“量化”爱情：关系流动性促进择偶计算心态

doi:10.3724/SP.J.1041.2025.1777

摘要/Abstract

摘要：

当下社会, 人们在相亲择偶时日趋“明码标价”。本研究将此现象概念化为择偶中的计算心态, 并提出在人际关系的建立与结束更加自由的高关系流动性环境中, 此种将择偶信息转换为流畅易加工的量化指标的认知策略具有认知减负的适应性功能。通过相亲网站文本分析、问卷测量和实验室实验, 三个研究(4个子研究)分别从地区和个体水平上揭示了关系流动性与择偶计算心态之间的相关和因果关系。研究结果将关系流动性的心理后效拓展至个人择偶领域, 一方面在理论上丰富了对于个体如何适应特定社会生态环境的认识, 另一方面可在实践上为制定符合社会转型期大众择偶心理的政策或营销策略提供参考。

关键词: 关系流动性, 择偶, 计算心态, 认知负荷

Abstract:

In today's China, people tend to break down their own and others' profiles into numbers and ranks during the blind date process, using these indexes to find perfect matches. For example, a female with 7 points might be matched with a male with 7 points. This study aims to address the following questions: What social mentality is reflected in this phenomenon, and why does it occur? Firstly, the tendency is conceptualized as a calculative mindset in mate choice—a cognitive process that translates dating information into a quantitative index. Thereafter, the study proposes that individuals tend to adopt this strategy in mate choice due to the increasing relational mobility in contemporary Chinese society, viewed from a socio-ecological perspective. With the rise in relational mobility, individuals are endowed with the freedom to choose their soulmate as they wish. However, this also imposes a greater cognitive load on the decision-making process. Therefore, the calculative mindset, which transforms qualitative profiles into comparable and matchable quantitative numbers, becomes prominent.

Hypotheses were tested through studies combining both macro and micro levels, including nationwide large-scale online data, self-report questionnaires, and experimental manipulations in the lab. Study 1 (N = 44, 703) utilized province-level statistical data from the nationwide statistical yearbook and online dating websites to preliminarily test our hypothesis on a macro scale across 31 provinces and regions. Specifically, the divorce-to-marriage rate was calculated to represent relational mobility, and the calculative mindset index was obtained based on self-introductions and mating requirements using text analysis with Python. Study 2 (N = 311) explored the questions at the individual level through questionnaires. It collected data on individuals' perceptions of relational mobility in their current environment and their inclination to use a calculative mindset for cognitively processing both others' profiles and their own in the mate choice process. In Study 3A (N = 130) and Study 3B (N = 132), which aimed to establish a causal relationship between variables, participants were induced with high or low relational mobility mindsets. Subsequently, they were asked to express their preferences regarding the use of graded profiles for assessing others and presenting themselves separately.

As for the results, using hierarchical linear modeling (HLM), Study 1 revealed that regional relational mobility positively and significantly predicted individuals' tendency to use quantified words in self-introductions and mating requirements, even after controlling for age, gender, education level, regional population, regional GDP, and regional residential mobility. Study 2 confirmed the hypotheses and identified a positive relationship between individuals’ perception of relational mobility and a calculative mindset in assessing others and themselves for aiding mating decisions. Lastly, Study 3 found that individuals primed with a high relational mobility mindset (versus a low relational mobility mindset) in laboratory settings were more inclined to use high grading profiles for choosing others (Study 3A) and presenting themselves (Study 3B), further confirming causality. In summary, the main hypothesis was confirmed through four studies conducted at varying levels, providing robust support for explaining the role of relational mobility in shaping a calculative mindset during mate choice.

This study initially introduced the concept of a calculative mindset in mate choice and revealed the driving forces of social transformation behind this phenomenon. It not only offers a novel perspective for comprehending the prevalent issue of mate selection in China but also enriches the theoretical understanding of the relational mobility research domain.

Key words: relational mobility, mate choice, calculative mindset, cognitive burden

中图分类号:

B849: C91.6

李雅雯, 蔡攀, 左世江, 王芳, 董煜, 周阳, 黄旎雯. (2025). “量化”爱情：关系流动性促进择偶计算心态. 心理学报, 57(10), 1777-1790.

LI Yawen, CAI Pan, ZUO Shijiang, WANG Fang, DONG Yu, ZHOU Yang, HUANG Niwen. (2025). Measuring love: Relational mobility promotes calculative mindset in mate choice. Acta Psychologica Sinica, 57(10), 1777-1790.

