Human Factors and Simulation最新文献

{"title":"Evolution of Workload Demands of the Control Room with Plant Technology","authors":"Jinchao Lin, G. Matthews, Jacquelyn L. Schreck, Kelly Dickerson, Niav Hughes","doi":"10.54941/ahfe1003562","DOIUrl":"https://doi.org/10.54941/ahfe1003562","url":null,"abstract":"The management and assessment of operator workload is a critical element of nuclear power plant (NPP) safety. Operators in the NPP main control room (MCR) often face workload that varies both quantitatively and qualitatively as immediate task demands change. Although workload is an intuitive construct, it is not easy to define and measure in practice. This paper reviews the conceptual and empirical challenges in workload assessment, discusses the evolution of workload in MCRs, and presents subjective workload data from recent U.S. Nuclear Regulatory Commission (NRC)’s Human Performance Test Facility (HPTF) studies. Designs for NPP control rooms will increasingly utilize new technology, ranging from digitization of I&C through automation of operator functions to eventual use of AI. Workload assessment can contribute to determining whether the technology reduces cognitive demands on operators or has detrimental effects, such as increasing the vulnerability to human errors. We advocate for a multidimensional workload assessment approach based on Multiple Resource Theory and workload assessment should be combined with measurements of other constructs such as situation awareness, teamwork, and trust to identify vulnerabilities to error in NPPs.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring driving simulator adaptation using EDA","authors":"Marie-Anne Pungu Mwange, F. Rogister, Luka Rukonić","doi":"10.54941/ahfe1001489","DOIUrl":"https://doi.org/10.54941/ahfe1001489","url":null,"abstract":"Most research about simulator adaptation focus on driving style and participants' comfort. However, in recent years, there is a growing interest in physiological data analysis as part of the user experience (UX) assessment. Furthermore, the application of machine learning (ML) techniques to those data may allow the automatic detection of stress and cognitive load. Previously, we noticed that new participants in experiments with our simulator were often in a constant state of tension. This prevented optimal training of our ML models as many of the collected data were not representative of a person's normal state.Our work focuses on improving driver's UX by keeping the cognitive load and stress at levels that do not interfere with the primary task of driving. We use a custom-made driving simulator as our testing platform and evaluate participants' emotional state with physiological signals, specifically electrodermal activity (EDA). EDA is the variation of the skin conductance created by sweat glands. It is linked to the sympathetic nervous system and is an indication of physiological and psychological arousal. We selected EDA because several studies have shown that it is a fast indicator of stress and cognitive load.To ensure that we are consistently collecting accurate data that could be fed to ML algorithms, we need to be able to correlate physiological reactions to external stimuli. We want to avoid them to be confused with general tension. Therefore, we need to determine the time it takes for most participants to physiologically adapt to our simulator. In this between-subjects study, we examined the impact of short time (ca. 10 min) exposures to the simulation and compared it with a longer exposure period (ca. 35 min).Another problem we faced was that some participants were too indisposed by driving in the simulator to complete testing sessions. Therefore, we needed to find a way to discriminate them during the recruitment process. Literature has shown that there might be a link between motion sickness and simulator sickness and in this study, we searched for a correlation between the motion sickness susceptibility questionnaire (MSSQ) and the self-reported simulator sickness using the simulator sickness questionnaire (SSQ).For our investigation, we recruited 22 people through an agency. They were divided in two groups. Group A (short-time exposures) had 10 participants between 25 and 69 years old (M=49.5; SD=17.1, 5 women, 5 men) and group B (long-time exposure) had 12 people between 28 and 65 years old (M=43; SD=12.8, 5 women, 7 men). We requested from the agency to recruit only active drivers of automatic transmissions cars as our simulator mimics this type of vehicle.Motion sickness susceptibility and discomfort felt in the simulator are moderately correlated. The coefficient value is 0.51. The number of participants of our study being small, further research is necessary to determine if the MSSQ can be used as a discriminator in the recru","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128007165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ergonomics, digital twins and time measurements for optimal workplace design","authors":"Nataša Vujica-Herzog, B. Buchmeister, Matic Breznik","doi":"10.54941/ahfe1001492","DOIUrl":"https://doi.org/10.54941/ahfe1001492","url":null,"abstract":"Ergonomics and Human Factors are both defined as a scientific discipline concerned with understanding the interactions between workers and other elements of a system. The implementation of ergonomics in industrial engineering, where workers are an integral part of the system, is very important in the development phase of the product/production and also in the planning of production technologies. The interaction between man and machine can be very intense in mass production, especially in assembly lines, and is therefore the focus of process optimization. In addition, appropriate workplace design has long-term effects on the worker. It is well known that it can prevent musculoskeletal complaints, increase productivity and reduce production costs.As part of the current trend of Industry 