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 …

The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in
human neuroscience has focused on acquiring either a few hours of data on many …

Considerable progress has recently been made in natural language processing: deep
learning algorithms are increasingly able to generate, summarize, translate and classify …

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 …

Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …

Several deep neural networks have recently been shown to generate activations similar to
those of the brain in response to the same input. These algorithms, however, remain largely …

Abstract Representations from transformer-based unidirectional language models are
known to be effective at predicting brain responses to natural language. However, most …

Speech comprehension is a complex process that draws on humans' abilities to extract
lexical information, parse syntax, and form semantic understanding. These sub-processes …

Self-supervised learning techniques are celebrating immense success in natural language
processing (NLP) by enabling models to learn from broad language data at unprecedented …

Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process
text, and now constitute the backbone of automatic translation, summarization and dialogue …