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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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Abstract  

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: yinwu0407@gmail.com
Issue Date: 21 May 2020
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Yinhua ZHANG
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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.
URL:  
http://journal.psych.ac.cn/xlkxjz/EN/10.3724/SP.J.1042.2020.01042     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2020/V28/I7/1042
  
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
  
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