Development and Validation of the System Trustworthiness Scale

OBJECTIVE

We created and validated a scale to measure perceptions of system trustworthiness.

BACKGROUND

Several scales exist in the literature that attempt to assess trustworthiness of system referents. However, existing measures suffer from limitations in their development and validation. The current study sought to develop a scale based on theory and methodological rigor.

METHOD

We conducted exploratory and confirmatory factor analyses on data from two online studies to develop the System Trustworthiness Scale (STS). Additional analyses explored the manipulation of the factors and assessed convergent and divergent validity.

RESULTS

The exploratory factor analyses resulted in a three-factor solution that represented the theoretical constructs of trustworthiness: performance, purpose, and process. Confirmatory factor analyses confirmed the three-factor solution. In addition, correlation and regression analyses demonstrated the scale's divergent and predictive validity.

CONCLUSION

The STS is a psychometrically valid and predictive scale for assessing trustworthiness perceptions of system referents.

APPLICATIONS

The STS assesses trustworthiness perceptions of systems. Importantly, the scale differentiates performance, purpose, and process constructs and is adaptable to a variety of system referents.

The Effect of Asset Degradation on Trust in Swarms: A Reexamination of System-Wide Trust in Human-Swarm Interaction

OBJECTIVE

The effects of asset degradation on trust in human-swarm interaction were investigated through the lens of system-wide trust theory.

BACKGROUND

Researchers have begun investigating contextual features that shape human interactions with robotic swarms-systems comprising assets that coordinate behavior based on their nearest neighbors. Recent work has begun investigating how human trust toward swarms is affected by asset degradation through the lens of system-wide trust theory, but these studies have been marked by several limitations.

METHOD

In an online study, the current work manipulated asset degradation and measured trust-relevant criteria in a within-subjects design and addressed the limitations of past work.

RESULTS

Controlling for swarm performance (i.e., target acquisition), asset degradation and trust (i.e., reliance intentions) in swarms were negatively related. In addition, as degradation increased, perceptions of swarm cohesion, obstacle avoidance, target acquisition, and terrain exploration efficiency decreased, the latter two of which (coupled with the reliance intentions criterion) support the tenets of system-wide trust theory as well as replicate and extend past work on the effects of asset degradation on trust in swarms.

CONCLUSION

Human-swarm interaction is a context in which system-wide trust is relevant, and future work ought to investigate how to calibrate human trust toward swarm systems.

APPLICATIONS

Based on these findings, design professionals should prioritize ways to depict swarm performance and system health such that humans do not abandon trust in systems that are still functional yet not over-trust those systems which are indeed performing poorly.

Return-to-Manual Performance can be Predicted Before Automation Fails

OBJECTIVE

This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control.

BACKGROUND

Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects.

METHOD

Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation.

RESULTS

For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance.

CONCLUSIONS

RTM performance degraded as trust in automation increased relative to participants' own average, but only for individuals with average or high levels of trust.

APPLICATIONS

Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

Towards Ethical AI: Empirically Investigating Dimensions of AI Ethics, Trust Repair, and Performance in Human-AI Teaming

OBJECTIVE

Determining the efficacy of two trust repair strategies (apology and denial) for trust violations of an ethical nature by an autonomous teammate.

BACKGROUND

While ethics in human-AI interaction is extensively studied, little research has investigated how decisions with ethical implications impact trust and performance within human-AI teams and their subsequent repair.

METHOD

Forty teams of two participants and one autonomous teammate completed three team missions within a synthetic task environment. The autonomous teammate made an ethical or unethical action during each mission, followed by an apology or denial. Measures of individual team trust, autonomous teammate trust, human teammate trust, perceived autonomous teammate ethicality, and team performance were taken.

RESULTS

Teams with unethical autonomous teammates had significantly lower trust in the team and trust in the autonomous teammate. Unethical autonomous teammates were also perceived as substantially more unethical. Neither trust repair strategy effectively restored trust after an ethical violation, and autonomous teammate ethicality was not related to the team score, but unethical autonomous teammates did have shorter times.

CONCLUSION

Ethical violations significantly harm trust in the overall team and autonomous teammate but do not negatively impact team score. However, current trust repair strategies like apologies and denials appear ineffective in restoring trust after this type of violation.

APPLICATION

This research highlights the need to develop trust repair strategies specific to human-AI teams and trust violations of an ethical nature.

Trust with increasing and decreasing reliability

OBJECTIVE

The primary purpose was to determine how trust changes over time when automation reliability increases or decreases. A secondary purpose was to determine how task-specific self-confidence is associated with trust and reliability level.

BACKGROUND

Both overtrust and undertrust can be detrimental to system performance; therefore, the temporal dynamics of trust with changing reliability level need to be explored.

METHOD

Two experiments used a dominant-color identification task, where automation provided a recommendation to users, with the reliability of the recommendation changing over 300 trials. In Experiment 1, two groups of participants interacted with the system: one group started with a 50% reliable system which increased to 100%, while the other used a system that decreased from 100% to 50%. Experiment 2 included a group where automation reliability increased from 70% to 100%.

RESULTS

Trust was initially high in the decreasing group and then declined as reliability level decreased; however, trust also declined in the 50% increasing reliability group. Furthermore, when user self-confidence increased, automation reliability had a greater influence on trust. In Experiment 2, the 70% increasing reliability group showed increased trust in the system.

CONCLUSION

Trust does not always track the reliability of automated systems; in particular, it is difficult for trust to recover once the user has interacted with a low reliability system.

