Dual-Task Interference in a Simulated Driving Environment: Serial or Parallel Processing?

Abbaszadeh, Mojtaba; Gholam‐Ali Hossein‐Zadeh; Vaziri-Pashkam, Maryam · 2021 · OpenAlex-citations

DOI: 10.3389/fpsyg.2020.579876

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Summary

This study investigates the cognitive mechanisms underlying dual-task interference in a naturalistic setting, specifically examining whether concurrent tasks are processed serially or in parallel. While dual-task interference—characterized by increased reaction times and decreased accuracy when tasks are temporally close—is well-documented in artificial laboratory paradigms, its underlying processes in real-world scenarios like driving remain unclear. The authors aimed to determine if the bottleneck theory (serial processing), capacity-sharing theory (parallel processing), or a hybrid model best explains performance declines in a simulated driving environment. Additionally, the study explored whether higher-order executive functions manage task order when the sequence of stimuli is unpredictable, a common feature of real-world multitasking. The researchers employed a dual-task paradigm involving a lane-change driving task and an image discrimination task (identifying faces vs. scenes) within a Unity 3D simulated driving environment. Twenty healthy adult participants performed these tasks while the Stimulus Onset Asynchrony (SOA) between the two tasks was systematically varied across eight intervals ranging from -600 ms to +600 ms. The experiment included both predictable and unpredictable task-order conditions. Reaction times and accuracies were recorded, and the data were analyzed using Drift-Diffusion Modeling (DDM) to decompose performance into evidence accumulation rates (drift rate) and non-decision times (perceptual and motor delays). This modeling approach allowed the authors to test specific predictions of competing dual-task theories regarding how processing stages are shared or bottlenecked. The results demonstrated that reaction times in dual-task conditions were significantly higher than in single-task conditions, with performance declining as the SOA decreased. Crucially, the DDM analysis revealed that dual-task interference affected both the rate of evidence accumulation and the non-decision times for the tasks. This pattern of results contradicted the pure bottleneck theory, which predicts only delays in non-decision time, and the pure capacity-sharing theory, which predicts only reduced drift rates. Instead, the findings supported a hybrid model where decision stages are processed in parallel but with shared resources, alongside a bottleneck in mapping decisions to motor responses. Furthermore, when the task order was unpredictable, participants dynamically adjusted the order of their responses based on task difficulty, which attenuated the effect of SOA on performance. These findings suggest that dual-task processing in naturalistic environments involves a hybrid mechanism combining parallel evidence accumulation with serial motor execution constraints. The study highlights the role of higher-level executive functions in optimizing task scheduling to minimize total reaction time, particularly when task order is unpredictable. By extending dual-task research from artificial to simulated real-world contexts, the paper provides a more ecologically valid understanding of cognitive limitations during complex activities like driving, implying that resource management and flexible task ordering are critical components of multitasking performance.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• dual task performance
• dual task multitasking
• temporal
• cognitive capacity variation
• mental demand
• manual

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: theory or model