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Advances in Psychological Science    2020, Vol. 28 Issue (7) : 1042-1055     DOI: 10.3724/SP.J.1042.2020.01042
Research Method |
The application of computational modelling in the studies of moral cognition
ZHANG Yinhua,LI Hong,WU Yin()
Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen 518060, China
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Moral cognition focuses on the processing of information underlying the moral behavior. Recently, researchers have begun to apply computational modelling to moral cognition as to explore how moral cognition is represented in the brain. However, the research on the computational modeling of moral cognition is still at its infancy. The application of computational modelling (the Drift Diffusion Models, Utility Models, Reinforcement Learning Models and Hierarchical Gaussian Filter) in the behavioral and physiological studies of moral cognition quantified the cognitive processes and neural mechanisms underlying moral decision-making, moral judgment, and moral inference. In addition, this new approach could help to understand antisocial behavior and mental disorders. Finally, the computational modeling needs to be improved and future research need to pay attention to the potential limitations.

Keywords moral cognition      computational modelling      moral decision      moral judgment      moral inference     
ZTFLH:  B841  
Corresponding Authors: Yin WU     E-mail:
Issue Date: 21 May 2020
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Yinhua ZHANG,Hong LI,Yin WU. The application of computational modelling in the studies of moral cognition[J]. Advances in Psychological Science, 2020, 28(7): 1042-1055.
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模型 道德决策 道德判断 道德推理
漂移扩散模型 Chen & Krajbich, 2018
Hutcherson et al., 2015
Krajbich et al., 2015
效用模型 Crockett et al., 2014, 2015, 2017
Gao et al., 2018
Hu et al., 2018
Sáez et al., 2015
Strombach et al., 2015
Yu et al., 2019
Zhu et al., 2014
Yu et al., 2019 Yu et al., 2019
强化学习模型 Yu et al., 2019 Hackel, et al., 2015
Hackel & Zaki, 2018
Shenhav & Greene, 2010, 2014
Yu et al., 2019
Hackel et al., 2015
Joiner et al., 2017
Suzuki et al., 2012
Yu et al., 2019
分层高斯过筛器模型 Siegel et al., 2018, 2019
[1] 参考消息. 中国“基因编辑婴儿”震惊世界!等待贺建奎的将是——. 2019-01-23取自参考消息.
[2] 刘媛媛, 丁一, 彭凯平, 胡传鹏. (2019). 多项式加工树模型在社会心理学中的应用. 心理科学, 42(2), 422-429.
[3] Ai, S. Z., Yin, Y. L., Chen, Y., Wang, C., Sun, Y., Tang, X. D., ... Shi, J. (2018). Promoting subjective preferences in simple economic choices during nap. eLife, 7, e40583.
pmid: 30520732 url:
[4] Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65(4), 550-562.
pmid: 20188659 url:
[5] Behrens, T. E. J., Hunt, L. T., & Rushworth, M. F. S . (2019). The computation of social behavior. Science, 324(5931), 1160-1164.
pmid: 19478175 url:
[6] Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S . (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214-1221.
pmid: 17676057 url:
[7] Beyens, U., Yu, H. B., Han, T., Zhang, L., & Zhou, X. L. (2015). The strength of a remorseful heart: Psychological and neural basis of how apology emolliates reactive aggression and promotes forgiveness. Frontiers in Psychology, 6(1611), 1-16.
[8] Bohnet, I., & Zeckhauser, R. (2004). Trust, risk and betrayal. Journal of Economic Behavior & Organization, 55(4), 467-484.
[9] Brown, J. W. (2014). The tale of the neuroscientists and the computer: Why mechanistic theory matters. Frontiers in neuroscience, 8(349), 1-3.
[10] Brown, V. M., Zhu, L. S., Wang, J. M., Frueh, B. C., King- Casas, B., & Chiu, P. H. (2018). Associability-modulated loss learning is increased in posttraumatic stress disorder. eLife, 7, e30150.
pmid: 29313489 url:
[11] Cameron, C. D., Payne, B. K., Sinnott-Armstrong, W., Scheffer, J. A., & Inzlicht, M. (2017). Implicit moral evaluations: A multinomial modeling approach. Cognition, 158, 224-241.
pmid: 27865113 url:
[12] Charpentier, C. J., & O’Doherty, J. P. (2018). The application of computational models to social neuroscience: Promises and pitfalls. Social Neuroscience, 13(6), 637-647.
pmid: 30173633 url:
[13] Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I. (2015). Reinforcement learning in depression: A review of computational research. Neuroscience & Biobehavioral Reviews, 55, 247-267.