图/表 6

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示例	自我介绍内容	分词结果	总词汇量	匹配词汇	匹配词汇量	择偶计算心态(匹配词汇量/总词汇量)
1	大家好, 我是一个开朗直率的女孩, 我在国有企业工作。对于我的另一半, 我希望他必须有房有稳定工作。	'大家好', '我', '是', '一个', '开朗', '直率', '的', '女孩', '我', '在', '国有企业', '工作', '对于', '我', '的', '另一半', '我', '希望', '他', '必须', '有房', '有', '稳定', '工作'	24	'国有企业', ‘有房’, '稳定'	3	0.13
2	32岁, 老家山东, 北京上的大学, 硕士毕业后留北京工作, 长相很一般。对于我的另一半, 我希望她是一个居家的女生。	'32岁', '老家', '山东', '北京', '上', '的', '大学', '硕士', '毕业', '后', '留', '北京', '工作', '长相', '很一般', '对于', '我', '的', '另一半', '我', '希望', '她', '是', '一个', '居家', '的', '女生'	27	'32岁', '硕士', '很一般', '居家'	4	0.15

示例	自我介绍内容	分词结果	总词汇量	匹配词汇	匹配词汇量	择偶计算心态(匹配词汇量/总词汇量)
1	大家好, 我是一个开朗直率的女孩, 我在国有企业工作。对于我的另一半, 我希望他必须有房有稳定工作。	'大家好', '我', '是', '一个', '开朗', '直率', '的', '女孩', '我', '在', '国有企业', '工作', '对于', '我', '的', '另一半', '我', '希望', '他', '必须', '有房', '有', '稳定', '工作'	24	'国有企业', ‘有房’, '稳定'	3	0.13
2	32岁, 老家山东, 北京上的大学, 硕士毕业后留北京工作, 长相很一般。对于我的另一半, 我希望她是一个居家的女生。	'32岁', '老家', '山东', '北京', '上', '的', '大学', '硕士', '毕业', '后', '留', '北京', '工作', '长相', '很一般', '对于', '我', '的', '另一半', '我', '希望', '她', '是', '一个', '居家', '的', '女生'	27	'32岁', '硕士', '很一般', '居家'	4	0.15

变量	1	2	3	4	5	6	7	8
1. 地区关系流动性	−
2. 性别	0.05^***	−
3. 年龄	−0.08^***	0.53^***	−
4. 教育水平	0.10^***	0.18^***	0.01^*	−
5. 地区人口总量	0.07	−0.19^***	−0.20^***	−0.08^***	−
6. 地区GDP	0.14	−0.10^***	−0.21^***	−0.03^***	0.85^***	−
7. 地区居住流动性	−0.00	−0.05^***	−0.01	−0.05^***	−0.29	−0.28	−
8. 择偶计算心态	0.02^***	−0.04^***	−0.06^***	0.02^***	0.02^***	0.03^***	−0.01^**	−
M	0.37	−	24.36	3.55	4478.52	27327.10	0.26	0.02
SD	0.12	−	2.29	1.31	2866.82	22186.91	0.11	0.04

变量	1	2	3	4	5	6	7	8
1. 地区关系流动性	−
2. 性别	0.05^***	−
3. 年龄	−0.08^***	0.53^***	−
4. 教育水平	0.10^***	0.18^***	0.01^*	−
5. 地区人口总量	0.07	−0.19^***	−0.20^***	−0.08^***	−
6. 地区GDP	0.14	−0.10^***	−0.21^***	−0.03^***	0.85^***	−
7. 地区居住流动性	−0.00	−0.05^***	−0.01	−0.05^***	−0.29	−0.28	−
8. 择偶计算心态	0.02^***	−0.04^***	−0.06^***	0.02^***	0.02^***	0.03^***	−0.01^**	−
M	0.37	−	24.36	3.55	4478.52	27327.10	0.26	0.02
SD	0.12	−	2.29	1.31	2866.82	22186.91	0.11	0.04

变量	择偶计算心态
变量	零模型	M1	M2
截距	1.64×10^-2***	2.34×10^-2***	2.04×10^-2***
M1:加入个体层次变量
性别		−3.86×10^-3***	−3.68×10^-3***
年龄		−3.08×10^-4**	−3.00×10^-4**
教育水平		6.86×10^-4***	6.71×10^-4***
M2:加入地区层次变量
地区人口总量			5.12×10^-8
地区GDP总量			1.87×10^-8
地区居住流动性			−2.85×10^-3
地区关系流动性			7.20×10^-3**
δ²	1.38×10^-3	1.37×10^-3	1.37×10^-3
τ₀₀	2.66×10^-6***	1.72×10^-6***	8.56×10^-7**
R²	0.0019	0.0057	0.0062
ΔR²		0.0038	0.0005