4.0 (I4.0), the traditional approach to workplace design is becoming intertwined with \"smart\" paradigms such as sensors, computing platforms, communication technology, control, simulation, data-intensive modelling, and predictive engineering. It is therefore important for companies to understand the great potential of the I4.0 concept and leverage its benefits in terms of moving from machine-dominated manufacturing to digital manufacturing.These technologies offer us the possibility to reproduce the work environment in a virtual scenario where it is possible to simulate manual tasks, evaluate ergonomic indices and perform time analysis at the same time. The idea of using ergonomic simulation software is not new. Several attempts have been made in Europe in the past. Starting with DELTA's ERGOMAS, ERGOMan systems, Siemens Jack and more recently Process simulate, both possibly supported by Xsens suit. With the I4.0 paradigm in mind, we examined the featured computing platforms developed from 1994 to the present to track the progress and changes made. For simulations, the most progress was made with the development of the Task Simulation Builder interface and later an important step was made with the development of sensor technology for motion capture. For example, for assembly lines, an integrated approach for setting working times was developed using the classical MTM approach and EAWS methods. With these technologies and accumulated knowledge, the design process changed rapidly and several published papers show the benefits of computer-aided approaches also for timing analysis. Based on the presented facts, the question arose: can computer-aided approaches integrated with ergonomics replace the existing standardised approaches for time determination? In our research, a case study of workplace design was conducted using two of the latest platforms, Siemens Jack and Process Simulate in conjunction with Xsens suit. A collaborative human-robot workplace was designed as a digital twin and tested in our lab with 6 subjects considering their anthropometric measurements. The human movements were converted into computer software and evaluated using OWAS analysis for ergonomi","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133970047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Participant Gaming Experience Predicts Mental Model Formation, Task Performance, and Teaming Behavior in Simulated Search and Rescue","authors":"Rhyse Bendell, J. Williams, S. Fiore, F. Jentsch","doi":"10.54941/ahfe1003569","DOIUrl":"https://doi.org/10.54941/ahfe1003569","url":null,"abstract":"Video gaming experience has been found to impact behavior and performance on experimental tasks, can influence cognitive processes, and may even transfer to tasking proficiency. The purpose of the investigations reported in this manuscript were jointly to examine the relationships between video game experience and mental model formation as well as experience and gameplay behaviors in the context of a game-based urban search and rescue mission. We hypothesized that differences in video game play experience would influence the formation of mental models, and that experience would also be associated with different behavioral tendencies during tasking. To test our hypotheses, we first conducted an investigation to evaluate the relationship between video game play experience and mental model formation given the context of a simulated urban search-and-rescue task (employing psychographic measures of gaming experience in addition to card sort mental model elicitation) and second drew on data collected under DARPA’s Artificial Social Intelligence Supporting Teams program (particularly, behavioral and performance metrics related to players' execution of mission critical actions and team supportive behaviors) to examine the influence of experience on individual and team tasking behaviors. Results of Study 1 support our hypothesis that greater video game experience was associated with more convergent mental models related to the game-based experimental task. Results of Study 2 indicate that participants with greater experience showed evidence of better overall performance and more strategic behavior. These findings suggest that video gaming experience impacts both the formation of task-related mental models as well as task performance and teaming behaviors. One critical takeaway from the results of these studies is that some aspects of generalized video game experience may transfer to novel task performance. Having found evidence of transfer in this context is particularly informative because the search-and-rescue task environment was essentially novel, and although it was based in Minecraft the task itself employed only the sandbox foundation and involved almost no features that appear in standard Minecraft survival or other modes. The video game experience measure, on the other hand, tapped general as well as Minecraft specific experience with respect to duration, frequency/intensity, and self-reported skill. These findings have implications for simulation based research methods, particularly with regards to identification and control of potential confounding variables, as well as the practical application of simulated training and testing. Further, we submit that gaming experience is emerging as a critical factor that may be used to profile participants across research, training, and operational domains for the purposes of predicting individual behavior and performance as well as to inform the formation and development of teams.