bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationships of eye gaze metrics between cognitive processes and strategy in spatial problem-solving
Authors: Hsing Hsiang-Wen , Lau Nathan , Bairaktarova Diana
Published: October 2022

Spatial strategies and cognitive processes have been separately found to indicate performance in spatial problem-solving, but their relationships have not been investigated quantitatively. This study employed eye-tracking to characterize and determine the relationship of the cognitive processes and spatial strategies employed by first year engineering students with low spatial ability. Eighty-eight engineering students with low spatial ability were recruited to complete test items in the Purdue Spatial Visualization Test: Rotations (PSVT:R) and the Santa Barbara Solids Test (SBST). Eye-tracking data were collected to indicate the encoding, transformation, confirmation cognitive processes as well as whether holistic or piecemeal strategy was employed. Strategy ratio correlated with encoding fixations more than other cognitive processes. This finding suggests that encoding may be more indicative of spatial strategies being employed than the transform and confirmation processes. Pedagogical practices for low spatial ability students may turn to improving the encoding process for better learning outcomes.
Information Access Effort: Are Head Movements “Cheap” or Even “Free”?
Authors: Warden Amelia C. , Wickens Christopher D. , Rehberg Daniel , Clegg Benjamin A. , Ortega Francisco R.
Published: October 2022

Augmented reality head-mounted displays (AR-HMDs) can present information at the center field of view (FOV) to 360° around the user. Deciding where to place virtual content when using an AR-HMD could directly impact the effort required to access information for different tasks. The current paradigm investigated the cost of information access effort for two different tasks presented on a virtual display using an AR-HMD. Participants made comparison judgments for two types of tasks (focused attention and computation integration) based on information presented at increasing lateral distances from the left side of the virtual display. Results showed no loss in performance as predicted by the Information Access Effort function. However, results show that evoking head movements played a significant role in restoring and preserving accuracy at greater visual eccentricities without hindering response time.
Investigating Cyber Attacker Team Cognition
Authors: Johnson Craig J. , Ferguson-Walter Kimberly J. , Gutzwiller Robert S. , Scott Dakota D. , Cooke Nancy J.
Published: October 2022

Cyber attackers commonly operate in teams, which may process information collectively and thus, may be best understood when the team is treated as the unit of analysis. Future research in Oppositional Human Factors (OHF) should consider the impact of team-influencing and team-level biases and the impact that defensive interventions have on team cognition in general. Existing measurement approaches using team interactions may be well suited for studying red teams, and how OHF interventions impact cyber attackers.
Repurposing design in bedrooms to improve home accessibility: task and motion analysis using the virtual reality environment
Authors: Luo Yue , Grimaldi Nicolas S , Lu Xiaojie , Ahrentzen Sherry , Hu Boyi
Published: October 2022

Functional disabilities, which have an association with a higher risk of falling in households, are common in the U.S. Individuals with functional limitations may benefit from accessible homes since they improve safety and reduce the risk of falling. Repurposing design is one strategy to improve home accessibility, by adapting home spaces and fixtures to serve a new purpose. This study aimed to propose a repurposing design in the bedroom area. The idea for a new bedroom repurposing design was proposed after analyzing a standard bedroom floor plan. The original standard bedroom layout and the redesigned bedroom layout were simulated in a virtual reality environment. To evaluate the accessibility of the new bedroom repurposing design, six participants (four females, 56.3 ± 14.1 years old) were recruited to perform two daily activities (i.e., relocating the robe & going to the bathroom) in both the original and redesigned bedrooms. The comparison of participants' task performance and trunk motion in both bedroom layouts revealed: (1) the sequences of actions required for two daily activities were similar between both bedroom layouts, (2) participants spent less time and walked for a shorter distance in the redesigned bedroom layout than in the original layout, (3) the motion smoothness when walking to the closet improved, whereas the RMS of trunk acceleration when opening the bathroom door decreased in the redesigned bedroom layout compared to the original layout.
Effect of Payment Methods in Crowdsourcing Platforms
Authors: Soratana Teerachart , Liu Yili , Jessie Yang X.
Published: October 2022

The use of an online crowdsourcing platform makes it easier and quicker for researchers to recruit participants online. It also allows researchers to adopt another payment method traditionally used on crowdsourcing platforms: the piece-rate payment method. This paper investigated the effects of the participants’ location, the payment method, and the compensation rate on subjective experience, motivation, and perceived payment fairness. This paper extends last year’s paper, in which the participants’ location and the payment method were the focus of the investigation. Two additional studies have been conducted using piece-rate payment method. The data were analyzed using a 3-way ANOVA and a chi-square test of independence. The results showed that the piece-rate payment method was less likely to attract workers who were motivated to work for money than the quota payment method. There was no difference in perceived payment fairness between using a quota payment method and a piece-rate payment method.
Adapting Crew Resource Management for Training in Unmanned Operations
Authors: Kreischer Noah , Sebok Angelia , Orchosky Lauren , “Roy” Rogers D.A.
Published: October 2022

