Trust in automation is an important topic in the field of human factors and has a substantial impact on both attitudes towards and performance with automated systems. One variable that has been shown to influence trust is the degree of human-likeness that is displayed by the automated system with the main finding being that increased human-like appearance leads to increased ratings of trust. In the current study, we investigate whether humanness unanimously leads to higher trust or whether the degree to which an agent is trusted depends on context variables (i.e., task type). For that purpose, we created a task with a social (i.e., judging emotional states from the eye region) and an analytical component (i.e., mathematical task) and measured how strongly participants complied to human, avatar or computer agents when performing the social versus the analytical version with them. We hypothesized that human-like agents are trusted more on social tasks, while machine-like agents are trusted more on analytical tasks. In line with our hypothesis, the results show that, human agents are in general not trusted more than automated agents but that the degree to which an agent is trusted depends on the anticipated expertise of an agent for a given task. The findings suggest that when designing automated systems that are supposed to interact with humans, the degree of humanness of the agent needs to match the degree to which a task requires social skills.

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This study explored whether a virtual agent in a feedback task would enhance social presence and motivation, as well as facilitate learning on a Chinese symbol retention task. Learning occurred with four different computer-based feedback sources: a computer-generated “Agent”, a “Human”, an “Inanimate” image and a non-image “Verbal” condition. Measures of presence, motivation, and learning were generally related, albeit with limited support for an Agent compared to other sources of feedback. Although performance with the Agent was comparable to the Human on several measures, it did not consistently differ from other conditions. Agent and Human conditions did elicit highest social presence, but this was not uniquely related to learning outcomes. Surprisingly, the Verbal condition resulted in optimal learning. Results suggest that the utility of virtual agents is dependent on task relevance and on subjects’ inference about the agent’s capacity; agent characteristics can be distracting as well as facilitating.