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Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain

MPS-Authors

Lopopolo,  Alessandro
Center for Language Studies , External Organizations;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Willems,  Roel M.
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Center for Language Studies , External Organizations;

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journal.pone.0177794.pdf
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Citation

Lopopolo, A., Frank, S. L., Van den Bosch, A., & Willems, R. M. (2017). Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain. PLoS One, 12(5): e0177794. doi:10.1371/journal.pone.0177794.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002D-4F97-E
Abstract
Language comprehension involves the simultaneous processing of information at the phonological, syntactic, and lexical level. We track these three distinct streams of information in the brain by using stochastic measures derived from computational language models to detect neural correlates of phoneme, part-of-speech, and word processing in an fMRI experiment. Probabilistic language models have proven to be useful tools for studying how language is processed as a sequence of symbols unfolding in time. Conditional probabilities between sequences of words are at the basis of probabilistic measures such as surprisal and perplexity which have been successfully used as predictors of several behavioural and neural correlates of sentence processing. Here we computed perplexity from sequences of words and their parts of speech, and their phonemic transcriptions. Brain activity time-locked to each word is regressed on the three model-derived measures. We observe that the brain keeps track of the statistical structure of lexical, syntactic and phonological information in distinct areas.