As automation is increasingly used on the flight deck and in unmanned aerial operations, the concept of “automation as a team member” becomes increasingly important. In addition to taking over certain aviation tasks there is a need to plan and train for the roles taken by automation. While training does exist to teach the flight crew how to interact with automation, the concept of automation as a team member is ill defined. In this work, we used the framework provided by Crew Resource Management (CRM) to begin addressing the needs for developing scenario-based training that address this gap.
Working Memory Load Impact on Effective Connectivity: a Dynamic Causal Modeling Study
Authors: Huang Jiali , Nam Chang S. , Fitts Edward P.
Published: October 2022

This study aims to examine the roles of the brain regions in working memory (WM) processing and the modulatory effects of WM load. Electroencephalogram (EEG) signals were recorded when participants perform a letter-version n-back task with three different levels of WM load. The directional causal connections between brain regions were estimated using Dynamic Causal Modeling (DCM). The directions and strengths of the connections were compared for different WM load conditions. The results showed a right-lateralized, backward-only connection pattern for the high WM load condition. The results also showed changes in the roles of the brain regions when the WM load increases. These findings of the modulatory effects of WM load may be utilized in measuring cognitive states, and designing adaptive automation in augmented cognition programs.
USE-Lite: Psychometric Item Reduction of the USE (Usefulness, Satisfaction, and Ease of Use) Questionnaire
Authors: Shanklin Roslyn , Kortum Philip , Oswald Frederick L.
Published: October 2022

The Usefulness, Satisfaction, and Ease of Use Questionnaire (USE) is a 30-item measure of subjective usability. The content of the USE allows the usability of a product to be interpreted along four important dimensions—Usefulness, Ease of Use, Ease of Learning, and Satisfaction—rather than as a global construct. Although the USE has been a valuable tool for human factors professionals, previous analyses revealed the need for psychometrically refining the measure. Additionally, the USE is too long for many usability testing protocols that are already time consuming. The current study addresses these issues by developing the USE-Lite, which reduces the USE psychometrically, then submits it to subsequent validation efforts. Regarding the latter, 194 participants evaluated Amazon.com and Microsoft Word using the USE-Lite and the System Usability Scale (SUS). Across products, the USE-Lite had very high reliability overall and when separated by dimension. Criterion-related validity was demonstrated by correlating the USE-Lite dimensions and SUS yielding r =.40 to.76. In terms of dimensionality, a principal axis factor analysis was performed on both product data sets. Results revealed four factors generally aligning with the original dimensions (Usefulness, Ease of Use, Ease of Learning, and Satisfaction). Further research and potential item refinement are needed to fully assess the practical application of the USE-Lite.
Estimating Trust in Conversational Agent with Lexical and Acoustic Features
Authors: Li Mengyao , Erickson Isabel M , Cross Ernest V. , Lee John D.
Published: October 2022

As NASA moves to long-duration space exploration operations, there is an increasing need for human-agent cooperation that requires real-time trust estimation by virtual agents. Our objective was to estimate trust using conversational data, including lexical and acoustic features, with machine learning. A 2 (reliability) × 2 (cycles) × 3 (events) within-subject study was designed to provoke various levels of trust. Participants had trust-related conversations with a conversational agent at the end of each event. To estimate trust, subjective trust ratings were predicted using machine learning models trained on three types of conversational features (i.e., lexical, acoustic, and combined). Results showed that a random forest model, trained on the combined lexical and acoustic features, best predicts trust in the conversational agent (R2adj = 0.67). Comparing models, we showed that trust is not only reflected in lexical cues but also acoustic cues. These results show the possibility of using conversational data to measure trust unobtrusively and dynamically.
Psychological Attributes in Future Spaceport Concepts
Authors: Cortelli Rosemary , Weger Kristin , Mesmer Bryan
Published: October 2022

Private U.S. companies are beginning the development of earth-based spaceports-sites for launching and receiving spacecraft. Existing research has examined individuals’ preferences for space travel, but not in the context of spaceports. We developed a mixed-method study that utilized qualitative and quantitative data to examine the psychological principles for founding a human factors perspective of spaceports and space habitats. We administered a survey to community members ( N = 204) to address closed and open-ended questions on general space travel, training, and team composition. After evaluating several chi square tests, we found a need for training regardless of space travel affinity, participants desired extensive training times, and participants preferred team compositions resembling existing NASA astronaut crews. These findings have implications for design and implementation of future spaceports. Further research is needed to expand the existing concepts of spaceport development, but consideration of training and team composition for commercial space travelers is necessary.
Individual Differences in the Acceptance and Adoption of AI-enabled Autonomous Systems
Authors: Weger Kristin , Easley Tiffanie , Branham Nathaniel , Tenhundfeld Nathan , Mesmer Bryan
Published: October 2022

