第三方惩罚行为的认知神经机制

doi:10.3724/SP.J.1042.2024.00398

摘要/Abstract

摘要：

第三方惩罚(third-party punishment, TPP)指个体作为第三方或者观察者为维护社会规范对违规者所实施的惩罚行为。大量研究为揭示TPP行为的神经机制提供了启示, 但鲜有研究关注不同功能性脑网络在其中发挥的整体作用。本文综述了近10年来TPP相关的研究, 对相关理论模型和脑网络进行总结, 并在此基础上提出TPP的认知神经网络模型, 系统地对TPP行为背后的神经机制进行解释和整合。在该模型中, 情绪系统和奖赏系统是TPP的动力来源, 认知系统主要负责责任评估以及惩罚的选择; 奖赏网络、突显网络、默认模式网络和中央执行网络分别参与不同认知加工阶段。该模型建立了TPP相关研究在心理层面和认知神经层面上的联系, 对TPP行为的发生和发展机制进行了更加整体、全面的解释。未来可以引入元分析或基于机器学习的分析方法, 在不同的背景信息和更加复杂的社交情境下探讨第三方干预偏好以及背后的认知神经机制。

关键词: 第三方惩罚, 认知神经机制, 脑网络, fMRI

Abstract:

Third-party punishment (TPP) is individuals punish the norm violator as unaffected third parties even at a personal cost. Many studies have provided insight into the neural mechanisms underlying TPP behavior from evidence at the electrophysiological and functional imaging levels. However, this evidence has been restricted to a single component or has focused only on the results of activation in independent brain regions. Moreover, there is still a lack of holistic understanding regarding the connections between the cognitive processes underlying TPP behavior and the functional brain networks. Therefore, this paper reviews the research related to TPP in the past decade. First, we summarize theories that can explain TPP behavior in order to deepen the understanding of the theoretical dimensions of TPP behavior. These theoretical models include the reciprocity model, which reflects individual preferences for cooperation and fairness, the emotion model of intuitive processing, and the dual-systems model, which integrates emotional and cognitive factors under a reinforcement learning perspective.

Second, we conduct a review of functional neuroimaging and electrophysiological evidence that pertains to TPP, with a particular emphasis on the inter- and intra-network connectivity within the brain. Taking into account the functions and activation patterns of the relevant brain networks in previous studies, we suggest that the generation of TPP behavior is divided into three phases: emotion generation, responsibility assessment and punishment selection. The corresponding brain networks are salient network, default mode network, and central executive network. In addition, the reward network collaborates in TPP processing, mainly playing the role of value representation and expected reward.

Finally, in order to explain the occurrence mechanism of TPP behavior from a more comprehensive perspective, we integrate the results of previous studies and propose a cognitive neural network model of TPP. In this model, the affective system and the reward system jointly function as the motivation system for TPP, playing a role in generating motivation for TPP behavior. The corresponding brain networks associated with these systems are the salience network and the reward network, respectively. The cognitive system consists of two subsystems: the social cognitive system and the executive control system. These subsystems play a role in two phases of TPP: responsibility assessment and punishment selection. The default mode network and the central executive network are the respective brain networks associated with these two phases. The components of the model cooperate and interact with each other, and ultimately the executive control system makes the decision of whether to punish and the intensity of punishment. The feedback information generated in turn influences the internal loop, enabling the individual to learn and refine their behavioral strategy based on each feedback. Over time, this process leads to the development of a stable behavioral pattern. The model establishes the connection between TPP behavior-related research at the psychological and cognitive-neural level. Moreover, it provides a more holistic and comprehensive explanation of the mechanisms of TPP behavior and suggests that TPP is a dynamic process with feedback and reinforcement involvement.

In the future, researchers can further explore TPP behavior from the following perspectives: (a) Starting from a more microscopic perspective such as neurotransmitters and hormones to reveal the neurophysiological mechanisms of TPP behavior; (b) Incorporating individual differences to explore the relationship between neurophysiological representations of TPP behavior and personality trait variables; (c) Introducing machine learning algorisms to further optimize and develop the relevant models to provide quantitative explanations and predictions of TPP behavior; (d) Utilizing meta-analysis to provide quantitative data support for the models to increase their reliability; (e) Exploring third-party intervention preferences and the underlying cognitive neural mechanisms in different contextual information or more complex social contexts.

Key words: third-party punishment, cognitive neural mechanisms, brain network, fMRI

中图分类号:

郑好, 陈荣荣, 买晓琴. (2024). 第三方惩罚行为的认知神经机制. 心理科学进展 , 32(2), 398-412.

ZHENG Hao, CHEN Rongrong, MAI Xiaoqin. (2024). The cognitive and neural mechanism of third-party punishment. Advances in Psychological Science, 32(2), 398-412.

