C. A. P. Smith

Interruption management: the use of attention-directing tactile cues.

Previous research has suggested that providing informative cues about interrupting stimuli aids management of multiple tasks. However, auditory and visual cues can be ineffective in certain situations. The objective of the present study was to explore whether attention-directing tactile cues aid or interfere with performance. A two-group posttest-only randomized experiment was conducted. Sixty-one participants completed a 30-min performance session consisting of aircraft-monitoring and gauge-reading computer tasks. Tactile signals were administered to a treatment group to indicate the arrival and location of interrupting tasks. Control participants had to remember to visually check for the interrupting tasks. Participants in the treatment group responded to more interrupting tasks and responded faster than did control participants. Groups did not differ on error rates for the interrupting tasks, performance of the primary task, or subjective workload perceptions. In the context of the tasks used in the present research, tactile cues allowed participants to effectively direct attention where needed without disrupting ongoing information processing. Tactile cues should be explored in a variety of other visual, interruptladen environments. Potential applications exist for aviation, user-interface design, vigilance tasks, and team environments.

Interruption management: A comparison of auditory and tactile cues for both alerting and orienting

Tactile and auditory cues have been suggested as methods of interruption management for busy visual environments. The current experiment examined attentional mechanisms by which cues might improve performance. The findings indicate that when interruptive tasks are presented in a spatially diverse task environment, the orienting function of tactile cues is a critical component, which directs attention to the location of the interruption, resulting in superior interruptive task performance. Non-directional tactile cues did not degrade primary task performance, but also did not improve performance on the secondary task. Similar results were found for auditory cues. The results support Posner and Petersons [1990. The attention system of the human brain. Annual Review of Neuroscience 13, 25-42] theory of independent functional networks of attention, and have practical applications for systems design in work environments that consist of multiple, visual tasks and time-sensitive information.

Overconfidence in projecting uncertain spatial trajectories

Objective The aim of this study was to understand factors that influence the prediction of uncertain spatial trajectories (e.g., the future path of a hurricane or ship) and the role of human overconfidence in such prediction. Background Research has indicated that human prediction of uncertain trajectories is difficult and may well be subject to overconfidence in the accuracy of forecasts as is found in event prediction, a finding that indicates that humans insufficiently appreciate the contributions of variance in nature to their predictions. Method In two experiments, our paradigm required participants to observe a starting point, a position at time T, and then make a prediction of the location of the trajectory at time NT. They experienced several trajectories from the same underlying model but perturbed by random variance in heading and speed. Results In Experiment 1A, people predicted linear paths well and were better in heading predictions than in speed predictions. However, participants greatly underestimated the variance in predicted location, indicating overconfidence. In Experiment 1B, the effect was replicated with frequencies rather than probabilities used in variance estimates. In Experiment 2, people predicted nonlinear trajectories poorly, and overconfidence was again observed. Overconfidence was reduced on the more difficult predictions. In both main experiments, those better at predicting the mean were not better at predicting the variance. Conclusions Predicting the level of uncertainty in spatial trajectories is not well done and may involve qualitatively different abilities than prediction of the mean. Application Improving real-world performance at prediction demands developing better understanding of variability, not just the average case. Biases in prediction of uncertainty may be addressed through debiasing training and/or visualization tools that could assist in more calibrated action planning.

Tactile interruption management: tactile cues as task-switching reminders

Tactile cuing has been suggested as a method of interruption management for busy visual environments. This study examined the effectiveness of tactile cues as an interruption management strategy in a multi-tasking environment. Sixty-four participants completed a continuous aircraft monitoring task with periodic interruptions of a discrete gauge memory task. Participants were randomly assigned to two groups; one group had to remember to monitor for interruptions while the other group received tactile cues indicating an interruption’s arrival and location. As expected, the cued participants evidenced superior performance on both tasks. The results are consistent with the notion that tactile cues transform the resource-intensive, time-based task of remembering to check for interruptions into a simpler, event-based task, where cues assume a portion of the workload, permitting the application of valuable resources to other task demands. This study is discussed in the context of multiple resource theory and has practical implications for systems design in environments consisting of multiple, visual tasks and time-sensitive information.

Mapping Spatial Uncertainty in Prediction

Past research in a range of domains has suggested that individuals tend to underestimate the contribution of random factors in predicting the future and therefore have difficulties understanding variance. This study explored understanding of central tendency versus understanding of underlying variance in a novel spatial task. Participants were required to predict the most likely outcome of an object’s somewhat uncertain movement, as well as estimate its probability of it being present at a number of potential locations. Our findings indicate that individuals became adept at predicting the target location, but they were relatively insensitive to the change variability in the underlying distribution and vastly overestimated the likelihood of samples around the mean. Additionally, there appeared to be no strong relationship between performance on prediction of the mean and performance on estimation of the likelihood. These findings highlight the need for interventions to improve human understanding of variability in order to facilitate prediction in real-world settings.

Effect of Visualization Training on Uncertain Spatial Trajectory Predictions

Objective: The goal of this study was to explore the ways in which visualizations influence the prediction of uncertain spatial trajectories (e.g., the unknown path of a downed aircraft or future path of a hurricane) and participant overconfidence in such prediction. Background: Previous research indicated that spatial predictions of uncertain trajectories are challenging and are often associated with overconfidence. Introducing a visualization aid during training may improve the understanding of uncertainty and reduce overconfidence. Method: Two experiments asked participants to predict the location of various trajectories at a future time. Mean and variance estimates were compared for participants who were provided with a visualization and those who were not. Results: In Experiment 1, participants exhibited less error in mean estimations when a linear visualization was present but performed worse than controls once the visualization was removed. Similar results were shown in Experiment 2, with a nonlinear visualization. However, in both experiments, participants who were provided with a visualization did not retain any advantage in their variance estimations once the visualization was removed. Conclusions: Visualizations may support spatial predictions under uncertainty, but they are associated with benefits and costs for the underlying knowledge being developed. Application: Visualizations have the potential to influence how people make spatial predictions in the presence of uncertainty. Properly designed and implemented visualizations may help mitigate the cognitive biases related to such predictions.

Purchasing Information to Reduce Uncertainty in Trajectory Prediction

Previous studies show people underestimate the amount of uncertainty in predictive states. This experiment tested an implication of such findings: that people will be less likely (than optimal) to acknowledge their uncertainty, and hence less likely to purchase information that reduces future uncertainty. In a trajectory prediction task, participants had the opportunity to reduce uncertainty by a constant amount by purchasing either an expensive (bad deal), neutral-cost, or cheap (bargain) sensor. Participants were biased, relative to expected value theory, in integrating cost with uncertainty reduction in their decisions to purchase. In contrast to our primary hypothesis that they would place more decision weighting on reducing the cost of the purchase, than on gaining information, our results revealed the opposite. The findings are interpreted in the context of models of attribute salience in decision-making.

Anchoring and Adjustment With Spatial Uncertainty in Trajectory Prediction

Anchoring and adjustment is a prevalent heuristic, common in a range of settings and decisions. While it is well studied using values, there has been limited research on its function in visual-spatial domains. The present study explored the role of anchoring and adjustment with visual displays containing uncertainty information related to spatial prediction. Participants were given a graphical briefing to anchor them on accurate, inaccurate, or no information regarding the future behavior of an object (both its average behavior and the variance in behavior). They then made predictions of future object location and estimated its likelihood at multiple locations. Overall individuals utilized the anchoring information and were able to adjust to incorrect anchors. However, individuals vastly overestimated the likelihood the object would be at any given location, suggesting that they were not anchored on the variance.

Attention Does Not Improve Impaired Understanding Of Variability In Spatial Prediction

Understanding variability of uncertain systems is often vital for decision makers, yet is habitually disregarded in favor of developing superior capability to predict most likely outcomes. One potential path to improving appreciation of variability is simply to attend more carefully to it. The present study explores a trade-off in the ability to predict average trajectories and estimate the variability in a spatial prediction task. Through instructional and task manipulations, some participants were encouraged to emphasize variability in a task that had previously shown good performance of mean prediction but poor estimation of variability. Overall estimation of variability was poor in both the prediction emphasis condition, and crucially in the variability emphasis condition. The results suggest overlooking variability is not just the result of neglecting that dimension, but rather represents a more systematic limitation in human performance. The implications of these findings are discussed.

Calibrating Uncertainty: Commonalities in the Estimation of Numeric Variability Versus Spatial Prediction

To assess whether there may be a common ability related to the understanding and calibration of instance variability and mean behavior, participants performed spatial prediction and numeric estimation tasks. In the first task, participants experienced variability in a set of spatial trajectories whose endpoints they predicted along with a central mean. In the second task, they experienced variability in a set of random numbers whose mean and variability they estimated. For both tasks, estimated variability was compared with the true variability of instances to derive measures of bias (e.g., over-or under-estimation) and precision. Correlations between these estimates across the two experiments revealed mixed evidence for a common ability to estimate variability, but suggested similar performance when estimating mean behavior. Implications for individual differences and interventions are discussed.