bluuebunny/crossref_metadata_2025 huggingface dataset:
Chondral Injury Associated With ACL Injury: Assessing Progressive Chondral Degeneration With Morphologic and Quantitative MRI Techniques
Authors: Davidson Emily J. , Figgie Caroline , Nguyen Joseph , Pedoia Valentina , Majumdar Sharmila , Potter Hollis G. , Koff Matthew F.
Published: October 2023

Background:

Anterior cruciate ligament (ACL) injuries are associated with a risk of post-traumatic osteoarthritis due to chondral damage. Magnetic resonance imaging (MRI) techniques provide excellent visualization and assessment of cartilage and can detect subtle and early chondral damage. This is often preceding clinical and radiographic post-traumatic osteoarthritis.

Hypothesis:

Morphologic and quantitative MRI techniques can assess early and progressive degenerative chondral changes after acute ACL injury.

Study Design:

Prospective longitudinal cohort.

Level of Evidence:

Level 3.

Methods:

Sixty-five participants with acute unilateral ACL injuries underwent bilateral knee MRI scans within 1 month of injury. Fifty-seven participants presented at 6 months, while 54 were evaluated at 12 months. MRI morphologic evaluation using a modified Noyes score assessed cartilage signal alteration, chondral damage, and subchondral bone status. Quantitative T1ρ and T2 mapping at standardized anatomic locations in both knees was assessed. Participant-reported outcomes at follow-up time points were recorded.

Results:

Baseline Noyes scores of MRI detectable cartilage damage were highest in the injured knee lateral tibial plateau (mean 2.5, standard error (SE) 0.20, P < 0.01), followed by lateral femoral condyle (mean 2.1, SE 0.18, P < 0.01), which progressed after 1 year. Longitudinal prolongation at 12 months in the injured knees was significant for T1ρ affecting the medial and lateral femoral condyles ( P < 0.01) and trochlea ( P < 0.01), whereas T2 values were prolonged for medial and lateral femoral condyles ( P < 0.01) and trochlea ( P < 0.01). The contralateral noninjured knees also demonstrated T1ρ and T2 prolongation in the medial and lateral compartment chondral subdivisions. Progressive chondral damage occurred despite improved patient-reported outcomes.

Conclusion:

After ACL injury, initial and sustained chondral damage predominantly affects the lateral tibiofemoral compartment, but longitudinal chondral degeneration also occurred in other compartments of the injured and contralateral knee.

Clinical Relevance:

Early identification of chondral degeneration post-ACL injury using morphological and quantitative MRI techniques could enable interventions to be implemented early to prevent or delay PTOA.
Reversible Cerebral Vasoconstriction Syndrome Secondary to Loperamide Ingestion: A Case Report
Authors: Jumah Ammar , Albanna Abdalla Jamal , Qureshi Momina , Malik Shaneela
Published: October 2023

Reversible cerebral vasoconstriction syndrome (RCVS) is a cerebrovascular disorder highlighted by diffuse and multifocal vasoconstriction of the cerebral circulation. This syndrome has been reported to be associated with provoking vasoactive agents, and the identification of such offenders is quite challenging. In our case, the patient’s RCVS was caused by the ingestion of loperamide. Although being reported in the cardiac literature, cerebral vasoconstriction due to loperamide has not been reported yet.
Effectiveness of early care information transfer among home-dwelling frail elderly patients in Japan: A cluster randomized trial
Authors: Ozaki Makiko , Matsumura Shinji , Bito Seiji
Published: October 2023

Introduction

Challenges are often observed during care transitions from home to hospital among frail elderly individuals who tend to be treated by different providers. This study evaluated the effectiveness of early care information transfer on the quality of care transitions among home-dwelling elderly patients in Japan who needed acute hospitalization.

Methods

A cluster randomized controlled trial with a clinic as a clustering unit was conducted with patients aged 65 years and older who had home-visit care and then needed to be hospitalized for acute care. The main outcomes were the quality of care transition perceived by the patient, measured by a self-administered questionnaire, and patient satisfaction, measured by the Hospital Patient Satisfaction Questionnaire. Multilevel regression analysis was used to adjust for clustering and covariates.

Results

Among 177 patients (78 patients in the intervention group vs. 99 patients in the control group) from 17 clinics (8 vs. 9 clinics) who were admitted to hospitals during the study period, 112 patients with main outcomes were included in the analysis (45 patients vs. 67 patients). Quality of care transition was not statistically significantly different between groups (understanding of home care situations: 58.8 vs. 58.2, p = 0.88; preference on where to be cared for: 58.1 vs. 59.6, p = 0.67; goal for discharge: 71.9 vs. 70.9, p = 0.79; care coordination: 66.3 vs. 63.8, p = 0.56).

