Syntactic Belief Update as the Driver of Garden Path Processing Difficulty
For psycholinguistics, this provides a non-lexical alternative to surprisal for explaining garden path effects, though it is domain-specific and incremental.
The authors propose a syntactic belief update metric based on Rényi divergence to predict garden path sentence processing difficulty, showing it fits human reading times better than lexical surprisal, which fails on such sentences.
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 probability 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 and will require a large update at the critical word. The magnitude of the update is measured with a generalized Rényi divergence. Crucially, this metric is dependent on lexical items, but is fully independent of the probability of lexical items. This Syntactic Belief Update provides a better fit to the human reading time data on garden path sentences. This suggests a new research direction examining purely non-lexical alternatives to surprisal for psycholinguistics.