[14] Chen, F., & Krajbich, I. (2018). Biased sequential sampling underlies the effects of time pressure and delay in social decision making. Nature Communications, 9(1), 3557.
pmid: 30177719 url:
[15] Cohn, A., Fehr, E., & Maréchal, M. A. (2014). Business culture and dishonesty in the banking industry. Nature, 516(7529), 86-89.
[16] Crockett, M. J., Clark, L., Hauser, M. D., & Robbins, T. W. (2010). Serotonin selectively influences moral judgment and behavior through effects on harm aversion. Proceedings of the National Academy of Sciences, 107(40), 17433-17438.
[17] Crockett, M. J., Kurth-Nelson, Z., Siegel, J. Z., Dayan, P., & Dolan, R. J. (2014). Harm to others outweighs harm to self in moral decision making. Proceedings of the National Academy of Sciences, 111(48), 17320-17325.
[18] Crockett, M. J., Siegel, J. Z., Kurth-Nelson, Z., Dayan, P., & Dolan, R. J. (2017). Moral transgressions corrupt neural representations of value. Nature neuroscience, 20(6), 879-885.
[19] Crockett, M. J., Siegel, J. Z., Kurth-Nelson, Z., Ousdal, O. T., Story, G., Frieband, C., ... Dolan, R. J. (2015). Dissociable effects of serotonin and dopamine on the valuation of harm in moral decision making. Current Biology, 25(14), 1852-1859.
pmid: 26144968 url:
[20] Debreu, G. (1954). Representation of a preference ordering by a numerical function. Decision Processes, 3, 159-165.
[21] Eikemo, M., Biele, G., Willoch, F., Thomsen, L., & Leknes, S. (2017). Opioid modulation of value-based decision- making in healthy humans. Neuropsychopharmacology, 42(9), 1833-1840.
pmid: 28294136 url:
[22] Eisenegger, C., Naef, M., Snozzi, R., Heinrichs, M., & Fehr, E. (2010). Prejudice and truth about the effect of testosterone on human bargaining behaviour. Nature, 463(7279), 356-359.
[23] Elqayam, S., Wilkinson, M. R., Thompson, V. A., Over, D. E., & Evans, J. S. B . (2017). Utilitarian moral judgment exclusively coheres with inference from is to ought. Frontiers in Psychology, 8, 1042.
[24] Engelmann, J. B., & Fehr, E. (2016). The slippery slope of dishonesty. Nature Neuroscience, 19(12), 1543-1544.
pmid: 27898084 url:
[25] Feldmanhall, O., Dunsmoor, J. E., Tompary, A., Hunter, L. E., Todorov, A., & Phelps, E. A. (2018). Stimulus generalization as a mechanism for learning to trust. Proceedings of the National Academy of Sciences of the United States of America, 115(7), E1690-E1697.
[26] Feldmanhall, O., Otto, A. R., & Phelps, E. A. (2018). Learning moral values: Another's desire to punish enhances one's own punitive behavior. Journal of Experimental Psychology: General, 147(8), 1211-1224.
[27] Gächter, S., & Schulz, J. F. (2016). Intrinsic honesty and the prevalence of rule violations across societies. Nature, 531(7595), 496-499.
[28] Gamer, M., Rill, H. G., Vossel, G., & Gödert, H. W. (2006). Psychophysiological and vocal measures in the detection of guilty knowledge. International Journal of Psychophysiology, 60(1), 76-87.
pmid: 16005091 url:
[29] Gao, X. X., Yu, H. B., Sáez, I., Blue, P. R., Zhu, L. S., Hsu, M., & Zhou, X. L. (2018). Distinguishing neural correlates of context-dependent advantageous-and disadvantageous- inequity aversion. Proceedings of the National Academy of Sciences, 115(33), E7680-7689.
