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来自卡夫卡的关联:接触意义威胁可改善人工语法的内隐学习。

Connections from Kafka: exposure to meaning threats improves implicit learning of an artificial grammar.

作者信息

Proulx Travis, Heine Steven J

机构信息

University of California, Santa Barbara, CA 93106, USA.

出版信息

Psychol Sci. 2009 Sep;20(9):1125-31. doi: 10.1111/j.1467-9280.2009.02414.x. Epub 2009 Jul 28.

Abstract

In the current studies, we tested the prediction that learning of novel patterns of association would be enhanced in response to unrelated meaning threats. This prediction derives from the meaning-maintenance model, which hypothesizes that meaning-maintenance efforts may recruit patterns of association unrelated to the original meaning threat. Compared with participants in control conditions, participants exposed to either of two unrelated meaning threats (i.e., reading an absurd short story by Franz Kafka or arguing against one's own self-unity) demonstrated both a heightened motivation to perceive the presence of patterns within letter strings and enhanced learning of a novel pattern actually embedded within letter strings (artificial-grammar learning task). These results suggest that the cognitive mechanisms responsible for implicitly learning patterns are enhanced by the presence of a meaning threat.

摘要

在当前的研究中,我们检验了这样一种预测:面对不相关的意义威胁时,新关联模式的学习会得到增强。这一预测源自意义维持模型,该模型假设,意义维持的努力可能会调用与原始意义威胁无关的关联模式。与处于对照条件下的参与者相比,暴露于两种不相关意义威胁之一的参与者(即阅读弗兰兹·卡夫卡的荒诞短篇小说或反对自身的自我统一性),既表现出更高的动机去察觉字母串中模式的存在,又在学习实际嵌入字母串中的新模式(人工语法学习任务)方面得到了增强。这些结果表明,负责隐性学习模式的认知机制会因意义威胁的存在而得到增强。

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