APPLICATIONS

This study provides initial evidence into the dynamics of trust for automation that gets better over time suggesting that users should only start interacting with automation when it is sufficiently reliable.

Understanding the Impact of Time Pressure and Automation Support in a Visual Search Task

OBJECTIVE

To understand the impact of time pressure and automated decision support systems (DSS) in a simulated medical visual search task.

BACKGROUND

Time pressure usually impairs manual performance in visual search tasks, but DSS support might neutralize this negative effect. Moreover, understanding the impact of time pressure and DSS support seems relevant for many real-world applications of visual search.

METHOD

We used a visual search paradigm where participants had to search for target letters in a simulated medical image. Participants performed the task either manually or with support of a highly reliable DSS. Time pressure was varied within-subjects by either a trialwise time-pressure manipulation (Experiment 1) or a blockwise manipulation (Experiment 2). Performance was assessed based on signal detection measures. To further analyze visual search behavior, a mouse-over approach was used.

RESULTS

In both experiments, results showed impaired sensitivity under high compared to low time pressure in the manual condition, but no negative effect of time pressure when working with a highly reliable DSS. Moreover, participants searched less under time pressure and when receiving DSS support, indicating participants followed the automation without thoroughly checking recommendations. However, the human-DSS team's sensitivity was always worse than that of the DSS alone, independent of the strength of time pressure.

CONCLUSION

Negative effects of time pressure can be ameliorated when receiving support by a DSS, but joint overall performance remains below DSS-alone performance.

APPLICATION

Highly reliable DSS seem capable of ameliorating the negative impact of time pressure in complex detection tasks.

Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts

OBJECTIVE

This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model.

BACKGROUND

Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations).

METHODS

In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance).

RESULTS

AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity (d'), and reliance increased in the high reliability AI condition, but there was no effect on response bias (c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition.

CONCLUSION

In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets).

APPLICATION

In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions.

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automated Driving Style?

OBJECTIVE

Automated driving is becoming a reality, and such technology raises new concerns about human-machine interaction on road. This paper aims to investigate factors influencing trust calibration and evolution over time.

BACKGROUND

Numerous studies showed trust was a determinant in automation use and misuse, particularly in the automated driving context.

METHOD

Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs. Distrustful) on drivers' behaviors and trust calibration during two sessions of simulated automated driving. The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human-machine early interactions. Trust was assessed over time through questionnaires. Drivers' visual behaviors and take-over performances during an unplanned take-over request were also investigated.

RESULTS

Results showed an increase of trust over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style.

CONCLUSION

Trust in automated driving increases rapidly when drivers' experience such a system. Initial level of trust seems to be crucial in further trust calibration and modulate the effect of automation performance. Long-term trust evolutions suggest that experience modify drivers' mental model about automated driving systems.

APPLICATION

In the automated driving context, trust calibration is a decisive question to guide such systems' proper utilization, and road safety.

How Terminology Affects Users' Responses to System Failures

OBJECTIVE

The objective of our research is to advance the understanding of behavioral responses to a system's error. By examining trust as a dynamic variable and drawing from attribution theory, we explain the underlying mechanism and suggest how terminology can be used to mitigate the so-called algorithm aversion. In this way, we show that the use of different terms may shape consumers' perceptions and provide guidance on how these differences can be mitigated.

BACKGROUND

Previous research has interchangeably used various terms to refer to a system and results regarding trust in systems have been ambiguous.

METHODS

Across three studies, we examine the effect of different system terminology on consumer behavior following a system failure.

RESULTS

Our results show that terminology crucially affects user behavior. Describing a system as "AI" (i.e., self-learning and perceived as more complex) instead of as "algorithmic" (i.e., a less complex rule-based system) leads to more favorable behavioral responses by users when a system error occurs.

CONCLUSION

We suggest that in cases when a system's characteristics do not allow for it to be called "AI," users should be provided with an explanation of why the system's error occurred, and task complexity should be pointed out. We highlight the importance of terminology, as this can unintentionally impact the robustness and replicability of research findings.

APPLICATION

This research offers insights for industries utilizing AI and algorithmic systems, highlighting how strategic terminology use can shape user trust and response to errors, thereby enhancing system acceptance.

Exploratory Development of Algorithms for Determining Driver Attention Status

OBJECTIVE

Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS).

BACKGROUND

Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state.

METHOD

A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof.

RESULTS

Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased.

CONCLUSION

At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers.

APPLICATION

This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.

The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?

OBJECTIVE

This study's purpose was to better understand the dynamics of trust attitude and behavior in human-agent interaction.

BACKGROUND

Whereas past research provided evidence for a perfect automation schema, more recent research has provided contradictory evidence.

METHOD

To disentangle these conflicting findings, we conducted an online experiment using a simulated medical X-ray task. We manipulated the framing of support agents (i.e., artificial intelligence (AI) versus expert versus novice) between-subjects and failure experience (i.e., perfect support, imperfect support, back-to-perfect support) within subjects. Trust attitude and behavior as well as perceived reliability served as dependent variables.

RESULTS

Trust attitude and perceived reliability were higher for the human expert than for the AI than for the human novice. Moreover, the results showed the typical pattern of trust formation, dissolution, and restoration for trust attitude and behavior as well as perceived reliability. Forgiveness after failure experience did not differ between agents.

CONCLUSION

The results strongly imply the existence of an imperfect automation schema. This illustrates the need to consider agent expertise for human-agent interaction.

APPLICATION

When replacing human experts with AI as support agents, the challenge of lower trust attitude towards the novel agent might arise.

