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Found 4 papers, 2 papers with code
Revisiting the Optimality of Word Lengths
Under this method, we find that a language's word lengths should instead be proportional to the surprisal's expectation plus its variance-to-mean ratio.
Testing the Predictions of Surprisal Theory in 11 Languages
We address this gap in the current literature by investigating the relationship between surprisal and reading times in eleven different languages, distributed across five language families.
A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans
We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena.
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior
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 context.
