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

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.

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© 2017 Lopopolo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Lopopolo, Alessandro1, 2, Author
Frank, Stefan L., Author
Van den Bosch, Antal1, 3, Author
Willems, Roel M.1, 4, 5, Author           
Affiliations:
1Center for Language Studies , External Organizations, ou_55238              
2International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society, Nijmegen, NL, ou_1119545              
3Meertens Institute, Royal Netherlands Academy of Science and Arts, Amsterdam, the Netherlands, ou_persistent22              
4Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792551              
5Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              

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 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.

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Language(s): eng - English
 Dates: 2017-05-18
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pone.0177794
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 12 (5) Sequence Number: e0177794 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850