Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System

Pathfinder networks are a method to represent mental models from empirically generated pairwise relatedness ratings. This study examined the effects of training exposure on mental model structures based on relatedness ratings collected using the Target Rating method. Forty-eight participants read instruction slides with or without explicit information on the functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively). Participants provided relatedness ratings and completed a comprehension test. The Advanced Mental Model group had more common links with the expected model, higher within-group network similarity scores, and higher mental model assessment questionnaire scores than the Basic Mental Model group. Both groups had coherence scores above the minimum threshold for internal consistency. Pathfinder network analysis was sensitive to changes produced by a simple exposure training intervention. In practice, a simple training program may effectively influence operator mental models in novel technological environments such as Advanced Air Mobility.
A Simple Tool to Evaluate Color for Legibility and Distinguishability when Designing Digital Displays

Color is important in helping users effectively process information like text and symbols when performing tasks on digital displays. Interface designers in safety- and mission-critical industries often utilize human factors design guidelines and standards to make important design decisions like the use of color for legibility and distinguishability. However, such guidance sometimes requires additional interpretation and calculation to readily apply to the design question at hand. This work describes a simple tool that can be used to evaluate the adequacy of color choices from a palette for being legible or distinguishable. The tool eliminates having to manually convert colors to a readable format while providing a specific set of colors that are acceptable from select human factors standards. This paper provides an exemplary case study applied to a digital modification at a nuclear power plant to highlight how the tool may be used in safety-and mission-critical industries.
USE-Lite: Psychometric Item Reduction of the USE (Usefulness, Satisfaction, and Ease of Use) Questionnaire

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.
Identifying And Improving Young Drivers’ Perceptions Towards Vulnerable Road Users

Vulnerable road users (VRUs) like bicyclists, pedestrians, and road users of other modalities, are at a higher risk of collision with young drivers when a complex traffic situation presents itself. Past research has established the importance of young drivers’ perceptions about VRUs around safe interactions. This research aimed to design and evaluate a novel persuasive intervention that can improve perceptions of young drivers towards VRUs. First, the study identified recently licensed (12 to 18 months) perceptions towards VRUs through structured interviews. Based on these findings, an interactive intervention was designed and evaluated. Results showed improvement over time in self-reported violations among groups who received the intervention or the control. Additionally, participants who received a citation showed lower violations and lapses. The findings have implications for designing future interventions for interactions with VRUs.
The Resource Pool is Dry: A Model for Surfing the Waves of Energy

Exhaustion impedes all humans from achieving their goals, be they during experiments or in daily life. The notion of mental resources has provided substantive discourse on attentional failures and operational mishaps. However, without empirical validation, we cannot assess whether the “resource” is an emergent or contrived phenomenon. We must refute the notion that it is an unspoken Law of Resources. In this paper we celebrate the contributions of the resource and issue a long-needed challenge – grounding it in psychophysiological reality. We outline the initial developments of a dynamic neural energetics model towards that goal. The fundamental question we pose is this – can we expand the capacity of resource theory? Emerging methods are discussed to measure energy waves, a more intuitive and testable metaphor. Our theory merges thermodynamic, information-theoretic, neuroscientific, and dynamical systems approaches into a single framework that argues “limitations” stand in paradox with energization methods.
Integrating Usability into the Design of Student Ratings of Teachers: The Teaching and Instructional Measure of Effectiveness (TIME)

At most universities, students are required to complete end-of-semester teacher evaluation forms, which may have undesirable effects on teacher ratings to the extent that students feel inconvenienced, annoyed, or disinterested when completing these forms. Addressing these concerns, we designed the Teaching and Instructional Measure of Effectiveness (TIME), a brief 6-item measure that captures relevant aspects of teaching effectiveness while retaining high levels of usability. The TIME was piloted in a small set of undergraduate psychology courses (k = 22 classrooms, cumulative N = 640 students). Students perceived the usability of the TIME to be good-to-exceptional; and overall, students indicated that the TIME was better than the teaching evaluation system currently employed by the university. Moreover, the TIME correlated very highly with the existing teaching evaluation form, but with fewer items and less time to complete. Together, these results suggest the TIME can reliably yet more conveniently reflect student ratings of teacher performance. Future research on the psychometric properties of the TIME can usefully expand from the current work by involving multiple universities, more disciplines, more diverse classrooms, and a wider range of institutional teaching evaluation forms.
Confirmative or Normative? Information Redundancy in Decision Support Systems

