Anthropomorphizing Artificial Intelligence (AI), i.e., ascribing human-like mind or emotions to it, is widespread but varies across individuals. We tested three proposed dispositional predictors of anthropomorphism (need for cognition, need for structure, loneliness) in a general population (N = 307) and an AI expert sample (N = 130). Using a vignette design based on excerpts from a dialogue between the large language model LaMDA and one of its engineers, we found that none of the three dispositional traits predicted anthropomorphism. Instead, higher levels of AI knowledge decreased anthropomorphism across both samples. Experts reported higher AI knowledge and lower anthropomorphism than laypersons. For laypersons, anthropomorphism increased intentions to use LaMDA. For experts it did not, but was correlated with discomfort. In both samples, anthropomorphism was associated with greater moral care, i.e., not switching off LaMDA against "its will". Our findings highlight the role of knowledge and expertise in perceptions of AI.

As autonomous robots become more common in public spaces, spontaneous encounters with laypersons are more frequent. For this, robots need to be equipped with communication strategies that enhance momentary transparency and reduce the probability of critical situations. Adapting these robotic strategies requires consideration of robot movements, environmental conditions, and user characteristics and states. While numerous studies have investigated the impact of distraction on pedestrians’ movement behavior [1]-[4], limited research has examined this behavior in the presence of autonomous robots. This research addresses the impact of robot type and robot movement pattern on distracted and undistracted pedestrians’ movement behavior. In a field setting, unaware pedestrians were videotaped while moving past two working, autonomous cleaning robots. Out of N = 498 observed pedestrians, approximately 8% were distracted by smartphones. Distracted and undistracted pedestrians did not exhibit significant differences in their movement behaviors around the robots. Instead, both the larger sweeping robot and the off-set rectangular movement pattern significantly increased the number of lateral adaptations compared to the smaller cleaning robot and the circular movement pattern. The off-set rectangular movement pattern also led to significantly more close lateral adaptations. Depending on the robot type, the movement patterns led to differences in the distances of lateral adaptations. The study provides initial insights into pedestrian movement behavior around an autonomous cleaning robot in public spaces, contributing to the growing field HRI research.

Mobile robots increasingly operate alongside humans but are often out of sight, so that humans need to rely on the sounds of the robots to recognize their presence. For successful human-robot interaction (HRI), it is therefore crucial to understand how humans perceive robots by their consequential sounds, i.e., operating noise. Prior research suggests that the sound of a quadruped Go1 is more detectable than that of a wheeled Turtlebot. This study builds on this and examines the human ability to localize consequential sounds of three robots (quadruped Go1, wheeled Turtlebot 2i, wheeled HSR) in Virtual Reality. In a within-subjects design, we assessed participants’ localization performance for the robots with and without an acoustic vehicle alerting system (AVAS) for two velocities (0.3, 0.8 m/s) and two trajectories (head-on, radial). In each trial, participants were presented with the sound of a moving robot for 3 s and were tasked to point at its final position (localization task). Localization errors were measured as the absolute angular difference between the participants’ estimated and the actual robot position. Results showed that the robot type significantly influenced the localization accuracy and precision, with the sound of the wheeled HSR (especially without AVAS) performing worst under all experimental conditions. Surprisingly, participants rated the HSR sound as more positive, less annoying, and more trustworthy than the Turtlebot and Go1 sound. This reveals a tension between subjective evaluation and objective auditory localization performance. Our findings highlight consequential robot sounds as a critical factor for designing intuitive and effective HRI, with implications for human-centered robot design and social navigation.

In this work, an integrative theoretical structure for the propensity to trust (PTT) is derived from literature.In an online study (N ¼ 669), the validity of the structure was assessed and compared in two domains: propensity to trust in humans (PTT-H) and propensity to trust in automated technology (PTT-A).Based on this, an economic scale to measure PTT-A was derived and its psychometric quality was explored based on the first and an additional second study.The observed correlational pattern to basic personality traits supports the convergent validity of PTT-A.Moreover, discriminative predictive validity of PTT-A over PTT-H was supported by its higher relationships to technology-related outcomes.Additionally, incremental validity of PTT-A over basic personality traits was supported.Finally, the internal validity of the scale was replicated in an independent sample and re-test reliability was established.The findings support the added value of integrating PTT-A in research on the interaction with automated technology.

