脑电图技术(EEG)在风险决策认知神经机制研究中的应用
Applications of Electroencephalography (EEG) in Research on the Cognitive Neural Mechanisms of Risky Decision-Making
摘要: 风险决策是人类在不确定情境下进行判断与选择的重要认知活动,其神经机制受到广泛关注。随着认知神经科学的发展,脑电图(EEG)凭借高时间分辨率、非侵入性和便捷性等优势,逐渐成为研究风险决策的重要技术手段。本文首先介绍风险决策的概念、相关理论及研究范式;其次阐述EEG信号的产生机制、频域特征及技术优势;最后综述EEG在风险决策领域中的相关研究,为深入理解风险决策的认知神经机制提供参考。
Abstract: Risky decision-making is an important cognitive activity in which individuals make judgments and choices under conditions of uncertainty, and its underlying neural mechanisms have attracted considerable attention. With the development of cognitive neuroscience, electroencephalography (EEG), characterized by its high temporal resolution, noninvasiveness, and convenience, has become an important tool for investigating risky decision-making. This paper first introduces the concept, theoretical foundations, and common research paradigms of risky decision-making. It then describes the generation mechanisms, frequency-domain characteristics, and technical advantages of EEG signals. Finally, it reviews EEG-related studies in the field of risky decision-making, with the aim of providing a reference for a deeper understanding of the cognitive and neural mechanisms underlying risky decision-making.
文章引用:郑景瑶 (2026). 脑电图技术(EEG)在风险决策认知神经机制研究中的应用. 心理学进展, 16(8), 8-14. https://doi.org/10.12677/ap.2026.168370

参考文献

[1] Abreu, R., Leal, A., & Figueiredo, P. (2018). EEG-Informed fMRI: A Review of Data Analysis Methods. Frontiers in Human Neuroscience, 12, Article 29.
https://doi.org/10.3389/fnhum.2018.00029
[2] Allais, M. (1953). Le Comportement de l’Homme Rationnel devant le Risque: Critique des Postulats et Axiomes de l’Ecole Americaine. Econometrica, 21, 503-546.
https://doi.org/10.2307/1907921
[3] Bachman, M. D., Watts, A. T. M., Collins, P., & Bernat, E. M. (2022). Sequential Gains and Losses during Gambling Feedback: Differential Effects in Time‐Frequency Delta and Theta Measures. Psychophysiology, 59, e13907.
https://doi.org/10.1111/psyp.13907
[4] Büchel, C., Brassen, S., Yacubian, J., Kalisch, R., & Sommer, T. (2011). Ventral Striatal Signal Changes Represent Missed Opportunities and Predict Future Choice. NeuroImage, 57, 1124-1130.
https://doi.org/10.1016/j.neuroimage.2011.05.031
[5] Burle, B., Spieser, L., Roger, C., Casini, L., Hasbroucq, T., & Vidal, F. (2015). Spatial and Temporal Resolutions of EEG: Is It Really Black and White? A Scalp Current Density View. International Journal of Psychophysiology, 97, 210-220.
https://doi.org/10.1016/j.ijpsycho.2015.05.004
[6] Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012). The Origin of Extracellular Fields and Currents—EEG, ECOG, LFP and Spikes. Nature Reviews Neuroscience, 13, 407-420.
https://doi.org/10.1038/nrn3241
[7] Cao, J., Zhao, Y., Shan, X., Wei, H., Guo, Y., Chen, L. et al. (2021). Brain Functional and Effective Connectivity Based on Electroencephalography Recordings: A Review. Human Brain Mapping, 43, 860-879.
https://doi.org/10.1002/hbm.25683
[8] Dantas, A. M., Sack, A. T., Bruggen, E., Jiao, P., & Schuhmann, T. (2023). Modulating Risk-Taking Behavior with Theta-Band TACs. NeuroImage, 283, Article ID: 120422.
https://doi.org/10.1016/j.neuroimage.2023.120422
[9] Edwards, W. (1954). The Theory of Decision Making. Psychological Bulletin, 51, 380-417.
https://doi.org/10.1037/h0053870
[10] Evans, J. S. B. T. (2003). In Two Minds: Dual-Process Accounts of Reasoning. Trends in Cognitive Sciences, 7, 454-459.
https://doi.org/10.1016/j.tics.2003.08.012
[11] Fields, E. C. (2023). The P300, the LPP, Context Updating, and Memory: What Is the Functional Significance of the Emotion-Related Late Positive Potential? International Journal of Psychophysiology, 192, 43-52.
