The micro-dynamic neural processing model of insight problem-solving

doi:10.3724/SP.J.1041.2026.0634

Abstract

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

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.

References

[1] Aziz‐Zadeh L., Kaplan J. T., & Iacoboni M. (2009). “Aha!”: The neural correlates of verbal insight solutions.Human Brain Mapping, 30(3), 908-916.
[2] Bartoli E., Devara E., Dang H. Q., Rabinovich R., Mathura R. K., Anand A., … Shofty B. (2024). Default mode network electrophysiological dynamics and causal role in creative thinking.Brain, 147(10), 3409-3425.
[3] Beaty R. E., Benedek M., Barry Kaufman S., & Silvia P. J. (2015). Default and executive network coupling supports creative idea production.Scientific Reports, 5(1), 10964.
[4] Benedek M., Christensen A. P., Fink A., & Beaty R. E. (2019). Creativity assessment in neuroscience research.Psychology of Aesthetics, Creativity, and the Arts, 13(2), 218-226.
[5] Betzel R. F., Erickson M. A., Abell M., O'Donnell B. F., Hetrick W. P., & Sporns O. (2012). Synchronization dynamics and evidence for a repertoire of network states in resting EEG.Frontiers in Computational Neuroscience, 6, 74.
[6] Bieth T., Ovando-Tellez M., Lopez‐Persem A., Garcin B., Hugueville L., Lehongre K.,. Volle E. (2024). Time course of EEG power during creative problem‐solving with insight or remote thinking.Human Brain Mapping, 45(1), e26547.
[7] Bilalić M., Graf M., Vaci N., & Danek A. H. (2021). The temporal dynamics of insight problem solving-restructuring might not always be sudden.Thinking & Reasoning, 27(1), 1-37.
[8] Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate problems.Behavior Research Methods, Instruments, & Computers, 35, 634-639.
[9] Bréchet L., Brunet D., Birot G., Gruetter R., Michel C. M., & Jorge J. (2019). Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI.Neuroimage, 194, 82-92.
[10] Britz J., Van De Ville D., & Michel C. M. (2010). BOLD correlates of EEG topography reveal rapid resting-state network dynamics.Neuroimage, 52(4), 1162-1170.
[11] Brouwer, H., & Hoeks, J. C. (2013). A time and place for language comprehension: Mapping the N400 and the P600 to a minimal cortical network.Frontiers in Human Neuroscience, 7, 758.
[12] Croce P., Zappasodi F., Spadone S., & Capotosto P. (2018). Magnetic stimulation selectively affects pre-stimulus EEG microstates.NeuroImage, 176, 239-245.
[13] Custo A., Van De Ville D., Wells W. M., Tomescu M. I., Brunet D., & Michel C. M. (2017). Electroencephalographic resting-state networks: Source localization of microstates.Brain Connectivity, 7(10), 671-682.
[14] Darsaud A., Wagner U., Balteau E., Desseilles M., Sterpenich V., Vandewalle G., … Maquet P. (2011). Neural precursors of delayed insight.Journal of Cognitive Neuroscience, 23(8), 1900-1910.
[15] Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.Journal of Neuroscience Methods, 134(1), 9-21.
[16] Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious thought.Perspectives on Psychological Science, 1(2), 95-109.
[17] Dohmatob E., Dumas G., & Bzdok D. (2020). Dark control: The default mode network as a reinforcement learning agent.Human Brain Mapping, 41(12), 3318-3341.
[18] Erickson B., Truelove-Hill M., Oh Y., Anderson J., Zhang F. Z., & Kounios J. (2018). Resting-state brain oscillations predict trait-like cognitive styles.Neuropsychologia, 120, 1-8.
[19] Fink A., Graif B., & Neubauer A. C. (2009). Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers.NeuroImage, 46(3), 854-862.
[20] Fleck, J. I., & Kounios, J. (2009). Intuition, creativity, and unconscious aspects of problem solving. In W. P. Banks (Ed.), Encyclopedia of consciousness(pp. 431-446). Elsevier.
[21] Gao, Y., & Zhang, H. (2014). Unconscious processing modulates creative problem solving: Evidence from an electrophysiological study.Consciousness and Cognition, 26, 64-73.
[22] Gilhooly, K. J. (2016). Incubation and intuition in creative problem solving.Frontiers in Psychology, 7, 1076.
[23] Haavold, P. Ø., & Sriraman, B. (2022). Creativity in problem solving: Integrating two different views of insight.ZDM-Mathematics Education, 54(1), 83-96.
