Impact of Perceived Agent Expertise on Trust in Computer Agent Recommendations

Computer agents are frequently anthropomorphized, giving them appearances and responses similar to humans. Research has demonstrated that users tend to apply social norms and expectations to such computer agents, and that people interact with computer agents in a similar fashion as they would another human. Perceived expertise has been shown to affect trust in human-human relationships, but the literature investigating how this influences trust in computer agents is limited. The current study investigated the effect of computer agent perceived level of expertise and recommendation reliability on subjective (rated) and objective (compliance) trust during a pattern recognition task. Reliability of agent recommendations had a strong effect on both subjective and objective trust. Expert agents started with higher subjective trust, but showed less trust repair. Agent expertise had little impact on objective trust resiliency or repair.

Bilateral (but not unilateral) interaction creates and cements norms at the covert psychophysical level: A behavioral and an fMRI study

Social norms, including values, beliefs and even perceptions about the world, are preserved and created through repeated interactions between individuals. However, whereas neuro-cognitive research on social norms has used the “unilateral influence” paradigm focusing on people’s reactions to extant standards, little is known about how our basic perceptions and judgments are shaped as new norms through bilateral interaction. Here, using a simple estimation task, we investigated the formation of perceptual norms using two experiments coupled with computational modeling. In the behavioral experiment, participants in dyads repeatedly estimated the number of dots on a screen and viewed each other’s answers. In the fMRI experiment, we manipulated the interaction process by pairing each participant with a computer agent which adjusted its estimations reciprocally to participants’ estimations (bilateral agent) or did not (unilateral). The results indicated that only the bilateral interaction yielded convergence of participants’ covert psychophysical functions (relations between subjective estimations and the actual number of dots) as well as overt behavioral responses within a pair. Bilateral interaction also increased the stability (reliability) of the covert function within each individual after interaction. Neural activity in the mentalizing network (right temporoparietal junction and dorsomedial prefrontal cortex) during interaction modulated the stabilization of the psychophysical function. These results imply that bilateral interaction helps people to cognitively anchor their views with each other. Such spontaneous perspective sharing can yield a shared covert “generative model” that enables endogenous agreement on totally new targets ― one of the key features of social norms.

Perceived Mistreatment and Emotional Capability Following Aggressive Treatment of Robots and Computers

Robots (and computers) are increasingly being used in scenarios where they interact socially with people. How people react to these agents is telling about the perceived empathy of such agents. Mistreatment of robots (or computers) by co-workers might provoke such telling reactions. This study examines perceived mistreatment directed towards a robot in comparison to a computer. This will provide some understanding of how people feel about robots in collaborative social settings. We conducted a two by two between-subjects study with 80 participants. Participants worked cooperatively with either a robot or a computer agent. An experiment confederate would either act aggressively or neutrally towards the agent. We hypothesized that people would not perceive aggressive speech as mistreatment when an agent was capable of emotional feelings and similar to themselves; that participants would perceive the robot as more similar in appearance and emotionally capable to themselves than a computer; and so would observe more mistreatment with a robot. The final results supported our hypotheses; the participants observed greater mistreatment for the robot, but not the computer. Also participants felt significantly more sympathetic towards the robot and believed that it was much more emotionally capable.

Technology-Enhanced Learning: An Optimal CPS Learning Application

Collaborative problem-solving (CPS) is highly valued in the sustainability of learning to foster the key soft power of talent for the future. In this study, a CPS learning application was built to train and assess individuals with the aim of increasing CPS skills. For effective learning to take place, several issues need to be carefully considered, and these were investigated while testing the proposed application. This study examined the impact of collaborative interactions (CIs) (human–computer agent (HCA) and human–human (HH) interactions) on the CPS performance of students. Gender and learning styles, which may have interaction effects with CIs on CPS performance, were also explored. The results show that the students’ CPS performance in HCA was significantly greater than that in HH. The interaction effect between gender and CI was not significant. The impact of learning style on CPS performance in HH was not significant. In contrast, in HCA, students with verbal, global, and reflective learning styles performed significantly better on CPS tasks than did students with visual, sequential, and active learning styles. Finally, we discussed the optimal ways to teach CPS and the practical effects of a CPS learning application.

Computer Agent Technologies in Collaborative Learning and Assessment

In recognition of the importance of collaborative and problem-solving skills, educators are realizing the need for effective and scalable learning and assessment solutions to promote the skillset in educational systems. In the settings of a comprehensive collaborative problem-solving assessment, each student should be matched with various types of group members and must apply the skills in varied contexts and tasks. One solution to these assessment demands is to use computer-based (virtual) agents to serve as the collaborators in the interactions with students. The chapter presents the premises and challenges in the use of computer agents in the assessment of collaborative problem solving and describes how human and computer agent collaborative assessments are used in an international learning and assessment project, Animalia.

Computer Agent Technologies in Collaborative Learning and Assessment

In recognition of the importance of collaborative and problem solving skills, educators are realizing the need for effective and scalable learning and assessment solutions to promote the skillset in educational systems. In the settings of a comprehensive collaborative problem solving assessment, each student should be matched with various types of group members and must apply the skills in varied contexts and tasks. One solution to these assessment demands is to use computer-based (virtual) agents to serve as the collaborators in the interactions with students. The chapter presents the premises and challenges in the use of computer agents in the assessment of collaborative problem solving and describes how human and computer agent collaborative assessments are used in international learning and assessment project Animalia.

Human-Agent Interruptions: A Systems Perspective

Detrimental effects of interruptions have been widely reported in the literature, particularly with laboratory-based studies. However, recent field-based studies suggest interruptions can be beneficial, even vital to maintaining or enhancing system performance. The literature seems to be at critical juncture; how do practitioners reconcile these perspectives? Do we ban interruptions or let them flow freely? To address this, we study how interruptions affect work performance over differing units of analysis (a dyad versus an individual) in a microworld scheduling task with 72 participants and a computer agent. We found that a team performance perspective shows more benefits from interruptions than an individual performance perspective. In other words, teams suffered less from the adverse effects of interruptions than individuals. Results show that systems-level aspects of interruptions, for both the individual and the team, plays a role in determining whether interruptions have a positive or negative effect.