Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

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

Departing from traditional linguistic models, advances in deep learning have resulted in a
new type of predictive (autoregressive) deep language models (DLMs). Using a self …

This volume outlines a model of language that can be characterized as functionalist, usage-
based, dynamic, and complex-adaptive. The core idea is that linguistic structure is not stable …

To what degree can language be acquired from linguistic input alone? This question has
vexed scholars for millennia and is still a major focus of debate in the cognitive science of …

Our ability to comprehend and produce language is one of humans' most impressive skills,
but it is not flawless. We must convey and interpret messages via a noisy channel in ever …

Natural language allows us to refer to novel composite concepts by combining expressions
denoting their parts according to systematic rules, a property known as\emph …

Proverbs are usually regarded as structurally fixed expressions. However, in daily
communication, language users often change them to suit their communicative purposes in …

Crosslinguistic Influence and Second Language Learning provides a comprehensive
overview of what is currently known about prior language knowledge and experience in …

Neural network models are potential tools for improving our understanding of complex brain
functions. To address this goal, these models need to be neurobiologically realistic …