Human Factors最新文献

ObjectiveOur objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness.BackgroundControl transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework.MethodWe used data from a driving simulation to develop an active inference model of takeover performance. After validating the model's predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors.ResultsThe model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states.ConclusionThe results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters.ApplicationThe active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

ObjectiveTo review and synthesise research on technological debiasing strategies across domains, present a novel distributed cognition-based classification system, and discuss theoretical implications for the field.BackgroundDistributed cognition theory is valuable for understanding and mitigating cognitive biases in high-stakes settings where sensemaking and problem-solving are contingent upon information representations and flows in the decision environment. Shifting the focus of debiasing from individuals to systems, technological debiasing strategies involve designing system components to minimise the negative impacts of cognitive bias on performance. To integrate these strategies into real-world practices effectively, it is imperative to clarify the current state of evidence and types of strategies utilised.MethodsWe conducted systematic searches across six databases. Following screening and data charting, identified strategies were classified into (i) group composition and structure, (ii) information design and (iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into eight categories.ResultsEighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen were ineffective and six were partially effective. Information design strategies were studied most, followed by procedural debiasing, and group structure and composition. Gaps and directions for future work are discussed.ConclusionThrough the lens of distributed cognition theory, technological debiasing represents a reconceptualisation of cognitive bias mitigation, showing promise for real-world application.ApplicationThe study results and debiasing classification presented can inform the design of high-stakes work systems to support cognition and minimise judgement errors.

ObjectiveThe study investigates users' tendency to access decision support (DS) systems as a function of the correlation between the DS information and the information users already have, the ongoing interaction with such systems, and the effect of correlated information on subjective trust.BackgroundPrevious research has shown inconclusive findings regarding whether people prefer information that correlates with information they already have. Some studies conclude that individuals recognize the value of noncorrelated information, given its unique content, while others suggest that users favor correlated information as it aligns with existing evidence. The impact of the level of correlation on performance, subjective trust, and the decision to use DS remains unclear.MethodIn an experiment (N = 481), participants made classification decisions based on available information. They could also purchase additional DS with different degrees of correlation with the available information.ResultsParticipants tended to purchase information more often when the DS was not correlated with the available information. Correlated information reduced performance, and the effect of correlation on subjective trust and performance depended on DS sensitivity.ConclusionAdditional information may not improve performance when it is correlated with available information (i.e., it is redundant). Hence, the benefits of additional information and DS depend on the information the system and the operator use.ApplicationIt is essential to analyze the correlations between information sources and design the available information to allow optimal task performance and possibly minimize redundancy (e.g., by locating sensors in different positions to capture independent data).

ObjectiveThe current study investigated the factors that predict drowsy drivers' decisions regarding whether to take breaks versus continue driving during long simulator drives.BackgroundDriver drowsiness contributes to substantial numbers of motor vehicle crashes, injuries, and deaths. Previous research has shown that taking a nap and consuming caffeine can temporarily mitigate drowsiness and enable continued safe driving.MethodNinety drivers completed a 150-mile highway drive in a driving simulator after a day of partial sleep restriction. Drivers passed several simulated rest areas where they could take breaks. To replicate drivers' motivation to reach their destination safely but also quickly, drivers were told that they would be paid more for completing the simulated drive faster but would forfeit their payment if they crashed.ResultsBreak taking was predicted by drivers' self-ratings of drowsiness and by the severity of lane departures. However, even at the highest levels of drowsiness, most drivers bypassed simulated rest areas without stopping. In comparing self-rated drowsiness to drowsiness measured by eye closures, drivers often under- and over-estimate their own level of drowsiness.ConclusionDrowsy drivers use their own self-assessed drowsiness when deciding whether to take breaks. These self-assessments are often incorrect, and even when drivers rate themselves as severely drowsy they are unlikely to stop to rest during long drives.ApplicationThe findings reveal the need for effective drowsy driving countermeasures to motivate drivers to stop to take breaks. Results underscore the need to educate and/or motivate drivers to respond sooner to warning signs of drowsiness.

ObjectiveThis study investigated the relationship between human team member contextual control and team performance under time constraints.BackgroundContextual control modes, which describe different strategies for action selection in dynamic environments, characterize how humans maintain performance under variable demands. Control modes have not yet been studied in teamwork settings. Modeling of the cross-level interaction between team members' control modes and emerging team behaviors can improve understanding of effective teamwork in dynamic environments.MethodA human-subjects study explored the relationship between individual contextual control and team performance. Questionnaires about contextual control were used to elicit individual control modes. Analysis compared team members' control modes and investigated how control modes changed under varying time pressures.ResultsParticipant's control modes differed in their look ahead horizon, the extensiveness of prior action evaluation, and their prior experience. Many team members shifted control modes during trials, resulting in both convergence and divergence of paired control modes. No effects on communication rate were found due to changes in team members' control modes, but partially significant findings may suggest that the control mode divergence affects performance.ConclusionTeams can operate in multiple control mode configurations that change dynamically according to context. Further research with an increased sample size is warranted to analyze how time constraints influence team members' control modes and overall teaming processes and whether divergence of team control mode is favorable under time pressures.ApplicationFurther study of contextual control in teams may help improve team design to better support teams in coping with time constraints in dynamic environments.

