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图学学报 ›› 2026, Vol. 47 ›› Issue (4): 915-926.DOI: 10.11996/JG.j.2095-302X.2026040915

• 工业设计 • 上一篇    下一篇

依恋回避调节的情感疗愈机器人交互机制研究

李洁1,2, 任宇航1, 郭士杰2,3()   

  1. 1 河北工业大学建筑与艺术设计学院天津 300130
    2 河北省机器人感知与人机融合重点实验室天津 300401
    3 河北工业大学机械工程学院天津 300401
  • 收稿日期:2026-01-20 接受日期:2026-05-07 出版日期:2026-08-31 发布日期:2026-08-31
  • 通讯作者:郭士杰,E-mail:guoshijie@hebut.edu.cn

Research on the interaction mechanisms of emotional healing robots moderated by attachment avoidance

LI Jie1,2, REN Yuhang1, GUO Shijie2,3()   

  1. 1 School of Architecture and Art Design, Hebei University of Technology, Tianjin 300130, China
    2 Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction, Tianjin 300401, China
    3 School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
  • Received:2026-01-20 Accepted:2026-05-07 Published:2026-08-31 Online:2026-08-31
  • Contact: GUO Shijie,E-mail:guoshijie@hebut.edu.cn

摘要:

针对情感疗愈机器人人机交互中认知机制不明晰、交互效能受个体心理特质影响显著的问题,面向高、低依恋回避用户开展情感交互设计研究。基于依恋理论、认知注意理论和人机交互理论构建情感疗愈机器人“视觉注意-情感整合”认知模型;通过混合实验2(拟人化:高/低)×2(身体比例:半身/特写)×2(交互风格:技术/社交导向),测量机器人拟人化、身体比例和交互风格对高、低依恋回避用户在情感联结、任务目标及满意度方面的影响;基于眼动实验解析高、低依恋回避用户在交互过程早期威胁评估、注意资源维持和认知加工深度方面的视觉认知机制;引入SSA-BP-SHAP方法,面向高、低依恋回避用户构建基于眼动数据的情感疗愈效果预测模型。研究发现高、低依恋回避用户在人机情感交互中存在2种不同认知模式,高依恋回避者表现为“评估性补偿”,面临情感负荷时出现注视延迟,高度依赖手部等非核心社交线索以缓解压力;低依恋回避者表现为“整合性沉浸”,能迅速将注意集中于面部获取情感共鸣;机器人设计要素对高低依恋回避用户视觉注意分配与主观疗愈体验的影响存在显著性差异,且预测模型可精准量化视觉特征与疗愈效能关系。研究结果为面向不同依恋回避用户的情感疗愈机器人个性化设计与交互优化提供理论依据。

关键词: 情感疗愈, 人机交互, 依恋回避, 眼动追踪, SSA-BP-SHAP

Abstract:

To address the issues of unclear cognitive mechanism and significant impact of individual psychological traits on interaction efficiency in the human-computer interaction with emotional healing robot, an emotional interaction design research was carried out for users with high and low attachment avoidance. Based on attachment theory, cognitive attention theory and human-computer interaction theory, the cognitive model of “visual attention-emotional integration” of emotional healing robots was constructed. Through a mixed experiment of 2 (anthropomorphic: high / low) ×2 (body proportion: semi-body / close-up) ×2 (interaction style: technical / social orientation), the effects of robot anthropomorphic levels, body proportion, and interaction style on emotional connection, task goal, and satisfaction of high and low attachment avoidance users were measured. Based on the eye movement experiment, the visual cognitive mechanism of high and low attachment avoidance users in early threat assessment, attention resource maintenance, and cognitive processing depth was analyzed. The SSA-BP-SHAP method was introduced to construct an emotional healing effect prediction model based on eye movement data for high and low attachment avoidance users. The study found that high and low attachment avoidance users had two different cognitive models in human-computer emotional interaction. High attachment avoidance users showed “evaluative compensation”, gaze delay when facing emotional load, and high dependence on non-core social cues such as hands to relieve stress; low attachment avoiders were characterized by “integrated immersion”, which enabled them to focus on the face to obtain emotional resonance ; there were significant differences in the influence of robot design elements on the visual attention distribution and subjective healing experience of high and low attachment avoidance users, and the prediction model could accurately quantify the relationship between visual features and healing efficacy. The research results provided a theoretical basis for the personalized design and interaction optimization of emotional healing robots for different attachment avoidance users.

Key words: emotional healing, human-robot interaction, attachment avoidance, eye-tracking, SSA-BP-SHAP

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