## 5 Discussion
What is unique is that the LBP feature, as a local feature, reflects the gray level relationship between each pixel and its neighboring pixels (local texture). The ERP data activated in the middle and left frontal lobes began to diverge from 300 ms and persisted until 1000 ms. This suggests that the LBP feature requires a more intricate processing procedure. [PERSON] et al. [55] also discovered in the abstract painting experiment that the retention and elimination of texture features would result in differences in aesthetic evaluation, and the original work with texture had a more significant impact. This disparity was reflected in a negative deflection occurring at the frontal and central scalp sites from 260 to 328 ms after stimulus presentation. Overall, in the process of the participants' aesthetic evaluation of abstract artworks, context continuously influences the cognitive process of aesthetics and will also affect the final aesthetic evaluation. The process generally follows the Global Precedence Hypothesis, the Hierarchical Theory of Visual Perception, and the Two Visual Pathways Theory. The overall performance is to process global features first, followed by local features. This study found that compared to the SVM model constructed using only image feature data, the model integrating neuroaesthetic data, computational aesthetic data, and contextual data has better quality and higher accuracy. This indicates that including human and context factors through an interdisciplinary approach that combines neuroaesthetics and advanced machine learning models can better establish an integrated aesthetic evaluation system between humans and Artificial Intelligence (AI), effectively simulating and predicting human aesthetic preferences [76; 77; 78]. Aesthetics is a significant area of study within psychology and neuroscience. Neuroaesthetics, a relatively new subfield, aims to understand the brain mechanisms underlying aesthetic experiences and related phenomena. However, there is currently an imbalance in the relationship between neuroaesthetics and the disciplines it draws upon for inspiration and guidance. Neuroaesthetics has much to learn from these fields, but at present, it offers relatively little in return [79; 80]. In this study, by combining computational aesthetics and neuroaesthetics research methods, the factors influencing aesthetics were explored. Through this interdisciplinary approach, on the one hand, in the study of neuroaesthetics, computational aesthetics methods are utilized to quantify features, broadening the research realm of neuroaesthetics. The feature extraction and classification of pictures employ a more objective quantitative approach, allowing for a clearer comprehension of the physical attributes of different features that impact aesthetics. Additionally, with the advancement of computational aesthetics quantification techniques, an increasing number of complex features can be quantified, providing more avenues for investigating the relationship between features and aesthetics. On the other hand, in the study of computational aesthetics, the research findings of neuroaesthetics are integrated. Building upon the original model that primarily relies on features as input, the data of the aesthetic subject and the aesthetic context are added, which not only enriches the model data but also considers the factors influencing aesthetics more comprehensively, resulting in a higher-quality model. Moreover, it offers a novel perspective for feature selection in modeling. Through the neuroaesthetics research method, the relationship between features and aesthetics can be more precisely determined, avoiding the inclusion of irrelevant features in the aesthetic model.