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

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.

Design and validation of a simulated multitasking environment for assessing the cognitive load on the infantry squad leader

The increasing cognitive load on infantry squad leaders is a common challenge in modern military operations. As this can increase health and safety risks, there is a need to study the factors responsible for the increase in cognitive load. Ecological situations inherently lack strong experimental controls; therefore, microworlds that simulate real tasks are the usual alternative to field studies. However, to the best of our knowledge, there are currently no microworlds that reproduce the main tasks of the squad leader during operations. This article adresses this gap by describing the design and validation of a new microworld: the Simulated Multitasking Environment for the Squad leader (SMES). Qualitative research was firstly conducted to highlight several squad leader's generic tasks (i.e., common to many situations in the field) that guided the design of the SMES. Psychometric validation of the SMES was then based on two experiments: (i) the first evaluated the microworld's psychometric qualities when tasks were performed individually; and (ii) the second explored concurrent tasks, reflecting real-world complexity. The results showed that the parameters manipulated for each task were relevant for inducing cognitive load, measured using a secondary detection response task and the NASA-TLX questionnaire. The SMES demonstrated satisfactory convergent and content validity in multitasking but not in single-task conditions. Performance in multitasking situations therefore does not seem to depend on task-specific skills, suggesting the existence of an independent factor-multitasking ability. Theoretical and practical implications of the SMES validation are discussed.

Attention switching through text dissimilarity: a cognition research on fragmented reading behavior

People tend to obtain information through fragmented reading. However, this behavior itself might lead to distraction and affect cognitive ability. To address it, it is necessary to understand how fragmented reading behavior influences readers' attention switching. In this study, the researchers first collected online news that had 6 theme words and 60 sentences to compose the experimental material, then defined the degree of text dissimilarity, used to measure the degree of attention switching based on the differences in text content, and conducted an EEG experiment based on P200. The results showed that even after reading the fragmented text content with the same overall content, people in subsequent cognitive tasks had more working memory capacity, lower working memory load, and less negative impact on cognitive ability with the text content with lower text dissimilarity. Additionally, attention switching caused by differences in concept or working memory representation of text content might be the key factor affecting cognitive ability in fragmented reading behavior. The findings disclosed the relation between cognitive ability and fragmented reading and attention switching, opening a new perspective on the method of text dissimilarity. This study provides some references on how to reduce the negative impact of fragmented reading on cognitive ability on new media platforms.

Examining post-error performance in a complex multitasking environment

Previous work on indices of error-monitoring strongly supports that errors are distracting and can deplete attentional resources. In this study, we use an ecologically valid multitasking paradigm to test post-error behavior. It was predicted that after failing an initial task, a subject re-presented with that task in conflict with another competing simultaneous task, would more likely miss their response opportunity for the competing task and stay 'tunneled' on the initially errored task. Additionally, we predicted that an error's effect on attention would dissipate after several seconds, making error cascades less likely when subsequent conflict tasks are delayed. A multi-attribute task battery was used to present tasks and collect measures of both post-error and post-correct performance. Results supported both predictions: post-error accuracy on the competing task was lower compared to post-correct accuracy, and error-proportions were higher at shorter delays, dissipating over time. An exploratory analysis also demonstrated that following errors (as opposed to post-correct trials), participants clicked more on the task panel of the initial error regardless of delay; this continued task-engagement provides preliminary support for errors leading to a cognitive tunneling effect.

Organizing for Mars: A Task Management Perspective on Work within Spaceflight Multiteam Systems

OBJECTIVE

The aim of this study was to examine how task, social, and situational factors shape work patterns, information networks, and performance in spaceflight multiteam systems (MTSs).

BACKGROUND

Human factors research has explored the task and individual characteristics that affect decisions regarding when and in what order people complete tasks. We extend this work to understand how the social and situational factors that arise when working in MTSs affect individual work patterns.

METHODS

We conducted a complex multi-site space analog simulation with NASA over the course of 3 years. The MTS task required participants from four teams (Geology, Robotics, Engineering, and Human Factors) to collaborate to design a well on Mars. We manipulated the one-way communication delay between the crew and mission support: no time lag, 60-second lag, and 180-second lag.

RESULTS

The study revealed that team and situational factors exert strong effects: members whose teams have less similar mental models, those whose teams prioritize their team goal over the MTS goal, and those working in social isolation and/or under communication delay engage longer on tasks. Time-on-task positively predicts MTS information networks, which in turn positively predict MTS performance when communication occurs with a delay, but not when it occurs in real-time.

CONCLUSION

Our findings contribute to research on task management in the context of working in teams and multiteam systems. Team and situational factors, along with task factors, shape task management behavior.

APPLICATION

Social and situational factors are important predictors of task management in team contexts such as spaceflight MTSs.

Modeling Task Scheduling Decisions of Emergency Department Physicians

OBJECTIVE

This study evaluated task-scheduling decisions in the context of emergency departments by comparing patterns of emergency physicians' task-scheduling models across levels of experience.

