Friederike Anne Eyssel

Social categorization of social robots: anthropomorphism as a function of robot group membership.

Previous work on social categorization has shown that people often use cues such as a persons gender, age, or ethnicity to categorize and form impressions of others. The present research investigated effects of social category membership on the evaluation of humanoid robots. More specifically, participants rated a humanoid robot that either belonged to their in-group or to a national out-group with regard to anthropomorphism (e.g., mind attribution, warmth), psychological closeness, contact intentions, and design. We predicted that participants would show an in-group bias towards the robot that ostensibly belonged to their in-group--as indicated by its name and location of production. In line with our hypotheses, participants not only rated the in-group robot more favourably--importantly, they also anthropomorphized it more strongly than the out-group robot. Our findings thus document that people even apply social categorization processes and subsequent differential social evaluations to robots.

'If you sound like me, you must be more human': on the interplay of robot and user features on human-robot acceptance and anthropomorphism

In an experiment we manipulated a robots voice in two ways: First, we varied robot gender; second, we equipped the robot with a human-like or a robot-like synthesized voice. Moreover, we took into account user gender and tested effects of these factors on human-robot acceptance, psychological closeness and psychological anthropomorphism. When participants formed an impression of a same-gender robot, the robot was perceived more positively. Participants also felt more psychological closeness to the same-gender robot. Similarly, the same-gender robot was anthropomorphized more strongly, but only when it utilized a human-like voice. Results indicate that a projection mechanism could underlie these effects.

Schema Effects of Rape Myth Acceptance on Judgments of Guilt and Blame in Rape Cases: The Role of Perceived Entitlement to Judge

Two experiments (N = 330) examined conditions that facilitate biasing effects of rape myth acceptance (RMA) on judgments of blame in rape cases. In both experiments, participants read a short vignette depicting a rape case. In Experiment 1, the amount of case-irrelevant information about defendant and plaintiff was varied. As predicted, high-RMA (vs. low-RMA) participants were less likely to blame the defendant the more irrelevant information they had read. In Experiment 2, participants in a social judgeability condition were made to believe that they had been subliminally exposed to additional case information although in fact no additional information had been presented. As predicted, compared to a control condition, participants’ blame judgments were more biased by their RMA under social judgeability. Our findings reveal that the mechanism underlying the biasing effects of RMA may be the subjective feeling of entitlement to judge.

Individualized gesturing outperforms average gesturing: evaluating gesture production in virtual humans

How does a virtual agents gesturing behavior influence the users perception of communication quality and the agents personality? This question was investigated in an evaluation study of co-verbal iconic gestures produced with the Bayesian network-based production model GNetIc. A network learned from a corpus of several speakers was compared with networks learned from individual speaker data, as well as two control conditions. Results showed that automatically GNetIc-generated gestures increased the perceived quality of an object description given by a virtual human. Moreover, gesturing behavior generated with individual speaker networks was rated more positively in terms of likeability, competence and human-likeness.

The social robot ‘Flobi’: Key concepts of industrial design

This paper introduces the industrial design of the social robot ‘Flobi’. In total, three key concepts influenced the industrial design: First, the robot head of Flobi appears as a cartoon-like character and has a ‘hole-free’ design without any visible conjunctions. Second, Flobi has dynamic features to display not only primary emotions, but also shame, a typical secondary emotion. Third, the structural design implements exchangeable modular parts. Through modular design, the underlying hardware is quickly accessible and the visual features of the robot (e.g., hairstyle, facial features) can be altered easily. A first study demonstrated the successful implementation of Flobis dynamic features, whereas a second study demonstrates that the exchangeable hair modules influence gender-schematic perceptions of the robot.

A second chance to make a first impression? how appearance and nonverbal behavior affect perceived warmth and competence of virtual agents over time

First impressions of others are fundamental for the further development of a relationship and are thus of major importance for the design of virtual agents, too. We addressed the question whether there is a second chance for first impressions with regard to the major dimensions of social cognition–warmth and competence. We employed a novel experimental set-up that combined agent appearance (robot-like vs. human-like) and agent behavior (gestures present vs. absent) of virtual agents as between-subject factors with a repeated measures design. Results indicate that ratings of warmth depend on interaction effects of time and agent appearance, while evaluations of competence seem to depend on the interaction of time and nonverbal behavior. Implications of these results for basic and applied research on intelligent virtual agents will be discussed .