ObjectiveWe aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.BackgroundTraditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.MethodEEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.ResultsThe developed XGBoost models demonstrated strong predictive performance with R² values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding p-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; p > 0.05).ConclusionThe findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training.ApplicationThe advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.

ObjectiveThis study investigated the effects of four takeover request (TOR) times and seven warning modalities on performance and trust in automated driving on a mildly congested urban road scenario, as well as the relationship between takeover performance and trust.BackgroundTakeover is crucial in L3 automated driving, where human-machine codriving is employed. Establishing trust in takeover scenarios among drivers can enhance the acceptance of autonomous vehicles, thereby promoting their widespread adoption.MethodUsing a driving simulator, data from 28 participants, including collision counts, takeover time (ToT), electrodermal activity (EDA) data, and self-reported trust scores, were collected and analyzed primarily using Generalized Linear Mixed Models (GLMM).ResultsCollisions during the takeover undermined participants' trust in the autonomous driving system. As TOR time increased, participants' trust improved, and the longer TOR time did not lead to participant confusion. There was no significant relationship between warning modality and trust. Furthermore, the combination of three warning modalities did not exhibit a notable advantage over the combination of two modalities.ConclusionThe study examined the effects of TOR time and warning modality on trust, as well as preliminarily explored the potential association between takeover performance, including collisions and ToT, and trust in autonomous driving takeovers.ApplicationResearchers and designers of automotive interactions were given referenceable TOR time and warning modality by this study, which extended the autonomous driving takeover scenarios. These findings contributed to boosting drivers' confidence in transferring control to the automated system.

Objective: This study investigated the effects of mass and vertical center-of-mass position of combat items attached to a tactical vest, as well as marksmanship posture on rifle marksmanship performance, postural sway, and perceived workload during a simulated rifle shooting task.

Background: A tactical vest serves as a load carriage system in addition to providing body protection. Its design, particularly the mass and vertical position of attached combat items, may impact postural control during rifle shooting and thus marksmanship performance.

Method: Thirty-two participants performed a simulated rifle shooting task on a force plate with a tactical vest on. Three independent variables were considered: load mass (4 levels), vertical load center-of-mass position (4 levels), and marksmanship posture (2 levels). The dependent variables were: 6 rifle marksmanship performance measures, 7 postural sway measures, and a perceived workload measure.

Results: Heavier load mass significantly degraded rifle marksmanship performance, and increased postural sway and perceived workload. Marksmanship posture significantly affected rifle marksmanship performance and postural sway. The kneeling posture resulted in less postural sway and better marksmanship performance than the standing posture. Vertical load center-of-mass position affected only part of the marksmanship performance measures and did not affect the measures of postural sway and perceived workload.

Conclusion: Reducing combat item mass on tactical vests and enhancing soldier postural control ability would improve rifle marksmanship and soldier lethality.

Application: The study findings inform the development of future military tactical vests and rifle marksmanship training, highlighting the need for lightweight gear design and postural control training.

Objective: An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human-vehicle interfaces (HVIs) and the selection of SA assessment methodologies.

Background: Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking.

Method: A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion.

Results: A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers.

Conclusion: The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs.

Application: This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

Objective: We investigated how people used cues to make Judgments of Difficulty (JODs) while observing automation perform a task and when performing this task themselves.

Background: Task difficulty is a factor affecting trust in automation; however, no research has explored how individuals make JODs when watching automation or whether these judgments are similar to or different from those made while watching humans. Lastly, it is unclear how cue use when observing automation differs as a function of experience.

Method: The study involved a visual search task. Some participants performed the task first, then watched automation complete it. Others watched and then performed, and a third group alternated between performing and watching. After each trial, participants made a JOD by indicating if the task was easier or harder than before. Task difficulty randomly changed every five trials.

Results: A Bayesian regression suggested that cue use is similar to and different from cue use while observing humans. For central cues, support for the UAH was bounded by experience: those who performed the task first underweighted central cues when making JODs, relative to their counterparts in a previous study involving humans. For peripheral cues, support for the MEH was unequivocal and participants weighted cues similarly across observation sources.

Conclusion: People weighted cues similar to and different from when they watched automation perform a task relative to when they watched humans, supporting the Media Equation and Unique Agent Hypotheses.

Application: This study adds to a growing understanding of judgments in human-human and human-automation interactions.