Computational Models of Mentalizing
Computational Models of Mentalizing
复制标题
心智化的计算模型
DOI：
10.31234/osf.io/4tyd9
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Luke J. Chang
中科院分区：
文献类型：
--
作者：
B. Gonzalez;Luke J. Chang
人类有一种非凡的能力来推断和代表他人的精神状态，如他们的信仰，目标，欲望，意图和感受。在这一章中，我们将回顾经济学、计算机科学和认知神经科学在对几种心智化操作的计算建模方面的一些创新。从广义上讲，这涉及到构建代理如何在受限环境中推断其他代理的心理状态的模型。这些模型包括用于以下的模块：表示代理的目标和期望（例如，最大化奖励或最小化尴尬），推断其他代理的精神状态（例如，信念、目标、欲望、意图和感受），并整合这些目标和心智化计算，以产生最佳的行为策略来导航环境。这些结构的数学操作化提供了一个通用框架，可以通过行为和神经记录技术进行验证，并在未来通过多个科学学科的贡献进行扩展。
Humans have a remarkable ability to infer and represent others’ mental states such as their beliefs, goals, desires, intentions, and feelings. In this chapter, we review some of the innovations that have developed in economics, computer science, and cognitive neuroscience in modeling the computations underlying several mentalizing operations. Broadly, this involves building models of how agents infer the mental states of other agents within constrained environments. These models include modules for: representing the goals and desires of an agent (e.g., maximize reward, or minimize embarrassment), inferring the mental states of other agents (e.g., beliefs, goals, desires, intentions, and feelings), and integrating these goals and mentalizing computations to produce optimal behavioral policies to navigate the environment. The mathematical operationalization of these constructs provides a general framework that can be validated by behavior and neural recording techniques and extended in the future by contributions from multiple scientific disciplines.
影响因子：
4.2
作者：
Peter C. Pantelis;Lisa Byrge;J. Tyszka;R. Adolphs;Daniel P. Kennedy
通讯作者：
Peter C. Pantelis;Lisa Byrge;J. Tyszka;R. Adolphs;Daniel P. Kennedy
影响因子：
4.2
作者：
Chang, Luke J.;Sanfey, Alan G.
通讯作者：
Sanfey, Alan G.