Discussion

Early care referral in care transition did not show effectiveness in the quality of care transition and patient satisfaction. Studies on information-sharing in the care transition from home to hospitals are needed.
A digital patient-reported outcome (electronic patient-reported outcome) system for patients with severe psychiatric disorders: User-centered development study and study protocol of a multicenter-controlled trial
Authors: Wiegmann Caspar , Quinlivan Esther , Michnevich Twyla , Pittrich Andreas , Ivanova Petja , Rohrbach Alissa Maresa , Kaminski Jakob
Published: October 2023

Background

The effective treatment of patients with severe psychiatric disorders primarily relies on subjective reporting of symptoms and side-effects. This information is crucial for a clinician's decision regarding medication adjustment. Treatment adjustment usually happens at a low frequency (∼4–8 weeks). In between points of care, patients are left alone with their symptoms and side-effects. This leads to uncertainty regarding the treatment, non-adherence, possible relapse, and rehospitalization.

Objectives

We aim to design a flexible electronic patient-reported outcome (ePRO) system, which allows patients with severe psychiatric disorders to: (a) record their symptoms using an app; (b) share the data with the clinical team at points of care; and (c) utilize the data to support therapy decisions.

Methods

In this article, we describe the development process which included the following steps: (a) formation of a co-design team; (b) stakeholder interviews with patients, practitioners, and digital health experts to access needs, requirements, and barriers; (c) prototype conceptualization and design; (d) user acceptance testing and refinement; and (e) finalization of the system for testing in a pilottrial.

Results

We included input from patients with lived experience of psychiatric disorders, clinical team members, software engineers, and researchers. A prototype system was refined, and iterative changes were made before finalization during a series of operational meetings. The system allows patients to digitally self-report their symptoms and provides longitudinal ePRO symptom data for export into the electronic health record.

Conclusions

Routine ePRO collection has the potential to improve outcomes and hereby also reduce health service costs. We have successfully developed a trial-ready ePRO system for severe psychiatric disorders. The findings were incorporated in the planning of a feasibility pilot trial. Assuming feasibility will be established, the system might be subjected to a certification process evaluation of safety and efficacy including a randomized controlled trial.
Interfaces of Dialogue: A Mixed Methods Approach to Investigating Intersubjectivity in Dyadic Improvisations
Authors: Smetana Monika , Bishop Laura , Stepniczka Irene
Published: October 2023

Music therapy provides a complex and dynamic environment and as such offers space for the emergence of nonverbal dialogue. We aimed to capture the relational, musical, and physiological aspects of intersubjectivity emerging during dyadic piano improvisations between music therapists and adult participants in a non-clinical feasibility study. Various data from 17 partner improvisations with adult participants and music therapists were collected, including audio, video, and MIDI recordings, physiological (ECG and EDA) measures, questionnaires, and semi-structured debriefing interviews. Parts of these data sets served for a mixed methods analysis to show (1) how instances of intersubjectivity were experienced by therapists and participants (drawing on interview data), (2) how their shared experiences were supported by coordination and contrast at the level of musical features (drawing on musical data), (3) how physiological arousal was compared between an initial resting baseline and the improvisation, and (4) how patterns in the event related electrodermal activity data related to qualities of intersubjectivity and musical interaction in an example of two case studies. In conclusion, intersubjectivity was characterized by overlapping experiences of relational qualities and complex patterns of musical interaction, including contrasting elements of convergence (in pitch contour) and divergence (key velocity and timing). Further, the case studies showed how differently-interpreted experiences can emerge from a homogenous musical structure, and how stronger intersubjectivity may be associated with more active experiential phenomena such as disruption or reciprocity (versus relaxation or security). Additionally, our findings highlight the complexity of musical interaction as an enactive process. Bringing together multi- and interdisciplinary perspectives enhanced a shared understanding of how subjective experiences that arise during dyadic improvisations relate to measures of musical coordination and physiological arousal. The complex mixed methods design proves to be feasible and, as a consequence, can be transferred to clinical settings in the future.
Visual Mental Imagery Evoked in Indian Classical Music: A Listener-Oriented Study
Authors: Borgohain Junmoni , Behera Rashmi Ranjan , Patnaik Priyadarshi , Suar Damodar
Published: October 2023

Music and visual mental imagery (VMI) are closely associated. The visual depiction of musical instruments and musical settings is central to music iconography and music-mediated visual culture. Music-evoked VMI, mediated by emotions, is another established field of study. Hindustani classical music (HCM) has a medieval musical pictorial tradition based on dhyanaslokas (meditative verses), which visually represent the ragas (musical modes) based on the evocation of their rasas or aesthetic emotion. This, incidentally, comes close to the contemporary understanding of music-evoked VMI. However, VMI evoked in response to music has received very little attention in studies on HCM, both for contemporary listeners and historically. In this context, we investigated whether HCM evoked VMI and whether its content was related to emotions, such as “calm” and “sad”, that dominate HCM. Additionally, the themes of the VMI in response to calm and sad clips were compared with corresponding VMI in Western music. Finally, medieval dhyanaslokas were analyzed for associated visual imagery (VI) that was compared with VMI generated in these contexts. Findings revealed that HCM evokes VMI, which is well differentiated for calm and sad clips. When the VMI for HCM was compared with Western VMI, though common themes emerged, cultural differences were identified. While VI in dhyanaslokas had similarities to VMI in response to HCM, marked differences also existed.
Factors Influencing the Professional Development of Engineering Students Under the “Plan for Educating and Training Outstanding Engineers”
Authors: Tang (唐旭亭) Xuting , Guo (郭卉) Hui
Published: October 2023