图/表 5

参考文献 132

[1]	陈新文, 李鸿杰, 丁玉珑. (2023). 探究事件相关脑电/脑磁信号中的神经表征模式:基于分类解码和表征相似性分析的方法. 心理科学进展, 31(2), 173-195. doi: 10.3724/SP.J.1042.2023.00173
[2]	陈瀛, 徐敏霞, 汪新建. (2020). 信任的认知神经网络模型. 心理科学进展, 28(5), 800-809. doi: 10.3724/SP.J.1042.2020.00800
[3]	董奇. (2004). 心理与教育研究方法 (修订版). 北京师范大学出版社.
[4]	刘映杰, 段亚妮, 刘昊馨, 刘佳, 王赫. (2022). 得失情境下第三方惩罚决策差异的神经机制:基于rTMS的研究. 心理科学, 45(4), 942-952.
[5]	罗艺, 封春亮, 古若雷, 吴婷婷, 罗跃嘉. (2013). 社会决策中的公平准则及其神经机制. 心理科学进展, 21(2), 300-308. doi: 10.3724/SP.J.1042.2013.00300
[6]	苏彦捷, 谢东杰, 王笑楠. (2019). 认知控制在第三方惩罚中的作用. 心理科学进展, 27(8), 1331-1343. doi: 10.3724/SP.J.1042.2019.01331
[7]	唐捷, 黄晓璇, 吴嵩, 崔芳. (2022). 财富越多, 责任越大:资金数量和来源对公共物品困境中第三方惩罚的影响. 心理科学, 45(3), 665-671.
[8]	吴燕, 罗跃嘉. (2011). 利他惩罚中的结果评价——ERP研究. 心理学报, 43(6), 661-673.
[9]	谢东杰, 苏彦捷. (2019). 第三方惩罚的演化与认知机制. 心理科学, 42(1), 216-222.
[10]	杨莎莎, 陈思静. (2022). 第三方惩罚中的规范错觉:基于公正世界信念的解释. 心理学报, 54(3), 281-299. doi: 10.3724/SP.J.1041.2022.00281
[11]	殷西乐, 李建标, 陈思宇, 刘晓丽, 郝洁. (2019). 第三方惩罚的神经机制:来自经颅直流电刺激的证据. 心理学报, 51(5), 571-583. doi: 10.3724/SP.J.1041.2019.00571
[12]	张慧, 马红宇, 徐富明, 刘燕君, 史燕伟. (2018). 最后通牒博弈中的公平偏好:基于双系统理论的视角. 心理科学进展, 26(2), 319-330. doi: 10.3724/SP.J.1042.2018.00319
[13]	张耀华, 林珠梅, 朱莉琪. (2013). 人类的利他性惩罚:认知神经科学的视角. 生物化学与生物物理进展, 40(9), 796-803.
[14]	Anne, L., Frank, K., Olga, D. M., Matteo, P., Sarah, J. P., Jeffrey, S., & Jordan, G. (2012). Damage to the left ventromedial prefrontal cortex impacts affective theory of mind. Social Cognitive and Affective Neuroscience, 7(8), 871-880. doi: 10.1093/scan/nsr071 pmid: 22021651
[15]	Asp, E. W., Gullickson, J. T., Warner, K. A., Koscik, T. R., Denburg, N. L., & Tranel, D. (2019). Soft on crime: Patients with ventromedial prefrontal cortex damage allocate reduced third-party punishment to violent criminals. Cortex, 119, 33-45. doi: S0010-9452(19)30155-8 pmid: 31071555
[16]	Baumgartner, T., Götte, L., R, Gügler, & Fehr, E. (2012). The mentalizing network orchestrates the impact of parochial altruism on social norm enforcement. Human Brain Mapping, 33(6), 1452-1469. doi: 10.1002/hbm.21298 pmid: 21574212
[17]	Baumgartner, T., Schiller, B., Rieskamp, J., Gianotti, L. R. R., & Knoch, D. (2014). Diminishing parochialism in intergroup conflict by disrupting the right temporo-parietal junction. Social Cognitive and Affective Neuroscience, 9(5), 653-660. doi: 10.1093/scan/nst023 pmid: 23482623
[18]	Bellucci, G., Camilleri, J. A., Iyengar, V., Eickhoff, S. B., & Krueger, F. (2020). The emerging neuroscience of social punishment: Meta-analytic evidence. Neuroscience and Biobehavioral Reviews, 113, 426-439. doi: S0149-7634(20)30066-X pmid: 32302599
[19]	Bellucci, G., Chernyak, S., Hoffman, M., Deshpande, G., Monte, O. D., Knutson, K., Grafman, J., & Krueger, F. (2017). Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence. Social Neuroscience, 12(2), 124-134. doi: 10.1080/17470919.2016.1153518 pmid: 26942651
[20]	Bernhard, H., Fehr, E., & Fischbacher, U. (2006). Group affiliation and altruistic norm enforcement. American Economic Review, 96(2), 217-221. doi: 10.1257/000282806777212594 URL
[21]	Bright, D. A., & Goodman-Delahunty, J. (2006). Gruesome evidence and emotion: Anger, blame, and jury decision- making. Law and Human Behavior, 30(2), 183-202. pmid: 16786406
[22]	Brüne, M., von Hein, S. M., Claassen, C., Hoffmann, R., & Saft, C. (2021). Altered third-party punishment in Huntington's disease: A study using neuroeconomic games. Brain and Behavior, 11(1), Article e01908.
[23]	Buckholtz, J. W., Asplund, C. L., Dux, P. E., Zald, D. H., Gore, J. C., Jones, O. D., & Marois, R. (2008). The neural correlates of third-party punishment. Neuron, 60(5), 930-940. doi: 10.1016/j.neuron.2008.10.016 pmid: 19081385
[24]	Buckholtz, J. W., & Marois, R. (2012). The roots of modern justice: Cognitive and neural foundations of social norms and their enforcement. Nature Neuroscience, 15(5), 655-661. doi: 10.1038/nn.3087 pmid: 22534578
[25]	Buckholtz, J. W., Martin, J. W., Treadway, M. T., Jan, K., Zald, D. H., Jones, O., & Marois, R. (2015). From blame to punishment: Disrupting prefrontal cortex activity reveals norm enforcement mechanisms. Neuron, 87(6), 1369-1380. doi: S0896-6273(15)00717-5 pmid: 26386518
[26]	Cheng, X., Zheng, L., Liu, Z., Ling, X., Wang, X., Ouyang, H., Chen, X., Huang, D., & Guo, X. (2022). Punishment cost affects third-parties’ behavioral and neural responses to unfairness. International Journal of Psychophysiology, 177, 27-33. doi: 10.1016/j.ijpsycho.2022.04.003 URL
[27]	Chung, J. C. Y., Bhatoa, R. S., Kirkpatrick, R., & Woodcock, K. A. (2023). The role of emotion regulation and choice repetition bias in the ultimatum game. Emotion, 23(4), 925-936
[28]	Ciaramidaro, A., Toppi, J., Casper, C., Freitag, C. M., Siniatchkin, M., & Astolfi, L. (2018). Multiple-brain connectivity during third party punishment: An EEG hyperscanning study. Scientific Reports, 8(1), Article 6822.
[29]	Civai, C. (2013). Rejecting unfairness: Emotion-driven reaction or cognitive heuristic? Frontiers in Human Neuroscience, 7, Article 126.
[30]	Civai, C., Crescentini, C., Rustichini, A., & Rumiati, R. I. (2012). Equality versus self-interest in the brain: Differential roles of anterior insula and medial prefrontal cortex. Neuroimage, 62(1), 102-112. doi: 10.1016/j.neuroimage.2012.04.037 pmid: 22548807
[31]	Civai, C., Huijsmans, I., & Sanfey, A. G. (2019). Neurocognitive mechanisms of reactions to second- and third-party justice violations. Scientific Reports, 9(1), Article 9271.
[32]	Contini, E. W., Wardle, S. G., & Carlson, T. A. (2017). Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions. Neuropsychologia, 105, 165-176. doi: S0028-3932(17)30059-3 pmid: 28215698
[33]	Craig, A. D. B. (2009). How do you feel—now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10(1), 59-70. doi: 10.1038/nrn2555 pmid: 19096369
[34]	Cui, F., Wang, C., Cao, Q., & Jiao, C. (2019). Social hierarchies in third-party punishment: A behavioral and ERP study. Biological Psychology, 146, Article 107722.
[35]	Cui, F., Wu, S., Wu, H., Wang, C., Jiao, C., & Luo, Y. (2018). Altruistic and self-serving goals modulate behavioral and neural responses in deception. Social Cognitive and Affective Neuroscience, 13(1), 63-71. doi: 10.1093/scan/nsx138 pmid: 29149322
[36]	de Quervain, D. J., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., & Fehr, E. (2004). The neural basis of altruistic punishment. Science, 305(5688), 1254-1258. doi: 10.1126/science.1100735 pmid: 15333831
[37]	Delgado, M. R., Locke, H. M., Stenger, V. A., & Fiez, J. A. (2003). Dorsal striatum responses to reward and punishment: Effects of valence and magnitude manipulations. Cognitive, Affective, & Behavioral Neuroscience, 3(1), 27-38.
[38]	Emanuele, E., Brondino, N., Bertona, M., Re, S., & Geroldi, D. (2008). Relationship between platelet serotonin content and rejections of unfair offers in the ultimatum game. Neuroscience Letters, 437(2), 158-161. doi: 10.1016/j.neulet.2008.04.006 pmid: 18448251
