Marijn van Vliet

Unstable word form representations and greater reliance on right hemisphere during word learning in adults with a history of developmental language disorder

This study examines word learning in adults with developmental language disorder (DLD) and typically developing (TD) controls, finding reduced recall and increased confusion in DLD participants, along with altered hemispheric involvement in word form processing.

When meaning becomes decodable: Linking neural markers of semantic processing and semantic representations

The paper investigates the relationship between N400 responses and semantic decoding performance in MEG data, finding that unrelated primes yield better decoding and suggest semantic information reshaping by context.

Predictive coding narrows the gap between convolutional networks and human brain function in misspelled-word reading

The paper enhances a convolutional neural network with predictive coding dynamics to better mimic human performance in recognizing misspelled words, reducing the accuracy gap between real and misspelled words.

Misspelled-word reading modulates late cortical dynamics

This study examines how the brain processes misspelled words, identifying three brain regions and showing late, sustained neural responses to varying degrees of misspelling.

Convolutional networks can model the functional modulation of the MEG responses associated with feed-forward processes during visual word recognition

The paper uses a convolutional neural network to model MEG responses during visual word recognition, showing it can predict key evoked response components and simulate reading processes.

Separating feedforward and feedback dynamics using time-frequency-resolved connectivity: a hybrid model of left ventral occipitotemporal cortex in word reading

This study investigates the functional role of left ventral occipitotemporal cortex during word reading by analyzing feedforward and feedback dynamics using MEG and connectivity metrics.

Cortical time-course of evidence accumulation during semantic processing

The paper uses magnetoencephalography and machine learning to track semantic processing dynamics, showing gradual accumulation of semantic information leading to a variable plateau across individuals.

Test-retest reliability of MEG functional brain connectivity related to language processing

This study examines the reliability of dynamic functional connectivity in MEG data related to language processing and identifies properties associated with reliable connections.

Post-hoc modification of linear models: combining machine learning with domain information to make solid inferences from noisy data

The paper introduces a framework to modify linear models using domain information to improve performance on noisy neuroimaging data.

Reconstructing meaning from bits of information

The paper investigates how the brain combines fragmented semantic features into complete object representations using machine-learning-based neural decoding.

Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

The paper provides a Python-based pipeline for analyzing functional connectivity and oscillatory power in MEG data, using DICS to estimate group-level brain networks from raw data to visualization.

Cracking the problem of neural representations of abstract words: grounding word meanings in language itself

The paper models semantic spaces using word co-occurrence statistics to decode brain activity related to abstract words, finding that abstractness emerges from language patterns and correlates with specific brain regions.