Modular Serial-Parallel Network (MSPN): A Unified Model for Hierarchical Cognitive and Perceptual Processes

We will present the Modular Serial-Parallel Network (MSPN) model, a comprehensive and unified theoretical framework for cognitive and perceptual processes across various behavioral domains. MSPN has the potential to generalize to cognitive neuroscience modeling and offers a detailed mechanistic analysis of mental processes involved. In the back end, MSPN synthesizes several perceptual and cognitive approaches, including memory representations, signal detection theory, rule-based decision-making, mental architectures, random walks, and process interactivity. The MSPN model has been applied to two domains to explore the hierarchical nature of mental representations. Firstly, in face perception, MSPN proposes a hierarchical organization of visual processing with low-level features processed first, followed by higher-level features, which is consistent with the two dominant approaches in facial perception: holistic and analytic facial encoding. Also, this is consistent with the idea that mental representations of faces are organized hierarchically. Secondly, in decision-making involving preferential gamble choices, MSPN proposes a similar hierarchical organization of processing, with low-level object attributes processed first, followed by higher-level integration of these properties, which is consistent with the so-called Heuristic- and Utility based approaches to decision making. Using the joint analysis of choice response time distributions, we compared several candidate stochastic models. The MSPN has shown impressive abilities in fitting choice response time distributions over other models in tested tasks. Thus, implying that MSPN can be used as a tool for further development and refinement of theoretical constructs, with the analysis of the model's parameter values providing insights into distinct properties of perceptual and cognitive processes.