Strategic resource allocation in memory encoding: An efficiency principle shaping language processing

How is the limited capacity of working memory efficiently used to support human linguistic behaviors? In this paper, we propose Strategic Resource Allocation (SRA) as an efficiency principle for memory encoding in sentence processing. The idea is that working memory resources are dynamically and strategically allocated to prioritize novel and unexpected information. From a resource-rational perspective, we argue that SRA is the principled solution to a computational problem posed by two functional assumptions about working memory, namely its limited capacity and its noisy representation. Specifically, working memory needs to minimize the retrieval error of past inputs under the constraint of limited memory resources, an optimization problem whose solution is to allocate more resources to encode more surprising inputs with higher precision. One of the critical consequences of SRA is that surprising inputs are encoded with enhanced representations, and therefore are less susceptible to memory decay and interference. Empirically, through naturalistic corpus data, we find converging evidence for SRA in the context of dependency locality from both production and comprehension, where non-local dependencies with less predictable antecedents are associated with reduced locality effect. However, our results also reveal considerable cross-linguistic variability, suggesting the need for a closer examination of how SRA, as a domain-general memory efficiency principle, interacts with language-specific phrase structures. SRA highlights the critical role of representational uncertainty in understanding memory encoding. It also provides a reinterpretation for the effects of surprisal and entropy on processing difficulty from the perspective of efficient memory encoding. • WM resources are strategically allocated to prioritize unexpected information. • Theoretically, SRA arises as an efficient solution to a computational problem of WM. • SRA predicts that high-surprisal inputs are encoded with higher precision. • Higher precision in turn suggests more robust representation against interference. • Empirically, we found that high-surprisal inputs exhibit reduced locality effect.