经济文化对心理健康的影响：道德中心性的中介作用
文献类型：学位论文
| 作者 | 吕思华 |
| 答辩日期 | 2024-06 |
| 文献子类 | 硕士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 朱廷劭 |
| 关键词 | 道德中心性 心理健康 中庸思维 收入分配不平等 大型语言模型 |
| 学位名称 | 应用心理硕士 |
| 学位专业 | 应用心理 |
| 其他题名 | Research on the Economic and Cultural Impact on Mental Health: The Mediating Role of Moral Centrality |
| 中文摘要 | In recent years, researchers have increasingly recognized the impact of economic and cultural factors (such as income distribution inequality and Zhongyong thinking style) on mental health. However, it is not clear how these external factors affect mental health through internal psychological mechanisms. As the macro environment in which individuals live, culture and economy shape people's values and make individuals have different levels of motivation orientation. Previous studies have indicated that individuals with a better ability to coordinate agency (a motivation representing selfinterest) and communion (a motivation representing altruism) tend to have a relatively high level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Thus, individuals are not only able to efficiently realize their personal values but also more easily allow for the attainment of eudaimonic well-being, thereby reducing the risk of mental health problems. Therefore, moral centrality may play a potential mediating role in the impact of economic and cultural factors on mental health. Overall, with income distribution inequality (economic factor) and Zhongyong thinking style (cultural factor) as independent variables, this study aims to explore the mechanisms through which these factors affect mental health, by examining how these factors influence moral centrality and, in turn, affect mental health. Our research not only enriches the theoretical foundation of the mental health field, but also provides a theoretical basis for interventions, and helps to formulate targeted strategies to improve the psychological well-being of the public. However, the research faces following challenges: (1) Although there are relatively mature methods for measuring moral centrality, it involves complex tasks of coding values in personal strivings, making the process complicated and labor-intensive. (2) While macroeconomic data such as Gini coefficients could reflect the level of income distribution inequality in a region, assessing moral centrality and mental health at the group level faces challenges, as the costs would increase with the number of regions and people involved. However, with the development of large language models, social media platform, and natural language processing technology, these tools offer new possibilities for text analysis and coding work, for measuring the moral centrality of local populations, and for monitoring mental health status. Accordingly, this study intends to utilize large language models, social media big data and natural language processing technology to explore how Zhongyong thinking style affects mental health through moral centrality, and how income distribution inequality affects mental health through moral centrality. Study 1 involves training GPT-3.5 Turbo to recognize values contained in personal strivings (achievement/power/universalism/benevolence) using differentiated prompts and evaluating its accuracy, precision and recall rates, in order to obtain a model meeting requirements for application, thereby reducing the labor and time costs in the process of encoding values while measuring moral centrality. Study 2 applies above models in the process of measuring moral centrality, exploring how moral centrality mediates the impact of Zhongyong thinking style on depression and anxiety. Study 3 used Weibo posts to extract word frequency features representing local population’s moral centrality and mental health through psycholinguistic dictionaries to examine how income distribution inequality affects regional negative emotions and suicide risk through moral centrality using panel data analysis. Study 4 attempts to combine moralcentrality-related psycholinguistic features and other lexicons related