[30] Garon, M., Lavallée, M. M., Estay, E. V., & Beauchamp, M. H. (2018). Visual encoding of social cues predicts sociomoral reasoning. PloS One, 13(7), e0201099.
[31] Garrett, N., Lazzaro, S. C., Ariely, D., & Sharot, T. (2016). The brain adapts to dishonesty. Nature Neuroscience, 19(12), 1727-1732.
[32] Gawronski, B., Conway, P., Armstrong, J., Friesdorf, R., & Hütter, M. (2018). Effects of Incidental emotions on moral dilemma judgments: An analysis using the CNI model. Emotion, 18(7), 989-1008.
pmid: 29389208 url:
[33] Gershman, S. J., & Niv, Y. (2010). Learning latent structure: Carving nature at its joints. Current Opinion in Neurobiology, 20(2), 251-256.
pmid: 20227271 url:
[34] Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535-574.
pmid: 17600525 url:
[35] Gray, J. (1987). The economic approach to human behavior: Its prospects and limitations. In Radnitzky, G., Bernholz, P.(Eds.), The economic method applied outside the field of economics
[36] Greene, J. D. (2007). Why are vmPFC patients more utilitarian? A dual-process theory of moral judgment explains. Trends in Cognitive Sciences, 11(8), 322-323.
[37] Greene, J. (2014). Moral tribes: Emotion, reason, and the gap between us and them. Penguin.
[38] Greene, J. D., & Paxton, J. M. (2009). Patterns of neural activity associated with honest and dishonest moral decisions. Proceedings of the National Academy of Sciences, 106(30), 12506-12511.
[39] Hackel, L. M., Doll, B. B., & Amodio, D. M. (2015). Instrumental learning of traits versus rewards: Dissociable neural correlates and effects on choice. Nature Neuroscience, 18(9), 1233-1235.
pmid: 25064850 url:
[40] Hackel, L. M., & Zaki, J. (2018). Propagation of economic inequality through reciprocity and reputation. Psychological science, 29(4), 604-613.
pmid: 29474134 url:
[41] Hill, C. A., Suzuki, S., Polania, R., Moisa, M., O’Doherty, J. P., & Ruff, C. C. (2017). A causal account of the brain network computations underlying strategic social behavior. Nature Neuroscience, 20(8), 1142-1149.
pmid: 28692061 url:
[42] Hu, Y., He, L. S., Zhang, L., Wölk, T., Dreher, J.-C., & Weber, B. (2018). Spreading inequality: Neural computations underlying paying-it-forward reciprocity. Social Cognitive and Affective Neuroscience, 13(6), 578-589.
[43] Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic choice and its implications. Neuron, 87(2), 451-462.
pmid: 26182424 url:
[44] Jiang, J. F., Summerfield, C., & Egner, T. (2016). Visual prediction error spreads across object features in human visual cortex. The Journal of Neuroscience, 36(50), 12746-12763.
pmid: 27810936 url:
[45] Joiner, J., Piva, M., Turrin, C., & Chang, S. W. C . (2017). Social learning through prediction error in the brain. npj Science of Learning, 2(1), 8, 1-9.
[46] Johnson, D. D. P., Blumstein, D. T., Fowler, J. H., & Haselton, M. G. (2013). The evolution of error: Error management, cognitive constraints, and adaptive decision-making biases. Trends in Ecology & Evolution, 28(8), 474-481.
[47] Jordan, J. J., Sommers, R., Bloom, P., & Rand, D. G. (2017). Why do we hate hypocrites? Evidence for a theory of false signaling. Psychological Science, 28(3), 356-368.
pmid: 28107103 url:
[48] Kamm, F. M.(2015). The trolley problem mysteries. Oxford University Press.
[49] Khalvati, K., Park, S. A., Mirbagheri, S., Philippe, R., Sestito, M., Dreher, J. C., & Rao, R. P. (2019). Modeling other minds: Bayesian inference explains human choices in group decision-making. Science Advances, 5(11), eaax8783
pmid: 31807706 url:
[50] Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., Hauser, M., & Damasio, A. (2007). Damage to the prefrontal cortex increases utilitarian moral judgements. Nature, 446(7138), 908-911.
pmid: 17377536 url:
[51] Konovalov, A., Hu, J., & Ruff, C. C. (2018). Neurocomputational approaches to social behavior. Current Opinion in Psychology, 24, 41-47.
pmid: 29738891 url:
[52] Konovalov, A., & Krajbich, I. (2019). Revealed indifference: Using response times to infer preferences. Judgment and Decision Making, 14(4), 381-394.
