The nature of the language input affects brain activation during learning from a natural language

• The study provides a natural language application of artificial language learning paradigm.
• Learning is visualized as it occurs rather than measured after a period of training.
• The presence of high transitional probabilities appears to attract attention resulting in broad-based brain activation.
• The absence of high transitional probabilities produces a limited learning network ICA analysis reveals the dynamics of early learning period.

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.