Deciding when to switch tasks in time-critical multitasking

Kushleyeva, Yelena; Salvucci, Dario D.; Lee, Frank J.

doi:10.1016/j.cogsys.2004.09.005

Cited by 35 publications

(21 citation statements)

References 9 publications

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“…Lee (personal communication, April 19, 2005) found that participants were most likely to examine wind direction right around the 30-sec mark. As another example, Kushleyeva, Salvucci, and Lee (2005) showed that participants tended to switch from one visual search task to another at the halfway point of the full 30-sec task period to ensure they were not penalized on the other task. In both cases, people tended to perform the timed tasks not as soon as possible, but rather approximately at the time they deemed appropriate, given the temporal characteristics of the task.…”

Section: The General Executive Is Dependent On Timementioning

confidence: 99%

A Multitasking General Executive for Compound Continuous Tasks

Salvucci

2005

Cognitive Science

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As cognitive architectures move to account for increasingly complex real-world tasks, one of the most pressing challenges involves understanding and modeling human multitasking. Although a number of existing models now perform multitasking in real-world scenarios, these models typically employ customized executives that schedule tasks for the particular domain but do not generalize easily to other domains. This article outlines a general executive for the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture that, given independent models of individual tasks, schedules and interleaves the models' behavior into integrated multitasking behavior. To demonstrate the power of the proposed approach, the article describes an application to the domain of driving, showing how the general executive can interleave component subtasks of the driving task (namely, control and monitoring) and interleave driving with in-vehicle secondary tasks (radio tuning and phone dialing).

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Section: The General Executive Is Dependent On Timementioning

confidence: 99%

A Multitasking General Executive for Compound Continuous Tasks

Salvucci

2005

Cognitive Science

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“…Elapsed time in a task is found to be a good predictor of urgency to switch away to another task (Kushleyeva et al 2005). Third, pre-knowledge of what is to be expected, in semantic memory, is used as a source for longer-term calibration of time-sharing.…”

Section: Users' Tactics and Strategies In Time-sharingmentioning

confidence: 98%

“…This notion is also relevant to mobile human-computer interaction where the competition is between mobility tasks (e.g., route planning, talking, waiting, estimating time-to-target, controlling personal space) and interaction tasks, which compete mainly for the visual and motor resources (Jameson and Klöckner 2005;Oulasvirta et al 2005). 2 Although one might easily think that external events are the root cause of diverted attention, time-sharing is actually largely driven by internal processes (Kushleyeva et al 2005). Satisfactory time-sharing requires the ability to create and schedule future intentions, the facility to remember, maintain and prioritize them, and the ability to switch from carrying out one intention to another when needed (Burgess 2000).…”

Section: Internal and External Constituents Of Time-sharingmentioning

confidence: 99%

Predicting time-sharing in mobile interaction

Miettinen

Oulasvirta

2007

User Model User-Adap Inter

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The era of modern personal and ubiquitous computers is beset with the problem of fragmentation of the user's time between multiple tasks. Several adaptations have been envisioned that would support the performance of the user in the dynamically changing contexts in which interactions with mobile devices take place. This paper assesses the feasibility of sensor-based prediction of time-sharing, operationalized in terms of the number of glances, the duration of the longest glance, and the total and average durations of the glances to the interaction task. The data used for constructing and validating the predictive models was acquired from a field study (N = 28), in which subjects performing mobile browsing tasks were observed for approximately 1 h in a variety of environments and situations. The predictive accuracy achieved in binary classification tasks was about 70% (about 20% above default), and the most informative sensors were related to the environment and interactions with the mobile device. Implications to the feasibility of different kinds of adaptations are discussed.

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“…Although the TSS metric is indirectly affected by task urgency, the effect should be modeled more directly. For further discussion of the impact of time pressure for cognitive state, see [24] or [25].…”

Section: B Statistical Analysismentioning

confidence: 99%

Real time modeling of the cognitive load of an Urban Search And Rescue robot operator

Colin

Smets²,

Mioch³

et al. 2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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Urban Search And Rescue (USAR) robots are used to find and save victims in the wake of disasters such as earthquakes or terrorist attacks. The operators of these robots are affected by high cognitive load; this hinders effective robot usage. This paper presents a cognitive task load model for real-time monitoring and, subsequently, balancing of workload on three factors that affect operator performance and mental effort: time occupied, level of information processing, and number of task switches. To test an implementation of the model, five participants drove a shape-shifting USAR robot, accumulating over 16 hours of driving time in the course of 485 USAR missions with varying objectives and difficulty. An accuracy of 69% was obtained for discrimination between low and high cognitive load; higher accuracy was measured for discrimination between extreme cognitive loads. This demonstrates that such a model can contribute, in a non-invasive manner, to estimating an operator's cognitive state. Several ways to further improve accuracy are discussed, based on additional experimental results.

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Deciding when to switch tasks in time-critical multitasking

Cited by 35 publications

References 9 publications

A Multitasking General Executive for Compound Continuous Tasks

A Multitasking General Executive for Compound Continuous Tasks

Predicting time-sharing in mobile interaction

Real time modeling of the cognitive load of an Urban Search And Rescue robot operator

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