[53] Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292-1298.
[54] Krajbich, I., Hare, T., Bartling, B., Morishima, Y., & Fehr, E. (2015). A common mechanism underlying food choice and social decisions. PLoS Computational Biology, 11(10), e1004371.
pmid: 26460812 url:
[55] Lee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., ... Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141-153.
[56] Lerche, V., & Voss, A. (2019). Experimental validation of the diffusion model based on a slow response time paradigm. Psychological Research, 83(6), 1194-1209.
pmid: 29224184 url:
[57] Levine, E. E., Barasch, A., Rand, D. G., Berman, J. Z., & Small, D. A. (2018). Signaling emotion and reason in cooperation. Journal of Experiment Psychology: General, 147(5), 702-719.
[58] Liu, Y., Li, S., Lin, W., Li, W., Yan, X., Wang, X., ... Ma, Y. (2019). Oxytocin modulates social value representations in the amygdala. Nature Neuroscience, 22(4), 633-644.
pmid: 30911182 url:
[59] Lopez-Persem, A., Rigoux, L., Bourgeois-Gironde, S., Daunizeau, J., & Pessiglione, M. (2017). Choose, rate or squeeze: comparison of economic value functions elicited by different behavioral tasks. PLoS Computational Biology, 13(11), e1005848.
pmid: 29161252 url:
[60] Mars, R. B., Shea, N. J., Kolling, N., & Rushworth, M. F. S . (2012). Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control. Quarterly Journal of Experimental Psychology, 65(2), 252-267.
[61] Mathys, C. D., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A bayesian foundation for individual learning under uncertainty. Frontiers in Human Neuroscience, 5(39), 1-20.
[62] Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K.E. (2014). Uncertainty in perception and the hierarchical gaussian filter. Frontiers in Human Neuroscience, 8, 825.
pmid: 25477800 url:
[63] Meder, D., Kolling, N., Verhagen, L., Wittmann, M. K., Scholl, J., Madsen K. H, … Rushworth, M. F. S. (2017). Simultaneous representation of a spectrum of dynamically changing value estimates during decision making. Nature Communications, 8(1942), 1-11.
[64] Mormann, M. M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgment and Decision Making, 5(6), 437-449.
[65] Nave, G., Camerer, C., & McCullough, M. (2015). Does oxytocin increase trust in humans? A critical review of research. Perspectives on Psychological Science, 10(6), 772-789.
pmid: 26581735 url:
[66] Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity. Nature, 437(7063), 1291-1298.
pmid: 16251955 url:
[67] Palminteri, S., Wyart, V., & Koechlin, E. (2017). The importance of falsification in computational cognitive modeling. Trends in Cognitive Sciences, 21(6), 425-433.
pmid: 28476348 url:
[68] Qu, C., Météreau, E., Butera, L., Villeval, M. C., & Dreher, J. C. (2019). Neurocomputational mechanisms at play when weighing concerns for extrinsic rewards, moral values, and social image. PLoS Biology, 17(6), e3000283.
pmid: 31170138 url:
[69] Randløv, J. & Alstrøm, P,(1998). Learning to drive a bicycle using reinforcement learning and shaping. Paper presented at the Proceedings of the Fifteenth International Conference on Machine Learning (USA), Madison, Wisconsin (pp. 463-471). The International Machine Learning Society.
[70] Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108.
[71] Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873-922.
pmid: 18085991 url:
[72] Ratcliff, R., Smith, P. L., Brown, S. D., & Mckoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20(4), 260-281.
pmid: 26952739 url:
[73] Ratcliff, R., Thapar, A., & Mckoon, G. (2003). A diffusion model analysis of the effects of aging on brightness discrimination. Perception & Psychophysics, 65(4), 523-535.
pmid: 12812276 url:
[74] Ratcliff, R., Thapar, A., & McKoon, G. (2004). A diffusion model analysis of the effects of aging on recognition memory. Journal of Memory and Language, 50(4), 408-424.