Purpose

This study investigates the role of the engineering education environment created by the implementation of the “Plan for Educating and Training Outstanding Engineers” (PETOE) on the professional capability development of engineering students from the perspective of students’ experience.

Design/Approach/Methods

This study uses data from the National Engineering Student Survey and multiple regression analysis to explore the role of institutional resources and support, teaching reforms, and interpersonal interaction on the professional development of engineering students.

Findings

The opportunities for on-campus practical activities provided by the institution, content of the teachers’ teaching, teaching methods, comprehensive coursework assessment, and interpersonal interactions contributed to the development of students’ engineering competencies, with limited contributions from research-based teaching methods. Internship opportunities and international exchange environments were negatively associated with the development of engineering skills in an “unexpected” manner.

Originality/Value

Focusing on students’ experiences of engineering education reform, this study comprehensively evaluates the implementation of the reform measures adopted under PETOE. Furthermore, it assesses how corresponding changes in the general educational environment relate to students’ professional capability development. Thus, this study addresses an important research gap in educational reform—the lack of domestic empirical studies in China.
Engaging Nepali Journalists in Good Road Safety Reporting: Evaluation of the Impact of Training Workshops
Authors: Pant Puspa Raj , Dahal Sudhamshu , Joshi Sunil Kumar , Mytton Julie
Published: October 2023

Most road traffic crash reports published in Nepali media provide little information about their causes, impacts, or preventability. Three workshops involving 31 journalists from diverse media platforms were held using published World Health Organisation resources, to provide training in good road safety reporting. Participants were invited to complete a survey exploring their motivation for participation and knowledge gained. Thirteen (42%) participants responded, reporting the acquisition of new knowledge for content generation, finding facts, active application of learning, and purposeful communication, and an intention to apply the skills learned in their work. A before and after assessment of 94 media outputs from workshop participants found an improvement in the use of story angles and ideas recommended in WHO resources. The study suggests that journalists are interested in developing their road safety reporting skills and that running such workshops has the potential to improve responsible reporting and the promotion of road safety.
Analyzing Discourses and the Communication of Sport: A Scoping Review and Suggestions for Future Endeavors
Authors: Wagner Ulrik , Sveinson Katherine
Published: October 2023

Scholars have been increasingly engaging with discourse analysis as theory and method. At its essence, this approach is centered around communicative acts, and thus is foundational to the study of sport media and communication. However, to our knowledge, no overview of the use of discourse analysis in sport, exercise, and physical education studies exists. Therefore, we conducted a structured scoping review by identifying 1892 papers in 277 journals from 2000 to April 2022, then narrowed the sample to 587 papers that specifically conducted a discourse analysis of sport. We sought to understand which theoretical traditions are commonly utilized, which sub-disciplines embrace discourse analysis with specific attention to communication, and how this approach can enrich and add to the methodological and theoretical development of media and communication studies in sport. Our review illustrated a heavy reliance on Foucauldian discourse analysis and critical discourse analysis across a multitude of sport disciplines, though discourse analysis generally appears less frequently in sport communication journals. To advance our field’s use of discourse analysis, we suggest the need to emphasis a multimodal approach to discourse analysis, the integration of technology within this approach, and directions to theorize beyond dominant approaches to focus on mediated social interactions.
A Systematic Literature Review for Measure, Estimation, and Mitigation of Motion Sickness in Vehicle Environment
Authors: Song Yein , Choi Myung Bin , Ahn Sung hee , Yun Myung Hwan
Published: October 2023

There are various studies to investigate the factors that affect driving experiences in the automobile environment and auditory experiences as variables. These factors might eventually affect users' sensitivity and performance of driving-related and non-driving-related tasks. Research on mechanical and electrical technology development is significant and has been conducted a lot. For driver- or passenger-centered auditory experiences, however, it is very important to understand people's perceptions and evaluations according to the technical design of the car sound. Therefore, in this paper, we would like to identify trends in 10 years of research on in-vehicle auditory experiences studied since 2013 and propose a human-centered automotive sound evaluation framework. The driving context, sound, and people were derived as factors affecting the car hearing experience, and specific sub-factors were derived. The results of this study are expected to provide insight into directionality in future automotive auditory.
Examination of Human Spatial Reasoning Capability and Simulated Autonomous Rendezvous and Docking Monitoring Performance
Authors: Larson Hannah , Stirling Leia
Published: October 2023