Decision Support Systems (DSS) provide users with information that is combined with their existing knowledge or information from other available sources. We investigate how redundancy of the information from the DSS affects the decision to purchase decision support, as well as the use of and trust in the support. Participants performed a classification task. Depending on the condition, they could purchase advice from either an independent, partly redundant, or fully redundant DSS. Purchase decisions, classification performance and trust were recorded. Participants preferred to purchase the independent system, performed better with it and trusted it more, compared to the partly or fully redundant systems. There was no significant learning effect across blocks. Overall, the results support a model of more normative, rather than confirmative preferences for decision support.
Cognitive Load and Website Usability: Effects of Contrast and Task Difficulty

The contrast of a website doesn’t just affect its usability for low-vision individuals; it affects everyone. This study aims to evaluate the effects of contrast (low, medium, or high) on web browsing experience in normally-sighted individuals. Cognitive load associated with the task was assessed using the NASA task load index (NASA-TLX) and the usability of the website was assessed by the systems usability scale (SUS). We found that NASA-TLX scores significantly increased with the amount of time a task took, indicating an effect of task difficulty. The impact of contrast was reported during end-of-study interviews, but no significant difference was found between the three chosen contrast levels in NASA-TLX or SUS scores. This study is part 1 of a multi-part research project and future research will compare low-vision individuals to their typically-sighted peers. Together, the studies in this project aim to quantitatively determine the effects of contrast users’ web browsing experience to further accessibility research and web development guidelines.
Examination of User Engagement and Subjective States in an Online Tetris Game

Engagement, as a multidimensional construct, fluctuates due to various factors. Previous research that examined engagement as a subjective experience was mainly concerned with the qualitative structure of engagement in different contexts (e.g., task difficulty, aesthetics). Few studies have examined the user’s subjective engagement as a given task progresses over time. This paper reports findings from an online study that aimed to assess the usefulness of subjective measures in capturing momentary feelings related to task engagement, under varying task duration and difficulty conditions. The Short Stress State Questionnaire (SSSQ) and the User Engagement Scale (UES) were used to capture self-reported engagement during a Tetris game. The sensitivity of scales to task conditions and relationships among subscales were examined. Results showed that changes in SSSQ were sensitive to difficulty levels, and Engagement obtained from the SSSQ was highly correlated with UES subscales. SSSQ may be a particularly useful tool to capture participants’ momentary feelings during a task via its Engagement, Distress, and Worry subscales.
“I mute my echo when I talk politics”: Connecting Smart Home Device Users’ Concerns to Privacy Harms Taxonomy

With the proliferation of Internet of Things devices, smart home devices are expected to increase in use. However, experts have raised privacy concerns regarding these devices. As the body of literature on understanding privacy concerns continues to emerge, we realize the need for a privacy concerns taxonomy to standardize and facilitate common understanding of privacy concerns. To address this gap, we conducted 25 interviews of smart home device users and analyzed their privacy concerns qualitatively. This paper contributes analysis of user privacy concerns from the angle of privacy taxonomy theory. It examines whether privacy concerns could be characterized by Solove’s taxonomy of privacy, which is a well-recognized privacy taxonomy for informational privacy. We further discuss results and their implications.
Investigating Crash Risks and Driving Performance Among the Oil and Gas Industry Workers

Motor vehicle crashes are the major cause of worker injuries and fatalities in the oil and gas industry. The current study uses an online survey to investigate driving safety and crash risk among the oil and gas industry workers. We examine driver characteristics and attentional and cognitive abilities associated with increased crash risks and evaluate the frequency of traffic warnings, tickets, crashes, and near-crashes among this driver population. The preliminary results from this ongoing study suggest a significant link between drivers’ age, experience, and attentional and cognitive deteriorations and increased motor vehicle crash risk. The survey responses also showed that crashes involving certain types and characteristics might be particularly prevalent among this driver group.
Performance and Relative Risk Dynamics during Driving Simulation Tasks under Distinct Automation Conditions

