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A major question in human-automation interaction is whether tasks should be traded or shared between human and automation. This work presents reflections-which have evolved through classroom debates between the authors over the past 10 years-on these two forms of human-automation interaction, with a focus on the automated driving domain. As in the lectures, we start with a historically informed survey of six pitfalls of automation: (1) Loss of situation and mode awareness, (2) Deskilling, (3) Unbalanced mental workload, (4) Behavioural adaptation, (5) Misuse, and (6) Disuse. Next, one of the authors explains why he believes that haptic shared control may remedy the pitfalls. Next, another author rebuts these arguments, arguing that traded control is the most promising way to improve road safety. This article ends with a common ground, explaining that shared and traded control outperform each other at medium and low environmental complexity, respectively. Practitioner summary: Designers of automation systems will have to consider whether humans and automation should perform tasks alternately or simultaneously. The present article provides an in-depth reflection on this dilemma, which may prove insightful and help guide design. Abbreviations: ACC: Adaptive Cruise Control: A system that can automatically maintain a safe distance from the vehicle in front; AEB: Advanced Emergency Braking (also known as Autonomous Emergency Braking): A system that automatically brakes to a full stop in an emergency situation; AES: Automated Evasive Steering: A system that automatically steers the car back into safety in an emergency situation; ISA: Intelligent Speed Adaptation: A system that can limit engine power automatically so that the driving speed does not exceed a safe or allowed speed.

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INTRODUCTION: Cars are increasingly computerized, and vehicle settings such as steering gain (SG) can now be altered during driving. However, it is unknown whether transitions in SG should be adaptable (i.e., triggered by driver input) or adaptive (i.e., triggered automatically). We examined this question for road segments expected to require different SG. OBJECTIVE: This paper aimed to investigate whether SG mode changes should be made by the driver or automatically. METHODS: Twenty-four participants drove under four conditions in a simulator: fixed low gain (FL), fixed high gain (FH), a machine-initiated steering system, which switched between the two SG levels at predetermined locations (MI), and a driver-initiated steering system, in which the SG level could be changed by pressing a button on the steering wheel (DI). RESULTS: Participants showed poorer lane-keeping and reported higher effort for FH compared to FL on straights, while the opposite held true on curved roads. On curved roads, the MI condition yielded better lane-keeping and lower subjective effort than the DI condition. However, a substantial portion of the drivers gave low preference rankings to the MI system. CONCLUSION: Drivers prefer and benefit from a steering system with a variable rather than fixed gain. Furthermore, although automatic SG transitions reduce effort, some drivers reject this concept. APPLICATION: As the state of technology advances, MI transitions are becoming increasingly feasible, but whether drivers would want to delegate their decision-making authority to a machine remains a moot point.

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Transcriptional coactivator p15 (PC4) encodes a structurally conserved but functionally diverse protein that plays crucial roles in RNAP-II-mediated transcription, DNA replication and damage repair. Although structures and functions of PC4 have been reported in most vertebrates and some invertebrates, the PC4 genes were less systematically identified and characterized in the bay scallop Argopecten irradians irradians. In this study, five PC4 genes (AiPC4s) were successfully identified in bay scallops via whole-genome scanning through in silico analysis. Protein structure and phylogenetic analyses of AiPC4s were conducted to determine the identities and evolutionary relationships of these genes. Expression levels of AiPC4s were assessed in embryos/larvae at all developmental stages, in healthy adult tissues and in different tissues (mantles, gills, hemocytes and hearts) being processed under 32 °C stress with different time durations (0 h, 6 h, 12 h, 24 h, 3 d, 6 d and 10 d). Spatiotemporal expression profiles of AiPC4s suggested the functional roles of the genes in embryos/larvae at all developmental stages and in healthy adult tissues in bay scallop. Expression regulations (up- and down-) of AiPC4s under high-temperature stress displayed both tissue-specific and time-dependent patterns with function allocations, revealing that AiPC4s performed differentiated functions in response to thermal stress. This work provides clues of molecular function allocation of PC4 in scallops in response to thermal stress and helps in illustrating how marine bivalves resist elevated seawater temperature.