[39]	Enge, S., Mothes, H., Fleischhauer, M., Reif, A., & Strobel, A. (2017). Genetic variation of dopamine and serotonin function modulates the feedback-related negativity during altruistic punishment. Scientific Reports, 7(1), Article 2996.
[40]	Etkin, A., Büchel, C., & Gross, J. J. (2015). The neural bases of emotion regulation. Nature Reviews Neuroscience, 16(11), 693-700. doi: 10.1038/nrn4044 pmid: 26481098
[41]	Fehr, E., & Fischbacher, U. (2003). The nature of human altruism. Nature, 425(6960), 785-791. doi: 10.1038/nature02043
[42]	Fehr, E., & Fischbacher, U. (2004a). Third-party punishment and social norms. Evolution and Human Behavior, 25(2), 63-87. doi: 10.1016/S1090-5138(04)00005-4 URL
[43]	Fehr, E., & Fischbacher, U. (2004b). Social norms and human cooperation. Trends in Cognitive Sciences, 8(4), 185-190. doi: 10.1016/j.tics.2004.02.007 URL
[44]	Fehr, E., & Gächter, S. (2002). Altruistic punishment in humans. Nature, 415(6868), 137-140. doi: 10.1038/415137a
[45]	Fehr, E., & Schurtenberger, I. (2018). Normative foundations of human cooperation. Nature Human Behaviour, 2(7), 458-468. doi: 10.1038/s41562-018-0385-5 pmid: 31097815
[46]	Feng, C., Deshpande, G., Liu, C., Gu, R., Luo, Y. J., & Krueger, F. (2016). Diffusion of responsibility attenuates altruistic punishment: A functional magnetic resonance imaging effective connectivity study. Human Brain Mapping, 37(2), 663-677. doi: 10.1002/hbm.23057 pmid: 26608776
[47]	Feng, C., Yang, Q., Azem, L., Atanasova, K. M., Gu, R., Luo, W., Hoffman, M., Lis, S., & Krueger, F. (2022). An fMRI investigation of the intention-outcome interactions in second- and third-party punishment. Brain Imaging and Behavior, 16(2), 715-727. doi: 10.1007/s11682-021-00555-z
[48]	Ferguson, E., Quigley, E., Powell, G., Stewart, L., Harrison, F., & Tallentire, H. (2019). To help or punish in the face of unfairness: Men and women prefer mutually-beneficial strategies over punishment in a sexual selection context. Royal Society Open Science, 6(9), Article 181441.
[49]	Gershman, S. J., Markman, A. B., & Otto, A. R. (2014). Retrospective revaluation in sequential decision making: A tale of two systems. Journal of Experimental Psychology: General, 143(1), 182-194. doi: 10.1037/a0030844 URL
[50]	Ginther, M. R., Bonnie, R. J., Hoffman, M. B., Shen, F. X., Simons, K. W., Jones, O. D., & Marois, R. (2016). Parsing the behavioral and brain mechanisms of third-party punishment. Journal of Neuroscience, 36(36), 9420-9434. doi: 10.1523/JNEUROSCI.4499-15.2016 pmid: 27605616
[51]	Glass, L., Moody, L., Grafman, J., & Krueger, F. (2016). Neural signatures of third-party punishment: Evidence from penetrating traumatic brain injury. Social Cognitive and Affective Neuroscience, 11(2), 253-262. doi: 10.1093/scan/nsv105 pmid: 26276809
[52]	Guala, F. (2012). Reciprocity: Weak or strong? What punishment experiments do (and do not) demonstrate. Behavioral and Brain Sciences, 35(1), 1-15. doi: 10.1017/S0140525X11000069 pmid: 22289303
[53]	Hu, J., Blue, P. R., Yu, H., Gong, X., Xiang, Y., Jiang, C., & Zhou, X. (2016). Social status modulates the neural response to unfairness. Social Cognitive and Affective Neuroscience, 11(1), 1-10. doi: 10.1093/scan/nsv086 pmid: 26141925
[54]	Hu, Y., Scheele, D., Becker, B., Voos, G., David, B., Hurlemann, R., & Weber, B. (2016). The effect of oxytocin on third-party altruistic decisions in unfair situations: An fMRI study. Scientific Reports, 6, Article 20236.
[55]	Hu, Y., Strang, S., & Weber, B. (2015). Helping or punishing strangers: Neural correlates of altruistic decisions as third- party and of its relation to empathic concern. Frontiers in Behavioral Neuroscience, 9, Article 24.
[56]	Ikemoto, S. (2010). Brain reward circuitry beyond the mesolimbic dopamine system: A neurobiological theory. Neuroscience and Biobehavioral Reviews, 35(2), 129-150. doi: 10.1016/j.neubiorev.2010.02.001 pmid: 20149820
[57]	Jamali, M., Grannan, B. L., Fedorenko, E., Saxe, R., Báez- Mendoza, R., & Williams, Z. M. (2021). Single-neuronal predictions of others' beliefs in humans. Nature, 591(7851), 610-614. doi: 10.1038/s41586-021-03184-0
[58]	Johnson, R., Henkell, H., Simon, E., & Zhu, J. (2008). The self in conflict: The role of executive processes during truthful and deceptive responses about attitudes. NeuroImage, 39(1), 469-482. pmid: 17919934
[59]	Jordan, J. J., Hoffman, M., Bloom, P., & Rand, D. G. (2016). Third-party punishment as a costly signal of trustworthiness. Nature, 530(7591), 473-476. doi: 10.1038/nature16981
[60]	Jordan, J., McAuliffe, K., & Rand, D. (2016). The effects of endowment size and strategy method on third party punishment. Experimental Economics, 19(4), 741-763. doi: 10.1007/s10683-015-9466-8 URL
[61]	Kanakogi, Y., Miyazaki, M., Takahashi, H., Yamamoto, H., Kobayashi, T., & Hiraki, K. (2022). Third-party punishment by preverbal infants. Nature Human Behaviour, 6(9), 1234-1242. doi: 10.1038/s41562-022-01354-2 pmid: 35680993
[62]	Kim, M., Decety, J., Wu, L., Baek, S., & Sankey, D. (2021). Neural computations in children’s third-party interventions are modulated by their parents’ moral values. NPJ Science of Learning, 6(1), Article 38.
[63]	Knoch, D., Nitsche, M. A., Fischbacher, U., Eisenegger, C., Pascual-Leone, A., & Fehr, E. (2008). Studying the neurobiology of social interaction with transcranial direct current stimulation—The example of punishing unfairness. Cerebral Cortex, 18(9), 1987-1990. doi: 10.1093/cercor/bhm237 URL
[64]	Knoch, D., Pascual-Leone, A., Meyer, K., Treyer, V., & Fehr, E. (2006). Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science, 314(5800), 829-832. doi: 10.1126/science.1129156 pmid: 17023614
[65]	Konishi, N., & Ohtsubo, Y. (2015). Does dishonesty really invite third-party punishment? Results of a more stringent test. Biology Letters, 11(5), Article 20150172.
[66]	Köster, R., Hadfield-Menell, D., Everett, R., Weidinger, L., Hadfield, G. K., & Leibo, J. Z. (2022). Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents. Proceedings of the National Academy of Sciences, 119(3), Article e2106028118.
[67]	Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the effectiveness of work groups and teams. Psychological Science in the Public Interest, 7(3), 77-124. doi: 10.1111/j.1529-1006.2006.00030.x pmid: 26158912
[68]	Krueger, F., & Hoffman, M. (2016). The emerging neuroscience of third-party punishment. Trends in Neurosciences, 39(8), 499-501. doi: S0166-2236(16)30055-8 pmid: 27369844
[69]	Krueger, F., Parasuraman, R., Moody, L., Twieg, P., de Visser, E., Mccabe, K., O’Hara, M., & Lee, M. (2012). Oxytocin selectively increases perceptions of harm for victims but not the desire to punish offenders of criminal offenses. Social Cognitive and Affective Neuroscience, 8(5), 494-498. doi: 10.1093/scan/nss026 URL
