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.

Combined Effects of Moderate Hypoxia and Sleep Restriction on Mental Workload

Aircraft pilots face a high mental workload (MW) under environmental constraints induced by high altitude and sometimes sleep restriction (SR). Our aim was to assess the combined effects of hypoxia and sleep restriction on cognitive and physiological responses to different MW levels using the Multi-Attribute Test Battery (MATB)-II with an additional auditory Oddball-like task. Seventeen healthy subjects were subjected in random order to three 12-min periods of increased MW level (low, medium, and high): sleep restriction (SR, <3 h of total sleep time (TST)) vs. habitual sleep (HS, >6 h TST), hypoxia (HY, 2 h, FIO2 = 13.6%, ~3500 m vs. normoxia, NO, FIO2 = 21%). Following each MW level, participants completed the NASA-TLX subjective MW scale. Increasing MW decreases performance on the MATB-II Tracking task (p = 0.001, MW difficulty main effect) and increases NASA-TLX (p = 0.001). In the combined HY/SR condition, MATB-II performance was lower, and the NASA-TLX score was higher compared with the NO/HS condition, while no effect of hypoxia alone was observed. In the accuracy of the auditory task, there is a significant interaction between hypoxia and MW difficulty (F(2-176) = 3.14, p = 0.04), with lower values at high MW under hypoxic conditions. Breathing rate, pupil size, and amplitude of pupil dilation response (PDR) to auditory stimuli are associated with increased MW. These parameters are the best predictors of increased MW, independently of physiological constraints. Adding ECG, SpO2, or electrodermal conductance does not improve model performance. In conclusion, hypoxia and sleep restriction have an additive effect on MW. Physiological and electrophysiological responses must be taken into account when designing a MW predictive model and cross-validation.

Derivation and Demonstration of a New Metric for Multitasking Performance

OBJECTIVE

We proposed and demonstrate a theory-driven, quantitative, individual-level estimate of the degree to which cognitive processes are degraded or enhanced when multiple tasks are simultaneously completed.

BACKGROUND

To evaluate multitasking, we used a performance-based cognitive model to predict efficient performance. The model controls for single-task performance at the individual level and does not depend on parametric assumptions, such as normality, which do not apply to many performance evaluations.

METHODS

Twenty participants attempted to maintain their isolated task performance in combination for three dual-task and one triple-task scenarios. We utilized a computational model of multiple resource theory to form hypotheses for how performance in each environment would compare, relative to the other multitask contexts. We assessed if and to what extent multitask performance diverged from the model of efficient multitasking in each combination of tasks across multiple sessions.

RESULTS

Across the two sessions, we found variable individual task performances but consistent patterns of multitask efficiency such that deficits were evident in all task combinations. All participants exhibited decrements in performing the triple-task condition.

CONCLUSIONS

We demonstrate a modeling framework that characterizes multitasking efficiency with a single score. Because it controls for single-task differences and makes no parametric assumptions, the measure enables researchers and system designers to directly compare efficiency across various individuals and complex situations.

APPLICATION

Multitask efficiency scores offer practical implications for the design of adaptive automation and training regimes. Furthermore, a system may be tailored for individuals or suggest task combinations that support productivity and minimize performance costs.

Examining Task Priority Effects in Multi-Task Management

The Strategic Task Overload Model (STOM) seeks to predict the choices of operators when they are confronted with multiple task options in an overloading situation. These situations characterize safety-critical incidents and work domains, such as a worker in a nuclear power plant or aircraft cockpit when multiple alarms sound, additional interruptions occur, and ongoing tasks must still be performed. Training and other safety considerations may instruct operators to “prioritize” certain tasks over others under these conditions, such as paying attention to the forward roadway while driving, instead of a passenger or a cell phone. However, priority may not exercise much power over the choice to switch tasks. Supporting prior data, the current experiment using a multi-task battery show priority instructions exerted no effect on the choice to switch task behavior; and in contrast to prior work, instructions also did not influence the time spent performing a prioritized task.

Examining conscientiousness as a key resource in resisting email interruptions: Implications for volatile resources and goal achievement

Within the context of the conservation of resources model, when a resource is deployed, it is depleted – albeit temporarily. However, when a ‘key’, stable resource, such as Conscientiousness, is activated (e.g., using a self‐control strategy, such as resisting an email interruption), we predicted that (1) another, more volatile resource (affective well‐being) would be impacted and that (2) this strategy would be deployed as a trade‐off, allowing one to satisfy task goals, at the expense of well‐being goals. We conducted an experience‐sampling field study with 52 email‐users dealing with their normal email as it interrupted them over the course of a half‐day period. This amounted to a total of 376 email reported across the sample. Results were analysed using random coefficient hierarchical linear modelling and included cross‐level interactions for Conscientiousness with strategy and well‐being. Our first prediction was supported – deploying the stable, key resource of Conscientiousness depletes the volatile, fluctuating resource of affective well‐being. However, our second prediction was not fully realized. Although resisting or avoiding an email interruption was perceived to hinder well‐being goal achievement by Conscientious people, it had neither a positive nor negative impact on task goal achievement. Implications for theory and practice are discussed.