[24] Hill A. T., Bailey N. W., Zomorrodi R., Hadas I., Kirkovski M., Das S., Lum J. A. G., & Enticott P. G. (2023). EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power.Human Brain Mapping, 44(18), 6484-6498.
[25] Huang F., Zhao Q., Zhou Z., & Luo J. (2019). People got lost in solving a set of similar problems.NeuroImage, 186, 192-199.
[26] Jung-Beeman M., Bowden E. M., Haberman J., Frymiare J. L., Arambel-Liu S., Greenblatt R.,. Kounios J. (2004). Neural activity when people solve verbal problems with insight.PLoS Biology, 2(4), e97.
[27] Khanna A., Pascual-Leone A., Michel C. M., & Farzan F. (2015). Microstates in resting-state EEG: Current status and future directions.Neuroscience & Biobehavioral Reviews, 49, 105-113.
[28] Koenig T., Prichep L., Lehmann D., Sosa P. V., Braeker E., Kleinlogel H.,. John E. R. (2002). Millisecond by millisecond, year by year: Normative EEG microstates and developmental stages.Neuroimage, 16(1), 41-48.
[29] Kohler, W. (1985). The mentality of apes. Routledge.
[30] Lehmann D., Ozaki H., & Pál I. (1987). EEG alpha map series: Brain micro-states by space-oriented adaptive segmentation.Electroencephalography and Clinical Neurophysiology, 67(3), 271-288.
[31] Lehmann D., Pascual-Marqui R. D., & Michel C. (2009). EEG microstates.Scholarpedia, 4(3), 7632.
[32] Leszczynski M., Chaieb L., Reber T. P., Derner M., Axmacher N., & Fell J. (2017). Mind wandering simultaneously prolongs reactions and promotes creative incubation.Scientific Reports, 7(1), 10197.
[33] Lin J., Chen Y., Xie J., & Mo L. (2022). Altered brain connectivity patterns of individual differences in insightful problem solving.Frontiers in Behavioral Neuroscience, 16, 905806.
[34] Liu C., Tu S., Guan J., Zhou Z., Ma J., & Shi Z. (2024). How does unconscious processing promote creative problem-solving? An examination using priming methods.Thinking & Reasoning, 31(3), 374-397.
[35] Liu D., Hao L., Han L., Zhou Y., Qin S., Niki K., … Luo J. (2023). The optimal balance of controlled and spontaneous processing in insight problem solving: fMRI evidence from Chinese idiom guessing.Psychophysiology, 60(7), e14240.
[36] Liu Y., Nour M. M., Schuck N. W., Behrens T. E. J., & Dolan R. J. (2022). Decoding cognition from spontaneous neural activity.Nature Reviews Neuroscience, 23(4), 204-214.
[37] Lloyd-Cox J., Chen Q., & Beaty R. E. (2022). The time course of creativity: Multivariate classification of default and executive network contributions to creative cognition over time.Cortex, 156, 90-105.
[38] Lopez K. L., Monachino A. D., Morales S., Leach S. C., Bowers M. E., & Gabard-Durnam L. J. (2022). HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings.NeuroImage, 260, 119390.
[39] Lu, Y., & Singer, W. (2023). Dynamic signatures of the Eureka effect: An EEG study.Cerebral Cortex, 33(13), 8679-8692.
[40] Luo, J. (2004). Neural correlates of insight.Acta Psychologica Sinica, 36(2), 219-234.
[罗劲. (2004). 顿悟的大脑机制.心理学报, 36(2), 219-234.]
[41] Mai X. Q., Luo J., Wu J. H., & Luo Y. J. (2004). “Aha!” effects in a guessing riddle task: An ERP study.Human Brain Mapping, 23(2), 128-128.
[42] Michel C. M., Bréchet L., Schiller B., & Koenig T. (2024). Current state of EEG/ERP microstate research.Brain Topography, 37(2), 169-180.
[43] Musso F., Brinkmeyer J., Mobascher A., Warbrick T., & Winterer G. (2010). Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks.Neuroimage, 52(4), 1149-1161.
[44] Oostenveld R., Fries P., Maris E., & Schoffelen J. M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.Computational Intelligence and Neuroscience, 2011, 156869.
[45] Pan D. N., Hoid D., Gu R. L., & Li X. (2020). Emotional working memory training reduces rumination and alters the EEG microstate in anxious individuals.NeuroImage: Clinical, 28, 102488.
[46] Pan M. W., Song J. Q., & Deng H. (2019). Development and validation of self-rating scales in online English writing diagnostic tests.Frontiers of Foreign Language Education Research, 2(4), 33-41.
[潘鸣威, 宋杰青, 邓华. (2019). 在线英语写作诊断测评中自评量表的开发与效度验证.外语教育研究前沿, 2(4), 33-41.]
[47] Poulsen A. T., Pedroni A., Langer N., & Hansen L. K. (2018). Microstate EEGlab toolbox: An introductory guide.BioRxiv, 289850.
