Priority influences task selection decisions in multi-task management.

The role of task priority on task selection in multi-task management is unclear based on prior work, leading to a common finding of 'priority neglect'. However, properties such as urgency and conflict may influence whether operators weigh priority in their decision. We examined the role of instructed task prioritization, bolstered by more urgent and conflicting conditions, on how operators select among emergent, concurrent tasks when multitasking. Using the Multi-Attribute Task Battery (MATB) multitasking platform we tested both an auditory communications task and a manual tracking task as the priority tasks. Results showed that instructed priority significantly increased target task selection under the conflicting task conditions for both tasks. Urgency itself may modulate whether instructions to prioritize affect task selection choices when multitasking, and therefore counter to prior results instructions may yet be useful for helping operators select a higher priority task under conflict, a generalizable effect to be further explored.

Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures.

OBJECTIVES: This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance. BACKGROUND: During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks. METHOD: Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives. RESULTS: The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3. CONCLUSION: Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two. APPLICATION: Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.

Exploring the Effects of Interruptions in Different Phases of Complex Decision-Making Tasks.

OBJECTIVE: The study aims to examine the effects of interruptions in major phases (i.e., problem-identification, alternative-development, and evaluation-and-selection) of complex decision-making tasks. BACKGROUND: The ability to make complex decisions is of increasing importance in workplaces. Complex decision-making involves a multistage process and is likely to be interrupted, given the ubiquitous prevalence of interruptions in workplaces today. METHOD: Sixty participants were recruited for the experiment to complete a procurement task, which required them to define goals, search for alternatives, and consider multiple attributes of alternatives to make decisions. Participants in the three experimental conditions were interrupted to respond to messages during one of these three phases, whereas participants in the control condition were not interrupted. The impacts of interruptions on performance, mental workload, and emotional states were measured through a combination of behavioral, physiological, and subjective evaluations. RESULTS: Only participants who were interrupted in the evaluation-and-selection phase exhibited poorer task performance, despite their positive feelings toward interruptions and confidence. Participants who were interrupted in the problem-identification phase reported higher mental workload and more negative perceptions toward interruptions. Interruptions in the alternative-development phase led to more temporal changes in arousal and valence than interruptions in other phases. CONCLUSION: Interruptions during the evaluation-and-selection phase undermine overall performance, and there is a discrepancy between behavioral outcomes and subjective perceptions of interruption effects. APPLICATION: Interruptions should be avoided in the evaluation-and-selection phase in complex decision-making. This phase information can be either provided by users or inferred from coarse-grained interaction activities with decision-making information systems.

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer.

OBJECTIVE: We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). BACKGROUND: Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. METHODS: Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver's right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. RESULTS: The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. CONCLUSION: The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Communication and Teamwork During Telemedicine-Enabled Stroke Care in an Ambulance.

OBJECTIVE: The purpose of this study is to understand the communication among care teams during telemedicine-enabled stroke consults in an ambulance. BACKGROUND: Telemedicine can have a significant impact on acute stroke care by enabling timely intervention in an ambulance before a patient reaches the hospital. However, limited research has been conducted on understanding and supporting team communication during the care delivery process for telemedicine-enabled stroke care in an ambulance. METHOD: Video recordings of 13 simulated stroke telemedicine consults conducted in an ambulance were coded to document the tasks, communication events, and flow disruptions during the telemedicine-enabled stroke care delivery process. RESULTS: The majority (82%) of all team interactions in telemedicine-enabled stroke care involved verbal interactions among team members. The neurologist, patient, and paramedic were almost equally involved in team interactions during stroke care, though the neurologist initiated 48% of all verbal interactions. Disruptions were observed in 8% of interactions, and communication-related issues contributed to 44%, with interruptions and environmental hazards being other reasons for disruptions in interactions during telemedicine-enabled stroke care. CONCLUSION: Successful telemedicine-enabled stroke care involves supporting both verbal and nonverbal communication among all team members using video and audio systems to provide effective coverage of the patient for the clinicians as well as vice versa. APPLICATION: This study provides a deeper understanding of team interactions during telemedicine-enabled stroke care that is essential for designing effective systems to support teamwork.

The Effect of Cognitive Load on Auditory Susceptibility During Automated Driving.

OBJECTIVE: We experimentally test the effect of cognitive load on auditory susceptibility during automated driving. BACKGROUND: In automated vehicles, auditory alerts are frequently used to request human intervention. To ensure safe operation, human drivers need to be susceptible to auditory information. Previous work found reduced susceptibility during manual driving and in a lesser amount during automated driving. However, in practice, drivers also perform nondriving tasks during automated driving, of which the associated cognitive load may further reduce susceptibility to auditory information. We therefore study the effect of cognitive load during automated driving on auditory susceptibility. METHOD: Twenty-four participants were driven in a simulated automated car. Concurrently, they performed a task with two levels of cognitive load: repeat a noun or generate a verb that expresses the use of this noun. Every noun was followed by a probe stimulus to elicit a neurophysiological response: the frontal P3 (fP3), which is a known indicator for the level of auditory susceptibility. RESULTS: The fP3 was significantly lower during automated driving with cognitive load compared with without. The difficulty level of the cognitive task (repeat or generate) showed no effect. CONCLUSION: Engaging in other tasks during automated driving decreases auditory susceptibility as indicated by a reduced fP3. APPLICATION: Nondriving task can create additional cognitive load. Our study shows that performing such tasks during automated driving reduces the susceptibility for auditory alerts. This can inform designers of semi-automated vehicles (SAE levels 3 and 4), where human intervention might be needed.