Risk has been a key factor influencing trust in Human-Automation interactions, though there is no unified tool to study its dynamics. We provide a framework for defining and assessing relative risk of automation usage through performance dynamics and apply this framework to a dataset from a previous study. Our approach allows us to explore how operators’ ability and different automation conditions impact the performance and relative risk dynamics. Our results on performance dynamics show that, on average, operators perform better (1) using automation that is more reliable and (2) using partial automation (more workload) than full automation (less workload). Our analysis of relative risk dynamics indicates that automation with higher reliability has higher relative risk dynamics. This suggests that operators are willing to take more risk for automation with higher reliability. Additionally, when the reliability of automation is lower, operators adapt their behavior to result in lower risk.
An HST/WFC3 Thermal Emission Spectrum of the Hot Jupiter HAT-P-7b

Secondary eclipse observations of several of the hottest hot Jupiters show featureless, blackbody-like spectra or molecular emission features, which are consistent with thermal inversions being present in those atmospheres. Theory predicts a transition between warmer atmospheres with thermal inversions and cooler atmospheres without inversions, but the exact transition point is unknown. In order to further investigate this issue, we observed two secondary eclipses of the hot Jupiter HAT-P-7b with the Hubble Space Telescope (HST) WFC3 instrument and combined these data with previous Spitzer and Kepler secondary eclipse observations. The HST and Spitzer data can be well fit by a blackbody with T = 2692 ± 14 K, and the Kepler data point constrains the geometric albedo to Ag = 0.077 ± 0.006. We modeled these data with a three-dimensional (3D) GCM and one-dimensional (1D) self-consistent forward models. The 1D models indicate that the atmosphere has a thermal inversion, weak heat redistribution, and water dissociation that limits the range of pressures probed. This result suggests that WFC3 observations of HAT-P-7b and possibly some other ultra-hot Jupiters appear blackbody-like because they probe a region near the tropopause where the atmospheric temperature changes slowly with pressure. Additionally, the 1D models constrain the atmospheric metallicity ( ) and the carbon-to-oxygen ratio (C/O < 1 at 99% confidence). The solar composition 3D GCM matches the Spitzer data but generally underpredicts the flux in the WFC3 bandpass and cannot reproduce its featureless shape. This discrepancy could be explained by high atmospheric drag or nightside clouds and may be better understood through further observation with the James Webb Space Telescope.
Methodology to Assess Individual Shooting Skills that is Predictive of Squad Level Performance in a Close Combat Training Engagement

Marksmanship, a critical aspect of military lethality training and readiness, is one area of military assessment that includes standardized quantifiable measures. However, assessment of marksmanship in a traditional live-fire setting can be costly, time consuming, and dangerous, while frequently only providing rudimentary objective measures of performance. Additionally, traditional methods of assessing individual marksmanship skills have never been associated with squad level performance in a close combat environment. This research created a comprehensive marksmanship assessment methodology which builds on earlier static and dynamic methodologies. Previous methodology metrics were integrated and additional pertinent aspects of active decision making and lethality were incorporated (i.e., detection, recognition, and identification or target discrimination) while minimizing execution time and employing a mobile, low-cost weapon simulator. This methodology provides streamlined metrics on the entire marksmanship process across two critical shooting styles, and can predict performance in squad level close combat training environments.
An Exploratory Analysis of Air Traffic Controller Speech Intelligibility Using Voice Data from a Simulation Experiment

Air Traffic Controllers (ATCs) communicate with pilots through radio communication. Speech intelligibility is vital in ensuring that the message is conveyed accurately. Factors such as speech rate affect this. Additionally, workload and stress have been shown to affect how people communicate significantly. In this paper, we attempt to analyze the voice data of ATCs who participated in a simulated experiment in the context of these non-verbal aspects of communication, particularly transmission length and speech rate. To better understand, we analyzed our data at two levels: aggregate and individual. Moreover, we focused on a single participant to see how such non-verbal characteristics evolve. Understanding these intricacies would contribute to building automated detectors in real-time voice transmissions that would leverage technology to avert any incidents brought about by stress and workload.
Development and Validation of Extended Reality Attentional Visual Field Tasks

A visuospatial attention measurement tool such as the Attentional Visual Field (AVF) task has been widely used in prior research to assess the spatial distribution of visual attention using a computerized task displaying visual targets on a computer screen. Although this computer-based task has been useful for examining individual differences in visuospatial attention and associated safety implications such as crash risk while driving, the computer-based task is limited due to its constraints. Newer AVF tasks were developed using virtual reality (VR) and augmented reality (AR) technology, and the results of the two evaluation studies provided partial evidence for the validities of the new AVF tasks. Overall, our current findings suggest that VR and AR versions of the AVF task can capture some aspects of visuospatial attention and that further validation and improvement are needed to establish the robustness of these new methods.
Driver Engagement Strategies for L3 ADS: A Review of the Literature

