the emerging Dreamcore aesthetic circulated online. The findings suggest that trait nostalgia (TN) functions as a unidimensional construct, whereas Dreamcore image liking (DIL) may be multidimensional, encompassing a darker/eerie dimension and a warmer/comforting dimension in this study. Despite these distinct structures, we observed a consistent association between TN and each DIL dimension, suggesting that nostalgic tendencies were broadly relevant to the appreciation of such surreal and somewhat unsettling imagery. Given the exploratory nature of this work, our findings provide preliminary insights into how individuals engage with emergent aesthetic forms. The rising popularity of Dreamcore may reflect nostalgia's amplified role in digital culture, driven by the need for emotional comfort and connection amid global uncertainty. Furthermore, the current study has implications for cultural and media research, highlighting the complexity of art preferences in the digital era and the role of individual differences in shaping aesthetic experiences.

Supplemental Material

sj-docx-1-art-10.1177_02762374251356955 - Supplemental material for What Makes People Like Dreamcore Aesthetics? the Role of Trait Nostalgia in Preferences for Dreamcore Imagery

Supplemental material, sj-docx-1-art-10.1177_02762374251356955 for What Makes People Like Dreamcore Aesthetics? the Role of Trait Nostalgia in Preferences for Dreamcore Imagery by Jia-Yu Song, Won-Ho Choi and Ru Li in Empirical Studies of the Arts

Footnotes

Acknowledgments

This work was supported by the Brain Korea 21, Future Visual Contents Creative Convergence Education Research Group.

ORCID iDs

Jia-Yu Song

Won-Ho Choi

Ru Li

Ethical Considerations

This study was approved by the Institutional Review Board of Dongseo University (1041493-A-2024-005). All procedures conformed to the ethical standards of national and institutional research committees on human experimentation and adhered to the 1964 Declaration of Helsinki and its subsequent amendments.

Author Contributions

JYS: Conceptualization, Investigation, Data Curation, Methodology, Writing - Original Draft; WHC: Conceptualization, Supervision, Funding acquisition, Writing - Review & Editing; RL: Conceptualization, Methodology, Formal analysis, Writing - Review & Editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Dongseo University,

Declaration of Conflicting Interests

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

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available in the OSF repository (https://osf.io/kyrw5/?view_only = ).

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Jia-Yu Song is a PhD Student in Visual Contents at Dongseo University. Her area of research is aesthetics, emerging art, and psychology of art.

Won-Ho Choi is a Professor in Visual Contents at Dongseo University. His area of research is psychology of film.

Ru Li is a Researcher in Psychology and Psychiatry at the University of Turku and Aarhus University. His area of research is personality, emotion, cognition, and behaviors.

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