Martin, A.

Meyer, L.

Nikulin, V.

Ten Oever, S.

Titone, L.

English

MPG.PuRe

MEG Experiment 1: ParadigmExposure PhaseMEG Experiment 1: Expected Results MEG Experiment 2: Expected ResultsFrequency AnalysisConnectivity AnalysisTesting PhaseEvent Related FieldsBehavioural PerformanceWhen PredictionsWhat PredictionsEvent Related FieldsPre-stimulus Phase AnalysisTime-Frequency Analysis Event Related FieldsExposure Phaseperutameputibodekaselimokamide0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.090.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09perutameputibodekaselimokamidetupirogolabupadotigolabubidaku1 1 0.3 1 1 0.3 1 1 0.3 1 1 0.3 1 11 1 0.3 1 1 0.3 1 1 0.3 1 1 0.3 1 1tupirogolabupadotigolabubidakuMEG Experiment 2: ParadigmTesting PhaseTesting PhaseNeural Oscillations in Language Processing: Tracking and PredictingLorenzo Titone1 , Sanne ten Oever2,3,4, Andrea E. Martin2,3, Vadim V. Nikulin1, & Lars Meyer1,5IMPRS Summer School, 2022ReferencesIntroduction• Neural oscillations = rhythmic cycles of neural activity• Neural oscillations support language processing by:1. Tracking exogenous acoustic units (e.g. syllables) andsuprasegmental acoustic cues (e.g. prosody)2. Predicting the when and what of forthcoming eventsOscillations in Language• Word learning relies on statistical regularities in speech• Neural tacking of prosody and of transitional probabilities(TPs) between syllables enables chunking and learning• Experiment 1: Which neural circuits are involved inthe tracking of prosodic and statistical cues?Neural Tracking and Chunking• Entrainment = neural inheritance of contextual rhythmsthat drives temporal—when—predictions• Internal language models = set of linguistic knowledgethat drives content—what—predictions• Experiment 2: How do oscillations help predictions?Neural Entrainment and PredictionsHypotheses• Distinct neural circuits should display periodicity at thechunk rate when TPs and/or prosodic cues are rhythmic• High-order regions sensitive to TPs should feedback top-down predictions of chunk boundaries, while acoustic aswell as prosodic cues should be processed bottom-up• Evoked responses (e.g. M400 amplitude) to test chunksshould be larger for non-words compared to words• Neural tracking of both statistical and prosodic rhythmsshould facilitate statistical learning of artificial wordsExperiment 1• Oscillations should carry predictions of when and what• When: entrained oscillations carry temporal predictions(pre-target phase bias) that surface in faster processing ofa downstream target that is aligned to an expected timepoint (post-target M300 latency reduction)• What: internal language models carry content predictions(alpha/beta power) that surface in facilitated processing ofa downstream target that is part of the lexicon (post-targetM400 amplitude reduction)Experiment 2• Experiment 1 could reveal that rhythmic processing ofTPs and prosodic cues embedded in a syllable stream:1. Is supported by distinct neural circuits2. Jointly impacts statistical learning of artificial words• Experiment 2 could reveal that neural oscillations play amechanistic role to optimize sensory processing:1. Entrainment supports temporal—when—predictions2. Internal models support content—what—predictions3. Oscillations impact downstream target processingImplicationsArnal & Giraud (2012). TRENDS COGN SCI 16(7), 390-8. — Arnal et al. (2015). CEREB CORTEX, 25(9), 3077–3085. — Batterink & Paller (2017). CORTEX, 90, 31–45. —Bourguignon et al. (2013). HUM BRAIN MAPP, 34(2), 314–326. — Breska & Deouell (2017). PLOSBIOL, 15(2), e2001665. — Buiatti et al, (2009). NEUROIMAGE, 44(2), 509–519. — Giraud & Poeppel (2012). NAT NEUROSCI, 15(1), 511–517. — Gross et al. (2013). PLOS BIOL, 11(12), e1001752. — Henin et al. (2021). SCI ADV 7(8), eabc4530. — Luo & Poeppel(2007). NEURON, 54(6), 1001–1010. — Martin (2020). J COGNITIVE NEURISCI, 32(8), 1407–1427. — Meyer et al. (2018). EUR J NEUROSCI, 48(7), 2609–2621. — Meyer et al. (2020). LANG COGN NEUROSCI, 35(9), 1089–1099. — Morillon & Baillet (2017). ANN NYACAD SCI, 1337(1), 26–31.— Obleser & Kayser (2019). TRENDS COGN SCI 23(11), 913-926. — Ordin et al. (2020). EUR J NEUROSCI, 51(9), 2008–2022. — Park et al. (2015). CURR BIO, 25(12), 1649–1653. — Park et al. (2020). LANG COGN NEUROSCI, 35(6),739–751. — Saffran et al. (1996). J MEM LANG, 35(4), 606–621. — Shukla et al. (2007). COGNITIVE PSYCHOL, 54(1), 1–32. — Stefanics et al. (2010). J NEUROSCI, 30(41), 13578–13585. — ten Oever & Martin (2021). ELIFE, 10, e68066.1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, DE · 2Max Planck Institute for Psycholinguistics, Nijmegen, NL · 3Donders Centre for Cognitive Neiroimaging, Nijmegen, NL · 4Maastricht University, NL · 5Clinic for Phoniatrics and Pedaudiology, University Hospital Münster, DEtitone@cbs.mpg.de

Neural oscillations in language processing: Tracking and predicting

http://hdl.handle.net/21.11116/0000-000A-B666-6

Neural oscillations in language processing: Tracking and predicting

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