Working Memory Guides Action Valuation in Model-based Decision-making Strategy

Abstract Humans use both model-free (or habitual) and model-based (or goal-directed) strategies in sequential decision-making. Working memory (WM) is essential for the model-based strategy; however, its exact role in these processes remains elusive. This study investigates the influence of WM processes on decision-making and the underlying cognitive computing mechanisms. Specifically, we used experimental data from two-stage decision tasks and found that delay and load, two WM-specific variables, impact goal-revisiting behaviors. Then, we proposed possible computational mechanisms by which WM participates in information processing and integrated them into the model-based system. The proposed Hybrid-WM model reproduced the observed experimental effects and fit human behavior better than the classic hybrid reinforcement learning model. These results were verified with independent data sets. Furthermore, differences in model parameters explain the age-related difference in sequential decision-making. Overall, this study suggests that WM guides action valuation in model-based strategies, highlighting the contribution of higher cognitive functions to sequential decision-making.