to depression to construct a depression prediction model using machine learning. The findings are as follows: (1) The GPT-3.5 Turbo demonstrated an accuracy rate of not less than 0.80 in recognizing values of power, achievement, universlaism, and benevolence, showing the potential application of ChatGPT in psychological research; (2) Moral centrality played a mediating role in the impact of Zhongyong thinking style on depression/anxiety. Specifically, individuals with a higher level of Zhongyong thinking style could better integrate agentic and communal motivations, enhancing their moral centrality, and thereby reducing levels of depression/anxiety; (3) Moral centrality mediated the impact of income distribution inequality on negative emotions/suicide risk, with regions of higher income inequality often having lower levels of moral centrality, leading to increased negative emotions/suicide risk; (4) The depression prediction model showed acceptable predictive validity (r=0.33, R²=0.10) and moderate split-half reliability (r=0.75). And we can see that moral-centrality-related psycholinguistic features contribute to the prediction of depression, further supporting the close relationship between moral centrality and mental health. In summary, this study utilized large language models, social media big data, and natural language processing technology to break through the technical limitations of traditional psychological research, exploring the mechanisms through which economic and cultural factors affect mental health and verifying the mediating role of moral centrality. It deepens our understanding of the mechanisms through which economic and cultural factors influence mental health, enriching the theoretical foundation of this field. Moreover, it suggests that policymakers could use the advantages of Zhongyong thinking culture, advocating for values emphasizing individual development while also focusing on collective well-being, improving individuals’ moral centrality, thereby mitigating the negative impact of economic inequality on mental health. |
| 英文摘要 | 近年来，研究人员认识到经济文化因素（如收入分配不平等、中庸思维）对心理健康的影响，然而这些外部因素是如何通过内在的心理机制对心理健康造成影响的还不甚明晰。文化和经济作为人们所处的宏观环境，塑造着人们的价值观，并拥有不同水平的动机取向。以往研究表明，当人们能较好地协调代 表“利己”的能动动机和代表“利他”的共生动机时，就会拥有相对高的道德中心性水平。道德中心性体现了内部动机系统的平衡状况，可以降低内在动机之间的冲突，促使两种动机相互支持、相互激励。在这种互助状态下，不仅能 够高效实现个人价值，还能通过寻找生活意义提高幸福感，进而减少产生心理健康问题的风险。因此，道德中心性或许在经济文化因素对心理健康的影响中发挥了潜在中介作用。综上，本研究希望以收入分配不平等（经济）和中庸思 维（文化）作为自变量，深入探究经济文化对心理健康的影响机制，了解经济文化是如何通过影响道德中心性进而影响心理健康，一方面丰富心理健康领域的理论基础，同时也为心理健康干预提供理论依据，有助于制定针对性的策略，以提升公众的心理福祉。 然而，课题当前依然存在以下难点：（1）尽管对于道德中心性的测量存在较为成熟的评估方法，但其涉及到对个人奋斗文本的价值观编码工作，因此测量过程较为复杂且人力成本较高；（2）虽然可以通过宏观经济数据如基尼系数反映地区收入分配不平等程度，但对地区民众进行道德中心性和心理健康评估时，面对的挑战和成本会随着涉及的地区和人数的增加而增大。然而，随着大型语言模型（比如 ChatGPT）、社交媒体大数据和自然语言处理技术的发展，这些工具为文本分析和编码工作、地区民众的道德中心性测量和心理健康状态的 监测提供了新的可能性。 综上，本研究拟借助大型语言模型、社交媒体大数据以及自然语言处理技术，从文化角度探究中庸思维是如何通过道德中心性影响心理健康水平，从经济角度探究地区收入分配不平等是如何通过道德中心性影响心理健康水平。研究一通过提示工程设计差异化提示词来训练 GPT-3.5 Turbo 识别个人奋斗中包含的价值观（成就/权力/博爱/仁爱），并对识别准确率、精确率和召回率进行评估，以得到符合要求、满足应用条件的识别模型，降低个人道德中心性测量过 程中价值观编码环节所需的人力与时间成本。在研究二中将上述模型应用于道德中心性的测量中，验证道德中心性在中庸思维对心理健康（抑郁和焦虑）影响中的中介作用。研究三利用地区微博用户发布的帖子，通过心理语义词典提取代表地区道德中心性以及地区心理健康水平的词频特征，采用面板数据分析考察收入分配不平等如何通过道德中心性影响地区民众的负面情绪和自杀风险。最后在研究四，我们将与道德中心性相关的心理语义特征以及其他与抑郁相关的心理语义特征结合起来，尝试通过这些特征利用机器学习方法构建抑郁预测 模型。 研究结果如下：（1）GPT-3.5 Turbo 大型语言模型在识别权力、成就、博爱和仁爱价值观的准确率不低于 0.80，展现了 ChatGPT 在心理学研究中的应用潜力；（2）道德中心性在中庸思维对抑郁/焦虑的影响中起到了中介作用，高中庸思维的个体能更有效地整合能动与共生动机，增强其道德中心性，从而降低抑郁/焦虑水平；（3）道德中心性在地区收入分配不平等对地区民众负性情绪/自杀风险的影响中起到了中介作用，收入分配不平等程度越高的地区往往伴随着越低的道德中心性水平，进而导致该地区的负性情绪/自杀风险增加。（4）抑郁预测模型表现出了可接受的预测效度（r=0.33，R²=0.10）与分半信度（r=0.75）， 我们可以看到，与道德中心性相关的心理语言特征有助于预测抑郁，进一步支持道德中心性与心理健康之间的密切关系。 综上所述，本研究利用大型语言模型、社交媒体大数据以及自然语言处理技术突破了传统心理学研究技术上的限制，探究了经济文化因素对心理健康的影响机制，验证了道德中心性在其中起到的中介作用。一方面加深了我们对经济文化因素影响心理健康机制的认识，丰富了该领域的理论基础；另一方面启示了政策制定者，可以尝试发挥中庸文化优势，倡导重视个人发展同时注重集体福祉的价值观，帮助民众形成协调的思维模式，进而减少经济发展不平等对人们心理健康的负面影响，维护和促进人民精神健康与社会的良性发展。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/48143]  |
| 专题 | 心理研究所_社会与工程心理学研究室 |
| 推荐引用方式 GB/T 7714 | 吕思华. 经济文化对心理健康的影响：道德中心性的中介作用[D]. 中国科学院心理研究所. 中国科学院大学. 2024. |
入库方式： OAI收割
来源：心理研究所
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