Abstract In recent years, there has been a debate on whether automated vehicles (AVs) should be equipped with novel external human–machine interfaces (eHMIs). Many studies have demonstrated how eHMIs influence pedestrians’ attitudes (e.g., trust in AVs) and behavior when they activate (e.g., encourage crossing by lighting up). However, very little attention has been paid to their effects when they do not activate (e.g., discourage crossing by not lighting up). We conducted a video-based laboratory study with a mixed design to explore the potential of two different eHMI messages to facilitate pedestrian-AV interactions by means of activating or not activating. Our participants watched videos of an approaching AV equipped with either a state eHMI (“I am braking”) or intent eHMI (“I intend to yield to you”) from the perspective of a pedestrian about to cross the road. They indicated when they would initiate crossing and repeatedly rated their trust in the AV. Our results show that the activation of both the state and intent eHMI was effective in communicating the AV’s intent to yield and both eHMIs drew attention to a failure to yield when they did not activate. However, the two eHMIs differed in their potential to mislead pedestrians, as decelerations accompanied by the activation of the state eHMI were repeatedly misinterpreted as an intention to yield. Despite this, user experience ratings did not differ between the eHMIs. Following a failure to yield, trust declined sharply. In subsequent trials, crossing behavior recovered quickly, while trust took longer to recover.

While conversational agents based on Large Language Models (LLMs) can drive progress in many domains, they are prone to generating faulty information. To ensure an efficient, safe, and satisfactory user experience maximizing benefits of these systems, users must be empowered to judge the reliability of system outputs. In this, both disclaimers and agents’ communicative style are pivotal design instances. In an online study with 594 participants, we investigated how these affect users’ trust and a mock-up agent’s persuasiveness, based on an established framework from social psychology. While prior information on potential inaccuracies or faulty information did not affect trust, an authoritative communicative style elicited more trust. Also, a trusted agent was more persuasive resulting in more positive attitudes regarding the subject of the conversation. Results imply that disclaimers on agents’ limitations fail to effectively alter users’ trust but can be supported by appropriate communicative style during interaction.

Mobile robots are increasingly being used in noisy environments for social purposes, e.g. to provide support in healthcare or public spaces. Since these robots also operate beyond human sight, the question arises as to how different robot types, ambient noise or cognitive engagement impacts the detection of the robots by their sound. To address this research gap, we conducted a user study measuring auditory detection distances for a wheeled (Turtlebot 2i) and quadruped robot (Unitree Go 1), which emit different consequential sounds when moving. Additionally, we also manipulated background noise levels and participants’ engagement in a secondary task during the study. Our results showed that the quadruped robot sound was detected significantly better (i.e., at a larger distance) than the wheeled one, which demonstrates that the movement mechanism has a meaningful impact on the auditory detectability. The detectability for both robots diminished significantly as background noise increased. But even in high background noise, participants detected the quadruped robot at a significantly larger distance. The engagement in a secondary task had hardly any impact. In essence, these findings highlight the critical role of distinguishing auditory characteristics of different robots to improve the smooth human-centered navigation of mobile robots in noisy environments.

Robots in public spaces need to communicate with lay persons who are not directly involved in the robot task to coordinate their movements and resolve critical situations. Hereby, this communication aims at salience and clarity and at the same time needs to be unobtrusive. While in automated cars, communication with uninvolved road members has been investigated with the label external human-machine interface (eHMI) in human-robot interaction (HRI) this has not been systematically discussed. This study investigates some of the mainly discussed eHMI concepts (blinker lights, beep, and speech) for solving critical situations in HRI. Six critical situations were presented together with five communication strategies (presented as videos) in an online study with N = 175 participants. Mainly, criticality and trust were measured as dependent variables. Overall, situations including visually or hearing-impaired persons were perceived as most critical. For all situations, criticality was reduced with added interaction modalities. The combination of blinker lights and voice was ranked as the most preferred strategy for five situations and led to a reduction in criticality of all situations and higher trust in the robot. The relation between perceived criticality and trust was partially mediated by predictability and transparency. Design recommendations for solving critical situations through robots’ communication strategies in the public are discussed.

With the emergence of automated vehicles (AVs), drivers’ understanding and expectations of AVs are crucial in their interaction decisions and actions. In a multi-agent driving simulator, participants encountered AVs and manually-driven vehicles (MVs) in a narrow passage. Controlled by a confederate, the vehicles communicated to yield or insist on priority, either distinctly or ambiguously. The ambiguous communication was repeated six times, involving three AVs and three MVs. The results revealed profound differences in expectations toward AVs and MVs, but similar passing times when communication was distinct. However, different learning curves emerged for AVs and MVs. Repeated exposure to ambiguous communication improved passing times for AVs, while no similar improvement was observed for MVs. The study highlights that when distinct bottom-up information is available, the influence of vehicle categories on drivers’ behavior is reduced. In turn, top-down processes become more effective when bottom-up information leaves room for interpretation and behavioral adaptation.

