谁更听信AI?个体依恋风格对人工智能建议采纳的影响*

舒聪; 贾永奇; 何凌南

doi:10.16719/j.cnki.1671-6981.20260301

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心理科学 ›› 2026, Vol. 49 ›› Issue (3) : 514-523. DOI: 10.16719/j.cnki.1671-6981.20260301

计算建模与人工智能

谁更听信AI?个体依恋风格对人工智能建议采纳的影响^*

舒聪 ¹ ,
贾永奇 ² ,
何凌南 ²^,^**

作者信息 +

Who Listens to AI? The Role of Attachment Styles in Adopting Artificial Intelligence Advice

Shu Cong ¹ ,
Jia Yongqi ² ,
He Lingnan ²^,^**

Author information +

文章历史 +

摘要

人工智能（AI）正日益成为个体获取建议的重要来源之一。依恋风格作为一种反映社会行为倾向的心理特质，塑造了个体对建议者相对稳定的预期与认知，为阐释AI建议采纳的个体差异提供了有力的理论框架。研究基于Bowlby的依恋理论，探究了个体依恋风格与建议来源（AI vs. 人类）对建议采纳的影响。两项实验研究均发现：（1）与人类建议相比，高依恋焦虑者更倾向于采纳AI的建议，即随着依恋焦虑程度的提升，个体对AI建议的采纳程度显著增加，但对人类建议的采纳程度没有显著变化；（2）高依恋回避者则未表现出对AI或人类建议的显著偏好。结果说明，依恋风格可以有效解释AI建议采纳的个体差异，依恋焦虑和依恋回避具有差异化的作用机制。

Abstract

In the era of intelligence, artificial intelligence (AI) and humans have become two important sources of advice. Attachment style, a classic social trait in psychology, reflects an individual's stable mental representation of an advisor and their positive or negative evaluations. It provides an effective theoretical framework for explaining individuals’ inherent preferences for advice from humans or AI. Based on Bowlby's attachment theory, current research aims to explore how attachment styles and the source of advice (AI or humans) jointly influence individuals' advice adoption.

Two experiments were conducted to examine the interaction between attachment styles and advice source on advice-taking behavior. Experiment 1 utilized a 2 (attachment style: attachment anxiety, attachment avoidance) × 2 (advice source: human, AI) between-subjects design. A total of 196 participants were recruited through an online survey platform. The primary dependent variable was the weight of advice (WOA), measured using the Judge-Advisor System (JAS) paradigm, which quantifies the extent to which participants adjust their initial judgments based on the advice received. Attachment styles were assessed using the Chinese version of the Experiences in Close Relationships Scale (ECR). Control variables included gender, age, education level, and self-reported AI knowledge. Participants were asked to estimate the weight of a person in a photograph (initial assessment) and then received either human or AI-generated advice on the weight. They were subsequently asked to provide a final weight estimate (post-assessment). The difference between the initial and post-assessment estimates, relative to the advice received, was used to calculate the WOA. Experiment 2 employed an attachment priming paradigm to replicate and extend the findings from Experiment 1. A total of 248 participants were randomly assigned to one of three attachment priming conditions (secure attachment, attachment anxiety, attachment avoidance) and then completed the same advice-taking task as in Experiment 1.

The research reveals that (1) Compared with human advice, individuals with high attachment anxiety are more likely to adopt advice from AI. That is, as the level of attachment anxiety increases, individuals' adoption of AI advice significantly rises, while their adoption of human advice does not change significantly; (2) Individuals with high attachment avoidance do not show a significant preference for either AI advice or human advice. Individuals with high attachment anxiety may find AI advice more appealing due to its perceived objectivity and lack of social threat, which aligns with their desire for support without the fear of social rejection. In contrast, attachment avoidance does not appear to drive a clear preference for either AI or human advice, suggesting that the underlying mechanisms influencing advice acceptance may differ between attachment anxiety and avoidance. Future research should explore additional mediators and moderators that may explain these differential effects and consider the impact of attachment styles on advice-taking in more complex and ecologically valid decision-making scenarios.

In terms of theoretical contributions, this study introduces the attachment theory into the research on advice interaction between humans and artificial intelligence. It enriches the theoretical research findings on human-Machine interaction and provides a novel theoretical framework for understanding the contradictory phenomena of algorithm appreciation and algorithm aversion in the intelligent era. In terms of practical implications, the findings of this study provide insights for the design of AI-based decision support systems, especially in contexts where user trust and acceptance are crucial for effective human-Machine collaboration. For individual advice adoption, the study offers a reference for individuals to optimize their advice adoption and decision-making patterns in the intelligent era. Specifically, those who can rationally analyze the capability boundaries between AI and humans, and comprehensively evaluate advice from different sources, are more likely to make effective decisions. Individuals with high attachment anxiety may exhibit irrational algorithm appreciation, which could lead to the adoption of erroneous suggestions. This, in turn, may distort and negatively impact their cognition, necessitating attention to the potential risk of over-reliance.

导出引用

舒聪, 贾永奇, 何凌南. 谁更听信AI?个体依恋风格对人工智能建议采纳的影响^*[J]. 心理科学. 2026, 49(3): 514-523 https://doi.org/10.16719/j.cnki.1671-6981.20260301

Shu Cong, Jia Yongqi, He Lingnan. Who Listens to AI? The Role of Attachment Styles in Adopting Artificial Intelligence Advice[J]. Journal of Psychological Science. 2026, 49(3): 514-523 https://doi.org/10.16719/j.cnki.1671-6981.20260301

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