Stress Outcomes of Four Types of Perceived Interruptions.

Objective We sought to define and measure four types of perceived interruptions and to examine their relationships with stress outcomes. Background Interruptions have been defined and measured in a variety of inconsistent ways. No study has simultaneously examined the subjective experience of all types of interruptions. Method First, we provide a synthesized definition and model of interruptions that aligns interruptions along two qualities: origin and degree of multitasking. Second, we create and validate a self-report measure of these four types of perceived interruptions within two samples (working undergraduate students and working engineers). Last, we correlate this measure with self-reported psychological and physical stress outcomes. Results Our results support the four-factor model of interruptions. Results further support the link between each of the four types of interruptions (intrusions, breaks, distractions, and a specific type of ruminations, discrepancies) and stress outcomes. Specifically, results suggest that distractions explain a unique portion of variance in stress outcomes above and beyond the shared variance explained by intrusions, breaks, and discrepancies. Conclusion The synthesized four-factor model of interruptions is an adequate representation of the overall construct of interruptions. Further, perceived interruptions can be measured and are significantly related to stress outcomes. Application Measuring interruptions by observation can be intrusive and resource intensive. Additionally, some types of interruptions may be internal and therefore unobservable. Our survey measure offers a practical alternative method for practitioners and researchers interested in the outcomes of interruptions, especially stress outcomes.

Impact of Frequent Interruption on Nurses' Patient-Controlled Analgesia Programming Performance.

OBJECTIVE: The purpose was to add to the body of knowledge regarding the impact of interruption on acute care nurses' cognitive workload, total task completion times, nurse frustration, and medication administration error while programming a patient-controlled analgesia (PCA) pump. BACKGROUND: Data support that the severity of medication administration error increases with the number of interruptions, which is especially critical during the administration of high-risk medications. Bar code technology, interruption-free zones, and medication safety vests have been shown to decrease administration-related errors. However, there are few published data regarding the impact of number of interruptions on nurses' clinical performance during PCA programming. METHOD: Nine acute care nurses completed three PCA pump programming tasks in a simulation laboratory. Programming tasks were completed under three conditions where the number of interruptions varied between two, four, and six. Outcome measures included cognitive workload (six NASA Task Load Index [NASA-TLX] subscales), total task completion time (seconds), nurse frustration (NASA-TLX Subscale 6), and PCA medication administration error (incorrect final programming). RESULTS: Increases in the number of interruptions were associated with significant increases in total task completion time ( p = .003). We also found increases in nurses' cognitive workload, nurse frustration, and PCA pump programming errors, but these increases were not statistically significant. APPLICATIONS: Complex technology use permeates the acute care nursing practice environment. These results add new knowledge on nurses' clinical performance during PCA pump programming and high-risk medication administration.

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis.

OBJECTIVE: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. BACKGROUND: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. METHOD: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. RESULTS: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. CONCLUSION: Effects of cognitive load on driving are strongly selective and task dependent. APPLICATION: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.

Is Talking to Your Car Dangerous? It Depends: Prologue to the Special Section.

Cognitive distraction represents an important and growing traffic safety issue, particularly with the increasing computerization of cars. The target paper in this special section describes a protocol for assessing the distraction potential of information and entertainment systems. Cognitive distraction has specific relevance to the challenges facing driving safety but also reflects the more pervasive challenge of generalizing findings in the face of complex contextual and compensatory influences. Peer commentaries from five driving safety experts sketch paths forward in assessing the distraction potential of in-vehicle information technology. A simple, definitive statement regarding the risk of talking to your car is appealing, but the complexity of driver behavior may make such a statement unachievable.

Cognitive Distraction in the Wild: Next Steps-Addressing a Not-So-Humdrum Conundrum.

Strayer et al. in this volume show that increases in cognitive workload caused by drivers' involvement in distracting activities that allow them to keep their eyes on the road lead to decrements in indices of safe driving performance. Although there is agreement that in-vehicle tasks that require drivers to take their eyes off the road increase crash risk, there is mounting controversy about whether in-vehicle tasks that do not require drivers to take their eyes off the forward roadway increase crash risk-thus the conundrum: How can there be an abundance of cognitively distracting activities and controversy about whether such activities increase crash risk?

Driver Compensation: Impairment or Improvement?

Strayer et al.'s conclusion that their "cognitive distraction scale" for auditory-vocal tasks indicates "significant impairments to driving" is not supported by their data. Additional analysis demonstrates that slower brake reaction times during auditory-vocal tasks were fully compensated for by longer following distances to the lead car. Naturalistic driving data demonstrate that cellular conversation decreases crash risk, the opposite of the article's assumption. Hence, the scale's internal and external validities for indicating driving impairment are highly questionable.