Artificial language studies have demonstrated that learners are able to segment individual word-like units from running speech using the transitional probability information. However, this skill has rarely been examined in the context of natural languages, where stimulus parameters can be quite different. In this study, two groups of English-speaking learners were exposed to Norwegian sentences over the course of three fMRI scans. One group was provided with input in which transitional probabilities predicted the presence of target words in the sentences. This group quickly learned to identify the target words and fMRI data revealed an extensive and highly dynamic learning network. These results were markedly different from activation seen for a second group of participants. This group was provided with highly similar input that was modified so that word learning based on syllable co-occurrences was not possible. These participants showed a much more restricted network. The results demonstrate that the nature of the input strongly influenced the nature of the network that learners employ to learn the properties of words in a natural language.

中文翻译：

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语言输入的性质影响从自然语言学习过程中的大脑激活

人工语言研究表明，学习者能够使用转换概率信息从运行的语音中分割出单个的类单词单元。然而，这种技能很少在自然语言的上下文中得到检验，在这种情况下，刺激参数可能大不相同。在这项研究中，两组说英语的学习者在三个 fMRI 扫描过程中接触了挪威语句子。一组被提供输入，其中转换概率预测句子中目标词的存在。该小组很快学会了识别目标词，fMRI 数据揭示了一个广泛且高度动态的学习网络。这些结果与第二组参与者的激活明显不同。该组提供了高度相似的输入，该输入经过修改，因此无法基于音节共现进行单词学习。这些参与者展示了一个更加受限的网络。结果表明，输入的性质强烈影响了学习者用来学习自然语言中单词属性的网络的性质。