More and more technical systems enter the vehicle impacting drivers’ experiences. In the human-centered design, an understanding of influencing factors for acceptance and usage is crucial to align in-vehicle technology with the user needs. Addressing the underlying psychological processes, this work modelled drivers’ usage intentions with motivational regulations (SDT), the TAM, and the UTAUT. An online study with 319 German drivers was conducted examining drivers’ positive or negative experiences with assistance and infotainment systems in the vehicle. In linear regressions, the TAM and UTAUT predicted the acceptance equally for assistance and navigation systems. Amotivation, identified regulation, and intrinsic regulation enhanced the prediction of usage intentions by 3.0–15.4% in addition to the UTAUT variables revealing the additional benefit of incorporating the motivational perspective into the modeling of in-vehicle technology acceptance. Future research and practitioners can build upon this theoretical basis and recommendations on improving motivation and well-being.

Domestic service robots become increasingly prevalent and autonomous, which will make task priority conflicts more likely. The robot must be able to effectively and appropriately negotiate to gain priority if necessary. In previous human-robot interaction (HRI) studies, imitating human negotiation behavior was effective but long-term effects have not been studied. Filling this research gap, an interactive online study ($N=103$) with two sessions and six trials was conducted. In a conflict scenario, participants repeatedly interacted with a domestic service robot that applied three different conflict resolution strategies: appeal, command, diminution of request. The second manipulation was reinforcement (thanking) of compliance behavior (yes/no). This led to a 3×2×6 mixed-subject design. User acceptance, trust, user compliance to the robot, and self-reported compliance to a household member were assessed. The diminution of a request combined with positive reinforcement was the most effective strategy and perceived trustworthiness increased significantly over time. For this strategy only, self-reported compliance rates to the human and the robot were similar. Therefore, applying this strategy potentially seems to make a robot equally effective as a human requester. This paper contributes to the design of acceptable and effective robot conflict resolution strategies for long-term use.

Human-robot conflicts might occur in the future, for instance, if a robot requests a public resource (e.g., an elevator). It needs to be investigated how the robot's request can be designed acceptably and effectively regarding the robot type, modality, and politeness. In this interactive video-based online study (N = 390), a robot requested priority over an elevator either using a polite or an assertive conflict resolution strategy presented via speech or on the robot's display. The robot was either humanlike, zoomorphic, or mechanoid. The mechanoid robot achieved more compliance than the humanoid robot if it used a verbal command. When the humanoid robot displayed the command instead of using its voice, more participants granted the robot priority. This might indicate that politeness norms are triggered more by a humanoid design and that if a robot makes a command, the modality should match the robot type.

For designing the interaction with robots in healthcare scenarios, understanding how trust develops in such situations characterized by vulnerability and uncertainty is important. The goal of this study was to investigate how technology-related user dispositions, anxiety, and robot characteristics influence trust. A second goal was to substantiate the association between hospital patients' trust and their intention to use a transport robot. In an online study, patients, who were currently treated in hospitals, were introduced to the concept of a transport robot with both written and video-based material. Participants evaluated the robot several times. Technology-related user dispositions were found to be essentially associated with trust and the intention to use. Furthermore, hospital patients' anxiety was negatively associated with the intention to use. This relationship was mediated by trust. Moreover, no effects of the manipulated robot characteristics were found. In conclusion, for a successful implementation of robots in hospital settings patients' individual prior learning history - e.g., in terms of existing robot attitudes - and anxiety levels should be considered during the introduction and implementation phase.

Due to the technical advances of automated vehicles (AVs), new uncertainties for human road users arise. To overcome these uncertainties, driving strategies of AVs might be aligned to human interaction styles. In vehicle-vehicle interactions, driving behavior is informed by remaining time gaps between vehicles. This video-based experiment investigated the influence of gap sizes and the measurement method on driving decisions. N=32 participants experienced a highly automated drive in which their AV approached narrow passages. The time to arrival (TTA) of the oncoming traffic was varied. Participants had to decide to drive first or second, indicate their decision certainty, and the situation’s criticality. The videos were presented in ascending, descending, and random order. Moreover, participants adjusted the TTA at which they would drive first and second. The results indicated a higher probability of driving first and lower criticality with increasing TTA. Decision certainty was lowest around the 50% threshold, while longer and shorter TTAs resulted in higher certainty. Results differed between the methods. The findings provide guidance for the design of automated systems to mimic human driving behavior.