[48] Qiu J., Li H., Jou J., Liu J., Luo Y., Feng T.,. Zhang Q. (2010). Neural correlates of the “Aha” experiences: Evidence from an fMRI study of insight problem solving.Cortex, 46(3), 397-403.
[49] Ritter, S. M., & Dijksterhuis, A. (2014). Creativity-the unconscious foundations of the incubation period.Frontiers in Human Neuroscience, 8, 215.
[50] Rominger C., Papousek I., Perchtold C. M., Weber B., Weiss E. M., & Fink A. (2018). The creative brain in the figural domain: Distinct patterns of EEG alpha power during idea generation and idea elaboration.Neuropsychologia, 118, 13-19.
[51] Satpute, A. B., & Lindquist, K. A. (2019). The default mode network’s role in discrete emotion.Trends in Cognitive Sciences, 23(10), 851-864.
[52] Schiller B., Gianotti L. R., Baumgartner T., Nash K., Koenig T., & Knoch D. (2016). Clocking the social mind by identifying mental processes in the IAT with electrical neuroimaging.Proceedings of the National Academy of Sciences, 113(10), 2786-2791.
[53] Shen W. B., Liu C., Luo J., & Yu J. (2012). Brain perceived intuitively mental impasses in insight problem solving: An ERP study.Acta Psychologica Sinica, 44(7), 924-935.
[沈汪兵, 刘昌, 罗劲, 余洁. (2012). 顿悟问题思维僵局早期觉察的脑电研究.心理学报, 44(7), 924-935.]
[54] Shen W., Tong Y., Li F., Yuan Y., Hommel B., Liu C., & Luo J. (2018). Tracking the neurodynamics of insight: A meta-analysis of neuroimaging studies.Biological Psychology, 138, 189-198.
[55] Sun Y., Cai Y., & Lu S. (2015). Hemispheric asymmetry in the influence of language on visual perception.Consciousness and Cognition, 34, 16-27.
[56] Tan T., Zou H., Chen C., & Luo J. (2015). Mind wandering and the incubation effect in insight problem solving.Creativity Research Journal, 27(4), 375-382.
[57] Tarailis P., Koenig T., Michel C. M., & Griškova-Bulanova I. (2024). The functional aspects of resting EEG microstates: A systematic review. Brain Topography, 37(2), 181-217.
[58] Van Der Werf J., Jensen O., Fries P., & Medendorp W. P. (2008). Gamma-band activity in human posterior parietal cortex encodes the motor goal during delayed prosaccades and antisaccades.The Journal of Neuroscience, 28(34), 8397-8405.
[59] Wang H., Guo Y., Tu Y., Peng W., Lu X., Bi Y., Iannetti G. D., & Hu L. (2023). Neural processes responsible for the translation of sustained nociceptive inputs into subjective pain experience.Cerebral Cortex, 33(3), 634-650.
[60] Wang T., Zhang Q., Li H., Qiu J., Tu S., & Yu C. (2009). The time course of Chinese riddles solving: Evidence from an ERP study.Behavioural Brain Research, 199(2), 278-282.
[61] Yang, J. (2012). The role of the right hemisphere in metaphor comprehension: A meta-analysis of functional magnetic resonance imaging studies.Human Brain Mapping, 35(1), 107-122.
[62] Yeshurun Y., Nguyen M., & Hasson U. (2021). The default mode network: Where the idiosyncratic self meets the shared social world.Nature Reviews Neuroscience, 22(3), 181-192.
[63] Yu Y., Oh Y., Kounios J., & Beeman M. (2022). Dynamics of hidden brain states when people solve verbal puzzles.NeuroImage, 255, 119202.
[64] Yue L., Iannetti G. D., & Hu L. (2020). The neural origin of nociceptive-Induced gamma-band oscillations.The Journal of Neuroscience, 40(17), 3478-3490.
[65] Zanesco A. P., Denkova E., & Jha A. P. (2021). Self-reported mind wandering and response time variability differentiate prestimulus electroencephalogram microstate dynamics during a sustained attention task.Journal of Cognitive Neuroscience, 33(1), 28-45.
[66] Zhao Q., Li S., Chen S., Zhou Z., & Cheng L. (2015). Dynamic neural processing mode of creative problem solving.Advances in Psychological Science, 23(3), 375-384.
[赵庆柏, 李松清, 陈石, 周治金, 成良. (2015). 创造性问题解决的动态神经加工模式.心理科学进展, 23(3), 375-384.]
[67] Zhao Q., Wei L., Li Y., Zhou Z., Zhao L., & Tang L. (2017). Right hemispheric dominance in forming novel semantic associations.Acta Psychologica Sinica, 49(11), 1370-1382.
[赵庆柏, 魏琳琳, 李瑛, 周治金, 赵黎莉, 唐磊. (2017). 新颖语义联结形成的右半球优势效应.心理学报, 49(11), 1370-1382.]
[68] Zhou S., Chen S., Wang S., Zhao Q., Zhou Z., & Lu C. (2018). Temporal and spatial patterns of neural activity associated with information selection in open-ended creativity.Neuroscience, 371, 268-276.