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Changing human's impression of empathy from agent by verbalizing agent's position

Takahiro Tsumura, Seiji Yamada

TL;DR

The study tackles how to foster trust and empathy toward anthropomorphic agents by manipulating pre-task cues: six self-disclosure attributes and two relationship framings (competitive vs cooperative). Using a 2-factor between-subjects design with online video stimuli and MDMT/IRI-based questionnaires, it finds that trust is not significantly altered pre-task, whereas empathy toward the agent is modulated by self-disclosure content and the agent's perceived empathy capacity is influenced by the stated relationship. Post-hoc analyses reveal specific self-disclosure attributes (e.g., work vs money) affecting empathy toward the agent, and cooperative framing increases perceived agent empathy capacity. These results suggest pre-interaction design features can shape early human-agent relations, informing how agents should present themselves before collaborative tasks in real-world deployments.

Abstract

As anthropomorphic agents (AI and robots) are increasingly used in society, empathy and trust between people and agents are becoming increasingly important. A better understanding of agents by people will help to improve the problems caused by the future use of agents in society. In the past, there has been a focus on the importance of self-disclosure and the relationship between agents and humans in their interactions. In this study, we focused on the attributes of self-disclosure and the relationship between agents and people. An experiment was conducted to investigate hypotheses on trust and empathy with agents through six attributes of self-disclosure (opinions and attitudes, hobbies, work, money, personality, and body) and through competitive and cooperative relationships before a robotic agent performs a joint task. The experiment consisted of two between-participant factors: six levels of self-disclosure attributes and two levels of relationship with the agent. The results showed that the two factors had no effect on trust in the agent, but there was statistical significance for the attribute of self-disclosure regarding a person's empathy toward the agent. In addition, statistical significance was found regarding the agent's ability to empathize with a person as perceived by the person only in the case where the type of relationship, competitive or cooperative, was presented. The results of this study could lead to an effective method for building relationships with agents, which are increasingly used in society.

Changing human's impression of empathy from agent by verbalizing agent's position

TL;DR

The study tackles how to foster trust and empathy toward anthropomorphic agents by manipulating pre-task cues: six self-disclosure attributes and two relationship framings (competitive vs cooperative). Using a 2-factor between-subjects design with online video stimuli and MDMT/IRI-based questionnaires, it finds that trust is not significantly altered pre-task, whereas empathy toward the agent is modulated by self-disclosure content and the agent's perceived empathy capacity is influenced by the stated relationship. Post-hoc analyses reveal specific self-disclosure attributes (e.g., work vs money) affecting empathy toward the agent, and cooperative framing increases perceived agent empathy capacity. These results suggest pre-interaction design features can shape early human-agent relations, informing how agents should present themselves before collaborative tasks in real-world deployments.

Abstract

As anthropomorphic agents (AI and robots) are increasingly used in society, empathy and trust between people and agents are becoming increasingly important. A better understanding of agents by people will help to improve the problems caused by the future use of agents in society. In the past, there has been a focus on the importance of self-disclosure and the relationship between agents and humans in their interactions. In this study, we focused on the attributes of self-disclosure and the relationship between agents and people. An experiment was conducted to investigate hypotheses on trust and empathy with agents through six attributes of self-disclosure (opinions and attitudes, hobbies, work, money, personality, and body) and through competitive and cooperative relationships before a robotic agent performs a joint task. The experiment consisted of two between-participant factors: six levels of self-disclosure attributes and two levels of relationship with the agent. The results showed that the two factors had no effect on trust in the agent, but there was statistical significance for the attribute of self-disclosure regarding a person's empathy toward the agent. In addition, statistical significance was found regarding the agent's ability to empathize with a person as perceived by the person only in the case where the type of relationship, competitive or cooperative, was presented. The results of this study could lead to an effective method for building relationships with agents, which are increasingly used in society.
Paper Structure (13 sections, 5 figures, 4 tables)

This paper contains 13 sections, 5 figures, 4 tables.

Figures (5)

  • Figure 1: Flowchart of experiment.
  • Figure 2: Competitive agent
  • Figure 3: Cooperative agent
  • Figure 4: Results of multiple comparisons on self-disclosure attributes. Red lines are medians, and circles are outliers.
  • Figure 5: Results of main effects on relationships with agent. Red lines are medians, and circles are outliers.