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小的添加项是通过直接从记忆中检索还是通过自动化计数程序解决的?对 Chen 和 Campbell(2018)的回应。

Are small additions solved by direct retrieval from memory or automated counting procedures? A rejoinder to Chen and Campbell (2018).

机构信息

SSP, Institute of Psychology, University of Lausanne, Géopolis Building, Room 4536, CH-1015, Lausanne, Switzerland.

University of Geneva, Geneva, Switzerland.

出版信息

Psychon Bull Rev. 2020 Dec;27(6):1416-1418. doi: 10.3758/s13423-020-01818-4. Epub 2020 Sep 23.

Abstract

Contrary to the longstanding and consensual hypothesis that adults mainly solve small single-digit additions by directly retrieving their answer from long-term memory, it has been recently argued that adults could solve small additions through fast automated counting procedures. In a recent article, Chen and Campbell (Psychonomic Bulletin & Review, 25, 739-753, 2018) reviewed the main empirical evidence on which this alternative hypothesis is based, and concluded that there is no reason to jettison the retrieval hypothesis. In the present paper, we pinpoint the fact that Chen and Campbell reached some of their conclusions by excluding some of the problems that need to be considered for a proper argumentation against the automated counting procedure theory. We also explain why, contrary to Chen and Campbell's assumption, the network interference model proposed by Campbell (Mathematical Cognition, 1, 121-164, 1995) cannot account for our data. Finally, we clarify a theoretical point of our model.

摘要

与长期以来的共识假设相反,即成年人主要通过直接从长期记忆中检索答案来解决小的个位数加法,最近有人认为成年人可以通过快速自动计数过程来解决小的加法。在最近的一篇文章中,Chen 和 Campbell(《心理学期刊:综述》,25,739-753,2018)回顾了这一替代假设的主要经验证据,并得出结论认为,没有理由抛弃检索假设。在本文中,我们指出 Chen 和 Campbell 通过排除一些需要考虑的问题来得出他们的一些结论,这些问题对于反对自动计数过程理论的正确论证是必要的。我们还解释了为什么与 Chen 和 Campbell 的假设相反,Campbell 提出的网络干扰模型(《数学认知》,1,121-164,1995)不能解释我们的数据。最后,我们澄清了我们模型的一个理论观点。

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本文引用的文献

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