Cognitive trust, the belief that a robot can accurately perform tasks, is crucial for effective human-robot interaction. While robot competence and reliability are known to build this trust, recent research shows that affective factors like attentiveness also matter. This study examines how competence and attentiveness interact to shape cognitive trust, specifically testing whether one factor can compensate for the other. Participants completed a search task with a robotic dog in a 2 × 2 design varying competence (high/low) and attentiveness (high/low). Results showed that high attentiveness compensates for low competence: participants working with an attentive but poorly performing robot reported trust levels similar to those working with highly competent robot. These findings suggest that building cognitive trust involves emotional processes often overlooked in traditional competence-based models.

Figure 1: We conducted a Virtual Reality driving simulation study to explore interactions between a human driver and an autonomous vehicle at traffic intersections in a pseudo-naturalistic setting to understand the influence of contingent driving behaviors in different contexts (two of four scenarios shown above).

Emergent leadership in teams is known as a special type of informal leadership that has unique benefits. Given that this form of leadership can be developed only organically from within the team, an informal leader may not emerge and the associated advantages can be missed. We explored the possibility of leveraging robotic social behavior to facilitate the emergence of leadership in a team. We evaluated whether a robot displaying explicit peer liking towards a specific team member would encourage that team member to take the lead. Two (stranger) participants were asked to engage in a search task together with a robotic dog who either presented peer-liking behavior toward one or both of them during the opening encounter. Our findings suggest that the robot's peer liking led the “Liked” participant to report higher sense of leadership and higher sense of responsibility over the task. Behavioral measures also indicated that the “Liked” participant managed the team's performance. We suggest that integrating a robot into human teams presents an opportunity to facilitate unique dynamics that would not develop when imposing a structure on the team from the outside.

Cognitive and affective trust are fundamental elements in human-robot interactions. Previous research suggests that robots' affective behaviors related to affective trust can also impact cognitive trust. In this work, we explored whether the opposite influence also exists and whether robots' cognitive behaviors related to cognitive trust can also impact affective trust. Our results revealed that the robot's cognitive capabilities significantly impacted cognitive trust but did not influence affective trust. Participants in the High Competence Robot condition reported higher cognitive trust scores and were more likely to trust the robot's suggestions compared to the Low Competence Robot and Baseline conditions. We did not observe any differences in affective trust, and almost all participants in all conditions reported low affective trust. We suggest that while robot cognitive performance can effectively build cognitive trust, comprehensive trust development may require affective-related behaviors.

The design of self-driving vehicles requires an understanding of the social interactions between drivers in resolving vague encounters, such as at un-signalized intersections. In this paper, we make the case for social situation awareness as a model for understanding everyday driving interaction. Using a dual-participant VR driving simulator, we collected data from driving encounter scenarios to understand how (N=170) participant drivers behave with respect to one another. Using a social situation awareness questionnaire we developed, we assessed the participants’ social awareness of other driver’s direction of approach to the intersection, and also logged signaling, speed and speed change, and heading of the vehicle. Drawing upon the statistically significant relationships in the variables in the study data, we propose a Social Situation Awareness model based on the approach, speed, change of speed, heading and explicit signaling from drivers.

Affective trust and cognitive trust are fundamental elements in human-robot interactions. They impact robots’ acceptance, the level of engagement, and the tendency to rely on robots in various contexts. Opposite effects are observed when the interaction with a robot is unreliable and untrustworthy. In this study, we examined the possibility of enhancing both aspects of trust by manipulating the robot’s level of attentiveness to the participant. We focused on robotic attentiveness since it can be easily applied even to highly simple non-humanoid robots, positioning it as a method for enhancing trust for robots with different morphologies. Specifically, we evaluated whether minimal attentive robotic gestures can enhance affective and cognitive aspects of trust and whether inattentive robotic behavior can decrease them. Quantitative and qualitative results indicated that the robot’s attentiveness impacted both aspects of trust. Participants in the Attentive Robot condition reported higher affective and cognitive trust scores, smaller interpersonal distance, and a higher number of participants reported that the robot would "be there for them", in comparison to the Inattentive Robot and Baseline conditions. Our findings suggest that an attentive robotic behavior, can support human affective and cognitive trust and enhance human-robot interaction.

Spatial skills are critical for robot teleoperation. For example, in order to make a judgment of relative direction when operating a robot remotely, one must take different perspectives and make decisions based on available spatial information. Training spatial skills is thus critical for robot teleoperation, yet, current training programs focus primarily on psycho-motoric skills of the task, and less on the essential cognitive aspects of spatial skills. This work addresses this need by considering previous findings on relative direction judgments in training robot teleoperation. We developed and tested a basic training paradigm of perspective taking skill targeting the cognitive skill rather than psycho-motoric skill. An experiment tested a basic training paradigm using a stationary robot, with a training group receiving perspective taking training and a control group without training, and both tested on a transfer test with the robot. The results show that participants who went through a targeted cognitive skill training reached mastery level during the training, and performed better than the control group in an analogue transfer of learning test. Moreover, results reveal that the training facilitated participants with initial poor perspective taking skills reach the level of the high-skilled participants in transfer test performance. The study validates the possibility to target only cognitive aspects of spatial skills and result in better robot teleoperation.

Abstract The crucial concept of modeling and synthesis/control of human motion (including face and body) for animation has been widely studied and explored in the literature. In this regard, the audience's perception of generated or recorded animation scenes is of critical importance. In this paper, we explore and conceptualize the general notions that need to be taken into account for human motion to maintain perceptual accuracy. We propose a paradigm called Perceptual Validity composed of four major components, which are discussed in detail. The model is concerned with different aspects of the scene such as correct illustration of the stimuli, context, and local/global relations of various visual cues present in human motion. Satisfying all the proposed principles, based on the literature, seems compulsory and vital for synthesis of perceptually valid animation scenes of human motion. We investigate the relative significance of the different components of the paradigm using feedback from expert animators and conduct a case study on one of the components of the paradigm. For further evaluation and exploration, Disney's principles of animation are discussed and compared against our proposed paradigm. We argue that while there are significant parallels and overlaps, our model is only focused on and more inclusive towards human motion and can therefore provide a valuable set of guidelines for animators in the field of character animation. Copyright © 2015 John Wiley & Sons, Ltd.

There is often a need to display logged information textually for real-time event-based supervisory tasks. Textual display design can follow several directions that reflect a tradeoff between a visual load and an operational load. The study reported here was designed in order to examine this tradeoff and its implications for such display design. An event-based monitoring and handling task was used with different event types having either a high or a low handling priority. The events were presented in four different display configurations varying in their degree of visual and operational load. The specific performance indices were event dwelling times, event handling proportion, and handling errors. In general it was found that the high priority events were handled faster and more accurately than the low priority events. In addition, performance with the various display configurations was dependent upon event type. These findings are discussed in terms of visual vs. operational load tradeoff and its context-sensitivity. Some implications for display design and further research on event presentation approaches are discussed.