人类对大语言模型的热情和能力感知

doi:10.3724/SP.J.1041.2025.2043

摘要/Abstract

摘要：

随着大语言模型(Large Language Models, LLMs)能力的提升及其广泛应用, 社会正逐步从传统的人际交互转向融合人际交互、人机交互和机机交互的多层次互动结构。在人类与LLMs交互日益深入的背景下, 研究人类如何感知LLMs成为了重要议题。本研究通过三项研究系统考察人类对LLMs的感知模式。研究1发现, 与对人类的感知一致, 人类主要通过热情和能力两个维度感知LLMs。然而, 在一般情境下, 不同于对人类感知中的热情优先, 人类在对LLMs的感知中能力优先。研究2探讨了热情和能力在不同态度预测中的优先效应, 结果表明, 热情与能力均能正向预测人类对LLMs的持续使用意愿和喜爱度, 其中能力对持续使用意愿的预测效力更高, 而热情对喜爱度的预测效力更高。研究3进一步探索了人类对LLMs与对他人的感知差异, 结果显示, 人类对LLMs的热情评价与人类无显著差异, 但对LLMs的能力评价显著高于人类。本研究为理解人类对LLMs的感知提供了理论基础, 并为人工智能的设计优化及人机协作机制的研究提供了新的视角。

关键词: 大语言模型, 社会认知, 热情, 能力

Abstract:

The rapid development and application of Large Language Models (LLMs) have significantly enhanced their capabilities, influencing human-machine interactions in profound ways. As LLMs evolve, society is shifting from traditional interpersonal interactions to a multilayered structure integrating human-to-human, human-to-machine, and machine-to-machine interactions. In this context, understanding how humans perceive and evaluate LLMs—and whether this follows the Big Two model of warmth and competence in interpersonal perception—has become critical. This study examines human perceptions of LLMs through three progressive empirical studies.

Participants with prior LLM experience were recruited for the studies. Study 1 comprised two sub-studies: Study 1a (N = 207) used a free-response task, asking participants to describe their impressions of LLMs using at least three words, which were analyzed using the Semi-Automated Dictionary Creation for Analyzing Text to identify key dimensions of perception. Study 1b (N = 219) involved a lexical rating task, in which participants rated the applicability of selected evaluation words to LLMs. Study 2 (N = 178) used a questionnaire, in which participants rated a familiar LLM and provided feedback on their willingness to continue using it and their liking of it. Study 3 (N = 207) employed a questionnaire survey to assess participants’ ratings of warmth and competence for both humans and LLMs.

Study 1 found that humans primarily perceive LLMs through warmth and competence, similar to how they perceive other humans. In general contexts, participants prioritized competence over warmth when evaluating LLMs, showing a significant priority effect (odds ratio = 2.88, z = 9.512, 95% CI [2.32, 3.59], p< 0.001). This contrasts with the typical warmth-priority effect in human-to-human perception. Study 2 investigated the relationship between perceptions of warmth and competence and human attitudes toward LLMs, specifically their emotional (e.g., liking) and functional (e.g., willingness to continue using) attitudes. Results showed that both dimensions positively predicted participants’ liking and willingness to continue using LLMs. Warmth had a stronger predictive effect on liking (warmth: β = 0.41, p< 0.001; competence: β = 0.27, p< 0.001), while competence had a stronger predictive effect on willingness to continue using (warmth: β = 0.19, p= 0.005; competence: β = 0.45, p< 0.001). This outcome suggests that the priority effect of warmth and competence shifts across attitude predictions. Study 3 examined specific LLMs ratings in terms of warmth and competence. Results showed no significant difference in warmth ratings between humans (M= 5.06, SD= 1.09) and LLMs (M= 5.11, SD= 1.23), t(206) = −0.60, p= 0.551. However, LLMs were rated significantly higher on competence (M= 5.16, SD= 1.20) than humans (M= 4.81, SD= 1.23), t(206) = −3.51, p< 0.001, Cohen’s d = −0.29.

This study makes two significant contributions to the field. First, it establishes a preliminary theoretical framework for understanding human perception of LLMs. Second, it offers new insights into human-machine interaction by emphasizing the importance of warmth and competence in shaping user attitudes. The findings have practical implications for AI design and policymaking, providing a framework for improving user acceptance, optimizing LLM design, and promoting responsible human-AI coexistence.

