Discrete task switching in overload: A meta-analyses and a model
DOI: 10.1016/j.ijhcs.2015.01.002
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Summary
This paper addresses the problem of sequential multitasking under conditions of cognitive overload, where operators cannot perform tasks concurrently and must choose whether to continue an ongoing task or switch to an alternative. The authors aim to model the decision-making process behind these switches, focusing on the "choice" aspect rather than the timing or performance quality, which are covered by existing micro-level models. The motivation stems from real-world failures, such as aviation accidents, where operators neglect high-priority tasks due to cognitive tunneling or fixation on less critical activities. To develop the Strategic Task Overload Management (STOM) model, the authors conducted a meta-analytic review of 31 experiments from the literature on applied task switching. They categorized studies into four classes, focusing primarily on those involving heterogeneous tasks and voluntary switching. The STOM model is a discrete-event simulation that determines task attractiveness based on five attributes derived from the validated Scanning Eye-Event (SEEV) model: Salience, Effort, Priority, Interest, and Difficulty. The model posits a fundamental tendency to avoid switching, weighing the "stickiness" of the ongoing task against the attractiveness of alternative tasks. The meta-analysis yielded specific quantitative estimates for model parameters. The primary finding was a strong switch avoidance tendency, with operators preferring to stay on the current task approximately 60% of the time (confidence interval 58–62%). Regarding task attributes, the analysis confirmed that operators prefer switching to easier tasks, with a mean preference of 63% for easy over difficult alternatives. The study also established the polarity of other attributes: higher priority, greater interest, and higher salience increase the likelihood of switching to a task, while higher difficulty decreases it. For instance, visual reminders were found to be more salient than auditory ones in certain contexts, and interest can sometimes override priority in driving scenarios. However, reliable weight estimates for priority, interest, and salience were not available from the existing literature, though their directional influence was confirmed. The significance of this work lies in providing a normative, multi-attribute decision model that complements existing fine-grained models of multitasking. By focusing on the coarse-grained decision of *what* to do rather than *how long* it takes, STOM helps explain phenomena like cognitive tunneling and task shedding in high-workload environments. The authors conclude that while the model’s architecture and switch avoidance parameter are well-supported, further empirical research is needed to establish precise weights for priority, interest, and salience, as well as to understand the effects of time-on-task. This framework offers a tool for predicting human behavior in complex systems, potentially aiding in the design of interfaces that mitigate overload-related errors.
Key finding
Operators exhibit a strong inherent tendency to avoid switching tasks during overload, preferring to stay on the current task approximately 60% of the time, and when they do switch, they favor tasks that are easier, more salient, higher in priority, and more interesting.
Methodology
meta_analysis
Sample size: 31
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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