[70]	Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014). Neural computations underlying arbitration between model-based and model-free learning. Neuron, 81(3), 687-699. doi: 10.1016/j.neuron.2013.11.028 pmid: 24507199
[71]	Lergetporer, P., Angerer, S., Glätzle-Rützler, D., & Sutter, M. (2014). Third-party punishment increases cooperation in children through (misaligned) expectations and conditional cooperation. Proceedings of the National Academy of Sciences of the United States of America, 111(19), 6916-6921. doi: 10.1073/pnas.1320451111 pmid: 24778231
[72]	Li, J., Li, S., Wang, P., Liu, X., Zhu, C., Niu, X., Wang, G., & Yin, X. (2018). Fourth-party evaluation of third-party pro-social help and punishment: An ERP study. Frontiers in Psychology, 9, Article 932.
[73]	Liu, Y., Bian, X., Hu, Y., Chen, Y.-T., Li, X., & Di Fabrizio, B. (2018). Intergroup bias influences third-party punishment and compensation: In-group relationships attenuate altruistic punishment. Social Behavior and Personality: An International Journal, 46(8), 1397-1408. doi: 10.2224/sbp.7193 URL
[74]	Lo Gerfo, E., Gallucci, A., Morese, R., Vergallito, A., Ottone, S., Ponzano, F., Locatelli, G., Bosco, F., & Romero Lauro, L. J. (2019). The role of ventromedial prefrontal cortex and temporo-parietal junction in third-party punishment behavior. NeuroImage, 200, 501-510. doi: S1053-8119(19)30544-0 pmid: 31233906
[75]	Lockwood, P. L., Apps, M. A. J., Valton, V., Viding, E., & Roiser, J. P. (2016). Neurocomputational mechanisms of prosocial learning and links to empathy. Proceedings of the National Academy of Sciences of the United States of America, 113(35), 9763-9768. doi: 10.1073/pnas.1603198113 pmid: 27528669
[76]	Marsh, N., Marsh, A. A., Lee, M. R., & Hurlemann, R. (2021). Oxytocin and the neurobiology of prosocial behavior. The Neuroscientist, 27(6), 604-619. doi: 10.1177/1073858420960111 URL
[77]	Martin, J. W., Martin, S., & McAuliffe, K. (2021). Third-party punishment promotes fairness in children. Developmental psychology, 57(6), 927-939. doi: 10.1037/dev0001183 pmid: 34424010
[78]	McAuliffe, K., Jordan, J. J., & Warneken, F. (2015). Costly third-party punishment in young children. Cognition, 134, 1-10. doi: 10.1016/j.cognition.2014.08.013 pmid: 25460374
[79]	Mclatchie, N., Giner-Sorolla, R., & Derbyshire, S. W. G. (2016). ‘Imagined guilt’ vs ‘recollected guilt’: Implications for fMRI. Social Cognitive and Affective Neuroscience, 11(5), 703-711. doi: 10.1093/scan/nsw001 pmid: 26746179
[80]	Meidenbauer, K. L., Cowell, J. M., & Decety, J. (2018). Children’s neural processing of moral scenarios provides insight into the formation and reduction of in‐group biases. Developmental Science, 21(6), Article e12676.
[81]	Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214(5-6), 655-667. doi: 10.1007/s00429-010-0262-0 URL
[82]	Moll, J., de Oliveira-Souza, R., Basilio, R., Bramati, I. E., Gordon, B., Rodríguez-Nieto, G.,... Grafman, J. (2018). Altruistic decisions following penetrating traumatic brain injury. Brain, 141(5), 1558-1569. doi: 10.1093/brain/awy064 pmid: 29590314
[83]	Morese, R., Rabellino, D., Sambataro, F., Perussia, F., Valentini, M. C., Bara, B. G., & Bosco, F. M. (2016). Group membership modulates the neural circuitry underlying third party punishment. PLoS One, 11(11), Article e0166357.
[84]	Morris, A., MacGlashan, J., Littman, M. L., & Cushman, F. (2017). Evolution of flexibility and rigidity in retaliatory punishment. Proceedings of the National Academy of Sciences, 114(39), 10396-10401. doi: 10.1073/pnas.1704032114 URL
[85]	Mothes, H., Enge, S., & Strobel, A. (2016). The interplay between feedback-related negativity and individual differences in altruistic punishment: An EEG study. Cognitive, Affective, & Behavioral Neuroscience, 16(2), 276-288.
[86]	Mussel, P., Hewig, J., & Weiß, M. (2018). The reward-like nature of social cues that indicate successful altruistic punishment. Psychophysiology, 55(9), Article e13093.
[87]	Naqvi, N., Shiv, B., & Bechara, A. (2006). The role of emotion in decision making: A cognitive neuroscience perspective. Current Directions in Psychological Science, 15(5), 260-264. doi: 10.1111/j.1467-8721.2006.00448.x URL
[88]	Nelissen, R. M. A., & Zeelenberg, M. (2009). Moral emotions as determinants of third-party punishment: Anger, guilt, and the functions of altruistic sanctions. Judgment and Decision Making, 4(7), 543-553. doi: 10.1017/S1930297500001121 URL
[89]	Ouyang, H., O’Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004). Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304(5669), 452-454. doi: 10.1126/science.1094285 pmid: 15087550
[90]	Ouyang, H., Yu, J., Duan, J., Zheng, L., Li, L., & Guo, X. (2021). Empathy-based tolerance towards poor norm violators in third-party punishment. Experimental Brain Research, 239(7), 2171-2180. doi: 10.1007/s00221-021-06128-2 pmid: 33978785
[91]	Pedersen, E. J., McAuliffe, W. H. B., & McCullough, M. E. (2018). The unresponsive avenger: More evidence that disinterested third parties do not punish altruistically. Journal of Experimental Psychology: General, 147(4), 514-544. doi: 10.1037/xge0000410 URL
[92]	Pessoa, L. (2017). A network model of the emotional brain. Trends in Cognitive Sciences, 21(5), 357-371. doi: S1364-6613(17)30036-0 pmid: 28363681
[93]	Piazza, J., & Bering, J. M. (2008). The effects of perceived anonymity on altruistic punishment. Evolutionary Psychology, 6(3), 487-501.
[94]	Qu, L., Dou, W., You, C., & Qu, C. (2014). The processing course of conflicts in third-party punishment: An event-related potential study. Psychology Journal, 3(3), 214-221. doi: 10.2307/1411116 URL
[95]	Rahal, R.-M., & Fiedler, S. (2022). Cognitive and affective processes of prosociality. Current Opinion in Psychology, 44, 309-314. doi: 10.1016/j.copsyc.2021.10.007 URL
[96]	Rai, T. S. (2022). Material benefits crowd out moralistic punishment. Psychological Science, 33(5), 789-797. doi: 10.1177/09567976211054786 URL
[97]	Raihani, N. J., & Bshary, R. (2015a). The reputation of punishers. Trends in Ecology & Evolution, 30(2), 98-103. doi: 10.1016/j.tree.2014.12.003 URL
[98]	Raihani, N. J., & Bshary, R. (2015b). Third-party punishers are rewarded, but third-party helpers even more so. Evolution, 69(4), 993-1003. doi: 10.1111/evo.12637 URL
[99]	Raihani, N. J., & McAuliffe, K. (2012). Human punishment is motivated by inequity aversion, not a desire for reciprocity. Biology Letters, 8(5), 802-804. doi: 10.1098/rsbl.2012.0470 pmid: 22809719
[100]	Rothschild, Z. K., & Keefer, L. A. (2018). Righteous or self- righteous anger? Justice sensitivity moderates defensive outrage at a third-party harm-doer. European Journal of Social Psychology, 48(4), 507-522. doi: 10.1002/ejsp.2018.48.issue-4 URL
