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Journal of Graphics ›› 2026, Vol. 47 ›› Issue (4): 915-926.DOI: 10.11996/JG.j.2095-302X.2026040915

• Industrial Design • Previous Articles     Next Articles

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 Online:2026-08-31 Published:2026-08-31
  • Contact: GUO Shijie

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

CLC Number: