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Single‐Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

https://doi.org/10.1111/cogs.12988
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A bit of progress in language modeling
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Contextual effects on word perception and eye movements during reading
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Syntax and Parsing
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Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Surprisal is Not Enough: Additive Effects of Grammaticality and Lexical Surprisal in Self-Paced Reading
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What’s Surprising About Surprisal
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How reliable are standard reading time analyses? Hierarchical bootstrap reveals substantial power over-optimism and scale-dependent Type I error inflation
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Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficulty
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Informativity enhances memory robustness against interference in sentence comprehension
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Language models that match reader experience are better predictors of reading times
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The lingering effect as memory persistence has distinct predictors from the garden-path effect.
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Large-scale evidence for logarithmic effects of word predictability on reading time
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Causality and signalling of garden-path sentences
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The Structure and Statistics of Language Jointly Shape Cross-frequency Neural Dynamics During Spoken Language Comprehension
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Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation
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Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle
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Large Language Models as Distributional Baselines for Language Tasks
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Agreement Errors Disrupt Eye Movements in Reading Only When They Are Detected
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The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing
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Word Frequency and Predictability Dissociate in Naturalistic Reading
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Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?
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Does entropy modulate the prediction of German long-distance verb particles?
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The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation
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Analyzing Wrap-Up Effects through an Information-Theoretic Lens
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Garden Path Traversal in GPT-2
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Incremental Processing of Principle B: Mismatches Between Neural Models and Humans
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Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities
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Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal
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Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics
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When Language Models Fall in Love: Animacy Processing in Transformer Language Models
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Plausibility Processing in Transformer Language Models: Focusing on the Role of Attention Heads in GPT
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A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing
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Does entropy modulate the prediction of German long-distance verb particles?
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Context ameliorates but does not eliminate garden-pathing: Novel insights from latent-process modeling
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Large-Scale Evidence for Logarithmic Effects of Word Predictability on Reading Time
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Communicative efficiency in multimodal language
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The Learnability of the Wh-Island Constraint in Dutch by a Long Short-Term Memory Network
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Surprisal does not explain syntactic disambiguation difficulty: evidence from a large-scale benchmark
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Information Theory as a Bridge Between Language Function and Language Form
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MCP: Multi-Chicken Pose Estimation Based on Transfer Learning
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Semantic Processing and Individual Variation: Experimental and Modeling Evidence from Quantifier Scope
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Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data
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Conscious rereading is confirmatory: Evidence from bidirectional self-paced reading
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AI linguistics, quo vadis?
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