Garden path sentences present a processing difficulty for humans— the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that shows that the initial interpretation was wrong. Lexical surprisal, a measure that usually predicts sentence processing difficulty quite well, fails to provide good predictions for garden path sentences.

We propose an alternative that actively predicts a distribution over syntactic trees (its syntactic belief) and updates that distribution after each new word. If a processor is led down a garden path, syntactic beliefs will be wrong, requiring a large update at the critical word. The magnitude of the update is measured with a Rényi divergence. Crucially, this metric is dependent on lexical items, but is distinct from lexical probability and measures a degree of belief update over non-lexical syntactic structure only. This Syntactic Belief Update provides a better fit to human reading time data on garden path sentences, suggesting a new research direction examining non-lexical alternatives to surprisal for psycholinguistics.