Autonomous rendezvous and docking (ARD) maneuvers are challenging tasks that require collaboration between a human and a spacecraft to be successful. As automation becomes more integrated into ARD systems, it is important to consider when and why a human may take control. Intrinsic human characteristics can influence these decisions. We consider how human spatial orientation capacity affects participants when monitoring a simulated ARD maneuver and initiating takeover when the system is perceived to be failing. Participants’ spatial reasoning capability was assessed and compared to performance in the monitoring task and perceived mental workload. While participants showed high rates of success in the task, they showed a wide range in spatial reasoning capacity and perceived mental demand. Spatial reasoning capacity did not indicate participants’ mental workload, which has implications for the human as the supervisor. These results inform future work on augmentative displays that may incorporate exocentric and egocentric views.
Explaining Trust Divergence: Bifurcations in a Dynamic System
Authors: Li Mengyao , Noejovich Sofia I. , Cross Ernest V. , Lee John D.
Published: October 2023

When people experience the same automation, their trust in automation can diverge. Prior research has used individual differences—trust propensity and complacency—to explain this divergence. We argue that bifurcation as an outcome of a dynamic system better explains trust divergence. Linear mixed-effect models were used to identify features to predict trust (i.e., individual differences, automation reliability, and exposure). Individual differences associated with trust propensity and complacency increases the R2 of the baseline model by 0.01, from R2 = 0.40 to 0.41. Furthermore, the Best Linear Unbiased Predictors (BLUPS) for random effect of participants were uncorrelated with trust propensity and complacency. In contrast, modeling trust divergence from a dynamic perspective, which considers the interaction between reliability and exposure along with the individual by-reliability variability fit the data well ( R2 = 0.84). These results suggest dynamic interaction with automation produce trust divergence and design should focus on state dependence and responsivity.
Designing for Trust and Situational Awareness in Automated Vehicles: Effects of Information Type and Error Type
Authors: Ding Yaohan , Jia Lesong , Du Na
Published: October 2023

Trust and situational awareness (SA) are crucial to the adoption and safety of automated vehicles (AVs). Appropriate design of AV explanations could promote drivers’ acceptance, trust, and SA, enabling drivers to get more benefits from the technology. This study investigated the effects of error type and information type of AV explanations on drivers’ trust and SA. We recruited 300 participants for an online video study with a 3 (information type) × 2 (error type) mixed design. Linear mixed model analyses showed that compared with false alarms, misses were associated with more trust decrease after the error and more trust decrease after the post-error recovery. Compared with why information, how information was associated with lower SA generally and risked potential over-trust in false alarms. Therefore, we recommend deploying AV decision systems that are less miss-prone and including why information in AV explanations.
Human Factors Extended Reality Showcase
Authors: Spain Randall , Goldberg Benjamin , Bailey Shannon , Fussell Stephanie , Bayro Allison , Hale Kelly , Jones Aaron , Regina Rachel , Thomas Bob , Owens Kevin , Lau Nathan , Dam Abhraneil , Chen Karen , Sturgeon Luke , Vaughn-Cooke Monifa , Enebechi Nuela Chidubem
Published: October 2023

This alternative format session provides a forum for human factors scholars and practitioners to showcase how state-of-the-art extended reality (XR) applications are being used in academia, defense, and industry to address human factors research. The session will begin with short introductions from each presenter to describe their XR application. Afterward, session attendees will engage with the presenters and their demonstrations, which will be set up around the demonstration floor room. This year’s showcase features XR applications in STEM education, medical and aviation training, agricultural data visualization, homeland security, training design, and visitor engagement in informal learning settings. Our goal is for attendees to experience how human factors professionals use XR to support human factors-oriented research and to learn about the exciting work being conducted with these emerging technologies.
Comparing Armband EMG-based Lifting Load Classification Algorithms using Various Lifting Trials
Authors: Taori Sakshi Pranay , Lim Sol
Published: October 2023

The objective of this study was to evaluate the performance of machine learning (ML) algorithms developed using surface electromyography (EMG) armband sensor data in predicting hand-load levels (5 lb and 15 lb) from diverse lifting trials. Twelve healthy participants (six male and six female) performed repetitive lifting with three different lifting conditions, i.e., symmetric (S), asymmetric (A), and free-dynamic (F) lifts. ML models were developed with four lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8%) compared to A (83.2%) and F (83.4%). Findings indicate that the ML model developed with controlled symmetric lifts was less accurate in predicting the load of more dynamic, unconstrained lifts, which is common in real-world settings.
Learning by Successful or Unsuccessful Experiences?
Authors: Kim Keum Joo , Santos Eugene
Published: October 2023