Effects of gesture on the perception of psychological anthropomorphism: a case study with a humanoid robot

Previous work has shown that gestural behaviors affect anthropomorphic inferences about artificial communicators such as virtual agents. In an experiment with a humanoid robot, we investigated to what extent gesture would affect anthropomorphic inferences about the robot. Particularly, we examined the effects of the robots hand and arm gestures on the attribution of typically human traits, likability of the robot, shared reality, and future contact intentions after interacting with the robot. For this, we manipulated the non-verbal behaviors of the humanoid robot in three experimental conditions: (1) no gesture, (2) congruent gesture, and (3) incongruent gesture. We hypothesized higher ratings on all dependent measures in the two gesture (vs. no gesture) conditions. The results confirm our predictions: when the robot used gestures during interaction, it was anthropomorphized more, participants perceived it as more likable, reported greater shared reality with it, and showed increased future contact intentions than when the robot gave instructions without using gestures. Surprisingly, this effect was particularly pronounced when the robots gestures were partly incongruent with speech. These findings show that communicative non-verbal behaviors in robotic systems affect both anthropomorphic perceptions and the mental models humans form of a humanoid robot during interaction.

When a Robot's Group Membership Matters Anthropomorphization of Robots as a Function of Social Categorization

Previous work has documented that humans categorize robots as members of different social groups, thereby using socially relevant cues such as a robot’s alleged gender or nationality. Importantly, these social categorization processes affect impressions people form about robots. In an experiment with N=45 participants, we utilized a minimal-group paradigm and tested whether categorizing the humanoid robot NAO as an in-group member vs. an out-group member based on socially non-relevant features would result in higher levels of anthropomorphism and more positive evaluations of the robot. Innovatively, to assess anthropomorphism, we utilized an implicit measurement procedure. Results support our hypotheses: Perceived in-group membership with the robot resulted in a greater extent of anthropomorphic inferences about the robot and more positive evaluations. Moreover, compared to the out-group condition, participants who perceived NAO as an in-group member showed greater willingness to interact with robots in general.

Cooperation makes it happen: Imagined intergroup cooperation enhances the positive effects of imagined contact

Imagined intergroup contact represents a new indirect contact strategy to reduce intergroup bias. Extending the literature on imagined contact, we tested whether the inclusion of cooperation into the imagination task would outperform the standard imagined contact scenarios used in previous research. 87 participants were instructed to imagine a neutral versus a positive versus a cooperative interaction with an out-group member. As predicted, after imagining a cooperative intergroup interaction, participants showed more empathy and trust toward the out-group than participants in the remaining experimental conditions. Furthermore, they also reported reduced prejudice and intergroup anxiety. Taken together, implementing cooperation in the imagined contact paradigm reduced intergroup bias, above and beyond basic imagined contact effects. Finally, the perceived quality of the imagined interaction with an out-group member mediated the experimental effects. Theoretical and practical implications of these novel findings will be discussed.

Activating elicited agent knowledge: How robot and user features shape the perception of social robots

A recent theoretical framework on anthropomorphism emphasizes the role of elicited agent knowledge in anthropomorphic inferences about nonhuman entities. According to the Three-Factor Model of psychological Anthropomorphism, people use anthropocentric knowledge structures when judging unfamiliar objects (e.g., robots). In the present research, our goal was to manipulate the accessibility of such elicited agent knowledge by varying features of a robots voice: Specifically, we examined effects of vocal cues that reflected both gender of robot (i.e., a male vs. female voice) and voice type (i.e., a human-like vs. robot-like voice). This was done to test the impact of these vocal features on anthropomorphic inferences about the robot and on human-robot interaction (HRI) acceptance. Our results demonstrate that a robots vocal cues clearly influence subsequent judgments of the robot and particularly so, when participant gender taken into account. Implications of our research for robotics will be discussed.