[101]	Ruff, C. C., & Fehr, E. (2014). The neurobiology of rewards and values in social decision making. Nature Reviews Neuroscience, 15(8), 549-562. doi: 10.1038/nrn3776 pmid: 24986556
[102]	Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science, 300(5626), 1755-1758. doi: 10.1126/science.1082976 pmid: 12805551
[103]	Schultz, W. (2007). Behavioral dopamine signals. Trends in Neurosciences, 30(5), 203-210. doi: 10.1016/j.tins.2007.03.007 pmid: 17400301
[104]	Schultz, W. (2013). Updating dopamine reward signals. Current Opinion in Neurobiology, 23(2), 229-238. doi: 10.1016/j.conb.2012.11.012 pmid: 23267662
[105]	Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19(10), 1286-1291. doi: 10.1038/nn.4384 pmid: 27669989
[106]	Singer, T., Critchley, H. D., & Preuschoff, K. (2009). A common role of insula in feelings, empathy and uncertainty. Trends in Cognitive Sciences, 13(8), 334-340. doi: 10.1016/j.tics.2009.05.001 pmid: 19643659
[107]	Stallen, M., Rossi, F., Heijne, A., Smidts, A., De Dreu, C. K. W., & Sanfey, A. G. (2018). Neurobiological mechanisms of responding to injustice. The Journal of Neuroscience, 38(12), 2944-2954. doi: 10.1523/JNEUROSCI.1242-17.2018 URL
[108]	Strobel, A., Zimmermann, J., Schmitz, A., Reuter, M., Lis, S., Windmann, S., & Kirsch, P. (2011). Beyond revenge: Neural and genetic bases of altruistic punishment. NeuroImage, 54(1), 671-680. doi: 10.1016/j.neuroimage.2010.07.051 pmid: 20673803
[109]	Sun, L., Tan, P., Cheng, Y., Chen, J., & Qu, C. (2015). The effect of altruistic tendency on fairness in third-party punishment. Frontiers in Psychology, 6, Article 820.
[110]	Thaler, R. H. (1988). Anomalies: The ultimatum game. Journal of Economic Perspectives, 2(4), 195-206. doi: 10.1257/jep.2.4.195 URL
[111]	Treadway, M. T., Buckholtz, J. W., Martin, J. W., Jan, K., Asplund, C. L., Ginther, M. R., Jones, O. D., & Marois, R. (2014). Corticolimbic gating of emotion-driven punishment. Nature Neuroscience, 17(9), 1270-1275. doi: 10.1038/nn.3781 pmid: 25086609
[112]	van Baar, J. M., Halpern, D. J., & FeldmanHall, O. (2021). Intolerance of uncertainty modulates brain-to-brain synchrony during politically polarized perception. Proceedings of the National Academy of Sciences of the United States of America, 118(20), Article e2022491118.
[113]	van der Helden, J., Boksem, M. A. S., & Blom, J. H. G. (2010). The importance of failure: Feedback-related negativity predicts motor learning efficiency. Cerebral Cortex, 20(7), 1596-1603. doi: 10.1093/cercor/bhp224 URL
[114]	Wang, L., Lu, X., Gu, R., Zhu, R., Xu, R., Broster, L. S., & Feng, C. (2017). Neural substrates of context- and person- dependent altruistic punishment. Human Brain Mapping, 38(11), 5535-5550. doi: 10.1002/hbm.v38.11 URL
[115]	Wang, R., Yu, R., Tian, Y., & Wu, H. (2022). Individual variation in the neurophysiological representation of negative emotions in virtual reality is shaped by sociability. NeuroImage, 263, Article 119596.
[116]	Wise, R. A., & Rompre, P.-P. (1989). Brain dopamine and reward. Annual Review of Psychology, 40(1), 191-225. doi: 10.1146/psych.1989.40.issue-1 URL
[117]	Wu, Y., Leliveld, M. C., & Zhou, X. (2011). Social distance modulates recipient’s fairness consideration in the dictator game: An ERP study. Biological Psychology, 88(2), 253-262. doi: 10.1016/j.biopsycho.2011.08.009 URL
[118]	Xiao, E., & Houser, D. (2005). Emotion expression in human punishment behavior. Proceedings of the National Academy of Sciences of the United States of America, 102(20), 7398-7401. pmid: 15878990
[119]	Xie, E., Liu, M., Liu, J., Gao, X., & Li, X. (2022). Neural mechanisms of the mood effects on third‐party responses to injustice after unfair experiences. Human Brain Mapping, 43(12), 3646-3661. doi: 10.1002/hbm.v43.12 URL
[120]	Xie, H., Karipidis, I. I., Howell, A., Schreier, M., Sheau, K. E., Manchanda, M. K.,... Saggar, M. (2020). Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm. Proceedings of the National Academy of Sciences of the United States of America, 117(37), 23066-23072.
[121]	Yamagishi, T., Li, Y., Fermin, A. S. R., Kanai, R., Takagishi, H., Matsumoto, Y., Kiyonari, T., & Sakagami, M. (2017). Behavioural differences and neural substrates of altruistic and spiteful punishment. Scientific Reports, 7(1), Article 14654.
[122]	Yang, C., Xiao, K., Ao, Y., Cui, Q., Jing, X., & Wang, Y. (2023). The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the Granger causality analysis. NeuroImage: Clinical, 37, Article 103295.
[123]	Yang, J., Gu, R., Liu, J., Deng, K., Huang, X., Luo, Y. J., & Cui, F. (2022). To blame or not? Modulating third-party punishment with the framing effect. Neuroscience Bulletin, 38(5), 533-547. doi: 10.1007/s12264-021-00808-3 pmid: 34988911
[124]	Yang, Q., Shao, R., Zhang, Q., Li, C., Li, Y., Li, H., & Lee, T. (2019). When morality opposes the law: An fMRI investigation into punishment judgments for crimes with good intentions. Neuropsychologia, 127, 195-203. doi: S0028-3932(18)30505-0 pmid: 30802462
[125]	Yang, Z., Zheng, Y., Yang, G., Li, Q., & Liu, X. (2019). Neural signatures of cooperation enforcement and violation: A coordinate-based meta-analysis. Social Cognitive and Affective Neuroscience, 14(9), 919-931. doi: 10.1093/scan/nsz073 pmid: 31593233
[126]	Zhao, Y., Wang, D., Wang, X., & Chiu, S. C. (2022). Brain mechanisms underlying the influence of emotions on spatial decision-making: An EEG study. Frontiers in Neuroscience, 16, Article 989988.
[127]	Zhong, S., Chark, R., Hsu, M., & Chew, S. H. (2016). Computational substrates of social norm enforcement by unaffected third parties. NeuroImage, 129, 95-104. doi: S1053-8119(16)00055-0 pmid: 26825438
[128]	Zhou, Y., Jiao, P., & Zhang, Q. (2017). Second-party and third-party punishment in a public goods experiment. Applied Economics Letters, 24(1), 54-57. doi: 10.1080/13504851.2016.1161709 URL
[129]	Zhou, Y., Wang, Y., Rao, L. L., Yang, L. Q., & Li, S. (2014). Money talks: Neural substrate of modulation of fairness by monetary incentives. Frontiers in Behavioral Neuroscience, 8, Article 150.
[130]	Zinchenko, O., Belianin, A., & Klucharev, V. (2019). The role of the temporoparietal and prefrontal cortices in a third-party punishment: A tDCS study. Psychology: Journal of the Higher School of Economics, 16(3), 529-550.
[131]	Zinchenko, O., & Klucharev, V. (2017). Commentary: The emerging neuroscience of third-party punishment. Frontiers in Human Neuroscience, 11, Article 512.
[132]	Zinchenko, O., Nikulin, V., & Klucharev, V. (2021). Wired to punish? Electroencephalographic study of the resting- state neuronal oscillations underlying third-party punishment. Neuroscience, 471, 1-10. doi: 10.1016/j.neuroscience.2021.07.012 pmid: 34302905