The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk

While trust in different types of automated vehicles has been a major focus for researchers and vehicle manufacturers, few studies have explored how people trust automated vehicles that are not cars, nor how their trust may transfer across different mobilities enabled with automation. To address this objective, a dual mobility study was designed to measure how trust in an automated vehicle with a familiar form factor-a car-compares to, and influences, trust in a novel automated vehicle-termed sidewalk mobility. A mixed-method approach involving both surveys and a semi-structured interview was used to characterize trust in these automated mobilities. Results found that the type of mobility had little to no effect on the different dimensions of trust that were studied, suggesting that trust can grow and evolve across different mobilities when the user is unfamiliar with a novel automated driving-enabled (AD-enabled) mobility. These results have important implications for the design of novel mobilities.

Enhancing component-specific trust with consumer automated systems through humanness design

Consumer automation is a suitable venue for studying the efficacy of untested humanness design methods for promoting specific trust in multi-component systems. Subjective (trust, self-confidence) and behavioural (use, manual override) measures were recorded as 82 participants interacted with a four-component automation-bearing system in a simulated smart home task for two experimental blocks. During the first block all components were perfectly reliable (100%). During the second block, one component became unreliable (60%). Participants interacted with a system containing either a single or four simulated voice assistants. In the single-assistant condition, the unreliable component resulted in trust changes for every component. In the four-assistant condition, trust decreased for only the unreliable component. Across agent-number conditions, use decreased between blocks for only the unreliable component. Self-confidence and overrides exhibited ceiling and floor effects, respectively. Our findings provide the first evidence of effectively using humanness design to enhance component-specific trust in consumer systems.Practitioner summary: Participants interacted with simulated smart-home multi-component systems that contained one or four voiced assistants. In the single-voice condition, one component's decreasing reliability coincided with trust changes for all components. In the four-voice condition, trust decreased for only the decreasingly reliable component. The number of voices did not influence use strategies.Abbreviations: ACC: adaptive cruise control; CST: component-specific trust; SWT: system-wide trust; UAV: unmanned aerial vehicle; CPRS: complacency potential rating scale; MANOVA: multivariate analysis of variance.

Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study

OBJECTIVE

Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other-effective connectivity (EC)-in the context of trust in automation.

BACKGROUND

Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions.

METHOD

Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions.

RESULTS

Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections.

CONCLUSION

Results indicated that trust and distrust can be two distinctive neural processes in human-automation interaction-distrust being a more complex network than trust, possibly due to the increased cognitive load.

APPLICATION

The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human-automation interface design but also in the proper use of automation in real-life situations.

An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.

Human Performance Consequences of Automated Decision Aids: The Impact of Time Pressure

OBJECTIVE

The study addresses the impact of time pressure on human interactions with automated decision support systems (DSSs) and related performance consequences.

BACKGROUND

When humans interact with DSSs, this often results in worse performance than could be expected from the automation alone. Previous research has suggested that time pressure might make a difference by leading humans to rely more on a DSS.

METHOD

In two laboratory experiments, participants performed a luggage screening task either manually, supported by a highly reliable DSS, or by a low reliable DSS. Time provided for inspecting the X-rays was 4.5 s versus 9 s varied within-subjects as the time pressure manipulation. Participants in the automation conditions were either shown the automation's advice prior (Experiment 1) or following (Experiment 2) their own inspection, before they made their final decision.

RESULTS

In Experiment 1, time pressure compromised performance independent of whether the task was performed manually or with automation support. In Experiment 2, the negative impact of time pressure was only found in the manual but not in the two automation conditions. However, neither experiment revealed any positive impact of time pressure on overall performance, and the joint performance of human and automation was mostly worse than the performance of the automation alone.

CONCLUSION

Time pressure compromises the quality of decision-making. Providing a DSS can reduce this effect, but only if the automation's advice follows the assessment of the human.

APPLICATION

The study provides suggestions for the effective implementation of DSSs in addition to supporting concerns that highly reliable DSSs are not used optimally by human operators.

Long-Term Evaluation of Drivers' Behavioral Adaptation to an Adaptive Collision Avoidance System

OBJECTIVE

Taking human factors approach in which the human is involved as a part of the system design and evaluation process, this paper aims to improve driving performance and safety impact of driver support systems in the long view of human-automation interaction.

BACKGROUND

Adaptive automation in which the system implements the level of automation based on the situation, user capacity, and risk has proven effective in dynamic environments with wide variations of human workload over time. However, research has indicated that drivers may not efficiently deal with dynamically changing system configurations. Little effort has been made to support drivers' understanding of and behavioral adaptation to adaptive automation.

METHOD

Using a within-subjects design, 42 participants completed a four-stage driving simulation experiment during which they had to gradually interact with an adaptive collision avoidance system while exposed to hazardous lane-change scenarios over 1 month.

RESULTS

Compared to unsupported driving (stage i), although collisions have been significantly reduced when first experienced driving with the system (stage ii), improvements in drivers' trust in and understanding of the system and driving behavior have been achieved with more driver-system interaction and driver training during stages iii and iv.

CONCLUSION

While designing systems that take into account human skills and abilities can go some way to improving their effectiveness, this alone is not sufficient. To maximize safety and system usability, it is also essential to ensure appropriate users' understanding and acceptance of the system.

APPLICATION

These findings have important implications for the development of active safety systems and automated driving.

