Humans: still vital after all these years of automation

Physiological measures of operators' mental state in supervisory process control tasks: a scoping review

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research.

Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.

Are humans still necessary?

Our long accepted and historically-persistent human narrative almost exclusively places us at the motivational centre of events. The wellspring of this anthropocentric fable arises from the unitary and bounded nature of personal consciousness. Such immediate conscious experience frames the heroic vision we have told to, and subsequently sold to ourselves. But need this centrality necessarily be a given? The following work challenges this, oft unquestioned, foundational assumption, especially in light of developments in automated, autonomous, and artificially-intelligent systems. For, in these latter technologies, human contributions are becoming ever more peripheral and arguably unnecessary. The removal of the human operator from the inner loops of momentary control has progressed to now an ever more remote function as some form of supervisory monitor. The natural progression of that line of evolution is the eventual excision of humans from access to any form of control loop at all. This may even include system maintenance and then, prospectively, even initial design. The present argument features a 'unit of analysis' provocation which explores the proposition that socially, and even ergonomically, the human individual no longer occupies priority or any degree of pre-eminent centrality. Rather, we are witnessing a transitional phase of development in which socio-technical collectives are evolving as the principle sources of what, may well be profoundly unhuman motivation. These developing proclivities occupy our landscape of technological innovations that daily act to magnify, rather than diminish, such progressive inhumanities. Where this leaves a science focused on work as a human-centred enterprise serves to occupy the culminating consideration of the present discourse.

Human-Autonomy Teaming: A Review and Analysis of the Empirical Literature

OBJECTIVE

We define human-autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions.

BACKGROUND

Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human-autonomy teams (HATs) achieve success. Human-autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective.

METHOD

We searched the literature on human-autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human-autonomy teaming. We found 76 articles that met our criteria for inclusion.

RESULTS

We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs.

CONCLUSION

There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.

Factors Influencing Attenuating Skill Decay in High-Risk Industries: A Scoping Review

The infrequent use of skills relevant in non-routine situations in highly automated and high-risk industries is a major safety issue. The infrequent use of skills can lead to skill decay. Research on skill decay has a long history, but not much is known about the relevant factors and refresher interventions to attenuate skill decay in highly automated environments. In the present study, a scoping review was conducted to determine whether the well-known factors in skill decay research are also relevant for complex cognitive skill decay and to identify refresher interventions that are deemed effective for attenuating decay. A scoping review aims at identifying, summarizing, and mapping the body of literature on a given topic. Searches in electronic databases, including PsycArticles, PsyINFO, and Psyndex, via EBSCOhost and Web of Science and Google Scholar were conducted, and documents were analyzed regarding the research question, which resulted in n = 58 studies. The findings demonstrate the relevance of task characteristics and method-related (cognitive-based, behavioral-based training) and person-related factors (e.g., cognitive ability, experience, motivation) to mitigate decay. Additionally, the results demonstrate that minor refresher interventions are effective at attenuating complex cognitive skill decay. Implications for industry and training providers that aim to implement training and refresher interventions to attenuate skill decay in high-risk industries are provided. Researchers may use the information about the influences of person- and method-related factors, task characteristics, and refresher interventions presented in this scoping review as a starting point to conduct further empirical research by taking skill acquisition, retention, and transfer into account.

Adaptable (Not Adaptive) Automation: Forefront of Human-Automation Teaming

OBJECTIVE

Identify a critical research gap for the human factors community that has implications for successful human-automation teaming.

BACKGROUND

There are a variety of approaches for applying automation in systems. Flexible application of automation such that its level and/or type changes during system operations has been shown to enhance human-automation system performance.

METHOD

This mini-review describes flexible automation in which the level of automated support varies across tasks during system operation, rather than remaining fixed. Two types distinguish the locus of authority to change automation level: adaptable automation (the human operator assigns how automation is applied) has been found to aid human's situation awareness and provide more perceived control versus adaptive automation (the system assigns automation level) that may impose less workload and attentional demands by automatically adjusting levels in response to changes in one or more states of the human, task, environment, and so on.

