Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
- URL: http://arxiv.org/abs/2603.23624v1
- Date: Tue, 24 Mar 2026 18:07:46 GMT
- Title: Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
- Abstract summary: We report experiments on English NP/Z garden-path sentences using Maze and self-paced reading.<n>We find no evidence for real-time digging-in effects.
- Score: 10.217186322563318
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Surprisal Theory is Tautological (without Rational Grounding) [60.917028769172795]
Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language model.<n>I argue this claim is a tautology without further constraint.<n>I conclude that breaking the tautology requires a rationalist intervention.
arXiv Detail & Related papers (2026-07-23T17:54:37Z) - HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models [70.67112531913669]
Hallucinations in vision language models are commonly treated as semantic errors.<n>Prior work mainly focuses on detecting or suppressing hallucinations at generation time.<n>We study Post Hallucination Reasoning (PHR), the stage in which hallucinated semantics enter the model's inference context.
arXiv Detail & Related papers (2026-07-08T15:02:11Z) - Trajectory Dynamics in Language Model Hidden States Predict Human Processing Costs Beyond Surprisal [0.0]
We show that word-level prediction error (surprisal) and sensitivity to the local momentum of the unfolding interpretation (trajectory extrapolation error) are dissociable components of processing cost.<n>The effect is especially pronounced in garden-path sentences.
arXiv Detail & Related papers (2026-06-03T18:44:41Z) - Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis [14.478855009044807]
Surprisal theory posits that the processing difficulty of a word is determined by its predictability in context.<n> LMs successfully predict reading times in naturalistic text, but systematically underpredict the magnitude of difficulty observed in controlled studies of syntactic ambiguity.<n>This suggests that differences in the number of simultaneous parses available to LMs and humans cannot reconcile LM-based surprisal with human sentence processing.
arXiv Detail & Related papers (2026-05-14T21:44:26Z) - Probing for Reading Times [60.30314214800421]
We probe language model representations for human reading times in English, Greek, Hebrew, Russian, and Turkish.<n>We find that the representations from early layers outperform surprisal in predicting early-pass measures such as first fixation and gaze duration.
arXiv Detail & Related papers (2026-04-20T18:12:59Z) - Algorithmic Consequences of Particle Filters for Sentence Processing: Amplified Garden-Paths and Digging-In Effects [10.217186322563318]
Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal.<n>Our best estimates of surprisal come from large language models, which have no explicit representation of structural ambiguity.<n>We show that particle filter models offer an alternative where structural hypotheses are explicitly represented as a finite set of particles.
arXiv Detail & Related papers (2026-03-12T00:56:42Z) - The Imperfective Paradox in Large Language Models [19.058068907991277]
We investigate the Imperfective Paradox, where the past progressive aspect entails event realization for activities but not for accomplishments.<n>We introduce ImperfectiveNLI, a diagnostic dataset designed to probe this distinction across diverse semantic classes.<n>We uncover a pervasive Teleological Bias: models systematically hallucinate completion for goal-oriented events, often overriding explicit textual negation.
arXiv Detail & Related papers (2026-01-14T10:57:16Z) - Temporal Predictors of Outcome in Reasoning Language Models [0.0]
Chain-of-thought (CoT) paradigm uses the elicitation of step-by-step rationales as a proxy for reasoning.<n>We show that, for harder questions, a drop in predictive accuracy highlights a selection artifact.<n>Overall, our results imply that for reasoning models, internal self-assessment of success tends to emerge after only a few tokens.
arXiv Detail & Related papers (2025-11-03T08:57:18Z) - UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations [62.71847873326847]
We investigate the ability to model unusual, unexpected, and unlikely situations.
Given a piece of context with an unexpected outcome, this task requires reasoning abductively to generate an explanation.
We release a new English language corpus called UNcommonsense.
arXiv Detail & Related papers (2023-11-14T19:00:55Z) - On the Effect of Anticipation on Reading Times [84.27103313675342]
We operationalize anticipation as a word's contextual entropy.
We find substantial evidence for effects of contextual entropy over surprisal on a word's reading time.
arXiv Detail & Related papers (2022-11-25T18:58:23Z) - Syntactic Surprisal From Neural Models Predicts, But Underestimates,
Human Processing Difficulty From Syntactic Ambiguities [19.659811811023374]
We propose a method for estimating syntactic predictability from a language model.
We find that treating syntactic predictability independently from lexical predictability indeed results in larger estimates of garden path effects.
Our results support the hypothesis that predictability is not the only factor responsible for the processing cost associated with garden path sentences.
arXiv Detail & Related papers (2022-10-21T18:30:56Z) - Naturalistic Causal Probing for Morpho-Syntax [76.83735391276547]
We suggest a naturalistic strategy for input-level intervention on real world data in Spanish.
Using our approach, we isolate morpho-syntactic features from counfounders in sentences.
We apply this methodology to analyze causal effects of gender and number on contextualized representations extracted from pre-trained models.
arXiv Detail & Related papers (2022-05-14T11:47:58Z) - On Hallucination and Predictive Uncertainty in Conditional Language
Generation [76.18783678114325]
Higher predictive uncertainty corresponds to a higher chance of hallucination.
Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties.
It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
arXiv Detail & Related papers (2021-03-28T00:32:27Z) - Mechanisms for Handling Nested Dependencies in Neural-Network Language
Models and Humans [75.15855405318855]
We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing.
Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement.
We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
arXiv Detail & Related papers (2020-06-19T12:00:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.