Human Performance of Novice Schedulers for Complex Spaceflight Operations Timelines

2021 ◽  
pp. 001872082110589
Author(s):  
Jessica J. Marquez ◽  
Tamsyn Edwards ◽  
John A. Karasinski ◽  
Candice N. Lee ◽  
Megan C. Shyr ◽  
...  
Keyword(s):  
Task Performance ◽  
Situation Awareness ◽  
Human Performance ◽  
Task Complexity ◽  
Lower Number ◽  
Scheduling Problems ◽  
Within Subjects ◽  
Complex Constraints ◽  
Design Scheduling ◽  
Main Effects

Objective Investigate the effects of scheduling task complexity on human performance for novice schedulers creating spaceflight timelines. Background Future astronauts will be expected to self-schedule, yet will not be experts in creating timelines that meet the complex constraints inherent to spaceflight operations. Method Conducted a within-subjects experiment to evaluate scheduling task performance in terms of scheduling efficiency, effectiveness, workload, and situation awareness while manipulating scheduling task complexity according to the number of constraints and type of constraints. Results Each participant ( n = 15) completed a set of scheduling problems. Results showed main effects of the number of constraints and type of constraint on efficiency, effectiveness, and workload. Significant interactions were observed in situation awareness and workload for certain types of constraints. Results also suggest that a lower number of constraints may be manageable by novice schedulers when compared to scheduling activities without constraints. Conclusion Results suggest that novice schedulers' performance decreases with a high number of constraints, and future scheduling aids may need to target a specific type of constraint. Application Knowledge on the effect of scheduling task complexity will help design scheduling systems that will enable self-scheduling for future astronauts. It will also inform other domains that conduct complex scheduling, such as nursing and manufacturing.

Measuring Situation Awareness in Training Systems: A Multivariate Approach

2000 ◽  
Vol 44 (11) ◽  
pp. 361-364
Author(s):  
Dennis A. Vincenzi ◽  
Robert T. Hays ◽  
Alton G. Seamon
Keyword(s):  
Situation Awareness ◽  
Human Performance ◽  
Cognitive Task ◽  
Training System ◽  
Multivariate Techniques ◽  
Performance Improvements ◽  
Significant Performance ◽  
Within Subjects ◽  
New Variables ◽  
Ship Handling

The Virtual Environment for Submarine Ship Handling Training (VESUB) project developed, demonstrated, and evaluated the training potential of a VE-based training system. Based on perceptual and cognitive task analyses, and input from subject matter experts, 15 performance variables were developed. These variables were combined and analyzed using multivariate techniques that provided a measure of situation awareness and general ship handling ability. These 2 new variables were then analyzed using an ANOVA with experience (novice or experienced) as the between subjects variable, and scenario session (pre-training or post-training) as the within subjects variable. The results indicated a significant improvement in situation awareness and general ship handling ability for both novice and experienced trainees. The VESUB training system provided the training needed to produce significant performance improvements for novice personnel who knew very little about ship handling, and also provided significant refresher training for experienced personnel. The use of multivariate analyses of specific embedded task measures provided an empirical link between situation awareness and human performance.

scholarly journals Beneficial and harmful explanatory machine learning

2021 ◽  
Author(s):  
Lun Ai ◽  
Stephen H. Muggleton ◽  
Céline Hocquette ◽  
Mark Gromowski ◽  
Ute Schmid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Beneficial Effect ◽  
Empirical Evidence ◽  
Task Performance ◽  
Human Performance ◽  
Human Learning ◽  
Science Literature ◽  
Definition Of ◽  
Self Learning

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

Current Task Activation Predicts General Effects of Advance Preparation in Task Switching

2006 ◽  
Vol 53 (4) ◽  
pp. 260-267 ◽  
Author(s):  
Edita Poljac ◽  
Ab de Haan ◽  
Gerard P. van Galen
Keyword(s):  
Task Performance ◽  
Task Switching ◽  
Shape Matching ◽  
Response Times ◽  
Error Rates ◽  
Matching Task ◽  
Task Switch ◽  
Current Task ◽  
Within Subjects ◽  
And Task

Two experiments investigated the way that beforehand preparation influences general task execution in reaction-time matching tasks. Response times (RTs) and error rates were measured for switching and nonswitching conditions in a color- and shape-matching task. The task blocks could repeat (task repetition) or alternate (task switch), and the preparation interval (PI) was manipulated within-subjects (Experiment 1) and between-subjects (Experiment 2). The study illustrated a comparable general task performance after a long PI for both experiments, within and between PI manipulations. After a short PI, however, the general task performance increased significantly for the between-subjects manipulation of the PI. Furthermore, both experiments demonstrated an analogous preparation effect for both task switching and task repetitions. Next, a consistent switch cost throughout the whole run of trials and a within-run slowing effect were observed in both experiments. Altogether, the present study implies that the effects of the advance preparation go beyond the first trials and confirms different points of the activation approach ( Altmann, 2002) to task switching.

