Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
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Abstract (arXiv)
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.
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References · 7
- 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
- 2018Colorless green recurrent networks dream hierarchicallycited by 210
- 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 137
- 2023Testing the Predictions of Surprisal Theory in 11 Languagescited by 75
- 2022minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Modelscited by 58
- 2021A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humanscited by 32
- 2022Analyzing Wrap-Up Effects through an Information-Theoretic Lenscited by 10
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