Anthropomorphic framing and failure comprehensibility influence different facets of trust towards industrial robots.

Utilizing anthropomorphic features in industrial robots is a prevalent strategy aimed at enhancing their perception as collaborative team partners and promoting increased tolerance for failures. Nevertheless, recent research highlights the presence of potential drawbacks associated with this approach. It is still widely unknown, how anthropomorphic framing influences the dynamics of trust especially, in context of different failure experiences.

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.

(Hu)man-Like Robots: The Impact of Anthropomorphism and Language on Perceived Robot Gender.

Implementing anthropomorphic features to robots is a frequently used approach to create positive perceptions in human-robot interaction. However, anthropomorphism does not always lead to positive consequences and might trigger a more gendered perception of robots. More precisely, anthropomorphic features of robots seem to evoke a male-robot bias.

Feeling with a robot-the role of anthropomorphism by design and the tendency to anthropomorphize in human-robot interaction.

The implementation of anthropomorphic features in regard to appearance and framing is widely supposed to increase empathy towards robots. However, recent research used mainly tasks that are rather atypical for daily human-robot interactions like sacrificing or destroying robots. The scope of the current study was to investigate the influence of anthropomorphism by design on empathy and empathic behavior in a more realistic, collaborative scenario.

Determinants of Laypersons' Trust in Medical Decision Aids: Randomized Controlled Trial.

Background: Symptom checker apps are patient-facing decision support systems aimed at providing advice to laypersons on whether, where, and how to seek health care (disposition advice). Such advice can improve laypersons' self-assessment and ultimately improve medical outcomes. Past research has mainly focused on the accuracy of symptom checker apps' suggestions.

Challenging presumed technological superiority when working with (artificial) colleagues.

Technological advancements are ubiquitously supporting or even replacing humans in all areas of life, bringing the potential for human-technology symbiosis but also novel challenges. To address these challenges, we conducted three experiments in different task contexts ranging from loan assignment over X-Ray evaluation to process industry. Specifically, we investigated the impact of support agent (artificial intelligence, decision support system, or human) and failure experience (one vs.

The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception.

Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of "Follow Me" robots influences how they are perceived by humans.