The completion of the driving task by Level 3 (L3) Automated Driving Systems (ADS) allows drivers to turn their attention away from the road and direct it towards other activities; however, the driver must remain receptive to requests to intervene (RTI) and be prepared to complete the driving task upon deactivation of the driving automation. This transfer of control is called a takeover. The objectives of this research activity were to understand the human factors underlying driver disengagement while using L3 ADS and to identify factors influencing takeover quality and performance when the ADS-equipped vehicle has exited its operational design domain. A structured, comprehensive literature review approach was used to synthesize findings in the following areas: common measures used to assess takeover performance, factors that affect takeovers, and strategies that improve driver takeover performance.
How Do Advanced Driver Assistance Systems Fit into Level of Automation Frameworks?

Advanced driver assistance systems (ADAS) have been increasingly incorporated in cars for nearly four decades and have changed the relationship of the driver to the driving task substantially. Over this period, original equipment manufacturers (OEMs) have developed similar ADAS functions (e.g., adaptive cruise control, lane keeping assist, forward collision warning), but these functions lack uniformity in their implementation such that there is the possibility of negative transfer of learning across different implementations of the same ADAS function. This brief theoretical paper aims to highlight issues around using some existing human-automation interaction (HAI) frameworks that have been used to classify vehicle automation and discuss considerations for better ADAS classification to inform their design and support safe and satisfactory use.
Impact of Monitoring Requests on Trust, Acceptance, Blame, and Praise of Autonomous Vehicles

Vehicles with autonomous features are more prevalent in today’s society, though as the level of autonomation increases, so does the vehicle system’s control of the vehicle. As the driving control shifts from human to the vehicle system, concerns arise regarding the attribution of responsibility and blame following critical events (e.g., collisions or near-misses). In this work-in-progress study, we aim to understand how the public attributes blame and praise to both humans and autonomous vehicles (AVs) following critical events. In addition, we examine how an AI driving assistant that administers Monitoring Requests influences blame and praise attributions. Furthermore, we examine differences in acceptance, trust, and perceived anthropomorphism between an AV with and without an AI driving assistant. Preliminary results are provided followed by a discussion of the expected results and potential impact for research and legal issues.
A mobile platform app to assist learning human kinematics in undergraduate biomechanics courses

Biomechanics examines different physical characteristics of the human body movement by applying principles of Newtonian mechanics to physical activities. Therefore, undergraduate biomechanics courses are highly demanding in mathematics and physics. While the inclusion of laboratory experiences can augment student comprehension of biomechanics concepts, the cost and the required expertise associated with motion tracking systems can be a burden of offering laboratory sessions. In this study, we developed a mobile platform app to facilitate learning human kinematics in biomechanics courses. An optimized computer-vision model that is based on convolutional pose machine (CPM), MobileNet V2 and TensorFlow Lite frameworks is adopted to reconstruct human pose first. A real-time human kinematics analysis then allows students to conduct human motion experiments. The proposed app can serve as a potential instructional tool in biomechanics courses.
Effects of predictive robot eyes on attentional processes in HRI

EXTENDED ABSTRACT Industrial collaborative robots interact in direct temporal and physical proximity with a human partner which creates new requirements for coordination and action prediction. Yet, to date robots only provide a limited amount of information about future movements to human collaborators. In human-human interaction, communication through gaze helps by intuitively directing attention to specific targets (Tomasello et al., 2007) and thus may also be considered a resource-efficient way to enhance human-robot interaction. The implementation of abstract eyes into robot design has already been shown to have great potential in numerous studies (e.g. Wiese et al., 2018; Moon et al., 2014). However, it is still unclear to what extent automatic, i.e. reflexive attentional shifts are triggered by anthropomorphic robot eyes (Onnasch et al, 2022; Admoni et al., 2011). The aim of the current study therefore was to investigate in a highly controlled experiment what the design of eyes for the use on a collaborative robot should look like in order to reflexively trigger attention reallocation, hence improving the prediction of robotic motion. In particular we compared human eyes with two kinds of anthropomorphic robot eyes and symbolic directional stimuli to gain differentiated insights on the mechanisms of (reflexive) cueing with different stimuli.