Surprisal theory (Hale, 2001; Levy, 2008) posits that a word's reading time is proportional to
its surprisal (ie, to its negative log probability given the proceeding context). Since we are …

Surprisal theory proposes that a word's predictability influences processing difficulty
because each word requires the comprehender to update a probability distribution over …

Prediction has been proposed as an overarching principle that explains human information
processing in language and beyond. To what degree can processing difficulty in …

In psycholinguistics, surprisal theory posits that the amount of online processing effort
expended by a human comprehender per word positively correlates with the surprisal of that …

Many studies of human language processing have shown that readers slow down at less
frequent or less predictable words, but there is debate about whether frequency and …

Recent studies have shown that as Transformer-based language models become larger and
are trained on very large amounts of data, the fit of their surprisal estimates to naturalistic …

Next-word probabilities from language models have been shown to successfully simulate
human reading behavior. Building on this, we show that, interestingly, instruction-tuned …

Word-by-word conditional probabilities from Transformer-based language models are
increasingly being used to evaluate their predictions over minimal pairs or to model the …

Reading research robustly finds that shorter and more frequent words are recognized faster
and skipped more often than longer and less frequent words. An empirical question that has …

A wide body of evidence shows that human language processing difficulty is predicted by
the information-theoretic measure surprisal, a word's negative log probability in context …