Modern deep neural networks achieve impressive performance in engineering applications
that require extensive linguistic skills, such as machine translation. This success has …

Efforts to understand the brain bases of language face the Map** Problem: At what level
do linguistic computations and representations connect to human neurobiology? We review …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

This work presents a linguistic analysis into why larger Transformer-based pre-trained
language models with more parameters and lower perplexity nonetheless yield surprisal …

Understanding spoken language requires transforming ambiguous acoustic streams into a
hierarchy of representations, from phonemes to meaning. It has been suggested that the …

Brain activity in numerous perisylvian brain regions is modulated by the expectedness of
linguistic stimuli. We leverage recent advances in computational parsing models to test what …

Neural networks models for NLP are typically implemented without the explicit encoding of
language rules and yet they are able to break one performance record after another. This …

A key component of research on human sentence processing is to characterize the
processing difficulty associated with the comprehension of words in context. Models that …

Research on speech processing is often focused on a phenomenon termed “entrainment”,
whereby the cortex shadows rhythmic acoustic information with oscillatory activity …

The activations of language transformers like GPT-2 have been shown to linearly map onto
brain activity during speech comprehension. However, the nature of these activations …