BACKGROUND

Task attributes (priority, difficulty, salience, and engagement) influence task-scheduling decisions. However, it is unclear how attributes interact to affect decisions, especially in complex contexts. An existing model of task scheduling, strategic task overload management-no priority (STOM-NP), found that an equal weighting of attributes can predict task-scheduling behavior. Alternatively, mathematical modeling estimated that priority alone could make similar predictions as STOM-NP in a parsimonious manner. Experience level may also influence scheduling decisions.

METHOD

An experimental design methodology shortened a judgment analysis approach to compare a priori task-scheduling decision strategies. Emergency physicians with two levels of experience rank-ordered 10 sets of 3 tasks varying on 4 task attributes in this complex environment.

RESULTS

Bayesian statistics were used to identify best-fit decision strategies. STOM-NP and priority-only provided the best model fits. STOM-NP fit the lower-experienced physicians best, whereas priority-only-using only one cue-fit the higher-experienced physicians best.

CONCLUSION

Models of decision strategies for task-scheduling decisions were extended to complex environments. Experts' level of experience influenced task-scheduling decisions, where the scheduling decisions of more-experienced experts was consistent with a more frugal decision process. Findings have implications for training and evaluation.

APPLICATION

We assessed models of cues that influence task-scheduling decisions, including a parsimonious model for task priority only. We provided a sample approach for shortening methods for understanding decisions.

Applying ergonomics within the multi-modelling paradigm with an example from multiple UAV control

This article presents a position statement on using ergonomics in conjunction with the multi-modelling paradigm. Multi-modelling is a computational approach to combine models of systems and components for design and simulation of cyber physical systems and systems of systems. Despite potentially significant benefits in terms of more human-centric system modelling, there is limited evidence of the application of ergonomics within multi-modelling. This article presents the case for applying ergonomics within multi-modelling. We open with an introduction to multi-modelling and benefits, applications and gaps for ergonomics in multi-modelling, and of potentially useful models from ergonomics. We then describe a proof-of-concept implementation of ergonomics within a multi-model of UAV control. This demonstrates that as well as user-centred modelling, this approach supports ergonomics in how we can access rich systems models, and the collaborative value of applying ergonomics theory in systems design. Practitioner Summary: Examines multi-modelling, a computational approach for complex modelling, and the contribution of ergonomics. An autonomous UAV test implementation demonstrates the application of ergonomics knowledge for improving design and evaluation processes, and how multi-modelling can give ergonomics access to rich systems models. Abbreviations: ACT-R: adaptive control of thought-rational; API: application programming interface; CFD: computational fluid dynamics; COTS: commerical off the shelf; CPS: cyber-physical system; CT: continuous time; DE: discrete event; DSE: design space exploration; FME: finite element modeling; FMI: functional mock-up interface; FMU: functional mock-up unit; GOMS: goals, operators, methods, selections; HCI: human-computer interaction; IMPRINT: improved performance research integration tool; INTO-CPS: integrated toolchain for cyber-physical system modeling; KLM: keystroke level model; MPC: model-predictive control; SysML: system markup language; SoS: system of system; UAV: unmanned aerial vehicle.

Global difficulty modulates the prioritization strategy in multitasking situations

There has been a considerable amount of research to conceptualize how cognition handle multitasking situations. Despite these efforts, it is still not clear how task parameters shape attentionnal resources allocation. For instance, many research have suggested that difficulty levels could explain these conflicting observations and very few have considered other factors such as task importance. In the present study, twenty participants had to carry out two N-Back tasks simultaneously, each subtask having distinct difficulty (0,1 or 2-Back) and importance (1 or 3 points) levels. Participants's cumulative dwell time were collected to assess their attentional strategies. Results showed that depending on the global level of difficulty (combination of the two levels of difficulty), attentional resources of people were driven either by the subtask difficulty (under low-global-difficulty) or the subtask importance (under high-global-difficulty), in a non-compensatory way. We discussed these results in terms of decision-making heuristics and metacognition.

Task Switching in Rock Climbing: Validation of a Computational Model for Different Skill Levels

Thirty-two rock climbers, all self-identifying as capable of lead climbing (place protective gear in the rock, to mitigate the risks of falling), climbed an outdoor route while placing what they believed was the necessary amount of protection. Cameras recorded the percentage of time they spent climbing upward (productivity) relative to placing protection (safety). We then applied STOM (strategic task overload model) to predict percent time-on-task, using the differences in their ratings of task interest, task priority, and task difficulty as predictors. The model significantly predicted time on task for the participants categorized as experts, but not for those categorized as non-experts. Time on the climbing (versus protection) task for the expert group, but not the non-expert group, was also predicted by a derived measure inferred to assess risk tolerance in climbing.

