Indexing prediction error during syntactic priming via pupillometry

Prediction is argued to be a key feature of human cognition, including in syntactic processing.
Prediction error has been linked to dynamic changes in syntactic representations in theoretical
models of language processing. This mechanism is termed error-based learning. Evidence from
syntactic priming research supports error-based learning accounts; however, measuring
prediction error itself has not been a research focus. Here we present a study exploring the use
of pupillometry as a measure of prediction error during syntactic priming. We found a larger
pupil response to the more complex and less expected passive structure. In addition, the pupil
response predicted priming while being weakly dependent on changes in expectations over the
experiment. We conclude that the pupil response is not only sensitive to syntactic complexity in
comprehension, but there is some evidence that its magnitude is related to the adjustment of
dynamic mental representations for syntax that lead to syntactic priming.