Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

Abstract Language behaviour is complex, but neuroscientific evidence disentangles it into
distinct components supported by dedicated brain areas or networks. In this Review, we …

From sequences of speech sounds, or letters, humans can extract rich and nuanced
meaning through language. This capacity is essential for human communication. Yet …

Prior work decoding linguistic meaning from imaging data has been largely limited to
concrete nouns, using similar stimuli for training and testing, from a relatively small number …

The nature of the representational code underlying conceptual knowledge remains a major
unsolved problem in cognitive neuroscience. We assessed the extent to which different …

Word embeddings are real-valued word representations able to capture lexical semantics
and trained on natural language corpora. Models proposing these representations have …

Cognitive Neuroscience of Language provides an up-to-date, wide-ranging, and
pedagogically practical survey of the most important developments in this exciting field. It …

We combined established psychological measures with techniques in machine learning to
measure changes in gender stereotypes over the course of the 20th century as expressed in …

Subtle features in people's everyday language may harbor the signs of future mental illness.
Machine learning offers an approach for the rapid and accurate extraction of these signs …

A key goal for cognitive neuroscience is to understand the neurocognitive systems that
support semantic memory. Recent multivariate analyses of neuroimaging data have …