With the deployment of AI-enabled autonomous systems in various fields of application, it is critical to understand the factors affecting the acceptance and in turn the adoption of such technology. While previous research has established that individual differences have an impact on the acceptance and adoption of technology, little research has examined the extent and specificity of those individual differences. The current research attempts to address this gap in the literature by examining individual differences concerning the willingness to accept and adopt general technology versus specific AI-enabled autonomous systems. Our findings reveal several significant results between an individual’s personality traits of Openness, Emotionality, and Honesty-Humility, as well as age, gender, and career field with an individual’s level of acceptance and adoption of general versus specific AI-enabled AS technology. Our findings call for future research to further explore these individual differences in detail and to validate current technology models.
Flip-Flops: A Survey of Risk Perception and Acceptance
Authors: Fortenbaugh David , Shibata Peggy , Meza-Arroyo Manuel , Thobe Keith , Welch Torrence
Published: October 2022

This study’s goal was to better understand risk tolerance associated with wearing flip-flops. A review of existing biomechanical data revealed substantial differences in gait kinematics and kinetics and an increased risk of falling when wearing flip-flops. However, users’ awareness of this increased risk and its potential influence on footwear decisions have not been studied. Nearly 800 participants were surveyed to evaluate usage behaviors of flip-flop wearers. Most participants found flip-flops comfortable and easy to wear, recognized the increased likelihood of a slip, trip, or misstep when wearing flip-flops, and reported experiencing such an event, mostly without injury. Older adults were less likely to have these attitudes and experiences regarding flip-flops. Despite the collective practical knowledge of an increased risk of a slip, trip, or misstep, most participants accepted the risk and chose to wear flip-flops, i.e., footwear choice was infrequently affected by awareness of the increased risk.
A Multiple Resource Examination of Time Sharing Performance for Egocentric and Allocentric Frames of Reference
Authors: Patton Colleen E.
Published: October 2022

Egocentric and allocentric frames of reference (FoR) can both be used in navigation aids, but each has benefits and drawbacks. Incorporating both FoR could be beneficial for offsetting the drawbacks of each viewpoint, but research on the timesharing ability of these FoR is lacking. The current study investigated whether FoR ought to be regarded as separate pools of cognitive resources under the framework of the Multiple Resource Model (MRM). In a dual task paradigm, results indicated that performance improved when both tasks used the same frame of reference. Such findings imply frames of reference are not separate pools in a multiple resources sense. Improved performance may be a result of task similarity, which can improve performance according to the shared processing routines hypothesis. Implications are discussed, including those for navigation aid design.
The Effects of Augmented Reality Remapping on Individual Differences in Task Performance, Workload, and Error Frequency within ICU Nurses
Authors: Salas Andrea Macedo , Richardson Khalaeb , McLaughlin Anne Collins , Coleman Maribeth Gandy , Zeagler Clint , Jordan Kala
Published: October 2022

Fast-paced environments require operators who perform in them to use their cognitive abilities such as perception and memory to complete tasks. Individual differences in ability can result in different outcomes. Previous research has shown that common errors in Intensive Care Units (ICUs) can be reduced if individual differences in cognition, such as attentional ability, are addressed. The goal of this research is to understand how cognitive aids in the form of Augmented Reality (AR) can reduce errors and perceived workload, as well as mitigate differences in performance and attentional ability between individuals.
Application of Augmented Reality for Remote Collaborative Work in Architecture, Engineering, and Construction – A Systematic Review
Authors: Bhanu Aasish , Sharma Harnish , Piratla Kalyan , Chalil Madathil Kapil
Published: October 2022

Augmented Reality (AR) enhances the user’s view of the existing world by adding a computer-generated model. AR technology is used in many domains to help users complete tasks more efficiently. This paper reports a systematic review of literature on where and how AR is used in the architecture, engineering, and construction industries for remote collaboration, identifying the devices adopted as well as their impact and challenges. The initial search resulted in a total of 695 studies, of which 12 were screened for final review. We found that integrating AR results in a significant reduction in time to complete the task, errors, and workload while at the same time improving the performance and accuracy of risk communication in construction projects. This review concludes with a discussion of the findings and the opportunities for future research
The Impact of Visualizing the Process of Disease Spread on Social Distancing Intentions and Attitudes
Authors: Witt Jessica , Hao Chenxu , Shah Priti
Published: October 2022

