Traditional research on product aesthetic evaluation has primarily relied on static images and outcome-oriented judgments, making it difficult to capture the formation process of aesthetic responses during dynamic viewing. To overcome this limitation, this study proposes a dynamic three-dimensional product aesthetic multimodal affective–cognitive integration (MACI) framework. Building on this framework, we propose a MACI-based guided design evaluation method tailored for dynamic product evaluation. This method organises multimodal physiological signals into process-based evidence, linking attention allocation, affective arousal, and cognitive processing to design-related evaluation outcomes. The study conducted a foundational validation study using dynamic 3D automotive presentation videos combined with eye-tracking, electrodermal activity (EDA), and electroencephalogram (EEG) data. The results indicate that the proposed method can reliably distinguish between different aesthetic levels; the KNN-based implementation achieved a test accuracy of 95.45% and an average accuracy of 92.88% in a 10-fold cross-validation. More importantly, multimodal evidence was further translated into interpretable design cues related to visual organisation, cognitive integration, and iterative refinement. Consequently, this study not only provides an empirical validation case but also establishes a process-oriented methodological pathway for integrating multimodal evidence into design review and optimisation processes.
Sun et al. (Mon,) studied this question.