【作者】 刘凯；

【Author】 Liu Kai;Department of Educational Science, Bohai University;

【机构】 渤海大学大学教育科学学院；

【摘要】 抑郁症病因始终是精神医学界和心理咨询界的共同关注重点和难点,但抑郁症的病理学机制至今不明,成为提升社会福祉的巨大挑战。作为人工智能领域近年迅速崛起的一支新生力量,通用人工智能(AGI)将人的大脑视为物理世界与主观精神世界影响的偶合体,认为生理和精神两个层面都存在诱发精神障碍症状的理论可能性。意味着也可能存在心理学标志物:在心理健康层面,精神问题的行为学症状表现也许与个体不良经验所构成的心理拓扑结构有关,从而衍生出某种特异性的稳定抽象结构。因此,假设抑郁个体主观精神世界中存在由其负性经验构成的心理拓扑结构——负性事件内联网络结构(即所谓"心结"),认为这是抑郁的心理病理学层面的关键致病因素。本研究以抑郁症心理学病因探索为科学问题,以挖掘负性事件内联结构及其与抑郁程度的关系为目标。结合复杂网络分析方法,自编和开发负性事件内联结构测量问卷及移动程序后:首先,进行预实验,对程序的可使用性进行初步检验,并且初步提取内联结构要素。在此基础上,对负性事件内联结构理论假设和测量程序进行进一步完善。继而,进行抑郁与负性事件内联结构测量,并将实验组抑郁被试与对照组非抑郁被试的负性事件内联结构要素进行比较分析。最后,对抑郁的连续发展特征进行研究。得到如下结论:(1)负性事件内联结构9个关键指标析取成功,每个指标在非抑郁组与抑郁组均差异显著。(2)最长闭合环代表指标,可作为一种心理学标志物预测抑郁发生。(3)负性事件内联结构要素在连续不同严重程度抑郁组别间的差异,尽管并未全部达到统计显著,却初步印证了抑郁强度具有从"正常"到"疾病"更为宽广的的连续谱发展态势。总体而言,当前心理健康问题需要重视,本研究发现不仅为抑郁的心理学标志物探索提供初步的测量与量化标准,也能为抑郁症的病理学研究提供新的理论思路及新的心理学诊断手段。更多还原

【Abstract】 The etiology of depression has long been a major concern and challenge in the fields of psychiatry and psychological counseling. Despite ongoing research, the underlying pathophysiology of depression remains unclear, presenting a significant obstacle to improving social welfare. In recent years, Artificial General Intelligence(AGI) has emerged as a rapidly growing force in the field of artificial intelligence. AGI views the human brain as a coupling between the physical world and the subjective spiritual world, suggesting that there may be a theoretical possibility of inducing mental disorder symptoms at both physiological and psychological levels.This implies that there may also be psychological markers for depression: at the level of mental health, behavioral symptoms of mental problems may be related to an individual’s psychological topological structure, which is formed by their negative experiences. This structure may give rise to specific, stable abstract structures.It is hypothesized that depressed individuals have a psychological topological structure in their subjective spiritual world that is composed of negative experiences-referred to as the negative event internal network structure or "knots"-which is considered to be a key pathogenic factor at the psychological level of depression.This study aims to explore the relationship between negative event internal linkage structures and depression levels,with the goal of shedding light on the psychological etiology of depression. Using complex network analysis methods and self-developed measurement questionnaires and mobile programs for negative event internal linkage structures, the study first conducted pre-experiments to test the usability of the program and extract internal linkage structure elements. Based on these results, the theoretical hypothesis and measurement program for negative event internal linkage structures were further refined. Depression and negative event internal linkage structures were then measured and compared between depressed subjects in the experimental group and non-depressed subjects in the control group. Finally, the continuous development characteristics of depression were studied.The study yielded several key findings:(1) Nine key indicators of negative event internal linkage structures were successfully analyzed, with each indicator showing significant differences between non-depressed and depressed groups.(2) The longest closed loop representative indicator can serve as a psychological marker for predicting the onset of depression.(3) While not all differences between elements of negative event internal linkage structures in continuous groups with varying severity levels of depression reached statistical significance,initial results suggest that depression intensity follows a broader continuous spectrum development trend from"normal" to "disease".In conclusion, addressing current mental health issues is of utmost importance. The findings of this study provide not only preliminary measurement and quantification standards for exploring psychological markers for depression but also offer new theoretical insights and diagnostic tools for pathological research on depression.更多还原