探索顿悟问题解决的微观动态神经加工模式*

doi:10.3724/SP.J.1041.2026.0634

摘要/Abstract

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。

关键词: 顿悟问题解决, 微状态, 无意识加工

Abstract:

Creative problem-solving relies upon distinct processes of reasoning and insight. Accumulating empirical evidence has demonstrated that insight, rather than manifesting as a transient ‘eureka!’ moment, constitutes a dynamic cognitive sequence. While descriptions of the ‘eureka moment’ itself provide information about potential neural markers present at the time of a problem is solved, our understanding of how multiple cognitive processes interact during insight problem-solving remains limited. Furthermore, unconscious processing is considered crucial for solving insight problems, yet due to its elusive nature, few studies have directly investigated this process.

Based on this, the present research investigated insight problem-solving in a simple random sample of 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, and English language proficiency was standardized as IELTS ≥ 7 / TOEFL ≥ 95 / the major of study at the university is English, and TEM4≥80 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 task performance. In 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 occurring at millisecond resolution) were conducted across these 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 rate of occurrence under the 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 rate of occurrence 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 rate of occurrence, 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, Microstate D demonstrated a significantly higher rate of occurrence during the initial problem presentation stage.

The experimental results revealed distinct neural response patterns across different problem-solving conditions at the electrophysiological level. Successful problem-solving 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 the insight condition. This suggests that unconscious cognitive processes may play a crucial role in insight problem-solving.

Key words: insight problem-solving, microstate, unconscious processing

中图分类号:

B842

陈岩, 李瑛, 刘冠雄, 于全磊, 梁正, 陈石, 赵庆柏. (2026). 探索顿悟问题解决的微观动态神经加工模式^*. 心理学报, 58(4), 634-650.

CHEN Yan, LI Ying, LIU Guanxiong, YU Quanlei, LIANG Zheng, CHEN Shi, ZHAO Qingbai. (2026). The micro-dynamic neural processing model of insight problem-solving. Acta Psychologica Sinica, 58(4), 634-650.

图/表 23

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交互项	df	F	p	η²
答案类型×时间窗口	4	1.13	0.346	0.05
答案类型×微状态	6	5.92	< 0.001	0.20
时间窗口×微状态	6	15.48	< 0.001	0.39
答案类型×时间窗口×微状态	12	11.56	< 0.001	0.33

交互项	df	F	p	η²
答案类型×时间窗口	4	1.13	0.346	0.05
答案类型×微状态	6	5.92	< 0.001	0.20
时间窗口×微状态	6	15.48	< 0.001	0.39
答案类型×时间窗口×微状态	12	11.56	< 0.001	0.33

交互项	df	F	p	η²
答案类型×时间窗口	4	1.16	0.336	0.05
答案类型×微状态	6	11.55	< 0.001	0.33
时间窗口×微状态	6	16.20	< 0.001	0.40
答案类型×时间窗口×微状态	12	8.28	< 0.001	0.26

交互项	df	F	p	η²
答案类型×时间窗口	4	1.16	0.336	0.05
答案类型×微状态	6	11.55	< 0.001	0.33
时间窗口×微状态	6	16.20	< 0.001	0.40
答案类型×时间窗口×微状态	12	8.28	< 0.001	0.26

交互项	df	F	p	η²
答案类型×时间	4	13.98	< 0.001	0.37
答案类型×转移类型	4	12.00	< 0.001	0.33
时间窗口×转移类型	4	7.79	< 0.001	0.25
答案类型×时间窗口×转移类型	8	3.76	< 0.001	0.14