探索顿悟问题解决的微观动态神经加工模式
Explore the micro-dynamic neural processing model of insight problem solving
陈岩 1李瑛 1刘冠雄 1于全磊 1梁正 2陈石 3赵庆柏1
作者信息
- 1. 人的发展与心理健康湖北省重点实验室;华中师范大学心理学院;青少年网络心理与行为教育部重点实验室
- 2. 人的发展与心理健康湖北省重点实验室;华中师范大学心理学院;清华大学心理与认知科学系;青少年网络心理与行为教育部重点实验室
- 3. 湖北时珍实验室;湖北中医药大学医学人文学院 湖北大健康产业发展研究中心
- 折叠
摘要
研究采用脑电微状态分析，探索复合远距离联想任务中顿悟问题解决的微观动态神经加工模式。主要结果表明：在问题呈现初期，相比于未解决条件，顿悟解决和非顿悟解决均表现出更高频率的微状态B(与视觉加工相关)以及更多的微状态B与D(与执行功能网络有关)的转换。相比于非顿悟解决条件，顿悟解决在中、后期则表现出更高频率的微状态C(与默认模式网络有关)，且微状态A(与感知、听觉加工相关)、C和D三者存在较高的相互转移概率。本研究初步考察了顿悟问题解决的微观动态神经加工模式，为揭示顿悟问题解决中执行功能调控下多种认知活动的复杂交互过程提供了电生理学依据，为无意识加工可能随顿悟问题解决进程如何变化提供了一定的启示。Abstract
Insight represents a distinctive form of creative problem-solving. Accumulating empirical evidence has consistently demonstrated that insight constitutes a dynamic cognitive sequence rather than a transient momentary event. While deBased on this, the present research used the simple random sampling 37 right-handed participants (average age 21.2 years old, 17 females) who spoke Chinese as their mother tongue and English as their second language, reached certain standards(IELTS7 or TOEFL95 or the major of study at the university is English, and TEM480) to participate in this experiment. The experiment employed the Compound Remote Associates (CRA) test, a classic verbal insight problem-solving paradigm. In this task, three words were simultaneously presented on the screen, requiring participants to generate a single word that could form a meaningful compound word or phrase with each of the three stimulus words. Electroencephalographic (EEG) activity was continuously recorded throughout the task performance. For data analysis, the problem-solving process was artificially divided into three distinct stages: initial problem presentation, the process of problem solving, and response execution stage. Statistical comparisons of the microstates (derived from cluster-based topographic maps that reveal cognitive processes potentially occurring at millisecond resolution) were conducted across different stages under insight, non-insight, and unresolved conditions. This approach aimed to characterize the neural response patterns associated with insight problem-solving.The main results show that: (1) Microstate C, which reflects components of the default mode network, demonstrated a significantly higher occurrence rate under insight condition and exhibited more frequent transitions with both Microstate A (associated with speech information processing) and Microstate D (linked to attentional processes and executive functions); (2) Microstate B, associated with visual processing, showed a significantly increased occurrence rate during the initial stage of both insight and non-insight problem-solving conditions. However, its presence persisted across all three processing stages exclusively in the non-insight condition; (3) In the unresolved condition, Microstate C displayed a significantly elevated occurrence rate, with its dominance progressively increasing throughout the problem-solving process; (4) Microstate D exhibited significantly more frequent transitions to both Microstate B and Microstate A across successful problem-solving conditions. Furthermore, it demonstrated a significantly higher occurrence rate during the initial problem presentation stage.The experimental results revealed distinct neural response patterns across different problem-solving conditions at the electrophysiological level. The successful problem resolution was found to depend on both the comprehensive representation of information and the active engagement of executive functions. Notably, the microstate associated with the default mode network (DMN) exhibited significant activation exclusively during insight condition. This suggests that unconscious cognitive processes may play a crucial role in insight problem solving. 关键词
顿悟问题解决/微状态/无意识加工Key words
insight problem-solving/microstate/unconscious processing引用本文复制引用
陈岩,李瑛,刘冠雄,于全磊,梁正,陈石,赵庆柏.探索顿悟问题解决的微观动态神经加工模式[EB/OL].(2025-11-17)[2025-11-20].https://chinaxiv.org/abs/202511.00086.学科分类
自然科学研究方法/生物科学理论、生物科学方法/信息科学、信息技术
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