Humans learn from both successful and unsuccessful experiences, because useful information about how to solve complex problems can be gleaned not only from success but also from failure. In this paper, we propose a method for investigating this difference by applying Preference based Inverse Reinforcement Learning to Double Transition Models built from replays of StarCraft II. Our method provides two advantages: (1) the ability to identify integrated reward distributions from computational models composed of multiple experiences, and (2) the ability to discern differences between learning by successes and failures. Our experimental results demonstrate that reward distributions are shaped depending on the trajectories utilized to build models. Reward distributions based on successful episodes were skewed to the left, while those based on unsuccessful episodes were skewed to the right. Furthermore, we found that players with symmetric triple reward distributions had a high probability of winning the game.
A Controlled Experiment on the Impact of Intrusion Detection False Alarm Rate on Analyst Performance
Authors: Layman Lucas , Roden William
Published: October 2023

Organizations use intrusion detection systems (IDSes) to identify harmful activity among millions of computer network events. Cybersecurity analysts review IDS alarms to verify whether malicious activity occurred and to take remedial action. However, IDS systems exhibit high false alarm rates. This study examines the impact of IDS false alarm rate on human analyst sensitivity (probability of detection), precision (positive predictive value), and time on task when evaluating IDS alarms. A controlled experiment was conducted with participants divided into two treatment groups, 50% IDS false alarm rate and 86% false alarm rate, who classified whether simulated IDS alarms were true or false alarms. Results show statistically significant differences in precision and time on task. The median values for the 86% false alarm rate group were 47% lower precision and 40% slower time on task than the 50% false alarm rate group. No significant difference in analyst sensitivity was observed.
Classification of Human Driver Distraction Using 3D Convolutional Neural Networks
Authors: Kwakye Kelvin , Aboah Armstrong , Seong Younho , Yi Sun
Published: October 2023

Distracted driving is a dangerous driving behavior that causes numerous accidents on US roads each year. It is critical to identify distracted drivers in order to prevent such accidents. Previous studies attempted to detect distracted driving using heuristics and machine learning; however, none of these methods could capture the problem's spatiotemporal features. As a result, the purpose of this study was to use a 3D convolutional neural network (CNN) that can capture both spatial and temporal information to classify distracted drivers based on facial features and behavioral cues. We used the Database to Enable Facial Analysis for Driving Studies (DEFADS), an open-source dataset containing 77 human subjects performing scripted driving-related activities, to achieve this goal. The PyTorch video library was used to train the model. The 3D CNN achieved an overall recall and precision of 97.6 and 98.1, respectively, indicating its efficacy in detecting distracted drivers in the real world.
Law Enforcement Perspectives on Police Vehicle Cybersecurity
Authors: Roberts Shannon C. , Zahabi Maryam , McCarthy Lauren , Lanclos Christopher , Romero Jonathan
Published: October 2023

Cyberattacks on law enforcement officers’ vehicles can compromise and disrupt emergency response, yet there is little knowledge of how law enforcement officers understand and respond to cybersecurity concerns within their work vehicles. This study gathered data on law enforcement officers’ perceptions of vehicle cybersecurity. A survey study was conducted with 80 law enforcement officers, focusing on gathering information to quantify the relationship between their experience as an officer, their cybersecurity experience, and their perception of vehicle cybersecurity. Overall, there was a lack of understanding and knowledge regarding vehicle cybersecurity. Results also revealed a small relationship between how many hours an officer spends in the vehicle and vehicle cybersecurity and an even stronger relationship between the officer’s cybercrime experience and their perceptions of vehicle cybersecurity. With appropriate interventions (e.g., training), there is an opportunity to impact law enforcement behavior to positively promote vehicle cybersecurity resiliency.
Willingness to Consider Pooled Rideshare?: An Exploratory Study on Influential Factors
Authors: Su Haotian , Gangadharaiah Rakesh , Rosopa Elenah , Brooks Johnell , Boor Lisa , Kolodge Kristin , Rosopa Patrick , Jia Yunyi
Published: October 2023

Rideshare use has grown significantly, beginning with solo riders and evolving to pooled rideshare. Pooled rideshare involves sharing a ride with stranger(s). Despite the growth in rideshare services worldwide, the use of pooled rideshare in the U.S. is relatively low within all rideshare trips and compared to other forms of transportation, e.g., driving one's personal vehicle. A national survey of 5,385 individuals was conducted to identify factors influencing riders' willingness to consider pooled rideshare. Exploratory and confirmatory factor analyses were performed. The survey results indicated five factors: service experience, time/cost, traffic/environment, privacy, and safety. Understanding these factors is crucial for the future of dynamic ridesharing services in the U.S.
May I, please?: Examining the Need for Improved Intention Communication on the Road Using Naturalistic Data
Authors: Song Miao , Ayoub Jackie , Tian Danyang , Perez Miguel , McClafferty Julie , Moradi Pari Ehsan
Published: October 2023