研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Baumgartner et al. (2014)	36名健康男性 M ± SD = 24.3 ± 4.2岁	rTMS 1. 刺激脑区：左/右侧TPJ 2. 被试作为玩家C, 观察玩家A、B (与C的关系为in/in, in/out, out/in, out/out)进行囚徒困境任务, 分为4个条件(合作/合作, 合作/背叛, 背叛/合作, 背叛/背叛)	对玩家A的惩罚程度情绪评级(愤怒、报复, 改善未来行为、公正)	对外群体违规成员的报复情绪在其中起到了中介作用心智化	TPJ	相比于内群体, 外群体违规会受到更多的惩罚, TPJ在其中起到重要作用。
Krueger et al. (2012)	26名健康成人 (男性13名) M ± SD = 26.0 ± 5.7岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 填写自由意志和决定论问卷	情绪效价情绪评分自由意志得分惩罚程度(0-100)	TPP程度与负性情绪强度相关对他人意图的推测	TPJ AIC	高自由意志主义者对低情感案件惩罚更强烈。
Treadway et al. (2014)	30名健康成人 (男性20名) 平均年龄22.8岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 2(形象语言, 直白语言)×2(故意, 无意)混合实验设计	惩罚程度(0-9)	负面情绪的增加导致了更大的惩罚对意图的推测：故意的伤害会有更大的惩罚	杏仁核 dlPFC	情绪和意图推断共同决定惩罚, 并且有时候意图推断比情绪更加重要。
Hu et al. (2015)	30名德国成人 (男性12名) M ± SD = 22.72 ± 2.85岁	fMRI 第三方惩罚/帮助的DG任务	特质共情评分惩罚/帮助程度	内在奖赏	纹状体	惩罚和帮助都和奖赏相关, 特质共情更高的个体更倾向于帮助。
Buckholtz et al. (2015)	66名健康成人 (男性33名) 18-30岁	rTMS 1. 刺激脑区：左/右dlPFC 2. 对犯罪场景进行惩罚程度评分, 分为R (负全责)以及DR (因胁迫、精神病导致责任减轻)两种场景	责备性评分惩罚程度(0-9)	责任评估和惩罚选择是两个独立的认知过程	dlPFC	抑制dlPFC活动降低了惩罚程度, 但不影响责备性评分。
Sun et al. (2015)	32名大学生 (男性10名) 18-24岁	ERP 1. 第三方惩罚的DG任务 2. 高利他主义者/低利他主义者	惩罚程度	不公平厌恶	MFN：预期违背 P300：情感、注意力	高利他主义者比低利他主义者有更强的不平等厌恶, 这可能导致更多的惩罚行为。
Morese et al. (2016)	23名男性大学生 M ± SD = 24.56 ± 1.87岁	fMRI 1. 第三方惩罚的DG任务 2. 博弈双方的群体关系为：in/in, in/out, out/in, out/out	惩罚程度	心智化、奖赏	vmPFC TPJ	被试对外群体成员的惩罚更加严厉。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Feng et al. (2016)	21名学生 (男性10名) M ± SD = 22.9 ± 1.6岁	fMRI 1. 第三方惩罚的DG任务 2. 2(公平, 不公平)×2(单独, 他人在场)被试内设计	情绪唤醒情绪效价责任感惩罚程度	不公平分配下, 情绪唤醒度更高, 愉悦感更低; 他人的存在使得被试责任感更低, 其评分可以显著预测惩罚水平; 责任扩散下, 个体以为别人会介入(心智化)	AIC dmPFC	责任扩散减少了第三方惩罚。
Ginther et al. (2016)	23名健康成人 (男性12名) 18-35岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 四个部分分开呈现：(B/C呈现顺序平衡) A：背景介绍; B：意图描述; C：伤害程度描述; D：选择惩罚	惩罚程度(0-9)	伤害程度评估涉及情感相关大脑区域; 意图评估和伤害程度评估是两个独立的过程	mPFC dlPFC 杏仁核	意图评估涉及默认模式网络、心智化网络, 伤害程度评估涉及情感相关大脑区域, 如杏仁核。dlPFC会综合以上信息做出最后的惩罚决定。
Zhong et al. (2016)	22名学生 (男性10名) M ± SD = 22.9 ± 3.2岁	fMRI 1. 第三方惩罚的DG任务 2. 有意(独裁者分配)和无意(计算机)分配条件	惩罚程度	不公平厌恶意图评估	ACC, 脑岛, dlPFC, vmPFC, TPJ	违规行为检测、意图评估, 主观价值表征相关的神经心理机制。
Glass et al. (2016)	脑损伤：114人 M ± SD = 63.36 ± 0.27岁对照组：32人 M ± SD = 63.41 ± 0.67岁	阅读犯罪故事, 判断其应该受到的惩罚程度	惩罚程度(0-100)	评估法律责任、确定适当的惩罚两种认知机制	mPFC dlPFC	表现为非典型TPP的患者在心智化(mPFC)和中央执行网络(dlPFC)的核心区域有特定的病变。
Bellucci et al. (2017)	26名健康成人 (男性13名) M ± SD = 26.0 ± 5.7岁	fMRI 阅读故意犯罪故事, 判断其应该受到的惩罚程度	情绪效价情绪评分惩罚程度(0-100)	负性情绪越强烈, 惩罚程度越大评估法律责任、确定适当惩罚	心智化网络和中央执行网络	TPP的两个基本认知功能：评估法律责任, 确定适当惩罚, 分别与心智化网络和中央执行网络相关。
Wang et al. (2017)	26名学生 (男性11名) M ± SD = 20.92 ± 2.04岁	fMRI 1. 第三方惩罚的DG任务 2. 单独完成/和其他两人一起完成	惩罚程度责任感公平感情绪唤醒情绪效价	负性情绪越强, 惩罚程度越大(突显网络) 心智化	dACC	被试对不公平分配的不公平感更高, 情绪唤醒度更高, 效价更消极, 且在单独游戏时, 责任感更强, 惩罚更大, dACC活动更弱。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
David et al. (2017)	50名学生 (男性23名) M ± SD = 24.6 ± 3.5岁	fMRI 1. 第三方惩罚/补偿的DG任务 2. 关注违规者(OB)/关注受害者(VB)/对照组	选择惩罚/帮助的比例	推断他人意图	TPJ dlPFC	相比于对照组, 当被试关注的焦点在违规者时, 更倾向于惩罚; 当关注的焦点在受害者时, 倾向于帮助, TPJ在其中起到重要作用。
Yamagishi et al. (2017)	453名非学生 20-50岁	MRI 1. 利他惩罚者(UG中拒绝, DG中惩罚)和恶意惩罚者(UG中拒绝, DG中不惩罚) 2. 经济游戏衡量亲社会偏好	在各个经济任务中的表现	奖赏	尾状核伏隔核	利他惩罚者从公平规范的执行中获得快乐, 恶意的惩罚者从看到目标经历负面后果中获得快乐。
Stallen et al. (2018)	55名男性 M ± SD = 21.2 ± 2.4岁	fMRI 1. 第三方惩罚的正义游戏 2. 催产素/安慰剂组	惩罚/补偿程度	情绪与惩罚程度相关	杏仁核 VS	情绪和奖赏在TPP中的作用。
Moll et al. (2018)	脑损伤：94名 M ± SD = 63.0 ± 2.4岁对照组：28名 M ± SD = 63.0 ± 4.1岁	脑损伤研究 1. 展示了各个社会组织及其工作 2. 判断是否惩罚/奖励/保留	惩罚/补偿程度	意图的表征	dmPFC	双侧dmPFC损伤增加了利他惩罚。
Yang, Shao et al. (2019)	30名女性大学生 M ± SD = 21.2 ± 2.28岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 意图好/意图坏	惩罚程度(0-9) 对犯罪者的愤怒、厌恶、同情程度评分; 道德可接受性评分	厌恶情绪体验心智化、认知控制	TPJ dlPFC dACC 脑岛	若犯罪意图好, 惩罚程度与道德可接受性负相关, 与厌恶情绪正相关。
殷西乐等 (2019)	90名大学生 (41名男性) 平均年龄22岁	tDCS 1. 刺激脑区：dlPFC 2. 第三方惩罚的DG任务有成本惩罚和无成本惩罚两种条件	情绪评分(1-5) 惩罚程度	负性情绪产生惩罚的内在冲动认知控制(自利权衡)	dlPFC	dlPFC活动影响第三方的负性情绪反应和自利加工过程。
Cui et al. (2019)	实验一：60名大学生(男性27名) M ± SD = 24.10 ± 2.33岁实验二：27名大学生(男性14名) M ± SD = 21.10 ± 2.83岁	实验一： 1. 第三方惩罚的DG游戏 2. 3(不公平程度)×3(独裁者地位)×3(接受者地位) 实验二：ERP 2. 第三方惩罚的DG游戏 2. 3(不公平程度)×3(接受者地位)	惩罚程度(0-5)	负性情绪预期违背认知努力	MFN：预期违背 LPC：认知努力	对地位高的接受者提出的不公平的分配更违反规范, MFN波幅更大, 惩罚程度越大。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Asp et al. (2019)	26名vmPFC损伤患者	脑损伤研究阅读犯罪故事, 判断其应该受到的惩罚程度	惩罚程度(0-9)	负性情绪影响TPP行为	vmPFC	vmPFC损伤的个体对情感唤起暴力罪犯分配了更轻的惩罚。
Civai et al. (2019)	40名健康成人 (7名男性) 平均年龄23.4岁	fMRI 1. 第三方惩罚的正义游戏 2. 被试观察到玩家A从B那里拿走了金币, 并且给B留下的更少, 决定是离开/惩罚A/补偿B	惩罚/补偿程度	情感体验、惩罚意愿与惩罚强度的关系	AIC 杏仁核	社会偏好和情感体验在TPP中的作用, 以及不同的认知神经机制。
Lo Gerfo et al. (2019)	60名学生 (25名男性) M ± SD = 23.0 ± 2.5岁	tDCS 1. 刺激脑区：TPJ、vmPFC 2. 第三方惩罚的DG任务	惩罚程度	奖赏系统, 心智化	vmPFC TPJ	奖赏系统和心智化系统是利他惩罚的基础。
Zinchenko et al. (2019)	实验1： 23名健康成人 (7名男性) 平均年龄21.5岁实验2： 21名健康成人 (10名男性) 平均年龄22.79岁	tDCS 第三方惩罚的DG任务实验1：单独刺激TPJ和dlPFC 实验2：联合刺激TPJ和dlPFC	惩罚程度	对意图推测	TPJ	阳极刺激TPJ的活动可以减少惩罚程度。
Ouyang et al. (2021)	29名大学生 (男性14名) M ± SD = 22.0 ± 2.39岁	ERP 1. 第三方惩罚的DG任务 2. 独裁者为穷人(高共情)/富人(低共情)	惩罚程度	共情	P200：共情 MFN：认知冲突 LPC：认知努力	个体更倾向于惩罚富人提出来的高度不公平提议, 而非穷人, 共情使个体更愿意容忍违规行为。
Zinchenko et al. (2021)	17名健康成人 (男性13人) M ± SD = 21.4 ± 3岁	EEG 第三方惩罚的DG任务	静息态神经元α振荡	—	TPJ dlPFC	右侧dlPFC和右侧TPJ之间的整体静息状态连接与TPP强度负相关; 右侧TPJ具有较强局部静息状态长时间相关性的个体表现出较低的TPP水平。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Brüne et al. (2021)	亨廷顿病患者29名(男性20名) M ± SD = 49.5 ± 8.9岁精神分裂症患者30名(男性20名) M ± SD = 42.8 ± 10.3岁对照组30名(男性10名) M ± SD = 42.8 ± 13.8岁	1. UG任务 2. 第三方惩罚的DG任务	个体作为接受者在UG中的拒绝率; 作为第三方在DG中的惩罚程度	认知控制、奖赏	dlPFC VS	亨廷顿病患者保留了对不公平的感知能力, 但是因为不良的执行功能以及难以体验奖赏导致TPP行为减少。
唐捷等 (2022)	24名健康成年人 (男性12名) M ± SD = 22.4 ± 3.4岁	ERP 1. 公共物品博弈 2. 初始资金数量、来源、贡献值对TPP行为影响	惩罚程度	负性情绪自利和维护社会规范的认知控制	FRN：预期违背 P3：注意	第三方会根据成员的经济水平和来源来评估其贡献。
刘映杰等 (2022)	实验一： 31名大学生 (男性12名) 平均年龄19.5岁实验二： 30名大学生 (男性15名) 平均年龄20.37岁	rTMS 左/右侧vmPFC/假刺激收益/损失情景：独裁者为接受者分配收益/损失(被试内) 实验一：第三方惩罚的DG任务实验二：第三方惩罚/补偿DG任务收益/损失情景	惩罚/补偿/保留	负性情绪是引起TPP的关键	vmPFC	rTMS抑制右侧vmPFC的功能显著降低了损失情境下的第三方惩罚, 收益情境下未发生改变。
Xie et al. (2022)	60名大学生 (男性32名) 18-24岁	fNIRS 1. 第三方的正义游戏 2. 优势/劣势不公平经验	惩罚/补偿程度情绪不公平感	负性情绪、高不公平感导致更严厉的惩罚	dlPFC	在经历不利不公平后, 被试的负性情绪更强, 不公平感更高, 更倾向于惩罚而非补偿。
Cheng et al. (2022)	20名学生 (9名男性) M ± SD = 23.7 ± 2.1岁	fMRI 1. 第三方惩罚的DG任务 2. 高成本惩罚(1:3)和低成本惩罚(1:6)	惩罚程度公平程度	未直接测量情绪, 但不公平情况下的AIC活动增加(负性情绪表征) 公平和自利机制的权衡	AI、dlPFC：不公平时更活跃 dACC：高成本惩罚情况下更活跃	惩罚决策是公平偏好和理性思考(经济)综合考量的结果。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Feng et al. (2022)	22名健康成人 (12名男性) M ± SD = 23.48 ± 3.30岁	fMRI 1. 第三方惩罚的DG任务 2. 意图和结果的交互作用, 4种条件：未违规、无意、企图、有意	惩罚程度(0, 2, 4, 6)	意图评估的重要性	TPJ dlPFC dmPFC	TPP意图和结果相互作用的神经心理机制, 心智化相关脑区在其中起到重要作用。

