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Cortico-striatal language pathways dynamically adjust for syntactic complexity: A computational study
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Institutet, Sweden.
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2017 (English)In: Brain and Language, ISSN 0093-934X, E-ISSN 1090-2155, Vol. 164, 53-62 p.Article in journal (Refereed) Published
Abstract [en]

A growing body of literature supports a key role of fronto-striatal circuits in language perception. It is now known that the striatum plays a role in engaging attentional resources and linguistic rule computation while also serving phonological short-term memory capabilities. The ventral semantic and the dorsal phonological stream dichotomy assumed for spoken language processing also seems to play a role in cortico-striatal perception. Based on recent studies that correlate deep Broca-striatal pathways with complex syntax performance, we used a previously developed computational model of frontal-striatal syntax circuits and hypothesized that different parallel language pathways may contribute to canonical and non-canonical sentence comprehension separately. We modified and further analyzed a thematic role assignment task and corresponding reservoir computing model of language circuits, as previously developed by Dominey and coworkers. We examined the models performance under various parameter regimes, by influencing how fast the presented language input decays and altering the temporal dynamics of activated word representations. This enabled us to quantify canonical and non-canonical sentence comprehension abilities. The modeling results suggest that separate cortico-cortical and cortico-striatal circuits may be recruited differently for processing syntactically more difficult and less complicated sentences. Alternatively, a single circuit would need to dynamically and adaptively adjust to syntactic complexity.

Place, publisher, year, edition, pages
Academic Press, 2017. Vol. 164, 53-62 p.
Keyword [en]
Cortico-striatal language network, Reservoir computing, Sentence comprehension, Striatum
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-202838DOI: 10.1016/j.bandl.2016.08.005ISI: 000392689600007Scopus ID: 2-s2.0-84994052108OAI: oai:DiVA.org:kth-202838DiVA: diva2:1082675
Note

QC 20170317

Available from: 2017-03-17 Created: 2017-03-17 Last updated: 2017-11-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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