The Role of Working Memory in Reward-based Learning over Long Timeframes: Opponent Bootstrapping and Interference Effects of Working Memory on Incremental Learning

Resumen

Learning from feedback is essential for effective decision-making in daily life. Previous experiments have indicated that feedback-based learning involves multiple processes, such as slower reinforcement learning (RL) and fast working memory (WM). However, most of these studies focused on the dynamics of RL and WM in short timeframes, ranging from a few seconds to minutes, whereas real-life learning may occur over hours, days, or weeks. This preregistered study aimed to investigate the interplay between RL and WM in long-term feedback-based learning. Instead of learning back-to-back iterations in quick succession, participants learned one or two iterations per day for 24 days. In this scenario, WM should only assist participants in conditions where a given item appears more than once in short succession. The results show that (1) when WM was unavailable, the size of the learning sets did not significantly influence learning performance; (2) when WM was available, it had both immediate and long-term effects in boosting learning; and (3) the deleterious interference effect of WM on long-term retention was observed during learning, but overshadowed in the test phase and modeling results by its ability to lead to more experience of rewards, illustrating the complexity of interactions between multiple processes supporting learning.

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Cómo citar (APA 7)

Zhang, Y., Chen, X., Rottman, B., & Collins, A. (2025). The Role of Working Memory in Reward-based Learning over Long Timeframes: Opponent Bootstrapping and Interference Effects of Working Memory on Incremental Learning. https://doi.org/10.31234/osf.io/vb9u6_v1