RESULTS

In contrast to vast investments in adaptive automation approaches, limited research has been devoted to adaptable automation. Experiments directly comparing adaptable and adaptive automation are particularly scant. These few studies show that adaptable automation was not only preferred over adaptive automation, but it also resulted in improved task performance and, notably, less perceived workload.

CONCLUSION

Systematic research examining adaptable automation is overdue, including hybrid approaches with adaptive automation. Specific recommendations for further research are provided.

APPLICATION

Adaptable automation together with effective human-factored interface designs to establish working agreements are key to enabling human-automation teaming in future complex systems.

Development of Rasmussen's risk management framework for analysing multi-level sociotechnical influences in the design of envisioned work systems

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.

Keeping the driver in the loop through semi-automated or manual lane changes in conditionally automated driving

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.

Evaluation of eye tracking for a decision support application

Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.

Reconfiguring and ramping-up ventilator production in the face of COVID-19: Can robots help?

As the COVID-19 pandemic expands, the shortening of medical equipment is swelling. A key piece of equipment getting far-out attention has been ventilators. The difference between supply and demand is substantial to be handled with normal production techniques, especially under social distancing measures in place. The study explores the rationale of human-robot teams to ramp up production using advantages of both the ease of integration and maintaining social distancing. The paper presents a model for faster integration of collaborative robots and design guidelines for workstations. The scenario is evaluated for an open source ventilator through continuous human-robot simulation and amplification of results in a discrete event simulation.

Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems.

Designing for Adaptation in Workers' Individual Behaviors and Collective Structures With Cognitive Work Analysis: Case Study of the Diagram of Work Organization Possibilities

OBJECTIVE

We demonstrate that the diagram of work organization possibilities, a recent addition to cognitive work analysis, can be used to develop designs that promote adaptation in the workplace.

BACKGROUND

Workers in sociotechnical systems adapt not just their individual behaviors but also their collective structures in dealing with instability, uncertainty, and unpredictability in their tasks. However, conventional design approaches are limited in supporting adaptations in both workers' behaviors and structures, especially during unforeseen situations. The work organization possibilities diagram has the potential to meet these requirements, but its value for design has not been established.

METHOD

We present a case study of a future system for maritime surveillance that provides an analytical demonstration of the utility of the diagram for design and empirical validation of the impact, uniqueness, and feasibility of this approach in an industrial setting.

RESULTS

This application results in a team design that is integrated with the career and training progression pathway of the crew in a way that maximizes the system's behavioral and structural possibilities for adaptation. Further, the approach has impact on practice, makes a distinct contribution to design relative to other techniques, and is implemented feasibly in an industrial setting.

CONCLUSION

The work organization possibilities diagram can contribute to the development of an integrated system design that supports actors' possibilities for behavioral and structural adaptation in a unified fashion.

APPLICATION

This research provides a basis for designing interfaces, teams, training, and automation that preserve a system's inherent capacity for adaptation.

How Can Physiological Computing Benefit Human-Robot Interaction?

As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators’ MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator’s MS to improve human-robot operations’ safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction.

Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations

The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model.

Trust is essential: positive effects of information systems on users' memory require trust in the system

Initial results suggest that decision support systems (DSSs) can trigger 'directed forgetting' in business settings if users trust in the DSS. In the present study, we further examined this trust effect on DSS-cued forgetting and related positive effects on users' cognitive resources, performance, and well-being. Moreover, we investigated how trust translates into behavioural intentions to use a DSS, and into actual usage of the DSS. Finally, we examined if risk-related framing of decision outcomes (loss vs. gain framing) moderates trust effects on directed forgetting and behavioural intentions. In line with our expectations, results of an experiment with N = 200 participants confirmed that trust significantly enhances directed forgetting, performance, and well-being. Behavioural intentions fully mediated the trust effect on DSS use. Framing of decision outcomes showed no moderation but a main effect on directed forgetting, with loss framing reducing the directed forgetting effect. Practitioner summary: This experimental study demonstrates the importance of trust in information systems to leverage positive effects of these systems on users' cognitive resources, performance, and well-being in a simulated complex business setting. Abbreviations: DSS(s): decision support system(s); R-F: remember-forget difference; ANOVA: difference analysis of variance.

