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Artificial Language Training Reveals the Neural Substrates Underlying Addressed and Assembled Phonologies

机译:人工语言培训揭示了已解决和已组装语音的神经底物

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摘要

Although behavioral and neuropsychological studies have suggested two distinct routes of phonological access, their neural substrates have not been clearly elucidated. Here, we designed an artificial language (based on Korean Hangul) that can be read either through addressed (i.e., whole word mapping) or assembled (i.e., grapheme-to-phoneme mapping) phonology. Two matched groups of native English-speaking participants were trained in one of the two conditions, one hour per day for eight days. Behavioral results showed that both groups correctly named more than 90% of the trained words after training. At the neural level, we found a clear dissociation of the neural pathways for addressed and assembled phonologies: There was greater involvement of the anterior cingulate cortex, posterior cingulate cortex, right orbital frontal cortex, angular gyrus and middle temporal gyrus for addressed phonology, but stronger activation in the left precentral gyrus/inferior frontal gyrus and supramarginal gyrus for assembled phonology. Furthermore, we found evidence supporting the strategy-shift hypothesis, which postulates that, with practice, reading strategy shifts from assembled to addressed phonology. Specifically, compared to untrained words, trained words in the assembled phonology group showed stronger activation in the addressed phonology network and less activation in the assembled phonology network. Our results provide clear brain-imaging evidence for the dual-route models of reading.
机译:尽管行为和神经心理学研究提出了语音访问的两种截然不同的途径,但尚未明确阐明它们的神经基础。在这里,我们设计了一种人工语言(基于朝鲜语韩语),可以通过寻址(即整个单词映射)或组合式(即字素到音素映射)阅读。在两个条件之一中训练了两组匹配的英语为母语的参与者,每天一小时,共八天。行为结果表明,两组在训练后正确命名了90%以上的训练单词。在神经水平上,我们发现了针对已解决和已组合语音系统的神经通路的明显分离:针对所解决的语音系统,前扣带回皮层,后扣带回皮层,右眶额叶皮层,角回和中颞回的参与更大,但是左中前回/额下回和上颌上回的激活作用更强,可用于组合音系。此外,我们发现了支持策略转移假说的证据,该假设假设,随着实践的发展,阅读策略从组装语音学转移到语音语音学。具体而言,与未训练的单词相比,组合语音组中的训练单词在寻址语音网络中显示出更强的激活,而在组合语音网络中显示出更少的激活。我们的结果为双途径阅读模型提供了清晰的脑成像证据。

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