Key words: large language model, social cognition, warmth, competence

中图分类号:

B849: C91

武月婷, 王博, 包寒吴霜, 李若男, 吴怡, 王嘉琪, 程诚, 杨丽. (2025). 人类对大语言模型的热情和能力感知. 心理学报, 57(11), 2043-2059.

WU Yueting, WANG Bo, BAO Han Wu Shuang, LI Ruonan, WU Yi, WANG Jiaqi, CHENG Cheng, YANG Li. (2025). Humans perceive warmth and competence in large language models. Acta Psychologica Sinica, 57(11), 2043-2059.

图/表 11

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维度	b	SE	z	p	95% CI
能力	−0.98	0.06	−15.11	<0.001	[−1.11, −0.85]
热情	−2.04	0.09	−22.53	<0.001	[−2.22, −1.86]
才智(Ability)	−1.14	0.07	−16.87	<0.001	[−1.27, −1.01]
自信(Assertiveness)	−3.14	0.15	−21.69	<0.001	[−3.42, −2.85]
道德(Morality)	−2.22	0.10	−22.84	<0.001	[−2.41, −2.03]
社会性(Sociability)	−3.63	0.18	−19.94	<0.001	[−3.99, −3.27]
地位(Status)	−2.83	0.13	−22.47	<0.001	[−3.07, −2.58]
外貌(Appearance)	−3.60	0.18	−20.06	<0.001	[−3.95, −3.25]
健康(Health)	−3.66	0.19	−19.82	<0.001	[−4.02, −3.30]
职业(Occupation)	−3.50	0.17	−20.42	<0.001	[−3.84, −3.17]
信念(Beliefs)	−4.03	0.22	−18.29	<0.001	[−4.46, −3.59]
异常行为(Deviance)	−4.18	0.24	−17.61	<0.001	[−4.65, −3.72]
情绪(Emotion)	−3.98	0.22	−18.48	<0.001	[−4.40, −3.56]
其他(Other)	−3.98	0.22	−18.49	<0.001	[−4.40, −3.56]
美貌(Beauty)	−5.13	0.38	−13.57	<0.001	[−5.87, −4.39]
地理(Geography)	−6.39	0.71	−9.06	<0.001	[−7.77, −5.01]
社会群体(Social groups)	−6.40	0.71	−9.03	<0.001	[−7.78, −5.01]

维度	b	SE	z	p	95% CI
能力	−0.98	0.06	−15.11	<0.001	[−1.11, −0.85]
热情	−2.04	0.09	−22.53	<0.001	[−2.22, −1.86]
才智(Ability)	−1.14	0.07	−16.87	<0.001	[−1.27, −1.01]
自信(Assertiveness)	−3.14	0.15	−21.69	<0.001	[−3.42, −2.85]
道德(Morality)	−2.22	0.10	−22.84	<0.001	[−2.41, −2.03]
社会性(Sociability)	−3.63	0.18	−19.94	<0.001	[−3.99, −3.27]
地位(Status)	−2.83	0.13	−22.47	<0.001	[−3.07, −2.58]
外貌(Appearance)	−3.60	0.18	−20.06	<0.001	[−3.95, −3.25]
健康(Health)	−3.66	0.19	−19.82	<0.001	[−4.02, −3.30]
职业(Occupation)	−3.50	0.17	−20.42	<0.001	[−3.84, −3.17]
信念(Beliefs)	−4.03	0.22	−18.29	<0.001	[−4.46, −3.59]
异常行为(Deviance)	−4.18	0.24	−17.61	<0.001	[−4.65, −3.72]
情绪(Emotion)	−3.98	0.22	−18.48	<0.001	[−4.40, −3.56]
其他(Other)	−3.98	0.22	−18.49	<0.001	[−4.40, −3.56]
美貌(Beauty)	−5.13	0.38	−13.57	<0.001	[−5.87, −4.39]
地理(Geography)	−6.39	0.71	−9.06	<0.001	[−7.77, −5.01]
社会群体(Social groups)	−6.40	0.71	−9.03	<0.001	[−7.78, −5.01]