Automation reliability, human-machine system performance, and operator compliance: A study with airport security screeners supported by automated explosives detection systems for cabin baggage screening

Using a simulated X-ray screening task, we tested 122 airport security screeners working with the support of explosives detection systems for cabin baggage screening (EDSCB) as low-level automation. EDSCB varied systematically on three automation reliability measures: accuracy, d', and positive predictive value (PPV). Results showed that when unaided performance was high, operator confidence was high, and automation provided only small benefits. When unaided performance was lower, operator confidence was lower, and automation with higher d' provided large benefits. Operator compliance depended on the PPV of automation: We found lower compliance for lower PPV. Automation with a high false alarm rate of 20% and a low PPV of .3 resulted in operators ignoring about one-half of the true automation alarms on difficult targets-a strong cry-wolf effect. Our results suggest that automation reliability described by d' and PPV is more valid than using accuracy alone. When the PPV is below .5, operators should receive clear instructions on how to respond to automation alarms.

Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation

OBJECTIVE

The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation.

BACKGROUND

Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities.

METHOD

This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems.

RESULTS

The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver-system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress.

CONCLUSIONS

Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

Sepsis surveillance: an examination of parameter sensitivity and alert reliability

Objective

To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth.

Materials and Methods

This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection.

Results

First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios.

Discussion

Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes.

Conclusion

Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.

Towards Mixed-Initiative Human-Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose.

Trust in Management Information Systems (MIS)

Abstract. Digitalization, enhanced storage capacities, and the Internet of Things increase the volume of data in modern organizations. To process and make use of these data and to avoid information overload, management information systems (MIS) are introduced that collect, process, and analyze relevant data. However, a precondition for the application of MIS is that users trust them. Extending accounts of trust in automation and trust in technology, we introduce a new model of trust in MIS that addresses the conceptual ambiguities of existing conceptualizations of trust and integrates initial empirical work in this field. In doing so, we differentiate between perceived trustworthiness of an MIS, experienced trust in an MIS, intentions to use an MIS, and actual use of an MIS. Moreover, we consider users’ perceived risks and contextual factors (e. g., autonomy at work) as moderators. The introduced model offers guidelines for future research and initial suggestions to foster trust-based MIS use.

A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis

This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.

Industry 4.0: defining the research agenda

Purpose

Industry 4.0 implies that global challenges exist within the manufacturing sector. Both theoretical and empirical research has been developed to support these transformations and assist companies in the process of changing. The purpose of this paper is to gather previous articles through an updated review and defines a research agenda for future investigation based on the most recent studies published in the field.

Design/methodology/approach

Key articles on the subject are analysed. The articles were published in 39 journals from which 107 papers dating from 2005 to 2018 have been selected.

Findings

The main findings imply the definition of a research agenda where: a common terminology should be created; the levels of implementation of Industry 4.0 should be defined; the stages of the development of Industry 4.0 should be identified; a lean approach for this industry is defined and the implications of Industry 4.0 in either a sustainable or circular economy should be understood; the consequences of human resources should be analysed; and the effects of the smart factory in the organisation are the areas identified and studied in the mentioned research agenda.

Research limitations/implications

This review has some limitations. First, a number of grey literature, such as reports from non-governmental organisations and front-line practitioners’ reflections, were not included. Second, only research studies in English and Spanish were reviewed.

Practical implications

This review helps practitioners in their implementation of Industry 4.0. Moreover, the identified future research areas may help to define priorities in this implementation.

Originality/value

After examining previous research, this paper proposes a research agenda covering issues about Industry 4.0. This research agenda should guide future investigations in the smart industry.

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust

PURPOSE

Self-driving cars are an extremely high level of autonomous technology and represent a promising technology that may help older adults safely maintain independence. However, human behavior with automation is complex and not straightforward (Parasuraman and Riley, 1997; Parasuraman, 2000; Rovira et al., 2007; Parasuraman and Wickens, 2008; Parasuraman and Manzey, 2010; Parasuraman et al., 2012). In addition, because no fully self-driving vehicles are yet available to the public, most research has been limited to subjective survey-based assessments that depend on the respondents' limited knowledge based on second-hand reports and do not reflect the complex situational and dispositional factors known to affect trust and technology adoption.

METHODS

To address these issues, the current study examined the specific factors that affect younger and older adults' trust in self-driving vehicles.

RESULTS

The results showed that trust in self-driving vehicles depended on multiple interacting variables, such as the age of the respondent, risk during travel, impairment level of the hypothesized driver, and whether the self-driving car was reliable.

CONCLUSION

The primary contribution of this work is that, contrary to existing opinion surveys which suggest broad distrust in self-driving cars, the ratings of trust in self-driving cars varied with situational characteristics (reliability, driver impairment, risk level). Specifically, individuals reported less trust in the self-driving car when there was a failure with the car technology; and more trust in the technology in a low risk driving situation with an unimpaired driver when the automation was unreliable.

Vehicle Politeness in Driving Situations

Future vehicles are becoming more like driving partners instead of mere machines. With the application of advanced information and communication technologies (ICTs), vehicles perform driving tasks while drivers monitor the functioning states of vehicles. This change in interaction requires a deliberate consideration of how vehicles should present driving-related information. As a way of encouraging drivers to more readily accept instructions from vehicles, we suggest the use of social rules, such as politeness, in human-vehicle interaction. In a 2 × 2 between-subjects experiment, we test the effects of vehicle politeness (plain vs. polite) on drivers’ interaction experiences in two operation situations (normal vs. failure). The results indicate that vehicle politeness improves interaction experience in normal working situations but impedes the experience in failure situations. Specifically, in normal situations, vehicles with polite instructions are highly evaluated for social presence, politeness, satisfaction and intention to use. Theoretical and practical implications on politeness research and speech interaction design are discussed.

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure < 1 year) benefitted substantially from EDSCB in detecting both improvised explosive devices and bare explosives. A comparison of all three conditions showed that automated decision provided better human-machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human-machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered.

Enhancing the effectiveness of human-robot teaming with a closed-loop system

With technological developments in robotics and their increasing deployment, human-robot teams are set to be a mainstay in the future. To develop robots that possess teaming capabilities, such as being able to communicate implicitly, the present study implemented a closed-loop system. This system enabled the robot to provide adaptive aid without the need for explicit commands from the human teammate, through the use of multiple physiological workload measures. Such measures of workload vary in sensitivity and there is large inter-individual variability in physiological responses to imposed taskload. Workload models enacted via closed-loop system should accommodate such individual variability. The present research investigated the effects of the adaptive robot aid vs. imposed aid on performance and workload. Results showed that adaptive robot aid driven by an individualized workload model for physiological response resulted in greater improvements in performance compared to aid that was simply imposed by the system.

Autopilot, Mind Wandering, and the Out of the Loop Performance Problem

To satisfy the increasing demand for safer critical systems, engineers have integrated higher levels of automation, such as glass cockpits in aircraft, power plants, and driverless cars. These guiding principles relegate the operator to a monitoring role, increasing risks for humans to lack system understanding. The out of the loop performance problem arises when operators suffer from complacency and vigilance decrement; consequently, when automation does not behave as expected, understanding the system or taking back manual control may be difficult. Close to the out of the loop problem, mind wandering points to the propensity of the human mind to think about matters unrelated to the task at hand. This article reviews the literature related to both mind wandering and the out of the loop performance problem as it relates to task automation. We highlight studies showing how these phenomena interact with each other while impacting human performance within highly automated systems. We analyze how this proximity is supported by effects observed in automated environment, such as decoupling, sensory attention, and cognitive comprehension decrease. We also show that this link could be useful for detecting out of the loop situations through mind wandering markers. Finally, we examine the limitations of the current knowledge because many questions remain open to characterize interactions between out of the loop, mind wandering, and automation.

