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 …

Neural language models are increasingly valued in computational psycholinguistics, due to
their ability to provide conditional probability distributions over the lexicon that are predictive …

The uniform information density (UID) hypothesis posits a preference among language
users for utterances structured such that information is distributed uniformly across a signal …

Expectation-based theories of language processing, such as Surprisal theory, are supported
by evidence of anticipation effects in both behavioural and neurophysiological measures …

Although computational models can simulate aspects of human sentence processing,
research on this topic has remained almost exclusively limited to the single language case …

Expectation-based theories of language comprehension, in particular Surprisal Theory, go a
long way in accounting for the behavioral correlates of word-by-word processing difficulty …

We investigate the extent to which word surprisal can be used to predict a neural measure of
human language processing difficulty-the N400. To do this, we use recurrent neural …

The processing difficulty of each word we encounter in a sentence is affected by both our
prior linguistic experience and our general knowledge about the world. Computational …

Probabilistic accounts of language processing can be psychologically tested by comparing
word-reading times (RT) to the conditional word probabilities estimated by language …

Finding the structure of a sentence—the way its words hold together to convey meaning—is
a fundamental step in language comprehension. Several brain regions, including the left …