Nested sets theory, full stop: Explaining performance on Bayesian inference tasks without dual-systems assumptions

David R. Mandel

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Nested sets theory, full stop: Explaining performance on Bayesian inference tasks without dual-systems assumptions

机译：嵌套集理论，句号：没有双重系统假设的贝叶斯推理任务的性能解释

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Consistent with Barbey & Sloman (B&S), it is proposed that performance on Bayesian inference tasks is well explained by nested sets theory (NST). However, contrary to those authors' view, it is proposed that NST does better by dispelling with dual-systems assumptions. This article examines why, and sketches out a series of NST's core principles, which were not previously defined.

机译：与Barbey＆Sloman（B＆S）一致，建议使用嵌套集理论（NST）很好地解释贝叶斯推理任务的性能。但是，与那些作者的观点相反，有人提出，通过消除双重系统假设，NST可以做得更好。本文探讨了为什么，并勾勒出了NST的一系列核心原则，这些原则以前没有定义。

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《Behavioral and Brain Sciences》 |2007年第3期|275-276|共2页
作者
David R. Mandel;
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Defence Research and Development Canada (Toronto), Toronto, ON M3M 3B9, Canada;

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正文语种 eng
中图分类心理学;
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机译：对观众效应的易感性解释了在心理学理论中有和没有自闭症的儿童之间的性能差距。

Nested sets theory, full stop: Explaining performance on Bayesian inference tasks without dual-systems assumptions

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