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Besides radically altering work, advances in automation and intelligent technologies have the potential to bring significant societal transformation. These transitional periods require an approach to analysis and design that goes beyond human-machine interaction in the workplace to consider the wider sociotechnical needs of envisioned work systems. The Sociotechnical Influences Space, an analytical tool motivated by Rasmussen's risk management model, promotes a holistic approach to the design of future systems, attending to societal needs and challenges, while still recognising the bottom-up push from emerging technologies. A study explores the concept and practical potential of the tool when applied to the analysis of a large-scale, 'real-world' problem, specifically the societal, governmental, regulatory, organisational, human, and technological factors of significance in mixed human-artificial agent workforces. Further research is needed to establish the feasibility of the tool in a range of application domains, the details of the method, and the value of the tool in design. Practitioner summary: Emerging automation and intelligent technologies are not only transforming workplaces, but may be harbingers of major societal change. A new analytical tool, the Sociotechnical Influences Space, is proposed to support organisations in taking a holistic approach to the incorporation of advanced technologies into workplaces and function allocation in mixed human-artificial agent teams.

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In the current study we investigated if drivers of conditionally automated vehicles can be kept in the loop through lane change maneuvers. More specifically, we examined whether involving drivers in lane-changes during a conditionally automated ride can influence critical take-over behavior and keep drivers' gaze on the road. In a repeated measures driving simulator study (n = 85), drivers drove the same route three times, each trial containing four lane changes that were all either (1) automated, (2) semi-automated or (3) manual. Each ride ended with a critical take-over situation that could be solved by braking and/or steering. Critical take-over reactions were analyzed with a linear mixed model and parametric accelerated failure time survival analysis. As expected, semi-automated and manual lane changes throughout the ride led to 13.5% and 17.0% faster maximum deceleration compared to automated lane changes. Additionally, semi-automated and manual lane changes improved the quality of the take-over by significantly decreasing standard deviation of the steering wheel angle. Unexpectedly, drivers in the semi-automated condition were slowest to start the braking maneuver. This may have been caused by the drivers' confusion as to how the semi-automated system would react. Additionally, the percentage gaze off-the-road was significantly decreased by the semi-automated (6.0%) and manual (6.6%) lane changes. Taken together, the results suggest that semi-automated and manual transitions may be an alarm-free instrument which developers could use to help maintain drivers' perception-action loop and improve automated driving safety.

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OBJECTIVE: Test the automation transparency design principle using a full-scope nuclear power plant simulator. BACKGROUND: Automation transparency is a long-held human factors design principle espousing that the responsibilities, capabilities, goals, activities, and/or effects of automation should be directly observable in the human-system interface. The anticipated benefits of transparency include more effective reliance, more appropriate trust, better understanding, and greater user satisfaction. Transparency has enjoyed a recent upsurge in use in the context of human interaction with agent-oriented automation. METHOD: Three full-scope nuclear power plant simulator studies were conducted with licensed operating crews. In the first two experiments, transparency was implemented for interlocks, controllers, limitations, protections, and automatic programs that operate at the local component level of the plant. In the third experiment, procedure automation assumed control of plant operations and was represented in dedicated agent displays. RESULTS: Results from Experiments 1 and 2 appear to validate the human performance benefits of automation transparency for automation at the component level. However, Experiment 3 failed to replicate these findings for automation that assumed control for executing procedural actions. CONCLUSION: Automation transparency appears to yield expected benefits for component-level automation, but caution is warranted in generalizing the design principle to agent-oriented automation. APPLICATION: The automation transparency design principle may offer a powerful means of compensating for the detrimental impacts of hidden automation influence at the component level of complex systems. However, system developers should exercise caution in assuming that the principle extends to agent-oriented automation.

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OBJECTIVE: The objective of this study was to test the predictions of the routine-failure trade-off (or lumberjack) model in a full-scope simulator study with expert operators performing realistic control tasks. BACKGROUND: A meta-study of degree of automation (DOA) studies concluded that DOA predicts task performance under both routine and automation failure conditions, workload, and situation awareness. Empirical support for this conclusion appears to be weak for complex work situations. METHOD: A full-scope nuclear power plant simulator experiment was conducted in which licensed operating crews completed realistic procedure execution tasks. Dependent measures selected from the lumberjack model were collected and analyzed for systematic effects. RESULTS: Situation awareness increased with increasing DOA, which contradicts the lumberjack model. Anticipated workload and failure task performance effects were not observed. CONCLUSION: The experimental results add further evidence challenging the applicability of the lumberjack model to complex work situations. APPLICATION: Practitioners should use caution when extending the predictions of the lumberjack model based on data from simple work situations to complex work situations. Researchers should invest more resources in testing the predictive power of the lumberjack model in complex work situations.

