Character-based Surprisal as a Model of Reading Difficulty in the Presence of Error
This work addresses reading difficulty in the presence of errors, providing insights for natural language processing and cognitive science, but it is incremental as it builds on prior error studies with a new computational model.
The study investigated how human readers handle text errors, finding that comprehension remains unimpaired but reading difficulty increases with errors, especially letter transpositions over misspellings and higher error rates. A character-based surprisal model was developed to explain these effects, attributing difficulty to unexpected letter combinations and degraded context.
Intuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter transpositions result in increased reading times, but it is unclear if this effect generalizes to more natural errors. In this paper, we report an eye-tracking study that compares two error types (letter transpositions and naturally occurring misspelling) and two error rates (10% or 50% of all words contain errors). We find that human readers show unimpaired comprehension in spite of these errors, but error words cause more reading difficulty than correct words. Also, transpositions are more difficult than misspellings, and a high error rate increases difficulty for all words, including correct ones. We then present a computational model that uses character-based (rather than traditional word-based) surprisal to account for these results. The model explains that transpositions are harder than misspellings because they contain unexpected letter combinations. It also explains the error rate effect: upcoming words are more difficultto predict when the context is degraded, leading to increased surprisal.