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133315000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Need Assessment Using Simulator Feature Framework","authors":"Olugbenga Gideon, Thomas Ulrich","doi":"10.54941/ahfe1003574","DOIUrl":"https://doi.org/10.54941/ahfe1003574","url":null,"abstract":"Full-scope nuclear control room simulators were developed to address operator skill deficits associated with several high-profile accidents occurring in the 1980s. Full-scope simulators are increasingly used to support plant modernization and advanced reactor research and development. New digital control room designs use full-scope simulators to develop and evaluate new concept of operations to support regulator required Human Factors Engineering Program Review Model (HFEPRM) activities. Modern simulator designs require more diverse and robust capabilities to serve the diverse needs of multiple user groups including researchers and educators. A common framework for evaluating features to support training, research, and education is critical to ensure future simulators enable research to support immediate and future plant modernization and advanced reactor deployment needs. An initial framework comprised of eight feature categories was developed by reviewing published simulator-based research and analyzing simulator features against research objectives and results (Gideon and Ulrich, 2022). A survey was administered to simulator users to evaluate the suitability of eight critical capabilities of a modified version of the framework to characterize and differentiate simulators across training, research, and education uses (n = 21). The results demonstrate the framework's effectiveness as a baseline for assessing the functionalities of simulators in line with their specific needs. Future work aims to validate the framework within a regulatory HFEPRM process to demonstrate its use as a tool to identify missing capabilities of existing simulators or to specify requirements for new simulators.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Camera Perspective and Image Throughput on Human Trust of a Quadrupedal Robot Scout","authors":"Ralph W. Brewer, Z. Guyton, Tyler Long, Angela Vantreese, M. Russell, Chad C. Kessens, E. Rovira","doi":"10.54941/ahfe1001490","DOIUrl":"https://doi.org/10.54941/ahfe1001490","url":null,"abstract":"The objective of this study is to understand user perceptions of robot behaviors. Specifically, we are interested in the possible effects of providing the user with different camera perspectives and with regular snapshots versus a continuous camera feed in the context of a small-unit military operation. The study will employ a mixed 2 (camera perspective: 1st person vs over the shoulder 3rd person) x 2 (camera feed: snapshots vs continuous) factorial design, with participants viewing a robot performing military tasks in both rural and urban operational settings. After viewing the robot’s performance, participants will answer performance questions based on the context of the military mission, as well as questionnaires that measure trust in the autonomous system. Dependent variables include performance outcomes from tactical performance questions and subjective results of the trust questionnaires. Data from participants will be analyzed with a 2x2 between subjects ANOVA. We anticipate that the findings will suggest that a third person perspective and continuous camera feed will result in the highest trust and best performance outcomes.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133403871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stress and Motivation on Reliance Decisions with Automation","authors":"Mollie McGuire, Miroslav Bernkopf","doi":"10.54941/ahfe1003579","DOIUrl":"https://doi.org/10.54941/ahfe1003579","url":null,"abstract":"The decision to rely on automation is crucial in high-stress environments where there is an element of uncertainty. It is equally vital in human-automation partnership that the human’s expectations of automation reliability are appropriately calibrated. Therefore, it is important to better understand reliance decisions with varying automation reliability. The current study examined the effects of stress and motivation on the decision to rely on autonomous partners. Participants were randomly assigned to a stress and motivation condition, using the Trier Social Stress Test (TSST) for stress induction, and monetary incentive for motivation. The main task was an iterative pattern learning task where one of two AI partners, one with high reliability and one with low reliability, gave advice at every iteration; the AI partner alternated every ten iterations. While motivation had a stronger effect than stress, both motivation and stress affected reliance decisions with the high reliability AI. The low reliability AI was affected to a lesser degree if at all. Overall, the decision to not rely on the AI partner, especially with the higher in reliability was slower than the decision to rely on the AI partner, with the slowest decision times occurring in the high stress condition with motivated participants, suggesting more deliberate processing was utilized when deciding against the advice of the AI higher in reliability.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Cognitive Model for Guiding Automation","authors":"Christopher A. Stevens, Christopher B. Fisher, Mary E. Frame","doi":"10.54941/ahfe1003575","DOIUrl":"https://doi.org/10.54941/ahfe1003575","url":null,"abstract":"A variety of systems and exist for managing human-machine team throughput and effectiveness. One example is autonomous managers (AMs), software that dynamically reallocates tasks to individual members of a team based on their workload and performance. Cognitive models can inform these technologies by projecting performance into the future and enabling “what-if” analyses. For example, would removing a task from an individual whose current performance is low cause them to improve? Conversely, can a team member who is currently performing well handle even more work without dropping performance? In the present study, we develop and validate a cognitive model built in the Adaptive Control of Thought – Rational (ACT-R) cognitive architecture in a novel empirical paradigm: The Intelligence, Surveillance, and