AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

We review information-theoretic measures of cognitive load during sentence processing that
have been used to quantify word prediction effort. Two such measures, surprisal and next …

Scientific studies of language behavior need to grapple with a large diversity of languages in
the world and, for reading, a further variability in writing systems. Yet, the ability to form …

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 …

Reading times on words in a sentence depend on the amount of information the words
convey, which can be estimated by probabilistic language models. We investigate whether …

Within human sentence processing, it is known that there are large effects of a word's
probability in context on how long it takes to read it. This relationship has been quantified …

This article introduces GECO, the Ghent Eye-Tracking Corpus, a monolingual and bilingual
corpus of the eyetracking data of participants reading a complete novel. English …

The control provided by digital displays over how visual information is presented to readers
has the potential to improve reading for each and every reader, regardless of ability or …

Recurrent neural networks (RNNs) have long been an architecture of interest for
computational models of human sentence processing. The recently introduced Transformer …

An interesting method of evaluating word representations is by how much they reflect the
semantic representations in the human brain. However, most, if not all, previous works only …