What's Driving Me? Exploration and Validation of a Hierarchical Personality Model for Trust in Automated Driving

OBJECTIVE

This paper presents a comprehensive investigation of personality traits related to trust in automated vehicles. A hierarchical personality model based on Mowen's (2000) 3M model is explored in a first and replicated in a second study.

BACKGROUND

Trust in automation is established in a complex psychological process involving user-, system- and situation-related variables. In this process, personality traits have been viewed as an important source of variance.

METHOD

Dispositional variables on three levels were included in an exploratory, hierarchical personality model (full model) of dynamic learned trust in automation, which was refined on the basis of structural equation modeling carried out in Study 1 (final model). Study 2 replicated the final model in an independent sample.

RESULTS

In both studies, the personality model showed a good fit and explained a large proportion of variance in trust in automation. The combined evidence supports the role of extraversion, neuroticism, and self-esteem at the elemental level; affinity for technology and dispositional interpersonal trust at the situational level; and propensity to trust in automation and a priori acceptability of automated driving at the surface level in the prediction of trust in automation.

CONCLUSION

Findings confirm that personality plays a substantial role in trust formation and provide evidence of the involvement of user dispositions not previously investigated in relation to trust in automation: self-esteem, dispositional interpersonal trust, and affinity for technology.

APPLICATION

Implications for personalization of information campaigns, driver training, and user interfaces for trust calibration in automated driving are discussed.

Dancing With Algorithms: Interaction Creates Greater Preference and Trust in Machine-Learned Behavior

OBJECTIVE

We examined a method of machine learning (ML) to evaluate its potential to develop more trustworthy control of unmanned vehicle area search behaviors.

BACKGROUND

ML typically lacks interaction with the user. Novel interactive machine learning (IML) techniques incorporate user feedback, enabling observation of emerging ML behaviors, and human collaboration during ML of a task. This may enable trust and recognition of these algorithms.

METHOD

Participants judged and selected behaviors in a low and a high interaction condition (IML) over the course of behavior evolution using ML. User trust in the outputs, as well as preference, and ability to discriminate and recognize the behaviors were measured.

RESULTS

Compared to noninteractive techniques, IML behaviors were more trusted and preferred, as well as recognizable, separate from non-IML behaviors, and approached similar performance as pure ML models.

CONCLUSION

IML shows promise for creating behaviors by involving the user; this is the first extension of this technique for vehicle behavior model development targeting user satisfaction and is unique in its multifaceted evaluation of how users perceived, trusted, and implemented these learned controllers.

APPLICATION

There are many contexts where the brittleness of ML cannot be trusted, but the advantage of ML over traditional programmed behaviors may be large, as in some military operations where they could be scaled. IML in this early form appears to generate satisfactory behaviors without sacrificing performance, use, or trust in the behavior, but more work is necessary.

Trusting Other Vehicles' Automatic Emergency Braking Decreases Self-Protective Driving

OBJECTIVE

We focused on drivers in close proximity to vehicles with advanced driver assistance systems (ADAS). We examined whether the belief that an approaching vehicle is equipped with automatic emergency braking (AEB) influences behavior of those drivers.

BACKGROUND

In addition to benefits of ADAS, previous studies have demonstrated negative behavioral adaptation, that is, behavioral changes after introduction of ADAS, by its users. However, little is known about whether negative behavioral adaptation can occur for nonusers in close proximity to vehicles with ADAS.

METHOD

Experienced (Experiment 1) and novice (Experiment 2) drivers drove a simulator vehicle without ADAS and tried to pass through intersections. We manipulated participants' belief about whether an approaching vehicle had AEB and time-to-arrival of the approaching vehicle. Participants kept constant speed or pressed the brake pedal before entering each intersection. In Experiment 2, participants rated their trust in AEB by a questionnaire after driving.

RESULTS

In both experiments, belief about the approaching vehicle's AEB did not influence braking probability; however, belief delayed initiation of braking. The effect of belief on braking latency was only observed when trust in AEB was higher in Experiment 2.

CONCLUSION

Negative behavioral adaptation can occur for nonusers in close proximity to users of AEB, and trust in AEB plays an important role.

APPLICATION

When evaluating the effect of ADAS, the possible behavioral change of surrounding nonusers as well as users should be taken into account. To establish consumers' trust accurately, advertisements (e.g., TV commercials) must carefully consider their messages.

Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation

Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings. The model is able to predict the effect of various potential disruptions, such as environmental changes or particular classes of adversarial intrusions on human trust in automation. Finally, we consider the use of model predictions to improve automation transparency that account for human cognitive biases in order to optimize the bidirectional interaction between human and machine through supporting trust calibration. The implications of our findings for the design of reliable and trustworthy automation are discussed.

Human Performance Benefits of The Automation Transparency Design Principle : Validation and Variation

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.

More Than a Feeling-Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety

With service robots becoming more ubiquitous in social life, interaction design needs to adapt to novice users and the associated uncertainty in the first encounter with this technology in new emerging environments. Trust in robots is an essential psychological prerequisite to achieve safe and convenient cooperation between users and robots. This research focuses on psychological processes in which user dispositions and states affect trust in robots, which in turn is expected to impact the behavior and reactions in the interaction with robotic systems. In a laboratory experiment, the influence of propensity to trust in automation and negative attitudes toward robots on state anxiety, trust, and comfort distance toward a robot were explored. Participants were approached by a humanoid domestic robot two times and indicated their comfort distance and trust. The results favor the differentiation and interdependence of dispositional, initial, and dynamic learned trust layers. A mediation from the propensity to trust to initial learned trust by state anxiety provides an insight into the psychological processes through which personality traits might affect interindividual outcomes in human-robot interaction (HRI). The findings underline the meaningfulness of user characteristics as predictors for the initial approach to robots and the importance of considering users’ individual learning history regarding technology and robots in particular.