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OBJECTIVE: This paper presents a theoretical model and two simulator studies on the psychological processes during early trust calibration in automated vehicles. BACKGROUND: The positive outcomes of automation can only reach their full potential if a calibrated level of trust is achieved. In this process, information on system capabilities and limitations plays a crucial role. METHOD: In two simulator experiments, trust was repeatedly measured during an automated drive. In Study 1, all participants in a two-group experiment experienced a system-initiated take-over, and the occurrence of a system malfunction was manipulated. In Study 2 in a 2 × 2 between-subject design, system transparency was manipulated as an additional factor. RESULTS: Trust was found to increase during the first interactions progressively. In Study 1, take-overs led to a temporary decrease in trust, as did malfunctions in both studies. Interestingly, trust was reestablished in the course of interaction for take-overs and malfunctions. In Study 2, the high transparency condition did not show a temporary decline in trust after a malfunction. CONCLUSION: Trust is calibrated along provided information prior to and during the initial drive with an automated vehicle. The experience of take-overs and malfunctions leads to a temporary decline in trust that was recovered in the course of error-free interaction. The temporary decrease can be prevented by providing transparent information prior to system interaction. APPLICATION: Transparency, also about potential limitations of the system, plays an important role in this process and should be considered in the design of tutorials and human-machine interaction (HMI) concepts of automated vehicles.

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Operators often fail to rely sufficiently on alarm systems. This results in a joint human-machine (JHM) sensitivity below the one of the alarm system. The 'confidence vs. trust hypothesis' assumes the use of the system depends on the weighting of both values. In case of higher confidence, the task is performed manually, if trust is higher, the user relies on the system. Thus, insufficient reliance may be due to operators' overconfidence in their own abilities and/or insufficient trust in the decision automation, but could be mitigated by providing feedback. That was investigated within a signal detection task, supported by a system with either higher sensitivity (HSS) or lower sensitivity (LSS) than the human, while being provided with feedback or not. We expected disuse of the LSS and insufficiently reliance on the HSS, in the condition without feedback. The feedback was expected to increase reliance on the HSS through an increase in trust and/or decreases in confidence, and thus, improve performance. Hypotheses were partly supported. Confidence in manual performance was similar to trust in the HSS even though humans' sensitivity was significantly lower than systems' sensitivity. While confidence had not effect on reliance or JHM sensitivity, trust was found to be positively related with both. We found disuse of the HSS, that could be improved through feedback, increasing also trust and JHM sensitivity. However, contrary to 'confidence vs. trust' expectations, participants were also found to make use of the LSS. This misuse could not be reduced by feedback. Results indicate the use of feedback being beneficial for the overall performance (with HSS only). Findings do not support the idea that misuse or disuse of the system may result from comparison of confidence and trust. We suppose it may rather be the product of users' wrong strategy of function allocation, based on the underlying idea of team work in combination with missing assignment of responsibility. We discuss this alternative explanation.

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OBJECTIVE: The aim of this study was to develop and validate a computational model of the automation complacency effect, as operators work on a robotic arm task, supported by three different degrees of automation. BACKGROUND: Some computational models of complacency in human-automation interaction exist, but those are formed and validated within the context of fairly simplified monitoring failures. This research extends model validation to a much more complex task, so that system designers can establish, without need for human-in-the-loop (HITL) experimentation, merits and shortcomings of different automation degrees. METHOD: We developed a realistic simulation of a space-based robotic arm task that could be carried out with three different levels of trajectory visualization and execution automation support. Using this simulation, we performed HITL testing. Complacency was induced via several trials of correctly performing automation and then was assessed on trials when automation failed. Following a cognitive task analysis of the robotic arm operation, we developed a multicomponent model of the robotic operator and his or her reliance on automation, based in part on visual scanning. RESULTS: The comparison of model predictions with empirical results revealed that the model accurately predicted routine performance and predicted the responses to these failures after complacency developed. However, the scanning models do not account for the entire attention allocation effects of complacency. APPLICATIONS: Complacency modeling can provide a useful tool for predicting the effects of different types of imperfect automation. The results from this research suggest that focus should be given to supporting situation awareness in automation development.

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Objective To study the performance evaluation of integration rendezvous and docking(RVD)control by man and machine in order to provide reference for function allocation between man and machine.Method By a rendezvous and docking experimental system,30 male volunteers aged 20～40 took part in the performance evaluation experiments.The RVD integration control success rates and total thruster ignition time were chosen as evaluation indices.Result The experiment showed that if less than three RVD parameters control tasks were completed by the subjects and the rest of parameters control task completed by automation,the RVD success rate would be larger than 80.47% and the fuel consumption would be optimized.In addition,there were ten subjects who finished the whole 6 RVD parameters control tasks by enough train.Conclusion If the astronauts role should be integrated into the RVD control,it is suitable for them to finish the heading,pitch and roll control in order to assure the man-machine system high performance.When astronauts were needed to finish all parameters control,two points should be taken into consideration,enough fuel and unconstraint operation time.