维度	估计边际均值	95% CI 下限	95% CI 上限
能力(Competence)	0.38	0.33	0.43
热情(Warmth)	0.13	0.11	0.16
才智(Ability)	0.32	0.28	0.37
自信(Assertiveness)	0.04	0.03	0.06
道德(Morality)	0.11	0.09	0.13
社会性(Sociability)	0.03	0.02	0.04
地位(Status)	0.06	0.05	0.08
职业(Occupation)	0.03	0.02	0.04
外貌(Appearance)	0.03	0.02	0.04
健康(Health)	0.03	0.02	0.04
其他(Other)	0.02	0.01	0.03
情绪(Emotion)	0.02	0.01	0.03
信念(Beliefs)	0.02	0.01	0.03
异常行为(Deviance)	0.02	0.01	0.02
美貌(Beauty)	0.01	0.00	0.01
地理(Geography)	0.00	0.00	0.01
社会群体(Social groups)	0.00	0.00	0.01

维度	估计边际均值	95% CI 下限	95% CI 上限
能力(Competence)	0.38	0.33	0.43
热情(Warmth)	0.13	0.11	0.16
才智(Ability)	0.32	0.28	0.37
自信(Assertiveness)	0.04	0.03	0.06
道德(Morality)	0.11	0.09	0.13
社会性(Sociability)	0.03	0.02	0.04
地位(Status)	0.06	0.05	0.08
职业(Occupation)	0.03	0.02	0.04
外貌(Appearance)	0.03	0.02	0.04
健康(Health)	0.03	0.02	0.04
其他(Other)	0.02	0.01	0.03
情绪(Emotion)	0.02	0.01	0.03
信念(Beliefs)	0.02	0.01	0.03
异常行为(Deviance)	0.02	0.01	0.02
美貌(Beauty)	0.01	0.00	0.01
地理(Geography)	0.00	0.00	0.01
社会群体(Social groups)	0.00	0.00	0.01

词汇	全部(N = 219)			男性(N = 134)			女性(N = 85)
词汇	因子1	因子2	共同度	因子1	因子2	共同度	因子1	因子2	共同度
智能的	0.83		0.70	0.80		0.65	0.87		0.82
实用的	0.81		0.70	0.80		0.75	0.71		0.66
耐心的	0.78		0.70	0.72		0.76	0.71		0.56
高级的	0.78		0.64	0.73		0.53	0.79		0.75
快速的	0.77		0.75	0.78		0.62
丰富的	0.77		0.70	0.69		0.49	0.80		0.71
信息多的	0.76		0.60	0.69		0.52	0.78		0.84
易用的	−0.76		0.66	−0.76		0.61
有效率的	0.76		0.66	0.82		0.68
全面的	0.76		0.63	0.73		0.55	0.79		0.67
好用的	0.75		0.65	0.78		0.61	0.75		0.62
博学的	0.74		0.60	0.77		0.60	0.71		0.57
有条不紊的	0.72		0.59	0.66		0.54	0.76		0.66
强大的	0.71		0.57	0.78		0.61
理解的	0.70		0.58	0.72		0.55	0.70		0.51
成熟的	0.69		0.72				0.79		0.63
活跃的	0.69		0.64	0.75		0.62	0.70		0.53
镇静的	0.66		0.50	0.52		0.52	0.66		0.47
简单的(R)	−0.65		0.46				−0.67		0.45
方便的	0.65		0.49				0.78		0.64
神奇的	0.65		0.43	0.63		0.47
开放的	0.64		0.59
准确的	0.59		0.61						0.50
按部就班的(R)	−0.56		0.47				−0.71		0.65
值得信赖的	0.54		0.58	0.62		0.48
聪明的							0.83		0.79
有趣的							0.68		0.53
宽厚的		0.88	0.79		0.90	0.82		0.91	0.88
低落的(R)		0.82	0.70		0.86	0.74		0.75	0.62
无恒心的(R)		0.81	0.67		0.86	0.74		0.74	0.57
孤僻的(R)		−0.80	0.66		−0.83	0.70		−0.75	0.60
缄默的(R)		−0.75	0.58		−0.76	0.58		−0.66	0.51
狡猾的(R)		0.73	0.57					0.64	0.47
动摇的(R)		0.70	0.53		0.74	0.57
热情的		0.68	0.55		0.65	0.46		0.65	0.48
掩饰的(R)		0.65	0.44					0.64	0.44
奇怪的(R)		0.62	0.41					0.70	0.54
小心的		−0.56	0.44		−0.55	0.46
特征值(旋转后)	12.83	6.27		10.13	5.12		11.21	4.82
贡献率(旋转后)	35.62	17.41		37.51	18.96		38.67	16.63
累计贡献率(旋转后)	35.62	53.04		37.51	56.47		38.67	55.30