Get Ready for Being Chauffeured : Passenger's Preferences and Trust While Being Driven by Human and Automation

OBJECTIVE

We investigated passenger's trust and preferences using subjective, qualitative, and psychophysiological measures while being driven either by human or automation in a field study and a driving simulator experiment.

BACKGROUND

The passenger's perspective has largely been neglected in autonomous driving research, although the change of roles from an active driver to a passive passenger is incontrovertible. Investigations of passenger's appraisals on self-driving vehicles often seem convoluted with active manual driving experiences instead of comparisons with being driven by humans.

METHOD

We conducted an exploratory field study using an autonomous research vehicle (N = 11) and a follow-up experimental driving simulation (N = 24). Participants were driven on the same course by a human and an autonomous agent sitting on a passenger seat. Skin conductance, trust, and qualitative characteristics of the perceived driving situation were assessed. In addition, the effect of driving style (defensive vs. sporty) was evaluated in the simulator.

RESULTS

Both investigations revealed a close relation between subjective trust ratings and skin conductance, with increased trust and by trend reduced arousal for human compared with automation in control. Even though driving behavior was equivalent in the simulator when being driven by human and automation, passengers most preferred and trusted the human-defensive driver.

CONCLUSION

Individual preferences for driving style and human or autonomous vehicle control influence trust and subjective driving characterizations.

APPLICATION

The findings are applicable in human-automation research, reminding to not neglect subjective attributions and psychophysiological reactions as a result of ascribed control duties in relation to specific execution characteristics.

Not All Information Is Equal: Effects of Disclosing Different Types of Likelihood Information on Trust, Compliance and Reliance, and Task Performance in Human-Automation Teaming

OBJECTIVE

The study examines the effects of disclosing different types of likelihood information on human operators' trust in automation, their compliance and reliance behaviors, and the human-automation team performance.

BACKGROUND

To facilitate appropriate trust in and dependence on automation, explicitly conveying the likelihood of automation success has been proposed as one solution. Empirical studies have been conducted to investigate the potential benefits of disclosing likelihood information in the form of automation reliability, (un)certainty, and confidence. Yet, results from these studies are rather mixed.

METHOD

We conducted a human-in-the-loop experiment with 60 participants using a simulated surveillance task. Each participant performed a compensatory tracking task and a threat detection task with the help of an imperfect automated threat detector. Three types of likelihood information were presented: overall likelihood information, predictive values, and hit and correct rejection rates. Participants' trust in automation, compliance and reliance behaviors, and task performance were measured.

RESULTS

Human operators informed of the predictive values or the overall likelihood value, rather than the hit and correct rejection rates, relied on the decision aid more appropriately and obtained higher task scores.

CONCLUSION

Not all likelihood information is equal in aiding human-automation team performance. Directly presenting the hit and correct rejection rates of an automated decision aid should be avoided.

APPLICATION

The findings can be applied to the design of automated decision aids.

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency

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.

Trust and Distrust of Automated Parking in a Tesla Model X

OBJECTIVE

The present study aims to evaluate driver intervention behaviors during a partially automated parking task.

BACKGROUND

Cars with partially automated parking features are becoming widely available. Although recent research explores the use of automation features in partially automated cars, none have focused on partially automated parking. Recent incidents and research have demonstrated that drivers sometimes use partially automated features in unexpected, inefficient, and harmful ways.

METHOD

Participants completed a series of partially automated parking trials with a Tesla Model X and their behavioral interventions were recorded. Participants also completed a risk-taking behavior test and a post-experiment questionnaire that included questions about trust in the system, likelihood of using the Autopark feature, and preference for either the partially automated parking feature or self-parking.

RESULTS

Initial intervention rates were over 50%, but declined steeply in later trials. Responses to open-ended questions revealed that once participants understood what the system was doing, they were much more likely to trust it. Trust in the partially automated parking feature was predicted by a model including risk-taking behaviors, self-confidence, self-reported number of errors committed by the Tesla, and the proportion of trials in which the driver intervened.

CONCLUSION

Using partially automated parking with little knowledge of its workings can lead to high degree of initial distrust. Repeated exposure of partially automated features to drivers can greatly increase their use.

APPLICATION

Short tutorials and brief explanations of the workings of partially automated features may greatly improve trust in the system when drivers are first introduced to partially automated systems.

Scared to Trust? - Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

The advantages of automated driving can only come fully into play if these systems are used in an appropriate way, which means that they are neither used in situations they are not designed for (misuse) nor used in a too restricted manner (disuse). Trust in automation has been found to be an essential psychological basis for appropriate interaction with automated systems. Well-balanced system use requires a calibrated level of trust in correspondence with the actual ability of an automated system. As for these far-reaching implications of trust for safe and efficient system use, the psychological processes, in which trust is dynamically calibrated prior and during the use of automated technology, need to be understood. At this point, only a restricted body of research investigated the role of personality and emotional states for the formation of trust in automated systems. In this research, the role of the personality variables depressiveness, self-efficacy, self-esteem, and locus of control for the experience of anxiety before the first experience with a highly automated driving system were investigated. Additionally, the relationship of the investigated personality variables and anxiety to subsequent formation of trust in automation was investigated. In a driving simulator study, personality variables and anxiety were measured before the interaction with an automated system. Trust in the system was measured after participants drove with the system for a while. Trust in the system was significantly predicted by state anxiety and the personality characteristics self-esteem and self-efficacy. The relationships of self-esteem and self-efficacy were mediated by state anxiety as supported by significant specific indirect effects. While for depression the direct relationship with trust in automation was not found to be significant, an indirect effect through the experience of anxiety was supported. Locus of control did not show a significant association to trust in automation. The reported findings support the importance of considering individual differences in negative self-evaluations and anxiety when being introduced to a new automated system for individual differences in trust in automation. Implications for future research as well as implications for the design of automated technology in general and automated driving systems are discussed.

Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human–subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.

Extending Three Existing Models to Analysis of Trust in Automation: Signal Detection, Statistical Parameter Estimation, and Model-Based Control

OBJECTIVE

The objective is to propose three quantitative models of trust in automation.

BACKGROUND

Current trust-in-automation literature includes various definitions and frameworks, which are reviewed.

METHOD

This research shows how three existing models, namely those for signal detection, statistical parameter estimation calibration, and internal model-based control, can be revised and reinterpreted to apply to trust in automation useful for human-system interaction design.

RESULTS

The resulting reinterpretation is presented quantitatively and graphically, and the measures for trust and trust calibration are discussed, along with examples of application.

CONCLUSION

The resulting models can be applied to provide quantitative trust measures in future experiments or system designs.

APPLICATIONS

Simple examples are provided to explain how model application works for the three trust contexts that correspond to signal detection, parameter estimation calibration, and model-based open-loop control.

Trusting Robocop: Gender-Based Effects on Trust of an Autonomous Robot

Little is known regarding public opinion of autonomous robots. Trust of these robots is a pertinent topic as this construct relates to one's willingness to be vulnerable to such systems. The current research examined gender-based effects of trust in the context of an autonomous security robot. Participants (N = 200; 63% male) viewed a video depicting an autonomous guard robot interacting with humans using Amazon's Mechanical Turk. The robot was equipped with a non-lethal device to deter non-authorized visitors and the video depicted the robot using this non-lethal device on one of the three humans in the video. However, the scenario was designed to create uncertainty regarding who was at fault - the robot or the human. Following the video, participants rated their trust in the robot, perceived trustworthiness of the robot, and their desire to utilize similar autonomous robots in several different contexts that varied from military use to commercial use to home use. The results of the study demonstrated that females reported higher trust and perceived trustworthiness of the robot relative to males. Implications for the role of individual differences in trust of robots are discussed.

Behavioral Adaptations to Lane Keeping Systems: Effects of Exposure and Withdrawal

OBJECTIVE

A driving simulator study was conducted to evaluate the longitudinal effects of an intervention and withdrawal of a lane keeping system on driving performance and cognitive workload.

BACKGROUND

Autonomous vehicle systems are being implemented into the vehicle fleet. However, limited research exists in understanding the carryover effects of long-term exposure.

METHODS

Forty-eight participants (30 treatment, 18 control) completed eight drives across three separate days in a driving simulator. The treatment group had an intervention and withdrawal of a lane keeping system. Changes in driving performance (standard deviation of lateral position [SDLP] and mean time to collision [TTC]) and cognitive workload (response time and miss rate to a detection response task) were modeled using mixed effects linear and negative binomial regression.

RESULTS

Drivers exposed to the lane keeping system had an increase in SDLP after the system was withdrawn relative to their baseline. Drivers with lane keeping had decreased mean TTC during and after system withdrawal compared with manual drivers. There was an increase in cognitive workload when the lane keeping system was withdrawn relative to when the system was engaged.

CONCLUSION

Behavioral adaptations in driving performance and cognitive workload were present during automation and persisted after the automation was withdrawn.

APPLICATION

The findings of this research emphasize the importance to consider the effects of skill atrophy and misplaced trust due to semi-autonomous vehicle systems. Designers and policymakers can utilize this for system alerts and training.

Under Pressure: Examining Social Conformity With Computer and Robot Groups

OBJECTIVE

The authors investigate whether nonhuman agents, such as computers or robots, produce a social conformity effect in human operators and examine to what extent potential conformist behavior varies as a function of the human-likeness of the group members and the type of task that has to be performed.

BACKGROUND

People conform due to normative and/or informational motivations in human-human interactions, and conformist behavior is modulated by factors related to the individual as well as factors associated with the group, context, and culture. Studies have yet to examine whether nonhuman agents also induce social conformity.

METHOD

Participants were assigned to a computer, robot, or human group and completed both a social and analytical task with the respective group.

RESULTS

Conformity measures (percentage of times participants answered in line with agents on critical trials) subjected to a 3 × 2 mixed ANOVA showed significantly higher conformity rates for the analytical versus the social task as well as a modulation of conformity depending of the perceived agent-task fit.

CONCLUSION

Findings indicate that nonhuman agents were able to exert a social conformity effect, which was modulated further by the perceived match between agent and task type. Participants conformed to comparable degrees with agents during the analytical task but conformed significantly more strongly on the social task as the group's human-likeness increased.

APPLICATION

Results suggest that users may react differently to the influence of nonhuman agent groups with the potential for variability in conformity depending on the domain of the task.

Understanding Is Key: An Analysis of Factors Pertaining to Trust in a Real-World Automation System

OBJECTIVE

This paper aims to explore the role of factors pertaining to trust in real-world automation systems through the application of observational methods in a case study from the railway sector.

