Martin, A.

Meyer, L.

Ten Oever, S.

Titone, L.

English

MPG.PuRe

The role of oscillations for the predictions of When and What during language comprehensionLorenzo Titone1, Sanne ten Oever2,3,4, Andrea E. Martin2,3, Lars Meyer1,51Max 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.5Clinic for Phoniatrics and Pedaudiology, University Hospital Münster, Germany.titone@cbs.mpg.deCognitive Neuroscience Society, 2022Introduction HypothesesExposure phase(Paradigm)Testing phase(Paradigm)Oscillations in language- Neural oscillations = rhythmic cycles of neural activity- Entrainment = neural inheritance of contextual rhythms:1. Drives temporal—when—predictions2. Is passively subserved by oscillations via bottom-up- Internal language model = set of linguistic knowledge:1. Drives linguistic—what—predictions2. Actively tunes neural excitability via top-down- Oscillations support language processing:1. By tracking exogenous signals (e.g., syllables)2. By endogenously predicting the when and the whatQ: How do oscillations help to predict target events?Statistical learning- Word learning relies on statistical regularities in speech- These regularities are shaped by transitional probabilities(TPs) between syllables- The extraction of TPs enables word learning- This process is subserved by neural oscillations- Learning an artificial lexicon adds linguistic knowledge tothe internal language model- This knowledge allows for new top-down predictions ontarget identityQ: Does statistical learning empower when and whatlinguistic predictions through neural oscillations?MethodsTesting phase(Expected results)i. Random TPs = no learning of the artificial lexiconii. Structured TPs = learning of the artificial lexiconConclusionsSummary- Statistical learning paradigm:i. TP-random syllable streamii. TP-structured syllable stream- Downstream target violations:A. Prediction of when (Timing)B. Prediction of what (Content)- Magnetoencephalography- Neural Frequency Tagging- Oscillatory power dynamics- Inter-trial phase coherence- Event-related fields (ERFs)- 2-AFC recognition task:- Accuracy and RTs2×2 Experimental designData acquisition & analyses123ReferencesDiscussiontupirogolabupadotigolabubidaku- Oscillations carry predictions of both when and what- When: contextual rhythms drive neural entrainment,carrying prediction of when the target will occur- What: internal language models (i.e. artificial lexicon)drive top-down linguistic prediction on target identity- Exposure to a random stream of syllables (i) generates nointernal model of the artificial lexicon1. Without a language model, only when predictions atthe syllable level are triggered, due to entrainment2. Entrainment biases phase at the syllable rate- Exposure to a structured stream of syllables (ii) generatesan internal model of the artificial lexicon1. An internal language model triggers when and whatlinguistic predictions at the word level2. Linguistic predictions bias phase at the word rate- When and what predictions should facilitate processing ofa target that is aligned to an expected time point and tothe linguistic knowledge of the artificial lexiconExposure phase(Expected results)Arnal et al. (2015). Cerebral Cortex, 25(9).Breska & Deouell (2017). PLOS Biology, 15(2).Ding et al. (2016). Nature Neuroscience, 19(1).Giraud & Poeppel (2012). Nature Neuroscience, 15(1).Gross et al. (2013). PLoS Biology, 11(12).Henin et al. (2021). Science Advances, 7(8).Martin (2020). Journal of Cognitive Neuroscience, 32(8).Meyer et al. (2018). European Journal of Neuroscience, 48(7).Meyer et al. (2020). Language, Cognition and Neuroscience, 35(9).Obleser & Kayser (2019). Trends in Cognitive Sciences 23(11).Park et al. (2020). Language, Cognition and Neuroscience, 35(6).Saffran et al. (1996). Journal of Memory and Language, 35(4).Stefanics et al. (2010). The Journal of Neuroscience, 30(41).ten Oever & Martin (2021). ELife, 10.- Depending on when and what predictions, we predict:- Modulations of pre-target oscillatory phase- Changes in pre/post-target oscillatory power- Downstream effects on ERFs (M300/M400)- Differences in behavioral performanceLimitations- 2×2 design: lack of parametric investigation of a gradientof model predictions (larger difference in predictability ↔larger phase shifts relative to isochronous)- In natural speech, the expected time—when—of the nextlinguistic unit may vary depending on what predictions4Ø Random TPs = neural tracking at the syllables rateØ Structured TPs = neural tracking at the word rateØ When prediction violations:Ø Pre-target phase clustering ↓Ø Post-target M300 latency ↑Ø What prediction violations:Ø On-target beta power desynchronization ↑Ø Post-target M400 amplitude ↑

The role of oscillations for the prediction of when and what during language comprehension

http://hdl.handle.net/21.11116/0000-000A-B63C-6

The role of oscillations for the prediction of when and what during language comprehension

Abstract

Similar works

Full text

Available Versions

MPG.PuRe