Level of Automation Forms a Key Aspect of Autonomy Design

2017 ◽  
Vol 12 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Mica R. Endsley
Keyword(s):  
Situation Awareness ◽  
Interface Design ◽  
Human Performance ◽  
Human Cognition ◽  
Level Of Automation ◽  
Out Of The Loop ◽  
Wide Range ◽  
Levels Of Automation ◽  
And Performance ◽  
The Impact

The concept of different levels of automation (LOAs) has been pervasive in the automation literature since its introduction by Sheridan and Verplanck. LOA taxonomies have been very useful in guiding understanding of how automation affects human cognition and performance, with several practical and theoretical benefits. Over the past several decades a wide body of research has been conducted on the impact of various LOAs on human performance, workload, and situation awareness (SA). LOA has a significant effect on operator SA and level of engagement that helps to ameliorate out-of-the-loop performance problems. Together with other aspects of system design, including adaptive automation, granularity of control, and automation interface design, LOA is a fundamental design characteristic that determines the ability of operators to provide effective oversight and interaction with system autonomy. LOA research provides a solid foundation for guiding the creation of effective human–automation interaction, which is critical for the wide range of autonomous and semiautonomous systems currently being developed across many industries.

The Role of Power Distance and Explanation Facility in Online Bargaining Utilizing Software Agents

2011 ◽  
pp. 73-90
Author(s):  
John Lim
Keyword(s):  
Factorial Design ◽  
Task Performance ◽  
Interaction Effect ◽  
Power Distance ◽  
Software Agents ◽  
Research Perspective ◽  
Online Transactions ◽  
Main Effects ◽  
Factorial Design Experiment

Online transactions have become increasingly popular and deserve greater attention from a research perspective. Whereas there are various aspects of online transactions, this study specifically examined an online bargaining scenario utilizing software agents. User’s performance and attitudes were studied in a 2x2 factorial-design experiment. The independent variables were power distance (a dimension of culture)-for reasons associated with increasing and irresistible globalization, and explanation facility-for its conjecturable benefits in helping users to better understand and work with their software agents. Results showed these factors to have an interaction effect on task performance; as well, explanation facility exhibited main effects on trust and satisfaction. The findings have implications for system designers and builders; they also help managers in tailoring their expectations on what technology can deliver-under which conditions.

scholarly journals Performance, Movement, Posture, and Perceived Discomfort in Active vs. Static Seating

2019 ◽  
Vol 63 (1) ◽  
pp. 1154-1158
Author(s):  
Jeremy D. Faulk ◽  
Cameron C. McKee ◽  
Heather Bazille ◽  
Michael Brigham ◽  
Jasmine Daniel ◽  
...  
Keyword(s):  
Task Performance ◽  
Motor Task ◽  
Fine Motor ◽  
Future Research ◽  
Trunk Movement ◽  
Significant Difference ◽  
Within Subjects ◽  
Computer Based ◽  
Preliminary Study ◽  
Static Conditions

Active seating designs may enable users to move more frequently, thereby decreasing physiological risks associated with a sedentary lifestyle. In this preliminary study, two active seating designs (QOR360, Ariel; QOR360, Newton) were compared to a static chair (Herman Miller, Aeron) to understand how active vs. static seating may affect task performance, movement, posture, and perceived discomfort. This within-subjects experiment involved n = 11 student participants who sat upon each of the three chairs for 20 minutes while performing a series of computer-based tasks. Participants showed increased trunk movement while also reporting higher levels of perceived discomfort in the two active chair conditions. There was no significant difference in either posture or fine motor task performance between the active and static conditions. Future research may benefit from additional physiological measurements along with a wider variety of tasks that require seated users to make postural adjustments.

scholarly journals Human factors challenges for the safe use of artificial intelligence in patient care

2019 ◽  
Vol 26 (1) ◽  
pp. e100081 ◽  
Author(s):  
Mark Sujan ◽  
Dominic Furniss ◽  
Kath Grundy ◽  
Howard Grundy ◽  
David Nelson ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Patient Care ◽  
Situation Awareness ◽  
Human Performance ◽  
Independent Evaluation ◽  
The Impact ◽  
Human Factors Research

The use of artificial intelligence (AI) in patient care can offer significant benefits. However, there is a lack of independent evaluation considering AI in use. The paper argues that consideration should be given to how AI will be incorporated into clinical processes and services. Human factors challenges that are likely to arise at this level include cognitive aspects (automation bias and human performance), handover and communication between clinicians and AI systems, situation awareness and the impact on the interaction with patients. Human factors research should accompany the development of AI from the outset.

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