During real-time language comprehension, our minds rapidly decode complex meanings
from sequences of words. The difficulty of doing so is known to be related to words' …

It is well known that real-time human language processing is highly incremental and context-
driven, and that the strength of a comprehender's expectation for each word encountered is …

During language comprehension, we routinely use information from the prior context to help
identify the meaning of individual words. While measures of online processing difficulty …

Human reading behavior is tuned to the statistics of natural language: the time it takes
human subjects to read a word can be predicted from estimates of the word's probability in …

What are the effects of word‐by‐word predictability on sentence processing times during the
natural reading of a text? Although information complexity metrics such as surprisal and …

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 …

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 …

Two eye movement while reading experiments address the issue of how reading of an
unpredictable word is influenced by the presence of a more predictable alternative. The …

Surprisal theory posits that less-predictable words should take more time to process, with
word predictability quantified as surprisal, ie, negative log probability in context. While …

This work presents a linguistic analysis into why larger Transformer-based pre-trained
language models with more parameters and lower perplexity nonetheless yield surprisal …