Exploring dual-process models of the Balloon Analogue Risk Task

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is poorly understood. Accurate depiction of the task requires modeling the mental representation of the object or the problem in the task environment and the cognitive processes operating on these representations to produce responses. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We crossed models of the BART with different assumptions about the interaction between the two systems (serial vs. parallel), and built a pool of computational dual-process models with varying representations and process configurations. This model framework was designed to explain both choice and response time data in the BART. A model comparison study examined the statistical properties of the models (i.e., parameter recovery, model recovery, and predictive accuracy). The best-performing subset of models was then evaluated by fitting them to data collected in three experiments whose manipulations were intended to engage the two systems in different ways. Results showed that the model assuming simultaneously activated two systems, a drift rate influenced by experience, and an evaluation process based on frequency representation, outperformed the others. Findings shed light on how modeling multiple processes and representations can benefit our understanding of sequential risk-taking behavior.