When service robots will be employed in private and public spaces, conflicts in human-robot interaction (HRI) might arise. To gain priority and continue its tasks, the robot would benefit from conflict resolution strategies (CRS) that are acceptable and effective. Previous studies have mainly investigated verbal or text-based CRS. As verbal interaction might not suit every application context or robot type, movement-based CRS were investigated. First, four possible implementations were pre-tested in an online study (N = 101). Then two CRS varying in modality (verbal vs. motoric) and assertiveness (submissive vs. dominant) were tested for acceptance and compliance in a lab study (N = 31) and compared for three robot types (humanoid, zoomorphic, and mechanoid). The verbal appeal was the most effective strategy to achieve user compliance. The motoric dominant strategy (moving back and forth) was perceived as most assertive and least polite if applied by the mechanoid cleaning robot but was not more effective than the verbal strategy. These studies provide insights into the influence of robot type on the acceptability and effectiveness of robot conflict resolution behavior depending on the modality.

Policymakers recommend that automated vehicles (AVs) display their automated driving status using an external human-machine interface (eHMI). However, previous studies suggest that a status eHMI is associated with overtrust, which might be overcome by an additional yielding intent message. We conducted a video-based laboratory study (N = 67) to investigate pedestrians’ trust and crossing behavior in repeated encounters with AVs. In a 2x2 between-subjects design, we investigated (1) the occurrence of a malfunction (AV failing to yield) and (2) system transparency (status eHMI vs. status+intent eHMI). Results show that during initial encounters, trust gradually increases and crossing onset time decreases. After a malfunction, trust declines but recovers quickly. In the status eHMI group, trust was reduced more, and participants showed 7.3 times higher odds of colliding with the AV as compared to the status+intent group. We conclude that a status eHMI can cause pedestrians to overtrust AVs and advocate additional intent messages.

Automated driving has the potential to reduce road fatalities. However, the public opinion to use automated driving can be described as skeptical. To increase the use of automated driving features, we investigate the persuasion principle of opt-out permission policies for enabling the automation, meaning automatically enabling the automation if users do not veto. In a driving simulator study (n = 19), participants drove on three different tracks (city, highway, rural). Three different interface concepts (opt-out, opt-in, control) were examined regarding their effects on automation use, trust, and acceptance. We found that an opt-out activation policy may increase automation usage for some participants. However, opt-out was perceived as more persuasive and more patronizing than the other conditions. Most importantly, opt-out can lead to mode confusion and therefore to dangerous situations. When such an opt-out policy is used in an automated vehicle, mode confusion must be addressed.

The design of a priori information about a conditionally automated driving (CAD) function influences the extent of effective usage of this function. The present online study investigated the effects of preliminary reliability and brand information on trust and acceptance for CAD. N = 519 participants were randomly assigned to (1) a reliability condition (high or low) and (2) an original equipment manufacturer (OEM) reputation condition (i.e., above average, average, below average, baseline). To measure the effect of CAD experience, participants were additionally exposed to four short videos of a driver interacting with a CAD function. Study results provide first evidence for an influence of OEM branding and reliability on CAD evaluation. We observed a trend towards more favorable attitudes for high compared to low reliability. This effect depends on the respective OEM reputation. The findings hold implications for the design of communication on automated vehicles to calibrate a priori assessment.

Simulator studies have been conducted in the automotive domain since the 1960s. Recently, automated driving studies have become more popular as real-world automated cars start to emerge but at this time not all levels of automation can be realized. A simulation does not entail all details of real driving, creating a realistic simulation experience - both on a psychological and physical level - proposes recurring challenges. These are among others: sample acquisition, simulator sickness, simulator training, interface design, take-over requests and secondary tasks in automated driving simulator studies. In this paper, we review existing literature and summarize important lessons from simulations in the domain of driving automation to provide considerations for studies investigating driver behavior in the age of highly automated driving.

Human errors are a major reason for traffic accidents. One of the aims of the introduction of automated driving functions in vehicles is to prevent such accidents as such systems are supposed to be more reliable, react faster with higher precision. Therefore, we assume that an increase of automation features will also increase safety. However, when drivers are not willing to relinquish control to the vehicle, safety benefits of automated vehicles do not take effect. Therefore, convincing drivers to actively make use of the automation when appropriate can increase traffic safety. In this paper we investigate the influence of system feedback in proactive, safety critical takeover situations in automated driving. In contrast to handover, which is initiated by the system, proactive takeover is initiated by the driver, who's intention for steering the car is the reason for driving manually. We compare auditory feedback with audio-visual feedback realized as a virtual co-driver in a user study. We conducted a virtual reality simulator study (n=38) to investigate how system feedback influences the willingness of drivers to relinquish control to the vehicle. There were three conditions of system feedback: in condition none no feedback was given, in condition audio spoken feedback was given, and in condition co-driver additionally to audio feedback, a virtual co-driver on the front passenger seat was displayed. Our research provides evidence that system feedback can lead to an increase of willingness to maintain automation and to follow its safety related advices.