https://doi.org/10.1016/j.ijpsycho.2023.08.005
[12] Helfrich, R. F., & Knight, R. T. (2019). Cognitive Neurophysiology: Event-Related Potentials. Handbook of Clinical Neurology, 160, 543-558.
https://doi.org/10.1016/B978-0-444-64032-1.00036-9
[13] Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L. et al. (2002). Evaluation of a Behavioral Measure of Risk Taking: The Balloon Analogue Risk Task (Bart). Journal of Experimental Psychology: Applied, 8, 75-84.
https://doi.org/10.1037/1076-898x.8.2.75
[14] Marschak, J. (1950). Rational Behavior, Uncertain Prospects, and Measurable Utility. Econometrica, 18, 111-141.
https://doi.org/10.2307/1907264
[15] Newson, J. J., & Thiagarajan, T. C. (2019). EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Frontiers in Human Neuroscience, 12, Article 521.
https://doi.org/10.3389/fnhum.2018.00521
[16] Olejniczak, P. (2006). Neurophysiologic Basis of EEG. Journal of Clinical Neurophysiology, 23, 186-189.
https://doi.org/10.1097/01.wnp.0000220079.61973.6c
[17] Padoa-Schioppa, C. (2011). Neurobiology of Economic Choice: A Good-Based Model. Annual Review of Neuroscience, 34, 333-359.
https://doi.org/10.1146/annurev-neuro-061010-113648
[18] Peng, M., Shi, Y., Tang, R., Yang, X., Yang, H., Cai, M. et al. (2025). Good Luck or Bad Luck? The Influence of Social Comparison on Risk‐taking Decision and the Underlying Neural Mechanism. Psychophysiology, 62, e14730.
https://doi.org/10.1111/Psyp.14730
[19] Rangel, A., Camerer, C., & Montague, P. R. (2008). A Framework for Studying the Neurobiology of Value-Based Decision Making. Nature Reviews Neuroscience, 9, 545-556.
https://doi.org/10.1038/nrn2357
[20] Ren, P., Ma, M., Zhuang, Y., Huang, J., Tan, M., Wu, D. et al. (2024). Dorsal and Ventral Fronto-Amygdala Networks Underlie Risky Decision-Making in Age-Related Cognitive Decline. GeroScience, 46, 447-462.
https://doi.org/10.1007/s11357-023-00922-2
[21] Roach, B. J., & Mathalon, D. H. (2008). Event-Related EEG Time-Frequency Analysis: An Overview of Measures and an Analysis of Early Gamma Band Phase Locking in Schizophrenia. Schizophrenia Bulletin, 34, 907-926.
https://doi.org/10.1093/schbul/sbn093
[22] Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006). Neuroeconomics: Cross-Currents in Research on Decision-Making. Trends in Cognitive Sciences, 10, 108-116.
https://doi.org/10.1016/j.tics.2006.01.009
[23] Schrooten, M., Vandenberghe, R., Peeters, R., & Dupont, P. (2019). Quantitative Analyses Help in Choosing between Simultaneous vs. Separate EEG and fMRI. Frontiers in Neuroscience, 12, Article 1009.
https://doi.org/10.3389/fnins.2018.01009
[24] Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69, 99-118.
https://doi.org/10.2307/1884852
[25] Timashkov, A., Anderson, S., & Zinchenko, O. (2025). Neural Correlates of Uncertainty Processing: Meta-Analysis of fMRI Studies. Frontiers in Neuroscience, 19, Article 1662272.
https://doi.org/10.3389/fnins.2025.1662272
[26] Toplak, M. E., Sorge, G. B., Benoit, A., West, R. F., & Stanovich, K. E. (2010). Decision-Making and Cognitive Abilities: A Review of Associations between Iowa Gambling Task Performance, Executive Functions, and Intelligence. Clinical Psychology Review, 30, 562-581.
https://doi.org/10.1016/j.cpr.2010.04.002
[27] Tóth‐Fáber, E., & Kóbor, A. (2026). α and θ Oscillations Differentiate Escalating Risk Levels during Reward Anticipation in Sequential Decision Making. Psychophysiology, 63, e70333.
https://doi.org/10.1111/psyp.70333
[28] Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5, 297-323.
https://doi.org/10.1007/bf00122574
[29] Xue, G., Chen, C., Lu, Z. L., & Dong, Q. (2010). Brain Imaging Techniques and Their Applications in Decision-Making Research. Acta Psychologica Sinica, 42, 120-137.
https://doi.org/10.3724/sp.j.1041.2010.00120