Novel diseases bring with them new fears, novel public health measures, and immense uncertainty. The uncertainty, coupled with distrust and politization of science, can lead to confusion about the importance of adopting interventions such as social distancing. We tested the impact of a well-designed visualization on attitudes towards and intentions to engage in social distancing. The visualization showed both the outcomes of social distancing and the process by which social distancing slowed the rate of disease transmission. We found that compared to visualizations that only showed the outcomes, process visualization increased the intention to engage in social distancing and the positive attitudes towards social distancing. The visualizations were also compelling as they were contained within the most-viewed on-line Washington Post article to date.
Identifying the Out Of The Loop phenomenon during driving automation using spontaneous gaze behavior
Authors: Chouchane Hanna , Nakamura Hiroki , Sato Kenji , Antona-Makoshi Jacobo , Abe Genya , Itoh Makoto
Published: October 2022

Human factors research that addresses driver-automation interaction is required to mitigate crashes related to the deployment of automated vehicles. While Automated Driving Systems still require driver supervision, there will remain room for human error. This paper presents results from the first in a series of experiments that aim to estimate driver situation awareness using spontaneous gaze behavior and to ensure the driver is ready for takeover. Spontaneous gaze behavior was studied and compared among 13 participants between partial driving automaton and driver assistance automations condition to extract indicators of the out of the loop phenomenon.
MazeWorld: A Game-Based Environment developed to Assess Teaming Behaviors
Authors: Francis Rick F. , Segal Jonathan , Farah Yvonne , Rozell Joseph , Dorneich Michael C. , Gilbert Stephen
Published: October 2022

A testbed has been developed to study multiple constructs of teamwork in a parameterized environment. MazeWorld is a testbed to learn and automate the collection of objective team metrics based on observable behaviors. Quantifying teamwork plays an important role in future agent cooperation. Challenges include differentiating individual skills from skills of the team to not only understand the team as an entity but each of the participating members. MazeWorld is a Unity-based cooperative game that involves three distinct roles working interdependently to achieve a goal. A series of trials were run in MazeWorld to test proposed observable measures of teaming constructs. Teams performed better as the trials progressed, and differences were found between teams. This effort serves as a proof of concept that MazeWorld can explore team behavior using automated team metrics to inform future work.
Using Red Light / Green Light to Understand Resiliency and Manage Risk in Complex Systems
Authors: Lawton Paige , Rosado Gabriela , Boquet Albert , Shappell Scott , Wiegmann Douglas
Published: October 2022

Threat and error management in complex systems has historically centered around failures rather than successes. This has led to an unbalanced view of risk within organizations in that information regarding resiliency within the system (those factors which help to negate failure) are typically ignored. Currently, the National Aeronautics and Space Administration (NASA) employs the Human Factors Analysis and Classification System (HFACS) to both identify human error involved in mishaps and assess those events which prevented more serious consequences from occurring. This paper demonstrates a Red Light/Green Light analysis of a case study, EVA 23, to outline an incident in which an astronaut came dangerously close to drowning in space due to the collection of water in his helmet.
Public Perception of UAM: Are we ready for the new mobility that we have dreamed of?
Authors: Park GeeBeum , Park HyunJae , Park HyunJoo , Chun Nathan , Kim Sang-Hwan , Lee Kyungwon
Published: October 2022

Urban Air Mobility (UAM) has lately emerged as a time-saving mode of air transportation in congested urban areas. However, several challenges to the commercialization and adoption of UAM vehicles exist, such as licensing and restrictions, security, and construction infrastructure. Besides these, public perception is one of the significant aspects of easing psychological stress and representing users’ needs. A survey study was carried out to understand the public perception of UAM and to comprehend how the public perceives and expects UAM aircraft in terms of user requirement analysis as part of human-centered design. To achieve this, a total of 2,847 valid data sets were analyzed. A preliminary data analysis revealed the general level of awareness of UAM machines, expected costs and values for specific scenarios, perceived advantages of UAM vehicles, and overall opinion, along with distinctions based on demographic information such as age groups, residential areas, and income levels. Future research topics might include in-depth data analysis and subsequent user surveys to discover underlying requirements.
“I Am Here”: Investigating the Relationship Between Sense ofDirection and Communication of Spatial Information
Authors: Cauffman Stephen , Cohen Myke C. , Yin Xiaoyun , Demir Federico Scholcover Mustafa , Huang Lixiao , Cooke Nancy
Published: October 2022

Navigation is critical for everyday tasks but is especially important for urban search and rescue (USAR) contexts. Aside from successful navigation, individuals must also be able to effectively communicate spatial information. This study investigates how differences in spatial ability affected overall performance in a USAR task in a simulated Minecraft environment and the effectiveness of an individual’s ability to communicate their location verbally. Randomly selected participants were asked to rescue as many victims as possible in three 10-minute missions. Results showed that sense of direction may not predict the ability to communicate spatial information, and that the skill of processing spatial information may be distinct from the ability to communicate spatial information to others. We discuss the implications of these findings for teaming contexts that involve both processes.
Feasibility of using depth cameras for evaluating human - exoskeleton interaction
Authors: Yoon Soocheol , Li-Baboud Ya-Shian , Virts Ann , Bostelman Roger , Shah Mili
Published: October 2022