Operator adaptation to changes in system reliability under adaptable automation

This experiment examined how operators coped with a change in system reliability between training and testing. Forty participants were trained for 3 h on a complex process control simulation modelling six levels of automation (LOA). In training, participants either experienced a high- (100%) or low-reliability system (50%). The impact of training experience on operator behaviour was examined during a 2.5 h testing session, in which participants either experienced a high- (100%) or low-reliability system (60%). The results showed that most operators did not often switch between LOA. Most chose an LOA that relieved them of most tasks but maintained their decision authority. Training experience did not have a strong impact on the outcome measures (e.g. performance, complacency). Low system reliability led to decreased performance and self-confidence. Furthermore, complacency was observed under high system reliability. Overall, the findings suggest benefits of adaptable automation because it accommodates different operator preferences for LOA. Practitioner Summary: The present research shows that operators can adapt to changes in system reliability between training and testing sessions. Furthermore, it provides evidence that each operator has his/her preferred automation level. Since this preference varies strongly between operators, adaptable automation seems to be suitable to accommodate these large differences.

Intuitive Cognition and Models of Human-Automation Interaction

OBJECTIVE

The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human-automation interaction, focusing on the Parasuraman et al. model and taxonomy.

BACKGROUND

Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems.

METHOD

One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature.

RESULTS

Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human-automation interaction that incorporates intuitive cognition is suggested.

APPLICATION

Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.

The use of adaptable automation: Effects of extended skill lay-off and changes in system reliability

This experiment aimed to examine how skill lay-off and system reliability would affect operator behaviour in a simulated work environment under wide-range and large-choice adaptable automation comprising six different levels. Twenty-four participants were tested twice during a 2-hr testing session, with the second session taking place 8 months after the first. In the middle of the second testing session, system reliability changed. The results showed that after the retention interval trust increased and self-confidence decreased. Complacency was unaffected by the lay-off period. Diagnostic speed slowed down after the retention interval but diagnostic accuracy was maintained. No difference between experimental conditions was found for automation management behaviour (i.e. level of automation chosen and frequency of switching between levels). There were few effects of system reliability. Overall, the findings showed that subjective measures were more sensitive to the impact of skill lay-off than objective behavioural measures.

Beyond Reliance and Compliance

Increasingly autonomous machines may lead to issues in human-automation systems that go beyond the typical concerns of reliance and compliance. This study used an interaction-oriented approach that considers interdependence in coordinating and cooperating on a joint task. A shared-resource microworld environment was developed to assess how changes in environmental demands and agent behavior affect cooperation and system performance. Seventy-two participants were recruited to perform a scheduling task that required coordination with a cooperative and a relatively uncooperative automated agent. Cooperative automation enhanced performance because it provided more resources to the person and because the person provided more resources to the automation. Considering interdependence theory and the associated structure, signal, strategy, and sequence of human-automation interaction can guide design for appropriate trust and cooperation.

Complacency and Automation Bias in the Enbridge Pipeline Disaster

Automation bias was likely the main contributing factor in the Enbridge pipeline disaster that occurred on July 26, 2010, when large amounts of crude oil were released into the Kalamazoo River and Talmadge Creek. An examination of the Enbridge oil pipeline accident suggests that complacency and automation bias played leading roles but were overlooked by regulators. Moreover, we believe that because the National Transportation Safety Board overlooked existing research on automation bias, its recommendations are flawed and could exacerbate, rather than alleviate, the problem. Industry, policy makers, and regulators need to consider automation bias when developing systems to reduce the likelihood of complacency errors.

The Impact of Automation Reliability and Operator Fatigue on Performance and Reliance

Reliability of automation is known to influence operator reliance on automation. What is less understood is how the influence of reliability and the effects of operator fatigue might interact. The present study investigated the impact of automation reliability on accuracy and reliance and how this impact changes with level of fatigue during simulated multiple unmanned aerial vehicle (UAV) operation. Participants ( N = 131) completed a two-hour simulated multi-UAV mission assisted by an automated decision making aid of either high or low reliability. A decrease in subjective task engagement and performance over time marked the induction of passive fatigue by the mission. Participants were more trusting in the high reliability condition than in the low reliability condition. Finally, reliance decreased with time at any reliability, but a significant interaction between reliability and time on task indicated that the decrease was of smaller magnitude when the automation was reliable.

