DOES BEAUTY FOLLOW PATTERNS? — A COMPUTATIONAL MECHANISM FOR AESTHETIC COGNITION BASED ON THE CLIP MODEL

Aesthetic cognition reflects both artistic experience and underlying neuropsychological state and cognitive function. Quantifying individual perceptual differences in complex visual stimuli provides a noninvasive engineering path for mental and psychological rehabilitation and cognitive impairment screening. To address the limitations of subjective and static cognitive assessments, this study constructs a cross-modal aesthetic cognitive computing framework based on generative artificial intelligence (AI). This framework is based on the contrastive language-image pre-training (CLIP) model, embedding structured aesthetic style features generated by Style-based generative adversarial network 2 (StyleGAN2) in the visual branch. Its latent spatial characteristics are utilized as a “gene map” of visual stimuli to enhance the representation of formal patterns. The text branch utilizes bidirectional encoder representation from transformers (BERT) based on transformers to construct a hierarchical semantic distillation module (HSDM), transforming abstract aesthetic concepts into computable vectors. By introducing a cognitive consistency loss function, the model jointly optimized the image text style embedding space, making its output highly fit the trajectory of human aesthetic cognition. Based on a self-built Surreal Symphonies dataset containing 30 genres and human cognitive behavior data, experiments showed that the accuracy of the enhanced model’s aesthetic style classification reached 92.7%, and the Pearson correlation coefficient with human evaluation scores was 0.89 ([Formula: see text]). This method effectively realizes the numerical deconstruction and anomaly detection of aesthetic perception. This study provides a potential generative AI-based tool and methodological reference for quantifying cognitive differences. This strategy advances personalized art research and supports future applications in mental health assessment and cognitive function research.