Automation trust and attention allocation in multitasking workspace

Previous research suggests that operators with high workload can distrust and then poorly monitor automation, which has been generally inferred from automation dependence behaviors. To test automation monitoring more directly, the current study measured operators' visual attention allocation, workload, and trust toward imperfect automation in a dynamic multitasking environment. Participants concurrently performed a manual tracking task with two levels of difficulty and a system monitoring task assisted by an unreliable signaling system. Eye movement data indicate that operators allocate less visual attention to monitor automation when the tracking task is more difficult. Participants reported reduced levels of trust toward the signaling system when the tracking task demanded more focused visual attention. Analyses revealed that trust mediated the relationship between the load of the tracking task and attention allocation in Experiment 1, an effect that was not replicated in Experiment 2. Results imply a complex process underlying task load, visual attention allocation, and automation trust during multitasking. Automation designers should consider operators' task load in multitasking workspaces to avoid reduced automation monitoring and distrust toward imperfect signaling systems.

Experimental Evaluation of STOM in a Business Setting

This study investigated the effect of autonomous prioritization on attention in a high-workload environment. It explored the “priority mystery,” or neglect of priority in switching behavior, found in previous Strategic Task Overload Model (STOM) experiments (Wickens Gutzwiller & Santamaria, 2015; Raby & Wickens 1994). We hypothesized that participants who could choose their own prioritization of tasks would view their prioritized task as more attractive and allocate more time and attention than participants who were not able to autonomously prioritize the tasks. Ninety-three participants were told to choose their own priority, given a priority, or not given any direction on priority of tasks. Participants had thirty minutes to complete two computer-based tasks and then were asked to rate the priority, interest, difficulty, and salience of each task. Priority influenced time on task most for the self-chosen priority group and not at all for the group given no priority direction. The more difficult task was also chosen less frequently. The implications and limitations of this study are discussed.

The Strategic Task Overload Model: History, Status, Challenges, and Extensions into New Domains

In this symposium we present the history, current status and challenges of the Strategic Task Overload Management (STOM) model of task switching, positing that parameters of priority, interest difficulty, salience and time-on-task are the factors that drive switching. We present four papers examining extensions of the model. In the first presentation, Wickens & Gutzwiller describe in detail the nature and background of the five parameters and their roots in models of scanning. In the second presentation, Gutzwiller & Sitzman consider the effects of multitasking and priority on task selection. Then Gilbert & Wickens show the extension of STOM to business tasks by presenting results of an experiment that systematically evaluate the role of task priority and goal setting. In the final presentation, Barg-Walkow & Rogers demonstrate the relevance of STOM to vitally important task management in the emergency room.

The Status of the Strategic Task Overload Model (STOM) for Predicting Multi-Task Management

A model for task switching which focuses on the decision making of operators in overloaded multitask conditions is reviewed and new research presented. The STOM model is an ongoing effort and as such, work is now accumulating, which serves to validate the model as a useful predictive method, but also is uncovering uncertainties that require further investigation. Here we summarize the origins of the model, which was informed by past modeling efforts, a literature review and a meta-analysis. We then describe in detail the basic parameters of STOM and the current status of each, before discussing future directions and six uncertainties uncovered when building our understanding of task switching choice.

Modeling Task Scheduling in Complex Healthcare Environments: Identifying Relevant Factors

Multiple task coordination involves scheduling tasks, completing tasks, and integrating tasks into a workflow. Task scheduling can influence outcomes of safety, satisfaction, and efficiency when completing tasks. This is especially important in complex life-critical environments such as healthcare, which incurs many situations where there are multiple tasks and limited resources for addressing all tasks. One approach for understanding tasks coordination is the Strategic Task Overload Management (STOM) model, which is a model for task scheduling behavior. In this theoretical paper, we discuss how this model can be extended to a complex healthcare environment. There are additional considerations (e.g., time) which must be considered when applying this model to healthcare. Ultimately, understanding how emergency physicians make multiple task scheduling decisions will advance theories and models, such as STOM, which can then in turn be implemented to improve scheduling behaviors in complex healthcare environments.

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.

Differences in Multitask Resource Reallocation After Change in Task Values

OBJECTIVE

The objective was to characterize multitask resource reallocation strategies when managing subtasks with various assigned values.

BACKGROUND

When solving a resource conflict in multitasking, Salvucci and Taatgen predict a globally rational strategy will be followed that favors the most urgent subtask and optimizes global performance. However, Katidioti and Taatgen identified a locally rational strategy that optimizes only a subcomponent of the whole task, leading to detrimental consequences on global performance. Moreover, the question remains open whether expertise would have an impact on the choice of the strategy.

METHOD

We adopted a multitask environment used for pilot selection with a change in emphasis on two out of four subtasks while all subtasks had to be maintained over a minimum performance. A laboratory eye-tracking study contrasted 20 recently selected pilot students considered as experienced with this task and 15 university students considered as novices.

RESULTS

When two subtasks were emphasized, novices focused their resources particularly on one high-value subtask and failed to prevent both low-value subtasks falling below minimum performance. On the contrary, experienced people delayed the processing of one low-value subtask but managed to optimize global performance.

CONCLUSION

In a multitasking environment where some subtasks are emphasized, novices follow a locally rational strategy whereas experienced participants follow a globally rational strategy.

APPLICATION

During complex training, trainees are only able to adjust their resource allocation strategy to subtask emphasis changes once they are familiar with the multitasking environment.