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From specific examples to general knowledge in language learning

机译:从特定的例子到语言学习的常识

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The extraction of general knowledge from individual episodes is critical if we are to learn new knowledge or abilities. Here we uncover some of the key cognitive mechanisms that characterise this process in the domain of language learning. In five experiments adult participants learned new morphological units embedded in fictitious words created by attaching new affixes (e.g., -afe) to familiar word stems (e.g., "sleepafe is a participant in a study about the effects of sleep"). Participants' ability to generalise semantic knowledge about the affixes was tested using tasks requiring the comprehension and production of novel words containing a trained affix (e.g., sailafe). We manipulated the delay between training and test (Experiment 1), the number of unique exemplars provided for each affix during training (Experiment 2), and the consistency of the form-to-meaning mapping of the affixes (Experiments 3-5). In a task where speeded online language processing is required (semantic priming), generalisation was achieved only after a memory consolidation opportunity following training, and only if the training included a sufficient number of unique exemplars. Semantic inconsistency disrupted speeded generalisation unless consolidation was allowed to operate on one of the two affix-meanings before introducing inconsistencies. In contrast, in tasks that required slow, deliberate reasoning, generalisation could be achieved largely irrespective of the above constraints. These findings point to two different mechanisms of generalisation that have different cognitive demands and rely on different types of memory representations. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
机译:如果我们要学习新的知识或能力,从各个情节中提取常识至关重要。在这里,我们揭示了语言学习领域中表征该过程的一些关键认知机制。在五个实验中,成年参与者学习了通过将新词缀(例如-afe)附加到熟悉的词干(例如“ sleepafe是有关睡眠影响的研究的参与者”)而在虚拟词中嵌入的新形态单元。使用要求理解和产生包含受过训练的词缀(例如,sailafe)的新颖单词的任务,测试了参与者概括关于词缀的语义知识的能力。我们操纵了训练和测试之间的延迟(实验1),训练期间为每个词缀提供的唯一示例的数量(实验2)以及词缀的形式到含义映射的一致性(实验3-5)。在需要加快在线语言处理速度(语义启动)的任务中,只有在训练后有记忆整合机会之后,并且仅在训练中包含足够数量的独特样本时,才能实现泛化。除非在引入不一致之前允许对两种词缀含义之一进行合并,否则语义不一致会打乱快速的概括。相反,在需要慢速,深思熟虑的任务中,无论上面的约束如何,都可以实现概括。这些发现指出了两种不同的概括机制,它们具有不同的认知要求,并依赖于不同类型的记忆表示。 (C)2015作者。由Elsevier Inc.发行。这是CC BY许可(http://creativecommons.org/licenses/by/4.0/)下的开放访问文章。

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