What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study

Keith J. Holyoak; Hongjing Lu

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What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study

机译：贝叶斯框架对理解认知的贡献：因果学习作为案例研究

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The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.

机译：因果学习和推理领域（在目标文章中大部分被忽略）为现代贝叶斯框架如何加深理论理解，解决长期存在的争议以及指导开发新的和更原则的算法模型提供了具有启发性的案例研究。这一进展在很大程度上是由多种贝叶斯替代模型的系统表述和经验比较所指导的。

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《Behavioral and Brain Sciences》 |2011年第4期|p.203-204|共2页
作者
Keith J. Holyoak; Hongjing Lu;
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Departments of Psychology,University of California, Los Angeles, CA 90095-1563;

rnDepartments of Psychology,University of California, Los Angeles, CA 90095-1563 Departments of Statistics,University of California, Los Angeles, CA 90095-1563;

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What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study

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