## 1 Introduction
In 2005, at the international academic conference on Computational Aesthetics in Graphics, Visualization, and Imaging of the Eurographics Association, computational aesthetics was initially put forward. Computational aesthetics is the research of computational methods that can make applicable aesthetic decisions in a similar fashion as humans can) [15]. The advantage of computational aesthetics lies in its ability to quantify various abstract aesthetic features, such as style, texture features, and so on [16]. By investigating the connection between these features and beauty, researchers are able to undertake tasks such as classification learning through the utilization of modeling techniques like Convolutional Neural Networks (CNNs), SVMs, and Back Propagation (BP) neural networks [17; 18; 19]. In addition to quantifying the aesthetic object, researchers have also started to incorporate more factors as the aesthetic subject. This includes combining visual and text data for quantification [20], analyzing the emotions in paintings [21], optimizing the model by integrating eye movement data, and optimizing the model by integrating eye movement data [22]. It can be observed that computational aesthetics excels at using mathematical methods to quantify the features of aesthetic objects. Subsequently, various mathematical techniques are employed to build models that simulate human aesthetic evaluations. Neuroaesthetics,on the other hand, primarily focuses on the study of the aesthetic subject. It employs psychological methods to investigate different aesthetic mechanisms and the impact of features, contexts, and other factors on aesthetics. This study proposes to integrate the research methods of computational aesthetics and neuroaesthetics to explore a comprehensive research approach. This approach involves delving into the internal relationships between features and aesthetics, as well as integrating and modeling the multimodal data of the aesthetic subject, aesthetic object, and aesthetic context. By leveraging the strengths of computational aesthetics in feature extraction and the advantages of neuroaesthetics in cognitive research, we aim to develop a more holistic understanding of aesthetic evaluation. Representational paintings have been proved to have higher judgments on the perceptual dimensions of the semantic dimension of the Illusion of Reality [23]. Therefore, this study employed abstract artworks as experimental materials. The specific methods are as follows:  
First, based on traditional concepts in art history, computational aesthetics is used to analyze and quantify the physical quantities corresponding to different features. Previous research has indicated that in the process of human visual perception, global processing is completed before more local analysis, that is, the Global Precedence Hypothesis: People typically perceive global features first in the visual processing and then notice local details [24; 25; 26; 27]. [PERSON] et al. [28] discovered via eye movement research that even when the local and global forms have equal conspicuity, there remains an advantage in the processing of the overall form. Of course, the global advantage also has individual differences. Expertise would affect saccade metrics, biasing them toward larger saccade amplitudes. Artists adopted a more global scan path strategy when performing a task related to art expertise [29; 30; 31]. Global features encompass the arrangement and interrelationships of diverse visual elements within an artwork, and this overall relationship can be summarized as the composition of the picture [32; 33]. Different compositions yield different visual effects. A suitable composition necessitates the rational allocation of subject space and negative space to achieve simplicity and balance. For instance, in traditional Chinese painting, a significant amount of blank space is frequently utilized to guide the viewer's attention, create dynamic visual balance, enhance the rhythm of the picture, and generate a simple and ethereal ambiance [34; 35].