Oscillatory neural dynamics have been steadily receiving more attention as a robust and temporally precise signature of network activity related to language processing. We have recently proposed that oscillatory dynamics in the beta and gamma frequency ranges measured during sentence-level comprehension might be best explained from a predictive coding perspective. Under our proposal we related beta oscillations to both the maintenance/change of the neural network configuration responsible for the construction and representation of sentence-level meaning, and to top-down predictions about upcoming linguistic input based on that sentence-level meaning. Here we zoom in on these particular aspects of our proposal, and discuss both old and new supporting evidence. Finally, we present some preliminary magnetoencephalography data from an experiment comparing Dutch subject- and object-relative clauses that was specifically designed to test our predictive coding framework. Initial results support the first of the two suggested roles for beta oscillations in sentence-level language comprehension.
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The relationship between the evoked responses (ERPs/ERFs) and the event-related changes in EEG/MEG power that can be observed during sentence-level language comprehension is as yet unclear. This study addresses a possible relationship between MEG power changes and the N400m component of the event-related field. Whole-head MEG was recorded while subjects listened to spoken sentences with incongruent (IC) or congruent (C) sentence endings. A clear N400m was observed over the left hemisphere, and was larger for the IC sentences than for the C sentences. A time-frequency analysis of power revealed a decrease in alpha and beta power over the left hemisphere in roughly the same time range as the N400m for the IC relative to the C condition. A linear regression analysis revealed a positive linear relationship between N400m and beta power for the IC condition, not for the C condition. No such linear relation was found between N400m and alpha power for either condition. The sources of the beta decrease were estimated in the LIFG, a region known to be involved in semantic unification operations. One source of the N400m was estimated in the left superior temporal region, which has been related to lexical retrieval. We interpret our data within a framework in which beta oscillations are inversely related to the engagement of task-relevant brain networks. The source reconstructions of the beta power suppression and the N400m effect support the notion of a dynamic communication between the LIFG and the left superior temporal region during language comprehension.
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In this study, we used electroencephalography to investigate the influence of discourse-level semantic coherence on electrophysiological signatures of local sentence-level processing. Participants read groups of four sentences that could either form coherent stories or were semantically unrelated. For semantically coherent discourses compared to incoherent ones, the N400 was smaller at sentences 2–4, while the visual N1 was larger at the third and fourth sentences. Oscillatory activity in the beta frequency range (13–21 Hz) was higher for coherent discourses. We relate the N400 effect to a disruption of local sentence-level semantic processing when sentences are unrelated. Our beta findings can be tentatively related to disruption of local sentence-level syntactic processing, but it cannot be fully ruled out that they are instead (or also) related to disrupted local sentence-level semantic processing. We conclude that manipulating discourse-level semantic coherence does have an effect on oscillatory power related to local sentence-level processing.
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There is a growing literature investigating the relationship between oscillatory neural dynamics measured using electroencephalography (EEG) and/or magnetoencephalography (MEG), and sentence-level language comprehension. Recent proposals have suggested a strong link between predictive coding accounts of the hierarchical flow of information in the brain, and oscillatory neural dynamics in the beta and gamma frequency ranges. We propose that findings relating beta and gamma oscillations to sentence-level language comprehension might be unified under such a predictive coding account. Our suggestion is that oscillatory activity in the beta frequency range may reflect both the active maintenance of the current network configuration responsible for representing the sentence-level meaning under construction, and the top-down propagation of predictions to hierarchically lower processing levels based on that representation. In addition, we suggest that oscillatory activity in the low and middle gamma range reflect the matching of top-down predictions with bottom-up linguistic input, while evoked high gamma might reflect the propagation of bottom-up prediction errors to higher levels of the processing hierarchy. We also discuss some of the implications of this predictive coding framework, and we outline ideas for how these might be tested experimentally.
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The role of neuronal oscillations during language comprehension is not yet well understood. In this paper we review and reinterpret the functional roles of beta- and gamma-band oscillatory activity during language comprehension at the sentence and discourse level. We discuss the evidence in favor of a role for beta and gamma in unification (the unification hypothesis), and in light of mounting evidence that cannot be accounted for under this hypothesis, we explore an alternative proposal linking beta and gamma oscillations to maintenance and prediction (respectively) during language comprehension. Our maintenance/prediction hypothesis is able to account for most of the findings that are currently available relating beta and gamma oscillations to language comprehension, and is in good agreement with other proposals about the roles of beta and gamma in domain-general cognitive processing. In conclusion we discuss proposals for further testing and comparing the prediction and unification hypotheses.
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Work on animals indicates that BOLD is preferentially sensitive to local field potentials, and that it correlates most strongly with gamma band neuronal synchronization. Here we investigate how the BOLD signal in humans performing a cognitive task is related to neuronal synchronization across different frequency bands. We simultaneously recorded EEG and BOLD while subjects engaged in a visual attention task known to induce sustained changes in neuronal synchronization across a wide range of frequencies. Trial-by-trial BOLD fluctuations correlated positively with trial-by-trial fluctuations in high-EEG gamma power (60. -80 Hz) and negatively with alpha and beta power. Gamma power on the one hand, and alpha and beta power on the other hand, independently contributed to explaining BOLD variance. These results indicate that the BOLD-gamma coupling observed in animals can be extrapolated to humans performing a task and that neuronal dynamics underlying high- and low-frequency synchronization contribute independently to the BOLD signal.
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