TRF

In this study we combine continuous MEG recording and forward modelling (temporal response functions) to measure phase-amplitude coupling evoked by distinct linguistic features in spoken language comprehension. We show that both structural and statistical language knowledge jointly shape neural dynamics.

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This study addresses a fundamental aspect of human speech processing: namely, how acoustic and linguistic features interact during comprehension.

Human language is unprecedented in its combinatorial capacity; we are capable of producing and understanding sentences we have never heard before. Although the mechanisms underlying this capacity have been described in formal linguistics and cognitive science, how they are implemented in the brain remains to a large extent unknown. A large body of earlier work from the cognitive neuroscientific literature implies a role for delta-band neural activity in the representation of linguistic structure and meaning. In this work, we combine these insights and techniques with findings from psycholinguistics to show that meaning is more than the sum of its parts; the delta-band MEG signal differentially reflects lexical information inside and outside sentence structures.

It remains unclear whether the proposed functional role of α and β oscillations in perceptual and motor function is generalizable to higher-level cognitive processes, such as spoken language comprehension. We found that syntactic features predict α and β power in language-related regions beyond low-level linguistic features when listening to naturalistic speech in a known language. We offer experimental findings that integrate a neuroscientific framework on the role of brain oscillations as “building blocks” with spoken language comprehension. This supports the view of a domain-general role of oscillations across the hierarchy of cognitive functions, from low-level sensory operations to abstract linguistic processes.

Spoken language comprehension requires rapid and continuous integration of information, from lower-level acoustic to higher-level …

A presentation and summary of my main PhD results

Word surprisal and word entropy, derived from a recurrent neural networl for language modelling, are represented in EEG signals in several freuqency bands. The neural encoding is quantified using temporal response functions with continuous EEG recorded during naturalsitic story listening.

Poster presented at SNL 2017