With the increased use of exoskeletons in a variety of fields such as industry, military, and health care, there is a need for measurement standards to understand the effects of exoskeletons on human motion. Optical tracking systems (OTS) provide high accuracy human motion tracking, but are expensive, require markers, and constrain the tests to a specified area where the cameras can provide sufficient coverage. This study describes the feasibility of using lower cost, portable, markerless depth camera systems for measuring human and exoskeleton 3-dimensional (3D) joint positions and angles. A human performing a variety of industrial tasks while wearing three different exoskeletons was tracked by both an OTS with modified skeletal models and a depth camera body tracking system. A comparison of the acquired data was then used to facilitate discussions regarding the potential use of depth cameras for exoskeleton evaluation.
Evaluation of Visual Acuity and Perceptual Field of View Using the Varjo XR-3 Headset in a Virtual Environment
Authors: Kappler Elizabeth , Figueroa Jacinto Rosemarie , Arndt Steve
Published: October 2022

Understanding the capabilities and limitations of virtual reality head-mounted displays are key to using virtual reality devices appropriately to display complex 3D information. This study sought to replicate and compare tests characterizing visual acuity and perception using the Oculus Rift and HTC Vive Pro head-mounted displays against the Varjo XR-3 headset. 14 participants tested the Varjo XR-3 in a series of visual and perceptual tasks. Performance in the central field of view was assessed using the Snellen and Landolt tests of visual acuity as well as contrast sensitivity circles. Visual performance in the peripheral field was measured by evaluating symbol and color detection at increasing levels of eccentricity. The Varjo XR-3 provides significantly better visual acuity compared to previously tested headsets. Symbol recognition in the peripheral region is similar when viewed through the Varjo XR-3 compared to in the real world and head movements play a significant role in increasing perceptual field of view.
Effects of Display Modality on Simulated On-Orbit Inspection Performance: Initial Results from Human Exploration Research Analog Campaign 6
Authors: Liu Andrew M. , Weiss Hannah , Stirling Leia
Published: October 2022

To reduce risk in future human spaceflights, free-flying teleoperated inspector robots could be a viable alternative to extravehicular activity inspection operations. Teleoperation depends on accurate operator situation awareness; consequently, a key to successful operations is providing appropriate feedback through an intuitive interface. During each week of a 45-day mission in NASA Human Exploration Research Analog Campaign 6, four crew members perform two simulated telerobotic inspection tasks (Synchronous Inspection task, Asynchronous Inspection task) while using three distinct visual displays (2D, 3D, AR). Preliminary results from Campaign 6 Mission 1 suggest that task performance and efficiency generally improved with each repetition of the task. Performance with the AR display did not show a performance benefit compared to using the 2D or 3D displays. These results are also in general agreement with a prior laboratory study using the same experiment protocol.
A Discussion on Accessibility, Inclusivity, Cultural Design Considerations, and Health & Safety Advisories for Virtual Reality Head Mounted Displays
Authors: Figueroa Jacinto Rosemarie , Kappler Elizabeth
Published: October 2022

With the exponential growth in availability of Virtual Reality (VR) head-mounted displays (HMDs), this study aims to discuss Health & Safety advisory information provided by VR products and observe the extent to which each platform considers accessibility and inclusive and culturally informed design. To this end, five commercially available PC-based VR HMDs, and their corresponding setup applications were assessed in this study - the Oculus Quest 2, Oculus Rift S, Samsung Odyssey, HTC Vive Pro, and Varjo XR-3. Three proto personas were created to evaluate these platforms from various perspectives. VR HMDs are moving in the right direction to consider diverse populations in animation design, but there is room for improvement regarding individuals with visual, auditory, or mobility restrictions. Advancements in multimodal sensory cues and inclusive design considerations may increase adoption of VR technologies as well as compliance with Health & Safety advisory information
Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning
Authors: Yin Wei , Chen Yinong , Reddy Curran , Zhang* Xudong
Published: October 2022

Knowledge of low-back loading is essential for understanding and mitigating the risk of low-back overexertion injuries. Conventional data acquisition methods for estimating joint loading are limited to laboratory settings, whereas wearable sensors can provide a mobile and cost-effective alternative. This study investigated the feasibility of learning prediction of L5S1 flexion moment based on kinematics and electromyography (EMG) measurements from flexible sensors. Four machine learning methods were compared, and different subsets of sensor inputs were explored. Results indicated that the support vector machine (SVM) method outperformed others, and a subset of four out of seven sensor locations, namely sacrum, thigh, shank, and thoracic erector spinae, yielded the best predictive accuracy. The study demonstrates that machine learning can unlock the potential of mobile miniaturized flexible sensors in field biomechanics or ergonomics studies.
The Effect of Support Level and Arm Angle on Passive Arm Support Exoskeleton Perceived Weight Reduction
Authors: Hitchcock William , Safron Julia , Wiese Elizabeth , Schwerha Diana J.
Published: October 2022

