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Objective: We examined the effect of interruption modality (visual or auditory) on primary task (visual) resumption to determine which modality was the least disruptive. Background: Theories examining interruption modality have focused on specific periods of the interruption timeline. Preemption theory has focused on the switch from the primary task to the interrupting task. Multiple resource theory has focused on interrupting tasks that are to be performed concurrently with the primary task. Our focus was on examining how interruption modality influences task resumption. We leverage the memory-for-goals theory, which suggests that maintaining an associative link between environmental cues and the suspended primary task goal is important for resumption. Method: Three interruption modality conditions were examined: auditory interruption with the primary task visible, auditory interruption with a blank screen occluding the primary task, and a visual interruption occluding the primary task. Reaction time and eye movement data were collected. Results: The auditory condition with the primary task visible was the least disruptive. Eye movement data suggest that participants in this condition were actively maintaining an associative link between relevant environmental cues on the primary task interface and the suspended primary task goal during the interruption. Conclusion: These data suggest that maintaining cue association is the important factor for reducing the disruptiveness of interruptions, not interruption modality. Application: Interruption-prone computing environments should be designed to allow for the user to have access to relevant primary task cues during an interruption to minimize disruptiveness.

Objective: A computational process model could explain how the dynamic interaction of human cognitive mechanisms produces each of multiple error types. Background: With increasing capability and complexity of technological systems, the potential severity of consequences of human error is magnified. Interruption greatly increases people's error rates, as does the presence of other information to maintain in an active state. Method: The model executed as a software-instantiated Monte Carlo simulation. It drew on theoretical constructs such as associative spreading activation for prospective memory, explicit rehearsal strategies as a deliberate cognitive operation to aid retrospective memory, and decay. Results: The model replicated the 30% effect of interruptions on postcompletion error in Ratwani and Trafton's Stock Trader task, the 45% interaction effect on postcompletion error of working memory capacity and working memory load from Byrne and Bovair's Phaser Task, as well as the 5% perseveration and 3% omission effects of interruption from the UNRAVEL Task. Conclusion: Error classes including perseveration, omission, and postcompletion error fall naturally out of the theory. Application: The model explains post-interruption error in terms of task state representation and priming for recall of subsequent steps. Its performance suggests that task environments providing more cues to current task state will mitigate error caused by interruption. For example, interfaces could provide labeled progress indicators or facilities for operators to quickly write notes about their task states when interrupted.

Objective: Computational models of identification and relative comparison tasks performed on color-coded data visualizations were presented and evaluated against two experiments. In this context, the possibility of a dual-use color scale, useful for both tasks, was explored, and the use of the legend was a major focus. Background: Multicolored scales are superior to ordered brightness scales for identification tasks, such as determining the absolute numeric value of a represented item, whereas ordered brightness scales are superior for relative comparison tasks, such as determining which of two represented items has a greater value. Method: Computational models were constructed for these tasks, and their predictions were compared with the results of two experiments. Results: The models fit the experimental results well. A multicolored, brightness-ordered dual-use scale supported high accuracy on both tasks and fast responses on a comparison task but relatively slower responses on the identification task. Conclusion: Identification tasks are solved by a serial visual search of the legend, whose speed and accuracy are a function of the discriminability of the color scales. Comparison tasks with multicolored scales are performed by a parallel search of the legend; with brightness scales, comparison tasks are generally solved by a direct comparison between colors on the visualization, without reference to the legend. Finally, it is possible to provide users a dual-use color scale effective on both tasks. Application: Trade-offs that must typically be made in the design of color-coded visualizations between speed and accuracy or between identification and comparison tasks may be mitigated.

This research adopted a model of goal activation to study the mechanisms underlying interrupted task performance. The effects of interruption timing, type of interruption, and age on task time and primary task resumption time were explored under conditions in which attention was switched back and forth between two tasks, much as when drivers shift attention between attending to the road and to an in-vehicle task. The timing of interruptions had a significant impact on task resumption times, indicating that the most costly time to interrupt task performance is during the middle of a task. However, this effect was overshadowed by age-related performance decrements for older participants. Interruptions that prevented strategic rehearsal of goals resulted in longer resumption times as compared with interruptions that allowed rehearsal. Actual or potential applications of this research include the design of in-vehicle device user interfaces, the timing of in-vehicle messages, and current metrics for assessing driver distraction.

Objective: This work investigated the impact of uncertainty representation on performance in a complex authentic visualization task, submarine localization. Background: Because passive sonar does not provide unique course, speed, and range information on a contact, the submarine operates under significant uncertainty. There are many algorithms designed to address this problem, but all are subject to uncertainty. The extent of this solution uncertainty can be expressed in several ways, including a table of locations (course, speed, range) or a graphical area of uncertainty. Method: To test the hypothesis that the representation of uncertainty that more closely matches the experts' preferred representation of the problem would better support performance, even for the nonexpert., performance data were collected using displays that were either stripped of the spatial or the tabular representation. Results: Performance was more accurate when uncertainty was displayed spatially. This effect was only significant for the nonexperts for whom the spatial displays supported almost expert-like performance. This effect appears to be due to reduced mental effort. Conclusion: These results suggest that when the representation of uncertainty for this spatial task better matches the expert's preferred representation of the problem even a nonexpert can show expert-like performance. Application: These results could apply to any domain where performance requires working with highly uncertain information.

Objective: A novel model of measuring clutter in complex geospatial displays was compared with human ratings of subjective clutter as a measure of convergent validity. The new model is called the color-clustering clutter (C3) model. Background: Clutter is a known problem in displays of complex data and has been shown to affect target search performance. Previous clutter models are discussed and compared with the C3 model. Method: Two experiments were performed. In Experiment 1, participants performed subjective clutter ratings on six classes of information visualizations. Empirical results were used to set two free parameters in the model. In Experiment 2, participants performed subjective clutter ratings on aeronautical charts. Both experiments compared and correlated empirical data to model predictions. Results: The first experiment resulted in a .76 correlation between ratings and C3. The second experiment resulted in a .86 correlation, significantly better than results from a model developed by Rosenholtz et al. Outliers to our correlation suggest further improvements to C3. Conclusions: We suggest that (a) the C3 model is a good predictor of subjective impressions of clutter in geospatial displays, (b) geospatial clutter is a function of color density and saliency (primary C3 components), and (c) pattern analysis techniques could further improve C3. Application: The C3 model could be used to improve the design of electronic geospatial displays by suggesting when a display will be too cluttered for its intended audience.