[75] Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H., Prokasy, W. F.(Eds.), Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.
[76] Riedmiller, M., Gabel, T., Hafner, R., & Lange, S. (2009). Reinforcement learning for robot soccer. Autonomous Robots, 27(1), 55-73.
[77] Rothkirch, M., Tonn, J., Köhler, S., & Sterzer, P. (2017). Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder. Brain, 140(4), 1147-1157.
pmid: 28334960 url:
[78] Sáez, I., Zhu, L. S., Set, E., Kayser, A., & Hsu, M. (2015). Dopamine modulates egalitarian behavior in humans. Current Biology, 25(7), 912-919.
pmid: 25802148 url:
[79] Schein, C., & Gray, K. (2015). The unifying moral dyad: Liberals and conservatives share the same harm-based moral template. Personality and Social Psychology Bulletin, 41(8), 1147-1163.
pmid: 26091912 url:
[80] Schein, C., & Gray, K. (2018). The theory of dyadic morality: Reinventing moral judgment by redefining harm. Personality and Social Psychology Review, 22(1), 32-70.
pmid: 28504021 url:
[81] Shenhav, A., & Greene, J. D. (2010). Moral judgments recruit domain-general valuation mechanisms to integrate representations of probability and magnitude. Neuron, 67(4), 667-677.
pmid: 20797542 url:
[82] Shenhav, A., & Greene, J. D. (2014). Integrative moral judgment: Dissociating the roles of the amygdala and the ventromedial prefrontal cortex. Journal of Neuroscience, 34(13), 4741-4749.
pmid: 24672018 url:
[83] Siegel, J. Z., Estrada, S., Crockett, M. J., & Baskin-Sommers, A. (2019). Exposure to violence affects the development of moral impressions and trust behavior in incarcerated males. Nature Communications, 10(1), 1942.
pmid: 31028269 url:
[84] Siegel, J. Z., Mathys, C., Rutledge, R. B., & Crockett, M. J. (2018). Beliefs about bad people are volatile. Nature Human Behaviour, 2(10), 750-256.
pmid: 31406285 url:
[85] Smith, P. L., Ratcliff, R., & Wolfgang, B. J. (2004). Attention orienting and the time course of perceptual decisions: Response time distributions with masked and unmasked displays. Vision Research, 44(12), 1297-1320.
pmid: 15066392 url:
[86] Strombach, T., Weber, B., Hangebrauk, Z., Kenning, P., Karipidis, I. I., Tobler, P. N., & Kalenscher, T. (2015). Social discounting involves modulation of neural value signals by temporoparietal junction. Proceedings of the National Academy of Sciences, 112(5), 1619-1624.
[87] Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. MIT press.
[88] Suzuki, S., Harasawa, N., Ueno, K., Gardner, J. L., Ichinohe, N., Haruno, M., ... Nakahara, H. (2012). Learning to simulate others' decisions. Neuron, 74(6), 1125-1137.
pmid: 22726841 url:
[89] Sven, C., Wolfgang, M. P., Peter, B., & John, O. (2017). Neural computations underlying inverse reinforcement learning in the human brain. eLife, 6, e29718.
pmid: 29083301 url:
[90] Szepesvari, C. (2010). Algorithms for reinforcement learning. In Synthesis Lectures on Artificial Intelligence and Machine Learning, 4.1, 1-103.
[91] Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), 58-68.
[92] Thapar, A., Ratcliff, R., & Mckoon, G. (2003). A diffusion model analysis of the effects of aging on letter discrimination. Psychology & Aging, 18(3), 415-429.
pmid: 14518805 url:
[93] Tyrer, P. P., Reed, G. M., & Crawford, M. J. (2015). Classification, assessment, prevalence, and effect of personality disorder. The Lancet, 385(9969), 717-726.
[94] Uhlmann, E. L., Pizarro, D. A., & Diermeier, D. (2015). A person-centered approach to moral judgment. Perspectives on Psychological Science, 10(1), 72-81.
pmid: 25910382 url:
[95] Uhlmann, E. L., & Zhu, L. (2014). Acts, persons, and intuitions: Person-centered cues and gut reactions to harmless transgressions. Social Psychological and Personality Science, 5(3), 279-285.
