# LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigms

arXiv: https://arxiv.org/abs/2602.04489 · HTML: https://mlsanity.com/paper/2602.04489/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 107 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://mlsanity.com/paper/2212.12131/) (2022)
- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMC](https://mlsanity.com/paper/1903.08008/) (2019)
- [Testing the Predictions of Surprisal Theory in 11 Languages](https://mlsanity.com/paper/2307.03667/) (2023)
- [Six Fallacies in Substituting Large Language Models for Human Participants](https://mlsanity.com/paper/2402.04470/) (2024)
- [Large Language Models Do Not Simulate Human Psychology](https://mlsanity.com/paper/2508.06950/) (2025)
- [Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention](https://mlsanity.com/paper/2405.16042/) (2024)
- [To model human linguistic prediction, make LLMs less superhuman](https://mlsanity.com/paper/2510.05141/) (2025)
- [Language Models Explain Word Reading Times Better Than Empirical Predictability](https://mlsanity.com/paper/2202.01128/) (2022)
- [Can Peanuts Fall in Love with Distributional Semantics?](https://mlsanity.com/paper/2301.08731/) (2023)
- [Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics](https://mlsanity.com/paper/2404.19178/) (2024)
- [Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling](https://mlsanity.com/paper/1612.04174/) (2016)
- [Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses](https://mlsanity.com/paper/1702.00564/) (2017)
- [Feature overwriting as a finite mixture process: Evidence from comprehension data](https://mlsanity.com/paper/1703.04081/) (2017)

## Built on by (most cited first, 1 of 1)

- [Syntactic Belief Update as the Driver of Garden Path Processing Difficulty](https://mlsanity.com/paper/2606.27206/) (2026)

## Abstract (arXiv)

Temporarily ambiguous garden-path sentences ("While the team trained the striker wondered... ") are known to cause processing difficulty, which can manifest itself in a variety of reading behaviors (in-situ slowdowns, rereading), as well as in miscomprehension or outright rejection of the sentence as ungrammatical. Which types of reading behavior are observed critically depends on the experimental method used to collect the data, which makes comparing results between reading paradigms difficult. To address this problem, we present a latent-process multinomial processing tree (MPT) model of human reading and comprehension/judgment behavior in garden-path sentences that we fit to combined data from four different reading paradigms (eye tracking, uni- and bidirectional self-paced reading, Maze). The model distinguishes between the probability of adopting an incorrect initial analysis, the cost of encountering an incompatible continuation, and the cost of syntactic reanalysis. By taking into account trials with inattentive reading, more realistic estimates of the cost parameters are obtained. Cross-validation reveals that the MPT model has a better predictive fit to human reading patterns and end-of-trial task data than a model based solely on LLM-derived surprisal values. We also test several models that assume an influence of surprisal within the MPT architecture, and find that adding surprisal as an additional predictor or reading time and/or garden-path cost further improves predictive fit.