研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Baumgartner et al. (2014)	36名健康男性 M ± SD = 24.3 ± 4.2岁	rTMS 1. 刺激脑区：左/右侧TPJ 2. 被试作为玩家C, 观察玩家A、B (与C的关系为in/in, in/out, out/in, out/out)进行囚徒困境任务, 分为4个条件(合作/合作, 合作/背叛, 背叛/合作, 背叛/背叛)	对玩家A的惩罚程度情绪评级(愤怒、报复, 改善未来行为、公正)	对外群体违规成员的报复情绪在其中起到了中介作用心智化	TPJ	相比于内群体, 外群体违规会受到更多的惩罚, TPJ在其中起到重要作用。
Krueger et al. (2012)	26名健康成人 (男性13名) M ± SD = 26.0 ± 5.7岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 填写自由意志和决定论问卷	情绪效价情绪评分自由意志得分惩罚程度(0-100)	TPP程度与负性情绪强度相关对他人意图的推测	TPJ AIC	高自由意志主义者对低情感案件惩罚更强烈。
Treadway et al. (2014)	30名健康成人 (男性20名) 平均年龄22.8岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 2(形象语言, 直白语言)×2(故意, 无意)混合实验设计	惩罚程度(0-9)	负面情绪的增加导致了更大的惩罚对意图的推测：故意的伤害会有更大的惩罚	杏仁核 dlPFC	情绪和意图推断共同决定惩罚, 并且有时候意图推断比情绪更加重要。
Hu et al. (2015)	30名德国成人 (男性12名) M ± SD = 22.72 ± 2.85岁	fMRI 第三方惩罚/帮助的DG任务	特质共情评分惩罚/帮助程度	内在奖赏	纹状体	惩罚和帮助都和奖赏相关, 特质共情更高的个体更倾向于帮助。
Buckholtz et al. (2015)	66名健康成人 (男性33名) 18-30岁	rTMS 1. 刺激脑区：左/右dlPFC 2. 对犯罪场景进行惩罚程度评分, 分为R (负全责)以及DR (因胁迫、精神病导致责任减轻)两种场景	责备性评分惩罚程度(0-9)	责任评估和惩罚选择是两个独立的认知过程	dlPFC	抑制dlPFC活动降低了惩罚程度, 但不影响责备性评分。
Sun et al. (2015)	32名大学生 (男性10名) 18-24岁	ERP 1. 第三方惩罚的DG任务 2. 高利他主义者/低利他主义者	惩罚程度	不公平厌恶	MFN：预期违背 P300：情感、注意力	高利他主义者比低利他主义者有更强的不平等厌恶, 这可能导致更多的惩罚行为。
Morese et al. (2016)	23名男性大学生 M ± SD = 24.56 ± 1.87岁	fMRI 1. 第三方惩罚的DG任务 2. 博弈双方的群体关系为：in/in, in/out, out/in, out/out	惩罚程度	心智化、奖赏	vmPFC TPJ	被试对外群体成员的惩罚更加严厉。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Feng et al. (2016)	21名学生 (男性10名) M ± SD = 22.9 ± 1.6岁	fMRI 1. 第三方惩罚的DG任务 2. 2(公平, 不公平)×2(单独, 他人在场)被试内设计	情绪唤醒情绪效价责任感惩罚程度	不公平分配下, 情绪唤醒度更高, 愉悦感更低; 他人的存在使得被试责任感更低, 其评分可以显著预测惩罚水平; 责任扩散下, 个体以为别人会介入(心智化)	AIC dmPFC	责任扩散减少了第三方惩罚。
Ginther et al. (2016)	23名健康成人 (男性12名) 18-35岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 四个部分分开呈现：(B/C呈现顺序平衡) A：背景介绍; B：意图描述; C：伤害程度描述; D：选择惩罚	惩罚程度(0-9)	伤害程度评估涉及情感相关大脑区域; 意图评估和伤害程度评估是两个独立的过程	mPFC dlPFC 杏仁核	意图评估涉及默认模式网络、心智化网络, 伤害程度评估涉及情感相关大脑区域, 如杏仁核。dlPFC会综合以上信息做出最后的惩罚决定。
Zhong et al. (2016)	22名学生 (男性10名) M ± SD = 22.9 ± 3.2岁	fMRI 1. 第三方惩罚的DG任务 2. 有意(独裁者分配)和无意(计算机)分配条件	惩罚程度	不公平厌恶意图评估	ACC, 脑岛, dlPFC, vmPFC, TPJ	违规行为检测、意图评估, 主观价值表征相关的神经心理机制。
Glass et al. (2016)	脑损伤：114人 M ± SD = 63.36 ± 0.27岁对照组：32人 M ± SD = 63.41 ± 0.67岁	阅读犯罪故事, 判断其应该受到的惩罚程度	惩罚程度(0-100)	评估法律责任、确定适当的惩罚两种认知机制	mPFC dlPFC	表现为非典型TPP的患者在心智化(mPFC)和中央执行网络(dlPFC)的核心区域有特定的病变。
Bellucci et al. (2017)	26名健康成人 (男性13名) M ± SD = 26.0 ± 5.7岁	fMRI 阅读故意犯罪故事, 判断其应该受到的惩罚程度	情绪效价情绪评分惩罚程度(0-100)	负性情绪越强烈, 惩罚程度越大评估法律责任、确定适当惩罚	心智化网络和中央执行网络	TPP的两个基本认知功能：评估法律责任, 确定适当惩罚, 分别与心智化网络和中央执行网络相关。
Wang et al. (2017)	26名学生 (男性11名) M ± SD = 20.92 ± 2.04岁	fMRI 1. 第三方惩罚的DG任务 2. 单独完成/和其他两人一起完成	惩罚程度责任感公平感情绪唤醒情绪效价	负性情绪越强, 惩罚程度越大(突显网络) 心智化	dACC	被试对不公平分配的不公平感更高, 情绪唤醒度更高, 效价更消极, 且在单独游戏时, 责任感更强, 惩罚更大, dACC活动更弱。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
David et al. (2017)	50名学生 (男性23名) M ± SD = 24.6 ± 3.5岁	fMRI 1. 第三方惩罚/补偿的DG任务 2. 关注违规者(OB)/关注受害者(VB)/对照组	选择惩罚/帮助的比例	推断他人意图	TPJ dlPFC	相比于对照组, 当被试关注的焦点在违规者时, 更倾向于惩罚; 当关注的焦点在受害者时, 倾向于帮助, TPJ在其中起到重要作用。
Yamagishi et al. (2017)	453名非学生 20-50岁	MRI 1. 利他惩罚者(UG中拒绝, DG中惩罚)和恶意惩罚者(UG中拒绝, DG中不惩罚) 2. 经济游戏衡量亲社会偏好	在各个经济任务中的表现	奖赏	尾状核伏隔核	利他惩罚者从公平规范的执行中获得快乐, 恶意的惩罚者从看到目标经历负面后果中获得快乐。
