Martin, A.

Meyer, L.

Nikulin, V.

Ten Oever, S.

Titone, L.

English

MPG.PuRe

Distinct neural circuits for tracking prosodic and statistical regularities in speech?Lorenzo Titone1, Sanne ten Oever2,3,4, Andrea E. Martin2,3, Vadim V. Nikulin5, Lars Meyer1,61Max Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. 2Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands.3Language and Computation in Neural Systems Group, Donders Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands.4Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.5Research Group Neural interactions and dynamics, Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.6Clinic for Phoniatrics and Pedaudiology University Hospital Münster, Germany.titone@cbs.mpg.deAdvances in Statistical Learning, 2022IntroductionTesting PhaseOscillations in languageExposure PhaseSummary1ReferencesDiscussionStatistical learning- Word learning relies on statistical regularities in speech- These regularities are shaped by transitional probabilities(TPs) between syllables- The extraction of TPs enables chunking and learning- Prosodic marking at chunk boundaries facilitates learningRQ: Do brain oscillations support statistical learning?- Distinct MEG-source reconstructed neural circuits shouldexhibit periodic activity at the chunk rate when TPs andprosodic cues are regularly presented in a syllable stream- Neural tracking of coherent statistical and prosodic cuesis expected to be associated with learning enhancement- Evoked responses (e.g., M400) to test chunks should belarger for non-words compared to words (− familiarity)- Neural tracking at the chunk rate in cortical regions thatare sensitive to TP should correlate with neural (evoked)responses and behavioral performance (learning)- Connectivity from high-order regions (e.g., sensorimotornetwork) feedbacks temporal predictions to sensory areasand top-down modulate neural tracking at the chunk rate- Acoustic and prosodic cues might instead be processedbottom-up, thus showing a reversed connectivity profileArnal & Giraud (2012). Trends in Cognitive Sciences, 16(7), 390–398. https://doi.org/10.1016/j.tics.2012.05.003Arnal et al. (2015). Cerebral Cortex, 25(9), 3077–3085. https://doi.org/10.1093/cercor/bhu103Assaneo et al. (2019). Frontiers in Integrative Neuroscience, 13, 28. https://doi.org/10.3389/fnint.2019.00028Batterink & Paller (2017). Cortex, 90, 31–45. https://doi.org/10.1016/j.cortex.2017.02.004Biau & Kotz (2018). Frontiers in Human Neuroscience, 12, 434. https://doi.org/10.3389/fnhum.2018.00434Bourguignon et al. (2013). Human Brain Mapping, 34(2), 314–326. https://doi.org/10.1002/hbm.21442Buiatti et al, (2009). NeuroImage, 44(2), 509–519. https://doi.org/10.1016/j.neuroimage.2008.09.015Ding et al. (2016). Nature Neuroscience, 19(1), 158–164. https://doi.org/10.1038/nn.4186Elmer et al. (2021). NeuroImage, 235, 118051. https://doi.org/10.1016/j.neuroimage.2021.118051Frost et al. (2015). Trends in Cognitive Sciences, 19(3), 117–125. https://doi.org/10.1016/j.tics.2014.12.010Giraud & Poeppel (2012). Nature Neuroscience, 15(1), 511–517. https://doi.org/10.1038/nn.3063Gross et al. (2013). PLoS Biology, 11(12), e1001752. https://doi.org/10.1371/journal.pbio.1001752Henin et al. (2021). Science Advances, 7(8), eabc4530. https://doi.org/10.1126/sciadv.abc4530Luo & Poeppel (2007). Neuron, 54(6), 1001–1010. https://doi.org/10.1016/j.neuron.2007.06.004Martin (2020). Journal of Cognitive Neuroscience, 32(8), 1407–1427. https://doi.org/10.1162/jocn_a_01552Meyer et al. (2018). European Journal of Neuroscience, 48(7), 2609–2621. https://doi.org/10.1111/ejn.13748Meyer et al. (2020). Language, Cognition and Neuroscience, 35(9), 1089–1099. https://doi.org/10.1080/23273798.2019.1693050Morillon & Baillet (2017). Annals of the New York Academy of Sciences, 1337(1), 26–31. https://doi.org/10.1111/nyas.12629Moser et al. (2021). NeuroImage, 240, 118378. https://doi.org/10.1016/j.neuroimage.2021.118378Ordin et al. (2020). European Journal of Neuroscience, 51(9), 2008–2022. https://doi.org/10.1111/ejn.14657Park et al. (2015). Current Biology, 25(12), 1649–1653. https://doi.org/10.1016/j.cub.2015.04.049Park et al. (2020). Language, Cognition and Neuroscience, 35(6), 739–751. https://doi.org/10.1080/23273798.2018.1506589Saffran et al. (1996). Journal of Memory and Language, 35(4), 606–621. https://doi.org/10.1006/jmla.1996.0032Shukla et al. (2007). Cognitive Psychology, 54(1), 1–32. https://doi.org/10.1016/j.cogpsych.2006.04.002- Neural oscillations = rhythmic cycles of neural activity- Neural oscillations support language processing by phasealignment to:1. Exogenous acoustic units in speech (syllables)2. Higher order acoustic marking (prosody)3. Endogenous abstract cues (TP patterns)RQ: Are TPs and prosodic cues concurrently trackedby distinct neural circuits for learning and chunking?RQ: Does statistical learning of artificial words rely ontop-down modulations from high-order cortical areas?MethodsStimuli- Controlled for frequency of use of syllables (L1: German)- Controlled for overlap and periodicity of phonetic featuresMagnetoencephalography:Neural frequency tagging (NFT)Inter-trial phase coherenceEvent related fields (ERFs)Expected ResultsConditions2-AFC word recognition task:a) Word vs. Non-wordb) Part-word     vs. Non-wordc) Word vs. Part-wordConfidence rating task:Likert scale (1 to 4)32 Expected Results4ConditionsData acquisition & analysesBehavioral performance:Explicit learning:- Word Recognition- Confidence RatingSyllable rate = 4 HzChunk rate = 1.33 HzChunk rate = 1.33 HzChunk rate = 1.33 Hz- Our study could show that rhythmic processing of TPsand prosodic cues embedded in a syllable stream:1. Is neurally dissociable2. Relies on feedback and feedforward connectivity3. Jointly impacts statistical learning of artificial wordsConclusionStatistical rhythmStatistical+ Prosodic rhythmAcoustic rhythmProsodic rhythm(Tupiro)(Pitula)ERFs Behavior- Learning effect:Ø Preference for words and part-words vs. non-wordsØ Behavioral performance in bothtasks signals explicit learningØ Learning should correlate withthe NFT and ERF effects- Words à extracted from artificial lexicon inventory- Part-words à one syllable deviates from a learned word- Non-words à syllable constructions never heard before- M400 effect:Ø M400 amplitude ↑ in responseto non-words vs. part-words vs.words in TP-structured streamsØ Tracking of TPs correlates withthis effectNFT effect Connectivity

Distinct neural circuits for tracking prosodic and statistical regularities in speech?

http://hdl.handle.net/21.11116/0000-000A-B657-7

Distinct neural circuits for tracking prosodic and statistical regularities in speech?

Abstract

Similar works

Full text

Available Versions

MPG.PuRe