[96] Valton, V., Romaniuk, L., Steele, J. D., Lawrie, S., & Seriès, P. (2017). Comprehensive review: Computational modelling of schizophrenia. Neuroscience & Biobehavioral Reviews, 83, 631-646.
pmid: 28867653 url:
[97] Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory & Cognition, 32(7), 1206-1220.
pmid: 15813501 url:
[98] Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to decision-making. Annual Review of Neuroscience, 30, 31-56.
pmid: 17417936 url:
[99] Xie, W. W., Yu, B. Y., Zhou, X. Y., Sedikides, C., & Vohs, K. D. (2014). Money, moral transgressions, and blame. Journal of Consumer Psychology, 24(3), 299-306.
[100] Yu, H. B., Siegel, J. Z., Crockett, M. J. (2019). Modeling morality in 3-D: Decision-making, judgment, and inference. Topics in Cognitive Science, 11(2), 409-432.
pmid: 31042018 url:
[101] Zhong, S. F., Chark, R. B., Hsu, M., & Chew, S. H. (2016). Computational substrates of social norm enforcement by unaffected third parties. NeuroImage, 129, 95-104.
pmid: 26825438 url:
[102] Zhu, L. S., Jenkins, A. C., Set, E., Scabini, D., Knight, R. T., Chiu, P. H., ... Hsu, M. (2014). Damage to dorsolateral prefrontal cortex affects tradeoffs between honesty and self-interest. Nature Neuroscience, 17(10), 1319-1321.
[103] Zhu, L. S., Jiang, Y. M., Scabini, D., Knight, R. T., & Hsu, M. (2019). Patients with basal ganglia damage show preserved learning in an economic game. Nature Communications, 10(802), 1-10.
[104] Zsuga, J., Biro, K., Papp, C., Tajti, G., & Gesztelyi, R. (2016). The “proactive” model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept. Behavioral Neuroscience, 130(1), 6-18.
pmid: 26795580 url:
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[2] ZHAN Ze,WU Baopei. Ubiquitous harm: Moral judgment in the perspective of the theory of dyadic morality[J]. Advances in Psychological Science, 2019, 27(1): 128-140.
[3] Yu YAN,Tong LI. An analysis of the reverse mechanism how the victim turn into an instigator on workplace incivility[J]. Advances in Psychological Science, 2018, 26(7): 1307-1318.
[4] Xiaomeng HU,Feng YU,Kaiping PENG. How does culture affect morality? The perspectives of between-culture variations, within-culture variations, and multiculturalism[J]. Advances in Psychological Science, 2018, 26(11): 2081-2090.
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[7] YE Hongyan; ZHANG Fenghua. Embodiment in Moral Judgment[J]. Advances in Psychological Science, 2015, 23(8): 1480-1488.
[8] WU Bao-Pei;GAO Shu-Ling. Moral Hypocrisy: An Opportunistic Adaptive Strategy[J]. , 2012, 20(6): 926-934.
[9] SHEN Wang-Bing;LIU Chang. Critical Review on Psychological Studies on Moral Hypocrisy[J]. , 2012, 20(5): 745-756.
[10] WU Bao-Pei;CHANG Lei. On the Relationship Between Disgust and Moral Judgment[J]. , 2012, 20(2): 309-316.
[11] YU Feng;PENG Kai-Ping;HAN Ting-Ting;CHAI Fang-Yuan;BAI Yang. Dilemma of Moral Dilemmas: The Conflict between Emotion and Reasoning in Moral Judgments[J]. , 2011, 19(11): 1702-1712.
[12] DU Xiao-Xiao; ZHENG Quan-Quan. A Brief Review of the Knobe Effect[J]. , 2010, 18(01): 91-96.
[13] XIE Xi-Yao; LUO Yue-Jia. The Role of Emotion in Moral Judgment: Evidence from Cognitive Neuroscience[J]. , 2009, 17(06): 1250-1256.
[14] Gao Cen Huichang. An Integration of Social Information Processing Modal
on Aggression and Moral Domain Theory
[J]. , 2008, 16(01): 91-97.
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