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Modular serial-parallel network for hierarchical facial representations

Mario Fific
Grand Valley State University ~ Psychology
Daniel R. Little
The University of Melbourne ~ Psychological Sciences
Prof. Cheng-Ta Yang
National Cheng Kung University ~ Department of Psychology

Researchers in facial perception foster competition between holistic and analytic encoding. Despite the popular belief that faces are perceived in a holistic fashion, both neural organization of the visual system and the phenomenological experience indicate that faces can also be examined analytically in terms of facial parts. This view is further corroborated by the concept of hierarchical object representation, in which selective neural populations are fine-tuned to detect visual properties ranging from simple features to more complex combinations of features. Thus, theoretical developers face two major challenges. The first one is to determine how to integrate both holistic and analytic encoding within the same framework, relying on the idea of hierarchical facial representations. The second is to further integrate these facial perception stages with the higher-level cognitive processes, such as memory and decisional processes. To answer these challenges, we proposed a computational framework of Modular Serial Parallel Network (MSPN), which is a synthesis of several successful approaches in both perceptual and cognitive domains that includes signal detection theory, rule-based decision making, mental architectures (serial and parallel processing), random walk 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 measure predictions. In a facial classification task, the MSPN model showed an impressive ability in fitting choice response time distributions, over other facial perception models. The MSPN can be used as a tool to further the development and refinement of hypotheses in facial perception. The analysis of the model’s parameter values, estimated from data, can be used to explore distinct properties of the perceptual and cognitive processes engaged in both analytic and holistic encoding. The MSPN could be generalized to other domains in both cognition and perception.


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Cite this as:

Fific, M., Little, D., & Yang, C.-T. (2022, July). Modular serial-parallel network for hierarchical facial representations. Paper presented at Virtual MathPsych/ICCM 2022. Via