The Relation between Alpha/Beta Oscillations and the Encoding of Sentence induced Contextual Information

Abstract

Pre-stimulus alpha (8–12 Hz) and beta (16–20 Hz) oscillations have been frequently linked to the prediction of upcoming sensory input. Do these frequency bands serve as a neural marker of linguistic prediction as well? We hypothesized that if pre-stimulus alpha and beta oscillations index language predictions, their power should monotonically relate to the degree of predictability of incoming words based on past context. We expected that the more predictable the last word of a sentence, the stronger the alpha and beta power modulation. To test this, we measured neural responses with magnetoencephalography of healthy individuals during exposure to a set of linguistically matched sentences featuring three levels of sentence context constraint (high, medium and low constraint). We observed fluctuations in alpha and beta power before last word onset, and modulations in M400 amplitude after last word onset. The M400 amplitude was monotonically related to the degree of context constraint, with a high constraining context resulting in the strongest amplitude decrease. In contrast, pre-stimulus alpha and beta power decreased more strongly for intermediate constraints, followed by high and low constraints. Therefore, unlike the M400, pre-stimulus alpha and beta dynamics were not indexing the degree of word predictability from sentence context.

Minimal phrase composition revealed by intracranial recordings

“…Beta oscillations in pars triangularis and ATL may be preparing a syntactic slot to be filled by predicted upcoming nominal content, or they could be initiating top-down local coordination of subsequent composition-indexing BGA in pSTS-TOJ. Following other recent findings (Terporten et al, 2019), it is possible that these beta dynamics do not pertain to lexico-semantic prediction specifically, but rather anticipatory stages pertaining to phrasal initiation. Our findings are also potentially in line with the notion that beta oscillations can index the construction and maintenance of sentence-level meaning (scaling up from minimal phrases) (Lewis et al, 2016), and also the claims that beta can index aspects of syntactic anticipation and phrasal category generation (Benítez-Burraco and Murphy, 2019; Murphy, 2020), since all phrases were predictably nominal phrases.…”

The Interaction of Context Constraints and Predictive Validity during Sentence Reading

“…The current study attempts to replicate the findings form Terporten et al (2019). Furthermore, it is tested whether alpha oscillations are related to linguistic prediction by investigating whether they are sensitive to the predictive validity of a sentential context.…”

“…CC-BY-NC 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 22, 2022. ; https://doi.org/10.1101/2022.09.21.508808 doi: bioRxiv preprint Rommers et al, 2017;Wang et al, 2017;Willems et al, 2008), though the direct link between alpha power and linguistic predictability has been challenged in a recent report (Terporten et al, 2019). Terporten and colleagues (2019) used varying degrees of sentential constraints to influence linguistic predictability.…”

“…We also explored later post-N400-positivities (PNP) as an event related effect that follows the N400, which has been shown to be sensitive to semantic plausibility and predictability (Van Petten & Luka, 2012). Based on our earlier approach (Terporten et al, 2019) we expected pre-target alpha power to be modulated by sentence context constraints, but we expected alpha desynchronization to be non-monotonically linked to the predictability of the target word. If a non-monotonic modulation of alpha power before target word onset is also mediated by predictive validity, the results would be in favor of the brain employing predictive mechanisms during the encoding of sentential context.…”

A Neural Model of Intrinsic and Extrinsic Hippocampal Theta Rhythms: Anatomy, Neurophysiology, and Function

“…As illustrated in Figure 10 (left panel), in all ART models, including SMART, a good enough top-down match with a bottom-up feature pattern triggers a feature-category resonance that drives learning of a new recognition category, or refinement of an already established one, as well as conscious recognition of the object that the category codes ( Grossberg, 2017b ). Both ART and SMART explain and simulate how sensory and cognitive information processing may be broken into cycles of match and mismatch ( Figures 7 , 10 ), or resonance and reset, which will be seen below to correspond to cycles of faster gamma oscillations (40–60 Hz) and slower beta oscillations (16–20 Hz); see Buffalo et al (2011) and Terporten et al (2019) . When theta rhythms (4–12 Hz) organize these cycles through time, then theta-modulated gamma oscillations and beta oscillations are the natural result.…”