This study tested 10 engineering students at Liberty University to determine the effect of various shoulder angles and support level settings on the perceived weight reduction of upper extremity exoskeletons (EXOs). It also examined the impact of support levels and the choice of two different EXOs. Effectiveness was determined by finding the minimum discrete value of a variable weight in a participant’s EXO supported arm at which the participant would assert that they could hold a control weight in their other, unsupported arm for an equal or longer duration. Results indicated that shoulder angle and support level setting were significant factors in determining the perceived weight of equivalence. Arm tested and choice of EXO did not prove significant. Findings indicate that arm support EXOs are more effective when the user’s upper arm is perpendicular to their body and when oriented 120º upward from resting position, and that higher support levels provide the highest reduction in perceived weight.
Identifying Individual-, Team-, and Organizational-Level Factors that Affect Team Performance in Complex Domains Based on Recent Literature
Authors: Atweh Jad A. , Marie Moacdieh Nadine , Riggs Sara Lu
Published: October 2022

Working in teams provides several advantages to dynamic, data-driven domains, but can also add a layer of complexity to operations. There have been several reviews on teams and team performance analysis; however, there has been limited work in the last five years that has examined micro- and macro-level factors that affect overall team performance. Previous research has proposed a framework within healthcare characterizing team characteristics into three categories: individual contributions, team processes, and organizational structures. However, it is still unclear how new emerging topics in the team literature fit within this framework. Here we provide more specific definitions of the three categories proposed and conduct a review that builds on this framework by adding topics identified from the current literature. To this end, we carried out a systematic search of the human factors literature to examine the research on team performance across various domains from the past five years centered. We then propose ideas for future research on team performance.
Investigating Underrepresented Perceptions of Inclusively Designed Voiced Automation
Authors: Watkins Heather , Pak Richard
Published: October 2022

Consumers are increasingly interacting with automated systems for various purposes (e.g., smart speaker, vehicle control). One of the most popular ways they communicate with these systems is via the spoken auditory channel (listening and speaking). However, most of these systems tend to default to a female voice. In addition, many specifically use a White-sounding female voice. While prior work has investigated how attributes of age and gender influence perceptions of voiced automation, an additional vocal attribute of humanness has not been well researched -race. The specific goal of this project is to examine how more inclusively designed AI voices (e.g. exhibiting attributes of race) affect Black users’ attitudes toward autonomous technology. Results are expected to show more preferential attitudes when automation exhibits similar vocal attributes to the user. Findings will add to our understanding of how a deliberately more inclusive design affects an underrepresented population’s attitudes toward technology.
Towards a more Connected Home: User Attitudes and Perceptions after an Integrated Home Technology Exposure
Authors: Cerino Lauren , FakhrHosseini Shabnam , Lee Chaiwoo , Lee Sheng-Hung , Son Heesuk , Coughlin Joseph
Published: October 2022

Recent advancements in Internet of Things (IoT) have introduced many smart home technologies that can provide higher levels of care and convenience to residents. This study aims at understanding user experience in a more connected home and the extent to which previous experience with and exposure to smart home systems can influence user acceptance and adoption of smart home technologies. Using a smart home prototype, a 3-week field trial was conducted to simulate an integrated home environment. Data were gathered after self-installation and after completion of the trial. Results show that technology exposure had positive influence on participants’ attitudes and opinions towards using smart home systems for various tasks. Moreover, participants reported high levels of satisfaction with the self-installation process which aimed to address consumers’ complaints regarding complex setup processes. Results will be discussed based on the practical and commercial implications regarding deployment of home devices.
Evaluating Team Metrics in Cooperative Video Games
Authors: Farah Yvonne A. , Dorneich Michael C. , Gilbert Stephen B.
Published: October 2022

This research aims to understand how cooperative game features lead to teamwork behaviors as a step in developing gamified testbeds where human-human and human-autonomy teaming can be evaluated. Multiplayer video games incorporate features that encourage cooperation and interdependency, making them potential task environments for evaluating team metrics. This study analyzed three cooperative video games through annotating play sessions by tracking cooperative features and teamwork behavioral markers. The results show how cooperative features lead to a high frequency of teamwork behaviors, including coordination, planning, backup behavior, and mutual monitoring. Additionally, game features such as complementary and shared obstacles and puzzles, community survival, and team spirit led to the highest frequencies of behavioral markers. This study can serve as an initial assessment to develop a list of requirements for the gamification of teaming testbeds that can support objective quantification of team measurement.
A Qualitative Analysis of Trust Dynamics in Human-Agent Teams (HATs)
Authors: Carmody Kendall , Ficke Cherrise , Nguyen Daniel , Addis Arianna , Rebensky Summer , Carroll Meredith
Published: October 2022