BACKGROUND

Trust in automation is widely acknowledged as an important mediator of automation use, but the majority of the research on automation trust is based on laboratory work. In contrast, this work explored trust in a real-world setting.

METHOD

Experienced rail operators in four signaling centers were observed for 90 min, and their activities were coded into five mutually exclusive categories. Their observed activities were analyzed in relation to their reported trust levels, collected via a questionnaire.

RESULTS

The results showed clear differences in activity, even when circumstances on the workstations were very similar, and significant differences in some trust dimensions were found between groups exhibiting different levels of intervention and time not involved with signaling.

CONCLUSION

Although the empirical, lab-based studies in the literature have consistently found that reliability and competence of the automation are the most important aspects of trust development, understanding of the automation emerged as the strongest dimension in this study. The implications are that development and maintenance of trust in real-world, safety-critical automation systems may be distinct from artificial laboratory automation.

APPLICATION

The findings have important implications for emerging automation concepts in diverse industries including highly automated vehicles and Internet of things.

Why Do I Have to Drive Now? Post Hoc Explanations of Takeover Requests

OBJECTIVE

It was investigated whether providing an explanation for a takeover request in automated driving influences trust in automation and acceptance.

BACKGROUND

Takeover requests will be recurring events in conditionally automated driving that could undermine trust as well as acceptance and, therefore, the successful introduction of automated vehicles.

METHOD

Forty participants were equally assigned to either an experimental group provided with an explanation of the reason for a takeover request or a control group without explanations. In a simulator drive, both groups experienced three takeover scenarios that varied in the obviousness of their causation. Participants rated their acceptance before and after the drive and rated their trust before and after each takeover situation.

RESULTS

All participants rated acceptance on the same high level before and after the drive, independent of the condition. The control group's trust ratings remained unchanged by takeover requests in all situations, but the experimental group showed decreased trust after experiencing a takeover caused by roadwork. Participants provided with explanation felt more strongly that they had understood the system and the reasons for the takeovers.

CONCLUSION

A takeover request did not lower trust or acceptance. Providing an explanation for a takeover request had no impact on trust or acceptance but increased the perceived understanding of the system.

APPLICATION

The results provide insights into users' perception of automated vehicles, takeover situations, and a fundament for future interface design for automated vehicles.

Prior Familiarization With Takeover Requests Affects Drivers' Takeover Performance and Automation Trust

OBJECTIVE

The objective for this study was to investigate the effects of prior familiarization with takeover requests (TORs) during conditional automated driving on drivers' initial takeover performance and automation trust.

BACKGROUND

System-initiated TORs are one of the biggest concerns for conditional automated driving and have been studied extensively in the past. Most, but not all, of these studies have included training sessions to familiarize participants with TORs. This makes them hard to compare and might obscure first-failure-like effects on takeover performance and automation trust formation.

METHOD

A driving simulator study compared drivers' takeover performance in two takeover situations across four prior familiarization groups (no familiarization, description, experience, description and experience) and automation trust before and after experiencing the system.

RESULTS

As hypothesized, prior familiarization with TORs had a more positive effect on takeover performance in the first than in a subsequent takeover situation. In all groups, automation trust increased after participants experienced the system. Participants who were given no prior familiarization with TORs reported highest automation trust both before and after experiencing the system.

CONCLUSION

The current results extend earlier findings suggesting that prior familiarization with TORs during conditional automated driving will be most relevant for takeover performance in the first takeover situation and that it lowers drivers' automation trust.

APPLICATION

Potential applications of this research include different approaches to familiarize users with automated driving systems, better integration of earlier findings, and sophistication of experimental designs.

Can We Study Autonomous Driving Comfort in Moving-Base Driving Simulators? A Validation Study

OBJECTIVE

To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting.

BACKGROUND

With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions.

METHOD

Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations.

RESULTS

We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system.

CONCLUSION

Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments.

APPLICATION

In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.

A Little Anthropomorphism Goes a Long Way

OBJECTIVE

We investigated the effects of exogenous oxytocin on trust, compliance, and team decision making with agents varying in anthropomorphism (computer, avatar, human) and reliability (100%, 50%).

BACKGROUND

Authors of recent work have explored psychological similarities in how people trust humanlike automation compared with how they trust other humans. Exogenous administration of oxytocin, a neuropeptide associated with trust among humans, offers a unique opportunity to probe the anthropomorphism continuum of automation to infer when agents are trusted like another human or merely a machine.

METHOD

Eighty-four healthy male participants collaborated with automated agents varying in anthropomorphism that provided recommendations in a pattern recognition task.

RESULTS

Under placebo, participants exhibited less trust and compliance with automated aids as the anthropomorphism of those aids increased. Under oxytocin, participants interacted with aids on the extremes of the anthropomorphism continuum similarly to placebos but increased their trust, compliance, and performance with the avatar, an agent on the midpoint of the anthropomorphism continuum.

CONCLUSION

This study provides the first evidence that administration of exogenous oxytocin affected trust, compliance, and team decision making with automated agents. These effects provide support for the premise that oxytocin increases affinity for social stimuli in automated aids.

APPLICATION

Designing automation to mimic basic human characteristics is sufficient to elicit behavioral trust outcomes that are driven by neurological processes typically observed in human-human interactions. Designers of automated systems should consider the task, the individual, and the level of anthropomorphism to achieve the desired outcome.

Cooperation in Human-Agent Systems to Support Resilience: A Microworld Experiment

OBJECTIVE

This study uses a dyadic approach to understand human-agent cooperation and system resilience.

