) 深圳大学师范学院心理学院; 深圳市情绪与社会认知科学重点实验室, 深圳 518060
收稿日期:2019-04-22出版日期:2020-07-15发布日期:2020-05-21通讯作者:吴寅E-mail:yinwu0407@gmail.com基金资助:* 国家自然科学基金(31872784);国家自然科学基金(31600923);广东省教育厅教育科学规划青年项目(2018GXJK150);深圳大学新教师科研启动项目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
Received:2019-04-22Online:2020-07-15Published:2020-05-21Contact:WU Yin E-mail:yinwu0407@gmail.com摘要/Abstract
摘要： 道德认知关注道德心理背后的信息加工。近年来, 研究者开始将计算模型应用于道德认知研究, 以探索道德认知如何在大脑中实现。但目前研究者对道德认知进行计算建模的研究处于起步阶段。计算模型(漂移扩散模型、效用模型、强化学习模型和分层高斯过筛器模型)在道德认知行为和生理研究上的运用量化了道德决策、道德判断和道德推理背后的认知过程和神经机制。此外, 这一新进展对理解反社会行为和精神障碍等有所助益。最后, 计算建模有待完善, 未来研究需要关注其潜在的问题。
图/表 2
图1分层高斯过筛器的更新方程与Rescorla-Wagner模型结构的对比。${{\mu }^{(k-1)}}$是前一后验概率; \[{{\mu }^{(k)}}\]是当前新的后验概率(具体参数参见Mathys et al., 2011)。
图1分层高斯过筛器的更新方程与Rescorla-Wagner模型结构的对比。${{\mu }^{(k-1)}}$是前一后验概率; \[{{\mu }^{(k)}}\]是当前新的后验概率(具体参数参见Mathys et al., 2011)。表1计算模型在道德认知研究中的应用总结
| 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 |
表1计算模型在道德认知研究中的应用总结
| 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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