Practitioner points

It may be necessary for highly Conscientious people to turn off their email interruption alerts at work, in order to avoid the strain that results from an activation‐resistance mechanism afforded by the arrival of a new email.

Deploying key resources means that volatile resources may be differentially spent, depending on one's natural tendencies and how these interact with the work task and context. This suggests that the relationship between demands and resources is not always direct and predictable.

Practitioners may wish to appraise the strategies they use to deal with demands such as email at work, to identify if these strategies are assisting with task or well‐being goal achievement, or whether they have become defunct through automation.

Circadian Effects on Simple Components of Complex Task Performance

The goal of this study was to advance understanding and prediction of the impact of circadian rhythm on aspects of complex task performance during unexpected automation failures, and subsequent fault management. Participants trained on two tasks: a process control simulation, featuring automated support; and a multi-tasking platform. Participants then completed one task in a very early morning (circadian night) session, and the other during a late afternoon (circadian day) session. Small effects of time of day were seen on simple components of task performance, but impacts on more demanding components, such as those that occur following an automation failure, were muted relative to previous studies where circadian rhythm was compounded with sleep deprivation and fatigue. Circadian low participants engaged in compensatory strategies, rather than passively monitoring the automation. The findings and implications are discussed in the context of a model that includes the effects of sleep and fatigue factors.

The Role of Individual Differences in Executive Attentional Networks and Switching Choices in Multi-Task Management

Individual differences in cognitive processing relate to critical performance differences in real-world environments. Task switching is required for many of them and especially for task management during overload. Research exploring individual differences related to switching behavior (both frequency, and adherence to “optimal” switch times) is, however, sparse. We examined these relationships here, using the attentional network task to index executive control, and an ongoing tracking task (within a larger suite of concurrent task demands) to examine switching behavior. The results failed to support a general relationship between executive control and frequency in a complex, heterogeneous multi-task environment. However, higher executive control participants more successfully exploited “optimal” switching times, highlighting the varying role of individual differences in task management, when choice is unconstrained.

Visual attention allocation between robotic arm and environmental process control

Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version, The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An unweighted model based on attributes of difficulty, interest and salience accounted for 96% of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task.

The Human Factors of Cyber Network Defense

Technology’s role in the fight against malicious cyber-attacks is critical to the increasingly networked world of today. Yet, technology does not exist in isolation: the human factor is an aspect of cyber-defense operations with increasingly recognized importance. Thus, the human factors community has a unique responsibility to help create and validate cyber defense systems according to basic principles and design philosophy. Concurrently, the collective science must advance. These goals are not mutually exclusive pursuits: therefore, toward both these ends, this research provides cyber-cognitive links between cyber defense challenges and major human factors and ergonomics (HFE) research areas that offer solutions and instructive paths forward. In each area, there exist cyber research opportunities and realms of core HFE science for exploration. We raise the cyber defense domain up to the HFE community at-large as a sprawling area for scientific discovery and contribution.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching

OBJECTIVE

The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible.

BACKGROUND

The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed.

METHOD

In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker.

RESULTS

Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation.

CONCLUSIONS

The multiattribute decision model provided a good fit to the data.

APPLICATIONS

The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.

Effort in human factors performance and decision making

OBJECTIVE

The aim of this study was to demonstrate the importance of effort in human factors.

BACKGROUND

Effort has made its appearance in several diverse formats and applications. Eight of these are integrated in the current writing related to learning, looking, task switching, visual search termination, information access, choosing decision strategies, and behaving safely.

METHOD

This is based upon a literature review.

RESULTS

The common elements of these different effort applications are highlighted, particularly, their manifestations in either implicit or explicit expected value decisions.

CONCLUSIONS

There is a need to show how the metrics of effort and workload assessment influence decisions in human factors, particularly, those related to safety.

Using Empirical Research and Computational Modeling to Predict Operator Response to Unexpected Events

This paper describes an effort to model and predict astronaut performance during sudden workload transitions in long duration missions. Our approach to the work is heavily based on empirical research. We have performed a set of meta-analyses 1) to identify the quantitative effects of poor sleep on task accuracy and task completion time, and 2) to develop a model of operator task selection during multitasking. We are currently developing a model, based on a literature review, to predict the effects of automation design factors on operator task performance. This paper gives an overview of the project, presents the overall model of operator performance during a workload transition, and describes the empirical and theoretical underpinnings of a model that predicts the effects of automation design on operator performance.