BACKGROUND

Increasingly capable technology fundamentally changes human-machine relationships. Rather than reliance on or compliance with more or less reliable automation, we investigate interaction strategies with more or less cooperative agents.

METHOD

A joint-task microworld scenario was developed to explore the effects of agent cooperation on participant cooperation and system resilience. To assess the effects of agent cooperation on participant cooperation, 36 people coordinated with a more or less cooperative agent by requesting resources and responding to requests for resources in a dynamic task environment. Another 36 people were recruited to assess effects following a perturbation in their own hospital.

RESULTS

Experiment 1 shows people reciprocated the cooperative behaviors of the agents; a low-cooperation agent led to less effective interactions and less resource sharing, whereas a high-cooperation agent led to more effective interactions and greater resource sharing. Experiment 2 shows that an initial fast-tempo perturbation undermined proactive cooperation-people tended to not request resources. However, the initial fast tempo had little effect on reactive cooperation-people tended to accept resource requests according to cooperation level.

CONCLUSION

This study complements the supervisory control perspective of human-automation interaction by considering interdependence and cooperation rather than the more common focus on reliability and reliance.

APPLICATION

The cooperativeness of automated agents can influence the cooperativeness of human agents. Design and evaluation for resilience in teams involving increasingly autonomous agents should consider the cooperative behaviors of these agents.

From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving

OBJECTIVE

The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study.

BACKGROUND

Earlier research from other domains indicates that drivers' automation trust might be inferred from gaze behavior, such as monitoring frequency.

METHOD

The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared.

RESULTS

Overall, there was a consistent relationship between drivers' automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency.

CONCLUSION

We suggest that (a) the current results indicate a negative relationship between drivers' self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers' automation trust during highly automated driving might be inferred from gaze behavior.

APPLICATION

Potential applications of this research include the estimation of drivers' automation trust and reliance during highly automated driving.

The Role of Trust as a Mediator Between System Characteristics and Response Behaviors

OBJECTIVE

The purpose of the current work was to clarify how subjective trust determines response behavior when interacting with a signaling system.

BACKGROUND

In multiple theoretical frameworks, trust is acknowledged as a prime mediator between system error characteristics and automation dependence. Some researchers have operationally defined trust as the behavior exhibited. Other researchers have suggested that although trust may guide operator responses, trust does not completely determine the behavior.

METHOD

Forty-four participants interacted with a primary flight simulation task and a secondary signaling system task. The signaling system varied in reliability (90%, 60%) and error bias (false alarm, miss prone). Trust was measured halfway through the experimental session to address the criterion of temporal precedence in determining the effect of trust on behavior.

RESULTS

Analyses indicated that trust partially mediated the relationship between reliability and agreement rate. Trust did not mediate the relationship between reliability and reaction time. Trust also did not mediate the relationships between error bias and reaction time or agreement rate. Analyses of variance generally supported specific behavioral and trust hypotheses, indicating that the paradigm employed produced similar effects on response behaviors and subjective estimates of trust observed in other studies.

CONCLUSION

These results indicate that strong assumptions of trust acting as the prime mediator between system error characteristics and response behaviors should be viewed with caution.

APPLICATION

Practitioners should consider assessing factors other than trust to determine potential operator response behaviors, which may be more predictive.

Trust in automation: integrating empirical evidence on factors that influence trust

OBJECTIVE

We systematically review recent empirical research on factors that influence trust in automation to present a three-layered trust model that synthesizes existing knowledge.

BACKGROUND

Much of the existing research on factors that guide human-automation interaction is centered around trust, a variable that often determines the willingness of human operators to rely on automation. Studies have utilized a variety of different automated systems in diverse experimental paradigms to identify factors that impact operators' trust.

METHOD

We performed a systematic review of empirical research on trust in automation from January 2002 to June 2013. Papers were deemed eligible only if they reported the results of a human-subjects experiment in which humans interacted with an automated system in order to achieve a goal. Additionally, a relationship between trust (or a trust-related behavior) and another variable had to be measured. All together, 101 total papers, containing 127 eligible studies, were included in the review.

RESULTS

Our analysis revealed three layers of variability in human-automation trust (dispositional trust, situational trust, and learned trust), which we organize into a model. We propose design recommendations for creating trustworthy automation and identify environmental conditions that can affect the strength of the relationship between trust and reliance. Future research directions are also discussed for each layer of trust.

CONCLUSION

Our three-layered trust model provides a new lens for conceptualizing the variability of trust in automation. Its structure can be applied to help guide future research and develop training interventions and design procedures that encourage appropriate trust.

The attribution of success when using navigation aids

Attitudes towards geographic information technology is a seldom explored research area that can be explained with reference to established theories of attribution. This article reports on a study of how the attribution of success and failure in pedestrian navigation varies with level of automation, degree of success and locus of control. A total of 113 participants took part in a survey exploring reflections on personal experiences and vignettes describing fictional navigation experiences. A complex relationship was discovered in which success tends to be attributed to skill and failure to the navigation aid when participants describe their own experiences. A reversed pattern of results was found when discussing the navigation of others. It was also found that navigation success and failure are associated with personal skill to a greater extent when using paper maps, as compared with web-based routing engines or satellite navigation systems.

PRACTITIONER SUMMARY

This article explores the influences on the attribution of success and failure when using navigation aids. A survey was performed exploring interpretations of navigation experiences. Level of success, self or other as navigator and type of navigation aid used are all found to influence the attribution of outcomes to internal or external factors.