Stallen et al. (2018)	55名男性 M ± SD = 21.2 ± 2.4岁	fMRI 1. 第三方惩罚的正义游戏 2. 催产素/安慰剂组	惩罚/补偿程度	情绪与惩罚程度相关	杏仁核 VS	情绪和奖赏在TPP中的作用。
Moll et al. (2018)	脑损伤：94名 M ± SD = 63.0 ± 2.4岁对照组：28名 M ± SD = 63.0 ± 4.1岁	脑损伤研究 1. 展示了各个社会组织及其工作 2. 判断是否惩罚/奖励/保留	惩罚/补偿程度	意图的表征	dmPFC	双侧dmPFC损伤增加了利他惩罚。
Yang, Shao et al. (2019)	30名女性大学生 M ± SD = 21.2 ± 2.28岁	fMRI 1. 阅读犯罪故事, 判断其应该受到的惩罚程度 2. 意图好/意图坏	惩罚程度(0-9) 对犯罪者的愤怒、厌恶、同情程度评分; 道德可接受性评分	厌恶情绪体验心智化、认知控制	TPJ dlPFC dACC 脑岛	若犯罪意图好, 惩罚程度与道德可接受性负相关, 与厌恶情绪正相关。
殷西乐等 (2019)	90名大学生 (41名男性) 平均年龄22岁	tDCS 1. 刺激脑区：dlPFC 2. 第三方惩罚的DG任务有成本惩罚和无成本惩罚两种条件	情绪评分(1-5) 惩罚程度	负性情绪产生惩罚的内在冲动认知控制(自利权衡)	dlPFC	dlPFC活动影响第三方的负性情绪反应和自利加工过程。
Cui et al. (2019)	实验一：60名大学生(男性27名) M ± SD = 24.10 ± 2.33岁实验二：27名大学生(男性14名) M ± SD = 21.10 ± 2.83岁	实验一： 1. 第三方惩罚的DG游戏 2. 3(不公平程度)×3(独裁者地位)×3(接受者地位) 实验二：ERP 2. 第三方惩罚的DG游戏 2. 3(不公平程度)×3(接受者地位)	惩罚程度(0-5)	负性情绪预期违背认知努力	MFN：预期违背 LPC：认知努力	对地位高的接受者提出的不公平的分配更违反规范, MFN波幅更大, 惩罚程度越大。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Asp et al. (2019)	26名vmPFC损伤患者	脑损伤研究阅读犯罪故事, 判断其应该受到的惩罚程度	惩罚程度(0-9)	负性情绪影响TPP行为	vmPFC	vmPFC损伤的个体对情感唤起暴力罪犯分配了更轻的惩罚。
Civai et al. (2019)	40名健康成人 (7名男性) 平均年龄23.4岁	fMRI 1. 第三方惩罚的正义游戏 2. 被试观察到玩家A从B那里拿走了金币, 并且给B留下的更少, 决定是离开/惩罚A/补偿B	惩罚/补偿程度	情感体验、惩罚意愿与惩罚强度的关系	AIC 杏仁核	社会偏好和情感体验在TPP中的作用, 以及不同的认知神经机制。
Lo Gerfo et al. (2019)	60名学生 (25名男性) M ± SD = 23.0 ± 2.5岁	tDCS 1. 刺激脑区：TPJ、vmPFC 2. 第三方惩罚的DG任务	惩罚程度	奖赏系统, 心智化	vmPFC TPJ	奖赏系统和心智化系统是利他惩罚的基础。
Zinchenko et al. (2019)	实验1： 23名健康成人 (7名男性) 平均年龄21.5岁实验2： 21名健康成人 (10名男性) 平均年龄22.79岁	tDCS 第三方惩罚的DG任务实验1：单独刺激TPJ和dlPFC 实验2：联合刺激TPJ和dlPFC	惩罚程度	对意图推测	TPJ	阳极刺激TPJ的活动可以减少惩罚程度。
Ouyang et al. (2021)	29名大学生 (男性14名) M ± SD = 22.0 ± 2.39岁	ERP 1. 第三方惩罚的DG任务 2. 独裁者为穷人(高共情)/富人(低共情)	惩罚程度	共情	P200：共情 MFN：认知冲突 LPC：认知努力	个体更倾向于惩罚富人提出来的高度不公平提议, 而非穷人, 共情使个体更愿意容忍违规行为。
Zinchenko et al. (2021)	17名健康成人 (男性13人) M ± SD = 21.4 ± 3岁	EEG 第三方惩罚的DG任务	静息态神经元α振荡	—	TPJ dlPFC	右侧dlPFC和右侧TPJ之间的整体静息状态连接与TPP强度负相关; 右侧TPJ具有较强局部静息状态长时间相关性的个体表现出较低的TPP水平。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Brüne et al. (2021)	亨廷顿病患者29名(男性20名) M ± SD = 49.5 ± 8.9岁精神分裂症患者30名(男性20名) M ± SD = 42.8 ± 10.3岁对照组30名(男性10名) M ± SD = 42.8 ± 13.8岁	1. UG任务 2. 第三方惩罚的DG任务	个体作为接受者在UG中的拒绝率; 作为第三方在DG中的惩罚程度	认知控制、奖赏	dlPFC VS	亨廷顿病患者保留了对不公平的感知能力, 但是因为不良的执行功能以及难以体验奖赏导致TPP行为减少。
唐捷等 (2022)	24名健康成年人 (男性12名) M ± SD = 22.4 ± 3.4岁	ERP 1. 公共物品博弈 2. 初始资金数量、来源、贡献值对TPP行为影响	惩罚程度	负性情绪自利和维护社会规范的认知控制	FRN：预期违背 P3：注意	第三方会根据成员的经济水平和来源来评估其贡献。
刘映杰等 (2022)	实验一： 31名大学生 (男性12名) 平均年龄19.5岁实验二： 30名大学生 (男性15名) 平均年龄20.37岁	rTMS 左/右侧vmPFC/假刺激收益/损失情景：独裁者为接受者分配收益/损失(被试内) 实验一：第三方惩罚的DG任务实验二：第三方惩罚/补偿DG任务收益/损失情景	惩罚/补偿/保留	负性情绪是引起TPP的关键	vmPFC	rTMS抑制右侧vmPFC的功能显著降低了损失情境下的第三方惩罚, 收益情境下未发生改变。
Xie et al. (2022)	60名大学生 (男性32名) 18-24岁	fNIRS 1. 第三方的正义游戏 2. 优势/劣势不公平经验	惩罚/补偿程度情绪不公平感	负性情绪、高不公平感导致更严厉的惩罚	dlPFC	在经历不利不公平后, 被试的负性情绪更强, 不公平感更高, 更倾向于惩罚而非补偿。
Cheng et al. (2022)	20名学生 (9名男性) M ± SD = 23.7 ± 2.1岁	fMRI 1. 第三方惩罚的DG任务 2. 高成本惩罚(1:3)和低成本惩罚(1:6)	惩罚程度公平程度	未直接测量情绪, 但不公平情况下的AIC活动增加(负性情绪表征) 公平和自利机制的权衡	AI、dlPFC：不公平时更活跃 dACC：高成本惩罚情况下更活跃	惩罚决策是公平偏好和理性思考(经济)综合考量的结果。
研究	被试信息	实验任务	因变量及测量	情绪唤醒/认知过程	相关脑区/ ERP成分	结论
Feng et al. (2022)	22名健康成人 (12名男性) M ± SD = 23.48 ± 3.30岁	fMRI 1. 第三方惩罚的DG任务 2. 意图和结果的交互作用, 4种条件：未违规、无意、企图、有意	惩罚程度(0, 2, 4, 6)	意图评估的重要性	TPJ dlPFC dmPFC	TPP意图和结果相互作用的神经心理机制, 心智化相关脑区在其中起到重要作用。