Modular serial-parallel network for hierarchical facial representations

Some evidence suggests that faces are perceived holistically, but subjective experience and research on the neural organization of the visual system indicate that facial parts can also be analyzed individually. Thus, advances in theories of face perception are met with two major challenges: How can hierarchical face representations be used to integrate holistic and analytic encoding within the same framework? And how can the stages of face processing be integrated with higher level cognitive processes, such as memory and decision making? We propose a novel computational framework termed the "Modular Serial-Parallel Network" (MSPN), which synthesizes several perceptual and cognitive approaches including memory representations, signal detection theory, rule-based decision making, mental architectures (serial and parallel processing), random walks, and process interactivity. MSPN provides a computational modeling account of four stages in face perception-(a) representational, (b) decisional, (c) logical-rule implementation, and (d) modular stochastic accrual of information-and can account for both choice probabilities and response-time predictions. We used MSPN to explore and validate theories across multiple paradigms including the composite face task, the part-to-whole task, and a new study of the other-race effect. The application of MSPN suggests that face processing varies based on the task and that holistic processing does not necessarily imply complete pooling into a single face object, but MSPN can reveal the precise nature of processing that gives rise to holistic effects in different paradigms. (PsycInfo Database Record (c) 2026 APA, all rights reserved).