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.

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.

Dancing With Algorithms: Interaction Creates Greater Preference and Trust in Machine-Learned Behavior.

OBJECTIVE: We examined a method of machine learning (ML) to evaluate its potential to develop more trustworthy control of unmanned vehicle area search behaviors. BACKGROUND: ML typically lacks interaction with the user. Novel interactive machine learning (IML) techniques incorporate user feedback, enabling observation of emerging ML behaviors, and human collaboration during ML of a task. This may enable trust and recognition of these algorithms. METHOD: Participants judged and selected behaviors in a low and a high interaction condition (IML) over the course of behavior evolution using ML. User trust in the outputs, as well as preference, and ability to discriminate and recognize the behaviors were measured. RESULTS: Compared to noninteractive techniques, IML behaviors were more trusted and preferred, as well as recognizable, separate from non-IML behaviors, and approached similar performance as pure ML models. CONCLUSION: IML shows promise for creating behaviors by involving the user; this is the first extension of this technique for vehicle behavior model development targeting user satisfaction and is unique in its multifaceted evaluation of how users perceived, trusted, and implemented these learned controllers. APPLICATION: There are many contexts where the brittleness of ML cannot be trusted, but the advantage of ML over traditional programmed behaviors may be large, as in some military operations where they could be scaled. IML in this early form appears to generate satisfactory behaviors without sacrificing performance, use, or trust in the behavior, but more work is necessary.

Bridging psychology and engineering to make technology work for people.

Engineering grand challenges and big ideas not only demand innovative engineering solutions, but also typically involve and affect human thought, behavior, and quality of life. To solve these types of complex problems, multidisciplinary teams must bring together experts in engineering and psychological science, yet fusing these distinct areas can be difficult. This article describes how Human Systems Engineering (HSE) researchers have confronted such challenges at the interface of humans and technological systems. Two narrative cases are reported-computer game-based cognitive assessments and medical device reprocessing-and lessons learned are shared. The article then discusses 2 strategies currently being explored to enact such lessons and enhance these kinds of multidisciplinary engineering teams: a "top-down" administrative approach that supports team formation and productivity through a university research center, and a "bottom-up" engineering education approach that prepares students to work at the intersection of psychology and engineering. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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.

Part-task training in the context of automation: current and future directions.

Automation often elicits a divide-and-conquer outlook. By definition, automation has been suggested to assume control over a part or whole task that was previously performed by a human (Parasuraman & Riley, 1997). When such notions of automation are taken as grounds for training, they readily invoke a part-task training (PTT) approach. This article outlines broad functions of automation as a source of PTT and reviews the PTT literature, focusing on the potential benefits and costs related to using automation as a mechanism for PTT. The article reviews some past work in this area and suggests a path to move beyond the type of work captured by the "automation as PTT" framework. An illustrative experiment shows how automation in training and PTT are actually separable issues. PTT with automation has some utility but ultimately remains an unsatisfactory framework for the future broad potential of automation during training, and we suggest that a new conceptualization is needed.