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回忆人名的困难:似是而非的语音假说。

The difficulty with recalling people's names: the plausible phonology hypothesis.

作者信息

Brennen T

机构信息

Institute of Psychology, University of Oslo, Norway.

出版信息

Memory. 1993 Dec;1(4):409-31. doi: 10.1080/09658219308258246.

DOI:10.1080/09658219308258246
PMID:7584280
Abstract

Recalling the name of a person is a simple, but often a problematic, everyday task. There are various explanations of this phenomenon, but here it is argued that the explanations offered so far, by failing to consider learning of names, have overlooked a simple account of name recall difficulty. The starting observation for this viewpoint is that names of people are often non-words, in that they have never been encountered before. This is not true of, say, names of professions. Not only does the relatively high rate of new exemplars mean that people's names are likely to be underlearned, but furthermore, even for equal degrees of learning, a person's name is at a disadvantage because of the high plausibility of most phonologies: "dreaner" is much more readily accepted as the name of a person than as the name of their profession. So specifying the phonology of people's names is inherently a more demanding task, compared to the phonology of other names. The implications of this view are explored with regard to explanations of empirically established name recall phenomena in normal subjects, the patterns of performance of anomic patients and the difficulty of name recall in different word domains. It is shown that these arguments, derived from a real world fact, account in a simple way for existing data and make predictions in different areas of research.

摘要

回忆一个人的名字是一项简单但常常存在问题的日常任务。对于这一现象有各种解释,但本文认为,迄今为止所提供的解释由于没有考虑名字的学习过程,而忽略了对名字回忆困难的一个简单解释。这一观点的起始观察点是,人的名字往往是无意义的词,因为它们此前从未被遇到过。比如说职业的名称就不是这样。新范例的相对高比例不仅意味着人们的名字可能学习不足,而且此外,即使对于同等程度的学习,一个人的名字也处于劣势,因为大多数语音组合的合理性很高:“dreaner”作为一个人的名字比作为他们职业的名字更容易被接受。所以,与其他名字的语音相比,确定人的名字的语音本质上是一项要求更高的任务。本文探讨了这一观点对于正常受试者中根据经验确定的名字回忆现象的解释、命名性失语症患者的表现模式以及不同词域中名字回忆困难的影响。结果表明,这些基于一个现实世界事实的论点,以一种简单的方式解释了现有数据,并在不同的研究领域做出了预测。

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