Beyond Likeability: Investigating Social Interactions with Artificial Agents and Objective Metrics

Frontiers in Psychology

Sebastian Loth¹ 

关键词: virtual agents;    social signals;    interaction design;    gaze aversion;    social eye gaze;    dialogue capability;    facial animation;   

DOI  :  10.3389/fpsyg.2017.01662

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

Numerous studies have investigated whether additional abilities of artificial agents improve the interaction with their users. Typically, a specific capability was added to a baseline system and participants interacted with both versions. Questionnaires (e.g., GOODSPEED, Bartneck et al., 2009) or time-related metrics combined with a questionnaire (e.g., PARADISE, Walker et al., 2000) were then evaluated. In most published work, the new capability demonstrably improved the user ratings. For example, agents with the capability to praise (Fasola and Mataric, 2012), err (Salem et al., 2013), and blink pleasantly (Takashima et al., 2008) were more likeable than their counterparts without this capability. However, subjective ratings can be unreliable (Cahill, 2009; Belz and Kow, 2010) and hardly address questions beyond likeability, e.g., whether an action is perceived as social or task-oriented, and when and why an agent is more or less comprehensible. But investigating human social behavior with controlled and ecologically valid experiments is notoriously difficult given the variance in natural interactions (e.g., Bergmann et al., 2010; Sciutti et al., 2015). Artificial agents can precisely reproduce interactive behavior in real-time but are still rarely used for investigations beyond likeability.

【 授权许可】

CC BY