Drivers need to constantly communicate their intention while sharing the road with other road users to attract attention, reduce confusion, and avoid collisions. With current advancements in the transportation system, particularly the increasing penetration of various levels of automation, the need to communicate intentions has become even more demanding and complex. Thus, it is critical to investigate the limitations and consequences of the existing communication channels and examine the need for improved communication. This study focused on two representative event types: lane change/merge and stop sign-controlled intersection in the SHRP 2 NDS dataset. Communication was deemed essential to the successful navigation of these maneuvers. Through exploratory analysis of driving behavior, insights were gained into the prevalence of lack of communication (LOC) among target events. Identified LOC events were further classified based on the scenario type. Moreover, descriptive observations of the interaction between drivers in these situations were developed and categorized.
Effects of Color Commonality of Overlay Clutter and Information Access Effort on Tasks Requiring Visual Search
Authors: Warden Amelia C. , Wickens Christopher D. , Clegg Benjamin A. , Ortega Francisco R.
Published: October 2023

The amount and color of overlay clutter can impede focusing attention on one layer of information from multiple sources, and influence dividing attention when comparing across layers. The current experiment examined the effect of information access effort and color commonality of overlay clutter on performance. Participants viewed maps with two domains of information that were either overlaid, adjacent, or separated, and answered questions about either both domains (integration) or one domain (focused attention). The overlaid information was either similar (green) or dissimilar (red) in color relative to the background. Overlaid displays benefited integration tasks but imposed a cost to focused attention tasks for accuracy but not response time. Increased display separation did not impose performance costs. Computational models account for some costs of clutter in overlay displays but the color similarity between databases also contributes to cost and benefits of overlay in a fashion not currently considered by such models.
How do Blame Attributions Impact Trust in Complex Task Environments?
Authors: Atchley Andrew , O’Hear Emily , Barr Hannah M. , Cotter Jenna E. , Hamblin Bryanna , Oswald Grace , Mesmer Bryan , Weger Kristen , Gholston Sampson , Menon Vineetha , Demir Mustafa , Tenhundfeld Nathan L.
Published: October 2023

Trust in automation is a multidimensional process in predicting the proper use of automated systems. Trust can be generalized across components in multi-component systems, leading to contagion effects. However, no research has considered how blame attributions influence the size of contagion effects. We conducted an experiment to address how attributions of blame and responsibility influenced the magnitude of contagion effects for trust. Participants were presented with a hypothetical scenario depicting an airplane environment consisting of six human and six automation components. Participants rated their trust in each component before and after an oxygen mask failure. The findings indicate that the percentage of blame attributed to a component predicted the degree to that trust decreased following an error. Thus, more research should focus on how users assign blame across systems.
Toward the Validation of a Novel Measure of Individuals’ Influence During Team Collaborations
Authors: Harrison Julie L. , Gorman Jamie C. , Reitman Jason G. , D’Mello Sidney
Published: October 2023

Collaborative problem solving (CPS) is an important skill in the modern workforce, and due to its interactive nature, is challenging to assess. The present study builds on work in team sciences to provide initial validation for a metric that quantifies CPS influence—the extent to which each individual contributes toward the team’s CPS processes—using average mutual information (AMI). The measure is investigated in teams collaborating in a computer programming task, where one teammate was assigned to a controller role (i.e., the only person who engaged with the task interface directly). Results suggest the controller had more influence over the team’s CPS processes than the other participants in the triad, providing initial validation for the influence metric. Future work will investigate the measure in classrooms and multiple modalities, and extend the metric in real-time to understand how influence fluctuates over the course of collaboration.
Comparing Trunk Kinematics Computed by Optical Marker-Based Motion Capture System and Inertial Measurement Units During Overground Trips
Authors: Lee Youngjae , Alexander Neil B. , Franck Christopher T. , Madigan Michael L.
Published: October 2023

Falls are the most common cause of non-fatal injuries, and trips are responsible for high percentages of those falls in the United States. Traditional method for estimating trunk kinematics during overground trips uses optical marker-based motion capture systems. However, their cost and space requirements can often be barriers in this research field. Inexpensive and portable inertial measurement units may be an appropriate alternative. This study compared trunk flexion angle and angular velocity at touchdown of the initial recovery step after laboratory-induced trips while walking captured by the optical markerbased motion capture system versus IMUs. Our results provide evidence that a sternum-worn IMU can provide trunk kinematic measurements of clinical relevance and may be used to provide meaningful data to understand kinematic responses to trips or trip-induced falls that occur in real life.
Multitasking in Complex Environments: An Attempt to Generalize Individual Differences in Multitasking to a Realistic Task Setting
Authors: Schindler Hanna , Bista Jayanti , Graffe Philipp , Ivanova Maria , Hahne Florian , Kubik Veit , Brüning Jovita , Rieger Tobias
Published: October 2023

