As urbanization quickens, the role of public art in urban landscape design gains prominence. Nevertheless, how stylistic characteristics of landscape public art affect aesthetic preferences remains insufficiently discussed, particularly with objective assessment methods. The use of event-related potential (ERP) can offer neurophysiological evidence to support research and practice in landscape art design. We employed a 2 (artistic features) × 2 (professional proficiency) repeated-measures design, involving abstract and figurative experimental stimuli; both experts and non-experts participated, with their aesthetic reactions and relevant electroencephalographic data recorded. Behavioral findings show a preference for figurative public artworks regardless of professional background. From neurophysiological outcomes, stimuli elicit an elevated N100 during early perceptual processing, signifying increased attentional resources. During aesthetic processing, figurative stimuli more effectively evoke positive emotions, particularly among professionals, yielding a heightened P200 response. Conversely, abstract stimuli may evoke a higher N200 amplitude, reflecting augmented negative biases. Nevertheless, non-experts exhibit no marked differences in their stimulus responses during aesthetic processing. Research indicates that low-level physical attributes of public artworks are initially noted, while the visual processing of artistic traits lies at a higher perceptual level, necessitating specialized expertise involvement. Furthermore, the complexity of visual perceptual processing plays a significant role in the assessment of landscape art preferences. This study not only offers crucial reference indices for designing urban landscapes that satisfy diverse public aesthetic needs but also lays the foundation for neural techniques to assess landscape design preferences and expands the field of landscape design research.

Introduction: The aesthetic preferences of people can determine the success of a design and are often closely related to design features. The discovery of designs that match user preferences can provide a reference for designers. Ceramic tiles are widely used in environmental design; however, little attention has been paid to the aesthetic preferences of people for tiles. This study aimed to explore the relationship between aesthetic preferences for tile design and neural responses.Materials and methods: In this study, two groups of tiles with different preference levels were randomly presented to 16 participants, and their electroencephalograms were recorded. The mean amplitudes of event-related potentials were analyzed by ANOVA.Results: The results showed that: (1) the aesthetic preferences of people for tiles could modulate brain activity; (2) tiles that people liked triggered higher N100 amplitudes; and (3) tiles that people disliked triggered higher P200 and late positive potential (LPP) amplitudes.Discussion: These results suggest that N100, P200, and LPP are significantly related to the aesthetic preferences of people for ceramic tiles. The difference in N100 and P200 amplitudes indicates that participants developed aesthetic perceptions of the tiles in the early and middle stages of vision and formed different attention allocations to tiles with varying levels of aesthetic preference; in the middle and late stages of visual processing, the difference in the LPP amplitude indicates that the impression of people for tiles is further deepened in the later stage, forming a top-down emotion-driven evaluation. Exploring the relationship between the aesthetic preferences of people and neural responses is significant in establishing objective aesthetic judgment indicators for tiles and understanding the process of aesthetic cognition. This study provides relevant information for quantitative aesthetic assessments of environmental design, interior design, and marketing involving ceramic tiles.

As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users’ willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users’ willingness to forgive compared to a rational style, primarily through the enhancement of users’ perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users’ willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.

In the face of rapid progress in heritage preservation and cultural tourism integration, landscape planning in historic cities is pivotal to showcasing regional identities and disseminating cultural value. However, the complexity of cultural characteristic identification and the imbalance in planning often restrict the progress of urban development. Additionally, existing studies predominantly rely on subjective methods and focus on a single cultural attribute, highlighting the urgent need for research on diversified cultural perception. Using Jingdezhen, a renowned historic cultural city, as an example, this study introduces a multi-label deep learning approach to examine cultural perceptions in tourism heritage landscapes. Leveraging social media big data and an optimized ResNet-50 model, a framework encompassing artifacts, production, folk, and living culture was constructed and integrated with ArcGIS spatial analysis and diversity indices. The results show: (1) The multi-label classification model achieves 92.35% accuracy, validating its potential; (2) Heritage landscapes exhibit a “material-dominated, intangible-weak” structure, with artifacts culture as the main component; (3) Cultural perception intensity is unevenly distributed, with core areas demonstrating higher recognition and diversity; (4) Diversity indices suggest that comprehensive venues display stronger cultural balance, whereas specialized ones reveal marked cultural singularity, indicating a need for improved integration across sites. This research expands the use of multi-label deep learning in tourism heritage studies and offers practical guidance for global heritage sites tackling mass tourism.

Background: With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human–computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human–computer interactions. Methods: This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users’ unconscious cognitive processing mechanisms during service failures. Results: The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. Conclusions: This study found that stance attribution explanations profoundly affect users’ mental models of AI, which determine their responses to service failure.

People spend most of their time indoors, and the visual characteristics of indoor building materials affect not only the quality of the indoor environment, but also the well-being of individuals. Ceramic tiles are widely used in interior decoration of buildings due to their aesthetic appeal and ease of maintenance. However, there is currently a lack of a comprehensive framework for assessing the visual comfort of ceramic tile design. This study established an evaluation system using the Analytic Hierarchy Process (AHP) and the Delphi method to collect perceptual words, extract evaluation indices, and calculate weights. A visual comfort scale for ceramic tiles, comprising three dimensions and twelve indices, was developed. A total of 342 questionnaires were analyzed using six types of tiles, and the multidimensional visual comfort scores of the various ceramic tile samples were statistically examined. An analysis of variance was conducted to investigate the effects of tile brightness, texture, and participant gender on visual comfort. The findings indicate that tile brightness and texture significantly affect the overall visual comfort score (p < 0.001; p < 0.001), with light-toned, non-textured tiles providing higher visual comfort (3.949). Although gender did not significantly affect the overall visual comfort scores, it did influence the evaluation scores in certain dimensions. Men rated the aesthetic comfort of tiles lower than women (p = 0.035), but they rated the emotional comfort of medium-toned and non-textured tiles higher (p = 0.003; p = 0.017). In terms of theoretical significance, the establishment of this evaluation model can expand the research content and methods of ceramic tiles, which are crucial architectural decoration materials. In terms of practical significance, this study provides an evaluation method and partial evaluation information for designers, enabling them to assess and enhance the visual experience of tiles based on the specific needs of interior spaces and the characteristics of the visual subject.