Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths

TL;DR

This study investigates whether real-time digging-in effects occur during human sentence processing by comparing experimental data with neural language model predictions, finding no evidence for such effects outside of confounding factors like wrap-up processes.

Contribution

The paper provides empirical evidence challenging the existence of real-time digging-in effects in human sentence processing, contrasting with predictions from self-organized and surprisal theories.

Findings

No evidence of real-time digging-in effects in human data.

Disambiguation position influences observed processing patterns.

Neural language models predict reverse trends consistent with experimental results.

Abstract

Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless lengthening genuinely shifts statistical expectations, and neural language models appear to show the opposite pattern. Whether digging-in is a robust real-time phenomenon in human sentence processing -- or an artifact of wrap-up processes or methodological confounds -- remains unclear. We report two experiments on English NP/Z garden-path sentences using Maze and self-paced reading, comparing human behavior with predictions from an ensemble of large language models. We find no evidence for real-time digging-in effects. Critically, items with sentence-final versus nonfinal disambiguation show qualitatively different patterns: positive digging-in trends appear only sentence-finally, where wrap-up effects confound interpretation. Nonfinal items -- the cleaner test of real-time processing -- show reverse trends consistent with neural model predictions.