Putting the Brakes on Autonomous Vehicle Control

The assimilation of automation in commuter vehicles is rapidly increasing, as too are the concerns with these technologies. Human interaction with autonomous vehicles must be thoroughly researched to understand the quantification and qualification of interactive behaviors with these systems. We developed a study using a high-fidelity driving simulator to mimic probable breakdowns with these systems to better understand the subsequent human responses and to explore the necessary technological requirements to overcome potential problems. 30 participants engaged in a driving scenario switching between manual and autonomous vehicle control. We accounted for individual differences in braking reaction time while simultaneously engaging in a secondary cognitive task during times of autonomous vehicle control. Results show the average RT for baseline scenarios without the cognitive task was 832.1 milliseconds while the average RT for baseline scenarios with the cognitive task was 908.4 milliseconds; a 9.17% significant increase. The average RT for the autonomous scenario was 1357.0 milliseconds; a significant increase of 49.38% over the baseline scenario with the cognitive task that can be attributed to the addition of automation. We found a positive linear correlation of time spent in autonomous control and subsequent braking reaction time. Additionally, cognitive task difficulty, attention allocation, self-reported mental demand, fatigue, and heart rate affect reaction time when cued to take control of the vehicle.

Automation Reliability and Other Contextual Factors in Multi-UAV Operator Selection

Multi-unmanned air vehicle (UAV) operation requires a unique set of skills and high demand for new operators requires selection from populations without previous flight training. To support developing criteria for multi-UAV operator selection, the present study investigated the role of multiple individual difference factors in performance under different multi-UAV specific contexts. Specifically, we compared performance under fatigue using a high- and low-reliability automated aid. Accuracy on surveillance tasks, as well as reliance on automation were assessed. Video gaming expertise was associated with reduced stress and less reliance with a low-reliability automated aid. Distress was the most robust predictor of performance accuracy, but high distress was harmful only when reliability was low. Personality correlates of performance varied with both automation reliability and gender. Our findings suggest that multi-UAV operator selection should take into account the reliability of the automated systems.

Reaction Times When Switching From Autonomous to Manual Driving Control

As autonomous vehicles become more prevalent in our everyday lives, we must succumb to the realities of technological deficiencies. Although a future of fully autonomous vehicles would be the pinnacle of safety and efficiency, the current reality leaves us in a transitional state requiring human interaction with autonomous systems. Therefore it is imperative to understand human-system interaction with the autonomous features in current and future technologies. To gain an improved understanding, we designed an investigational study to gain a better understanding of human performance parameters at the moment they relieve and regain control of autonomous systems. The current findings show that reaction time increases as time disengaged from the task of driving increases, regardless of cognitive engagement.

The Development and Evaluation of Countermeasures to Tactile Change Blindness

OBJECTIVE

The goal of the present study was to develop and empirically evaluate three countermeasures to tactile change blindness (where a tactile signal is missed in the presence of a tactile transient). Each of these countermeasures relates to a different cognitive step involved in successful change detection.

BACKGROUND

To date, change blindness has been studied primarily in vision, but there is limited empirical evidence that the tactile modality may also be subject to this phenomenon. Change blindness raises concerns regarding the robustness of tactile and multimodal interfaces.

METHOD

Three countermeasures to tactile change blindness were evaluated in the context of a highly demanding monitoring task. One countermeasure was proactive (alerting the participant to a possible change before it occurred) whereas the other two were adaptive (triggered after the change upon an observed miss). Performance and subjective data were collected.

RESULTS

Compared to the baseline condition, all countermeasures improved intramodal tactile change detection. Adaptive measures resulted in the highest detection rates, specifically when signal gradation was employed (i.e., when the intensity of the tactile signal was increased after a miss was observed).

CONCLUSION

Adaptive displays can be used to counter the effects of change blindness and ensure that tactile information is reliably detected. Increasing the tactile intensity after a missed change appears most promising and was the preferred countermeasure.

APPLICATION

The findings from this study can inform the design of interfaces employing the tactile modality to support monitoring and attention management in data-rich domains.