Efficient Allocation of Working Memory Resource for Utility Maximization in Humans and RNNs
Qingqing Yang, Hsin-Hung Li
Working memory (WM) supports the temporary retention of information but is limited in capacity and subject to noise. Flexibly allocating WM resource is therefore critical for adaptive behavior. While some studies show that humans can prioritize information based on attention, others suggest WM is insensitive to reward—raising open questions about whether humans can efficiently allocate WM resource based on utility or environmental statistics. To address this, we conducted behavioral experiments where participants learned stimulus–reward associations and performed a delayed-estimation WM task. WM precision was influenced by both environmental statistics and rewards. When rewards were uniform, memory was more precise for cardinal orientations (the oblique effect)—reflecting sensitivity to the naturalistic priors. When rewards were manipulated, high-reward information was maintained more stably, with effects increasing over time—indicating dynamic, value-guided resource allocation. To interpret these findings, we (1) evaluated an ideal observer model with efficient coding, showing that optimal WM allocation should reflect both stimulus probability and reward, and that these effects accumulate over delays. (2) We trained recurrent neural networks (RNNs) to maximize utility under a 2×2 design: prior (uniform vs. environmental) × reward policy (baseline vs. reward context). In the RNNs, memory is more stable for stimuli associated with higher probability or rewards, mirroring human behavior in leveraging both environmental statistics and rewards. Together, these results provide converging behavioral and computational evidence that WM resource allocation is shaped by both environmental statistics and rewards. This work offers insight into how intelligent systems can dynamically optimize memory under resource constraints.