SearcharxivSearch

arXiv · 2403.14557

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

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Takahiro Tsumura, Seiji Yamada. 2024-03-21. Changing human's impression of empathy from agent by verbalizing agent's position. https://doi.org/10.1109/ro-man60168.2024.10731291

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

ShellVis: Sandboxed Live Programming for Shell Scripts

Live programming provides visibility to programmers by running and tracing programs as they are edited. However, for programs with potentially harmful side effects, liveness can turn mistakes into disasters. We propose enabling live programming in environments with side effects via sandboxing: confining effects to a simulation of the true environment. We apply sandboxed live programming in the challenging context of shell scripting: a ubiquitous and powerful---yet notoriously opaque and error-prone---tool. ShellVis provides line-by-line feedback on a shell script's run-time behavior, with file operations sandboxed via a safe overlay of the file system. A qualitative user evaluation finds ShellVis to be helpful to participants, replacing tedious existing practices and instilling confidence. Participant responses also reveal areas for future research, particularly bridging the gulf of execution alongside the gulf of evaluation. ShellVis serves as a case study of how sandboxing can bring live-programming techniques into the many real-world programming contexts where side effects are important.

cs.HC

Visual-Motion-Induced Modulation of Pedestrian Trajectories Using Spatially Distributed Multi-Display Signage in Public Spaces

Multi-display signage (MDS), now ubiquitous in urban environments, has the potential to influence human behavior and experience in public spaces. However, despite its unique capability to present spatially distributed dynamic visual stimuli, its current use is mainly limited to advertising. In this study, we propose a perception-based approach for laterally modulating pedestrian trajectories as a nonverbal means of guiding pedestrians in public spaces. The approach is motivated by vection, the illusion of self-motion, and uses laterally moving monochrome stripes, a standard stimulus in vection research, presented across spatially distributed displays to elicit postural responses that may bias pedestrian trajectories. We evaluated the approach through a controlled laboratory experiment and a real-world field deployment involving actual pedestrian flows in a national museum. The laboratory experiment examined whether the MDS setup induced trajectory shifts in the direction predicted by prior research on the behavioral effects of vection. The field deployment investigated whether comparable effects would emerge in aggregate pedestrian behavior during unconstrained movement under conditions closer to those of urban public spaces. In the laboratory, full-screen motion significantly biased walking trajectories in the direction of visual motion, whereas partial-stripe motion produced no significant directional effect. In the field deployment, opposing full-screen motion conditions produced direction-consistent differences in aggregate pedestrian positions. The field results, observed despite the substantial variability in real-world pedestrian flows, extend the controlled laboratory findings and provide ecologically valid evidence supporting practical MDS-based pedestrian modulation in public settings. The results further suggest that sufficient visual-motion coverage may be important.

cs.HC

How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding

The utility of AI in multi-coder qualitative coding has been widely discussed, yet little empirical evidence exists to delineate the contexts in which it performs reliably. We address this gap by quantifying the effectiveness of multi-agent LLM coding across varied qualitative datasets, revealing key contextual and structural factors that mediate coding outcomes. We developed a literature-informed baseline pipeline that enables AI agents to independently code, debate, and reconcile disagreements. Results revealed that coding accuracy depends on factors such as codebook length, qualitative data similarity, and agent disagreement. Notably, intense and unresolved debates between agents led to higher accuracy. Our analysis showed that while LLMs emulate many human discussion behaviors, they lack adaptive responsiveness to context. From these findings, we offer design recommendations for building automated coding systems. Our open-source AI discussion dataset and methodological framework lay the groundwork for advancing the design of AI-mediated automated thematic analysis.

cs.HC