Balancing Risk and Reward: Cognitive Processes in Decision-Making Explored Through the Modular Serial-Parallel Network

The Modular Serial-Parallel Network (MSPN) framework provides a robust approach to understanding the cognitive mechanisms underlying decision-making, particularly in risk-reward scenarios exemplified by the classical gamble task. By facilitating the diagnosis of processing order (serial or parallel), stopping rules (exhaustive or self-terminating), and the interdependency of mental processes, the MSPN model bridges the gap between two prominent theoretical approaches: utility-based models and heuristic-based models. Our study utilized the MSPN to explore how participants navigate decisions involving risk, revealing diverse strategies—some participants relied on serial processing, others on parallel processing, and many exhibited a dynamic mix of both depending on the trial. Notably, individual subject analysis highlighted significant variability, with some participants showing consistent preferences for one processing style, while others flexibly switched between strategies. These findings challenge the dominance of pure utility-based models and underscore the importance of considering heuristics and individual differences in decision-making. Furthermore, the MSPN’s capability to validate or falsify cognitive assumptions enhances our understanding of the risk-reward calculus in human judgment. This dual role positions the MSPN as a pivotal tool in advancing both theoretical insights and practical applications in cognitive research.