This study investigated how and why trust changes over time between differing levels of autonomy (LOA) for an intelligence, surveillance, and reconnaissance (ISR) task involving four unmanned aerial vehicles (UAV). Four LOAs were implemented in the study ranging from low to high (manual, advice, consent, veto). The analysis examined if trust increased, decreased, or stayed the same during transitions between two different LOAs. Through a thematic analysis, themes influencing the participant’s trust in agents were identified. Current findings suggest trust increases most when a participant moves between moderate LOAs. Specifically, moving from the condition in which the role of the agent is to provide advice, to the agent making decisions on their own with the consent of the human. Trust was also shown to increase when the human moved from the highest LOA (human merely vetoes) to the manual condition. The most prevalent themes influencing trust included perceived agent errors and false alarms.
Perception of Robot Power: Scale Development
Authors: Benton Rachel A. , McLaughlin Anne Collins , Rovira Ericka M.
Published: October 2022

Robots are increasingly utilized to work with humans in collaborative tasks. While there is a growing body of research investigating individual measures that impact human-robot interaction (HRI), to our knowledge, no measure exists to quantify an individual's perception of robot power. How powerful one perceives robots could be a driving factor in an individual's attitudes toward robots and their trust in HRI. This study aims to develop and validate a scale to quantify peoples' general perception of the power of robots. Preliminary results from exploratory factor analyses with nearly 60% of planned participants revealed three potential factors: companion/task robots, social coworking robots, and domineering robots. Future work will examine whether and how the scale predicts behavior to continue to refine the scale and isolate its measurement.
A User Study on the Feasibility of Topic-aware Misinformation Warning on Social Media
Authors: Xie Jingyi , Yamashita Michiharu , Cai Zekun , Xiong Aiping
Published: October 2022

Misinformation is one of the most fundamental problems in social media with increasing cases and underlying harmful effects on users. To mitigate such problem, misinformation warnings have been developed, including alerting with warning messages and hiding the contents. Previous studies mainly explored the most effective, one-size-fits-all design. Therefore, little has been known about customizable and flexible warning designs. In this study, we propose a “topic-aware misinformation warning” where users’ preferences for warning designs can vary on topics. To illustrate our ideas, we developed Twitter-like pages using three topics (i.e., politics, gossip, and Covid-19) and three designs (i.e., interstitial, contextual, and highlight). We conducted semi-structured interviews with 18 participants to explore their preferences and opinions on the designs. Our results show that users’ preferences for misinformation warnings are diverse in topics. Thus, topic-aware misinformation warning is promising to alleviate misinformation problems on Twitter.
Open-Loop Naturalistic Driving: An Assessment of Human Behavior, Performance, and Physiology to Support the Development of Shared Control Strategies
Authors: Luster Maya S. , Pitts Brandon J.
Published: October 2022

Advanced systems that require shared control are becoming increasingly pervasive. One advantage of a shared control approach is that the human and machine work together to accomplish safe operations. However, data about the human is needed to implement successful strategies. The goal of this study was to quantify naturalistic driving by collecting performance and physiological data during manual, open-loop driving. Sixteen participants performed a single drive that included four sudden obstacles of increasing difficulty (road debris, construction, inclement weather, and an animal). Participants were asked to traverse each obstacle using self-employed judgement and strategies. Action selection, lane deviation, speed, and heart rate data were recorded. Results showed two distinct driving strategies for avoiding the moving obstacle/animal (left vs. right lane navigation). Also, maximum speed was affected by obstacle type, but heart rate variability was not. Results can be used to inform shared control algorithms designed to combat poor driving performance.
Preparing for the lasting effects of COVID-19 on medical training: The design and pilot study of a low-fidelity virtual reality prototype for Central Venous Catheterization training
Authors: Gonzalez-Vargas Jessica , Tzamaras Haroula , Brown Dailen , Moore Jason , Miller Scarlett
Published: October 2022

Over the last several decades we have seen a shift from in-person to online training that has been exasperated by the COVID-19 pandemic. Researchers believe that many of these effects will be lasting which makes it even more important that the Human Factors community seek to step back and understand how to best train complex skills in a virtual world. The current paper is focused on understanding the utility of Virtual Reality (VR) in medical education for a hands-on procedural heavy procedure - ultrasound-guided Internal Jugular Central Venous Catheterization. Specifically, the objective of this study is to identify the potential utility of VR in US-IJCVC training through the design of a low-fidelity prototype and user interviews with three subject-matter experts. Results showed that the VR prototype designed is useful and provides a depth of knowledge and educational value which can be used to design innovative VR training approaches.
Model Based Approach of Integrating Spatial and Temporal Tools compared to traditional Time Studies
Authors: Li Yifan , Armstrong Thomas J.
Published: October 2022