Multitasking is a common feature of many modern work and home environments, and this study investigated the relationship between multitasking performance in two different paradigms: a more controlled task-switching paradigm (TSWP) and a more complex, semi-ecologically valid multitasking paradigm (SynWin). The study also explored whether parallel processors may have performance advantages in a complex dual-task environment. Results showed no significant correlation between individual multitasking efficiency in the TSWP and SynWin paradigms. Additionally, the results indicated that the combination of subtasks was the primary factor affecting performance in dual-task variants of the SynWin, rather than the use of a parallel processing mode. We conclude that there may be constraints with respect to the experimental conditions necessary to generalize findings from controlled multitasking paradigms to semi-ecologically valid tasks scenarios. Future research should prioritize efforts to understand how people multitask in more realistic settings and the underlying cognitive processes involved.
Frustration, Confusion, Surprise, Confidence, And Self-Doubt: Cyber Operators’ Affects During A Realistic Experiment
Authors: Gutzwiller Robert S. , Gilbert Madison , Drescher T. J. , Ferguson-Walter Kimberly J. , Mikanda Noella , Johnson Craig J. , Scott Dakota D.
Published: October 2023

In this paper we examine data from a complex two-day experiment with professional cyber red teamers to determine what creates, or contributes, to affective states that may further disrupt attackers. Participants rated their frustration, surprise, confidence, self-doubt, and confusion at the end of each day, and these responses were scored on a Likert scale and using Thematic analysis. We found several elements strongly contributed to the frequency and severity of these affective states, supporting our initial hypothesis, and further that (1) experiencing a Lack of Progress impacted four of the five states with moderate severity, and (2) interacting with elements in the environment that Deviated from what was expected was associated strongly with surprise. Implications for cyber defense and limitations of the method are discussed.
Comparing mouse versus trackpad input in a web-based app for assessing motor learning
Authors: Watral Alexandra T. , Morley Abby , Pastel Robert , Trewartha Kevin M.
Published: October 2023

Current laboratory approaches to measuring motor learning are not accessible to all populations, limiting research about developmental processes and medical conditions that impact motor control. We recently created a web-based application remote assessment of visuomotor adaptation, a gold-standard approach to studying motor learning. Previously, we validated this application in younger and older adults. However, preliminary analyses suggested that the input device (mouse or trackpad) may have impacted performance. The current study directly evaluated performance differences in younger adults using the application with a mouse compared to a trackpad. Results showed no statistically significant differences in learning curves or movement times between groups, but reaction time was significantly faster in mouse users. While the input device had very little impact on motor learning, slower reaction time when using a trackpad may be related to increased cognitive demands or reduced movement efficiency compared to using a mouse for this task.
Project AVIAN: Implications of Utilizing the Novel AVIAN-S Machine Learning Model in Analyzing Aviation Safety Event Reports
Authors: Kinsella Amelia , Bynum Edward , Hinson R. Jordan , Berry Katherine , Sawyer Michael
Published: October 2023

Voluntary Safety Reporting Programs (VSRPs) create an opportunity for actively identifying potential safety issues within aviation operations. However, manually reading and analyzing these reports can be labor-intensive and heavily relies on subject-matter experts. The full potential of VSRP data is difficult to achieve due to limited resources available to extract meaningful human factors (HF) data from reports. New machine learning (ML) techniques involving natural language processing (NLP) offer opportunities to label factors of interest within safety reports more efficiently and effectively. A novel ML model was developed to identify HF issues within aviation safety reports. The AVIAN-S model was trained on over 50,000 rows of manually classified VSRPs. The model uses ML and NLP to automate the process of labeling aviation safety reporting data and coding reporter narratives according to an established HF taxonomy. Preliminary results and implications for using the AVIAN-S model for analyzing aviation safety event reports are described.
A new taxonomy to categorize flexible work arrangements for post-covid organizational work planning
Authors: Wang Wenbi , Le Jimmy
Published: October 2023

Flexible work arrangements (FWA) widely proliferated around the world during the covid pandemic lockdown. A new multi-dimensional taxonomy was proposed in this paper to classify different forms of FWA according to the degree of autonomy that a policy offers to employees with respect to their spatial mobility, temporal flexibility, and the degree of freedom from supervision. This taxonomy reflects the defining features of contemporary flexible working. It enables researchers and business decision-makers to categorize different forms of FWA, meaningfully compare their impacts on organizational and individual performance metrics, and support an evidence-based approach to inform the establishment of post-pandemic FWA policies.
Designing for Older Adults with Cognitive Impairment: A Panel Discussion
Authors: Rogers Wendy A. , Czaja Sara J. , Charness Neil , Boot Walter R.
Published: October 2023