Reconnaissance Multi-attribute Task Battery (ISR-MATB). In this task, participants engage in a mock ISR task in which they must integrate information from several subtasks to arrive at a decision about a situation. These tasks include searching visual displays, listening for audio chatter, making decisions based on multiple cues, and remaining vigilant for signals. The tasks are based upon analogous laboratory psychology tasks to improve empirical rigor. Eight participants completed the task under two 30-minute conditions: easy and difficult. The difficult task required searching more complex stimuli in the audio and visual domain than in the easy condition. In addition, subjective workload ratings (NASA-TLX) were collected. We describe the preliminary behavioral and self-report results, as well as the ACT-R model’s fit to the behavioral data. Further, we describe a new method for workload visualization and task decomposition using model-based analyses.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127998816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The importance of assessing both expert and non-expert populations to inform expert performance","authors":"S. Mitroff, Emma M. Siritzky, S. Nag, Patrick H. Cox, Chloe Callahan-Flintoft, Andrew J. Tweedell, Dwight J. Kravitz, Kelvin S. Oie","doi":"10.54941/ahfe1001486","DOIUrl":"https://doi.org/10.54941/ahfe1001486","url":null,"abstract":"Realizing the benefits of research for human factors applications requires that academic theory and applied research in operational environments work in tandem, each informing the other. Mechanistic theories about cognitive processing gain insight from incorporating information from practical applications. Likewise, human factors implementations require an understanding of the underlying nature of the human operators that will be using those very implementations. This interplay holds great promise, but is too often thwarted by information from one side not flowing to the other. On one hand, basic researchers are often reluctant to accept research findings from complex environments and a relatively small number of highly-specialized participants. On the other hand, industry decision makers are often reluctant to believe results from simplified testing environments using non-expert research participants. The argument put forward here is that both types of data are fundamentally important, and explicit efforts should bring them together into unified and integrated research programs. Moreover, effectively understanding expert performance requires assessing non-expert populations.For many fields, it is critically important to understand how operators (e.g., radiologists, aviation security officers, military personnel) perform in their professional setting. Extensive research has explored a breadth of factors that can improve, or hinder, operators’ success, however, the vast majority of these research endeavors hit the same roadblock—it is practically difficult to test specialized operators. They can be hard to gain access to, have limited availability, and sometimes there just are not enough of them to conduct the needed research. Therefore, non-expert populations can provide a much-needed resource. Specifically, it can be highly useful to create a closed-loop ecosystem wherein an idea rooted in an applied realm (e.g., radiologists are more likely to miss an abnormality if they just found another abnormality) is explored with non-experts (e.g., undergraduate students) to affordably and extensively explore a number of theoretical and mechanistic possibilities. Then, the most promising candidate outcomes can be brought back to the expert population for further testing. With such a process, researchers can explore possible ideas with the more accessible population and then only use the specialized population with vetted research paradigms and questions.While such closed-looped research practices offer a way to best use available resources, the argument here is also that it is necessary to assess non-experts to fully understand expert performance. That is, even if researchers have full access to a large number of experts, they still need to test non-experts. Specifically, assessing non-experts allows for quantifying fundamentally important factors, such as strategic vs. perceptual drivers of performance and the time course of learning. Many of the potenti","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124600804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer of nuclear maintenance skills from virtual environments to reality - Toward a methodological guide","authors":"Valérian Faure, J. Chardonnet, D. Mestre, F. Ferlay, Michaël Brochier, L. Joblot, F. Mérienne, C. Andriot","doi":"10.54941/ahfe1003566","DOIUrl":"https://doi.org/10.54941/ahfe1003566","url":null,"abstract":"Nuclear maintenance operations require several types of cognitive and motor skills that can be trained in immersive environments. However, there is a lack of normalized methodological approaches to classify tasks and guide them for a potential transposition to immersive training. This paper proposes a methodological approach to classify nuclear maintenance tasks based on their complexity and the potential transfer of training obtainable from each type of immersion techniques and their related interactions.This proposed methodology provides a novel approach to compare various immersive technologies and interactions in a normalized way for a same industrial task.This paper aims at serving as a base for a methodological guide dedicated to the transposition of nuclear maintenance skills learned in immersive environments to real environment setups and proposes two future use cases based on this methodological approach.","PeriodicalId":102446,"journal":{"name":"Human Factors and Simulation","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133952165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}