This work aims to demonstrate the use of CAD-based workplace models that describe the spatial relationships of a given job and the use with Predetermined Time Systems (PTS) - namely MODAPTS - in lieu of traditional time studies. MODAPTS was chosen for this work as it is widely used and has relative simplicity. This work shows that CAD and PTS models can be combined in an underlying model for predicting work time and work methods that can then be used for interpreting various fatigue risks. It also gives insight on where MODAPTS categorization of movement patterns falls short, and provides taxonomy updates so that a model-based approach can more accurately reflect the job. It also provides a framework that can serve as a foundation for interpreting observation-based results, for identifying specific risk factors, and for designing engineering interventions.
Investigating the Efficacy of Behavioral Models to Predict Use of Health Technology: A Scoping Review
Authors: Zahed Karim , Fields Sherecce , Sasangohar Farzan
Published: October 2022

Chronic conditions are widespread and need serious behavioral habits to manage or mitigate. There is a gap in understanding the role of beliefs to assess intention to use and to sustain using technology that can aid in performing healthy behaviors. This review aims to understand the common behavioral models used in the literature to understand intention to use health-related technologies. The scoping review included 36 articles, and results show that integrating constructs from more than one behavioral model may add to the capability of behavioral models to predict intention. The research also shows that intention needs to be assessed longitudinally and compared with actual behavior performed to capture a realistic assessment of the relationship between intention and action.
Drivers’ Knowledge of and Preferences for Connected and Automated Vehicles
Authors: Hung Ya-Hsin , Proctor Robert W. , Chen Yunfeng , Zhang Jiansong , Feng Yiheng
Published: October 2022

Connected and automated vehicles (CAVs) offer many potential advantages, including improved traffic flow, reduction of traffic accidents, and increased freedom for adolescents and adults with restricted mobility. However, successful implementation of CAVs depends on several factors, especially acceptance and preferences by people. Specifically, during the earlier stage of deployment, CAVs will have to share the roads with human-driven vehicles (HDVs), which requires communication between CAVs and HDVs regarding their intentions and future actions. Therefore, as a first step in our research program, we conducted a survey of 182 U.S. drivers to assess their knowledge of CAVs and their thoughts about implementation. We report the survey results, accompanied by our interpretations.
Machine Learning Techniques for Prediction of Stress-Related Mental Disorders: A Scoping Review
Authors: Razavi Moein , Ziyadidegan Samira , Sasangohar Farzan
Published: October 2022

The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.
Deidentification of Drivers’ Face Videos: Scope and Challenges in Human Factors Research
Authors: Thapa Surendrabikram , Cook Julie , Sarkar Abhijit
Published: October 2022

Data sharing across disciplines helps to build collaboration, and advance research. With recent development in data-driven models, there is an unprecedented need for data. However, data collected from human research subjects are required to follow proper ethical guidelines. Researchers have an obligation to protect the privacy of research participants and address ethical and safety concerns when data contains personally identifying information (PII). This paper addresses this problem with a focus on sharing drivers’ face videos for transportation research. The paper first gives an overview of the multitude of problems that are associated with sharing drivers’ videos. Then it demonstrates the possible directions for data sharing by de-identifying drivers’ faces using artificial intelligence-based techniques. The results achieved through the proposed techniques were evaluated qualitatively and quantitatively to prove the validity of the suggested methods. We specifically demonstrated how face-swapping algorithms can effectively de-identify faces while still preserving important attributes related to human factor research including eye movements, head movements, mouth movements, etc. Finally, we discuss possible measures to share such de-identified videos with the greater research community.
Analysis of Driver Behavior in Mixed Autonomous and Non-autonomous Traffic Flows
Authors: Liu Jundi , Boyle Linda Ng
Published: October 2022

Autonomous vehicles are expected to improve road safety and efficiency in future transportation systems. A driving simulator study was designed to identify driving styles and the cooperation between human drivers and other AVs. The study captured driver’s following behavior in a fully autonomous driving environment at unsignalized intersections. Participants were asked to make a series of maneuvers (straight through intersection, left turn, and right turn) in two different speed conditions (30, 40 mph) and two different traffic density conditions (with or without other traffic). Analysis of Variance showed that drivers had a significantly larger deviation (defined as the area between two trajectories) during left turn maneuvers when they were traveling at higher speeds. Moreover, the first turning operation had smaller deviation than the second turning operation. The findings have implications for the design of driver-assistance guidance systems in future mixed autonomous and non-autonomous traffic flows.