How the Working Memory with Distributed Executive Control Model Accounts for Task Switching and Dual-Task Coordination Costs

Vandierendonck, André · 2021 · Crossref

DOI: 10.5334/joc.138

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Summary

This paper presents a computational implementation of the Working Memory with Distributed Executive Control (WMDEC) model to explain mechanisms underlying task switching and dual-task coordination costs. The research addresses how human cognition reconciles the need for action persistence with the flexibility required for rapid task changes. Unlike models relying on a central executive, WMDEC posits that executive control is distributed across low-level processes embedded in working memory. The model extends Baddeley’s multicomponent framework by replacing the central executive with an executive memory module dedicated to storing task-set information, including goals, means, and situational constraints. The study utilizes a computational simulation of the WMDEC model to generate predictions for published experiments involving task switching, attention switching, and dual-task coordination. The model’s architecture includes declarative and procedural long-term memory, sensory memories, and four working memory modules: the phonological buffer, visuospatial module, episodic buffer, and executive module. Control is achieved through condition-action production rules that match environmental states and trigger actions. The simulation operates in 10-millisecond cycles, allowing parallel processing of modules while maintaining sequential execution of rules. Key parameters govern activation growth, lateral inhibition, phonological decay, and rule learning rates. The model accounts for capacity limits in the episodic buffer and uses attentional refreshment to maintain information. The simulations demonstrate that the WMDEC model can account for performance costs observed in multitasking scenarios. By modeling the maintenance of task sets and the competition between memory traces, the model explains delays and errors associated with switching tasks or coordinating dual tasks. The distributed nature of executive control allows the model to simulate how learned rules manage goal-directed activities without a central agent. The results show that the model successfully replicates human performance patterns in experiments requiring the coordination of memorization and task switching demands. The significance of this work lies in providing a unified account of working memory and executive control. By integrating storage and control functions within a single computational framework, the WMDEC model offers an alternative to theories relying on a central executive. The findings support the view that executive control emerges from distributed, low-level processes acquired through associative learning. This approach has implications for understanding cognitive load and multitasking efficiency, suggesting that task coordination costs arise from the interaction of memory maintenance and rule-based control processes rather than a bottleneck in a central executive system.

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Topics

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• executive function
• task switching
• working memory