Face processing is widely regarded in cognitive science as the integration of individual features into a holistic percept. However, recent neuroscience research highlights a more nuanced interplay between holistic and featural mechanisms, with specific facial features receiving greater emphasis during early perception. Event-related potential studies reveal that the number and type of parafoveal features significantly influence neural response delays, yet the underlying mechanistic model remains unclear. This paper examines these phenomena through the lens of the predictive coding network, a biologically plausible alternative to traditional deep neural networks. Our findings show that predictive coding networks accurately simulate the influence of parafoveal features on neural response times while upholding the saliency hierarchy of facial features. These results provide a computational explanation for the observed neural delays and highlight the potential of predictive coding as a robust framework for understanding face perception in the human brain.