Technology Category
Natural Language Processing: Lexical Semantics and MorphologyCognitive Modeling & Cognitive Systems: AnalogySearch and Optimization: Metareasoning and Metaheuristics
Strategic resource allocation in memory encoding: An efficiency principle shaping language processing
🤖 AI Summary
This study investigates how working memory resources are strategically allocated under capacity constraints to optimize language comprehension and production. Methodologically, it adopts a resource-rational framework, formalizes a principle of memory encoding efficiency, and integrates natural-corpus statistical modeling, dependency distance analysis, and dual-perspective empirical testing (comprehension and production) across typologically diverse languages. The key contributions are: (1) identification of a cross-linguistically robust, dynamic encoding mechanism that prioritizes novel, low-predictability information; (2) evidence that long-distance dependencies with low predictability attenuate locality effects—providing convergent cross-linguistic support for strategic resource allocation; and (3) discovery of significant typological variation, clarifying systematic interactions between syntactic structure and resource allocation strategies. Collectively, these findings advance our understanding of the adaptive, context-sensitive regulatory mechanisms governing working memory in real-time language processing.
Technology Category
Application Category
📝 Abstract
How is the limited capacity of working memory efficiently used to support human linguistic behaviors? In this paper, we investigate strategic resource allocation 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, enhancing their representations to make them less susceptible to memory decay and interference. Theoretically, from a resource-rational perspective, we argue that this efficiency principle naturally arises from two functional assumptions about working memory, namely, its limited capacity and its noisy representation. Empirically, through naturalistic corpus data, we find converging evidence for strategic resource allocation in the context of dependency locality from both the production and the comprehension side, where non-local dependencies with less predictable antecedents are associated with reduced locality effect. However, our results also reveal considerable cross-linguistic variability, highlighting the need for a closer examination of how strategic resource allocation, as a universal efficiency principle, interacts with language-specific phrase structures.
Problem
Research questions and friction points this paper is trying to address.
Efficient use of working memory in language processing
Strategic allocation of memory resources for novel information
Cross-linguistic variability in memory encoding strategies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic strategic allocation of working memory resources
Prioritization of novel and unexpected information
Resource-rational perspective on memory encoding efficiency
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