The population is aging and a subset of older adults will have some level of cognitive impairment. Human factors/ergonomics (HF/E) practitioners must consider them in the design process but often are not prepared to do so, lacking relevant experience or training. In this panel, an interdisciplinary team with expertise in aging, in general, and cognitive impairment, in particular, will (a) provide insights into cognitive challenges confronting older adults with cognitive impairments; (b) discuss impacts of these challenges on everyday performance (e.g., transportation, computer use, health); (c) explore the design cycle as it relates to cognitive impairment; and (d) provide examples of accommodations necessary for research and user testing for this population. Interactive audience discussion will focus on future directions and challenges for design and testing of emerging technologies for persons aging with cognitive impairment. This panel will be of interest to a broad range of HF/E domains.
Using Sensitivity and Bias in Signal Detection Theory to Predict Proportion Correctness: Simulation and Case Study on ADAS Mental Model Evaluation
Authors: Huang Chunxi , He Dengbo
Published: October 2023

The sensitivity ( d’) and bias ( c) in signal detection theory can reflect respondents’ objective performance in understanding a system and their bias towards saying yes. Thus, d’ and c can be used as alternatives of proportion correctness (PC) when evaluating drivers’ mental models of advanced driving assistance system (ADAS). The adoption of d’ and c as mental model metrics also allows cross-study comparisons as their values are independent of signal (i.e., ADAS function present) and noise (i.e., ADAS function absent) ratio. However, there is no closed-form solution of the relationships among d’, c, and PC. Hence, using numerical simulations, we extracted an empirical equation that quantifies how d’ and c can estimate PC. The equation was then validated based on participants’ responses from a survey study that targeted towards drivers’ ADAS mental model. The results show that the empirical equation reached a satisfying performance (R2 > 0.8) in estimating PC.
Level of detail in visualization for human autonomy teaming: Speed, accuracy, and workload effects
Authors: Wang Tianzi , Lau Nathan
Published: October 2023

For human autonomy teaming, information for promoting transparency could lead to information overload, negatively impacting performance and workload. This paper presents an empirical study investigating how different level of details (LODs) about the autonomy represented on the user interface would influence speed, accuracy, and workload. Specifically, we compared visualizations of a lost person model at four different LODs to aid in directing human and unmanned aerial vehicles searchers in search and rescue missions. The lowest LOD was found to support higher accuracy but at the expense of speed. The highest LOD induced the highest workload, while the other three LODs induced lower and similar levels of workload. The results indicate that the LOD in transparent displays could induce a speed and accuracy tradeoff.
ChatGPT as the Ultimate Travel Buddy or Research Assistant: A Study on Perceived Attitudes and Usability
Authors: Flores-Cruz Gabriela , Hinkle Sean D. , Roque Nelson A. , Mouloua Mustapha
Published: October 2023

The purpose of this study was to investigate participants’ perceived attitudes and usability with OpenAI’s ChatGPT AI chatbot. Participants were asked to watch screen recorded videos of a researcher exploring the AI’s ability to create a quantum mechanics experiment and to plan a trip to New York City. Thirty percent of participants had previously used the AI before the study. Attitudes towards the AI were in the middle of the scale, and prior use did not affect these attitudes. Additionally, ratings on usability were higher for planning a trip compared to creating an experiment, but no differences were found depending on prior use. Future research should examine attitudes and usability when participants interact with the AI chatbot directly in different scenarios. The study also emphasizes the need to examine the potential effects of AI on user experience, and safety, given the prevalence of ChatGPT in the general population.
Sensor-based Stress Level Monitoring: An Exploratory Study
Authors: Li Jiaxin , Soh Robyn , Kim Ji-Eun
Published: October 2023

Stress is a common concern in modern workplaces. However, traditional stress measurements such as selfreported questionnaires have limited application in real-world settings. In this exploratory study, we collected physiological signals via a wristband and an eye tracker from five participants while they were executing a stress-inducing task. Our mixed-effect model revealed that several physiological responses, including electrodermal activity, skin temperature, and average pupil diameter, can be used as indicators of perceived stress levels. Our findings suggest the potential of using physiological sensors to monitor individuals’ perceived stress in real-world scenarios and thus facilitate workplace stress management and intervention.
Exploratory Analysis of Automated Vehicle Crashes Using an NLP Pipeline
Authors: Sharma Anjnesh , Du Na
Published: October 2023

This study utilized a recently released crash dataset of Level 3 automated vehicles (AVs) made publicly available by the National Highway Traffic Safety Administration (NHTSA). The primary objective was to investigate various crash types and identify factors that influence crash severity. To achieve this, we employed a lightweight Natural Language Processing (NLP) pipeline to automatically extract relevant information from crash narratives and categorized the crashes into 15 distinct types. By analyzing the dependency triples derived from the crash narrative using the Stanford CoreNLP library, we determined the similarity between each narrative and the predefined categories. Our findings highlight safety-critical crash scenarios based on real-world data encompassing diverse operational design domains (ODDs), revealing a statistically significant impact of lighting conditions on crash severity. These results contribute to a better understanding of AV crashes and provide valuable insights to enhance the safe testing, integration, and development of AVs in real-world environments.