Computational Sentence‐Level Metrics of Reading Speed and Its Ramifications for Sentence Comprehension
Bibliographic Data
| ID | 7150369 |
|---|---|
| Authors | Kun Sun (0000-0001-9766-269X, School of Foreign Languages Tongji University, corresponding author), Rong Wang (0000-0003-1070-1460, Department of Linguistics University of Tübingen, corresponding author) |
| Year | 2025 |
| Volume | 49 |
| Issue | 7 |
| Pages | e70092-e70092 |
| Publication date | 2025-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cognitive Science (JOURNAL) |
| Journal identifiers | ISSN: 0364-0213 • E-ISSN: 1551-6709 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/cogs.70092 |
| PMID | 40692511 |
| OpenAlex | W4412579047 |
| Language | EN |
| References cited | 72 |
The majority of research in computational psycholinguistics on sentence processing has focused on word‐by‐word incremental processing within sentences, rather than holistic sentence‐level representations. This study introduces two novel computational approaches for quantifying sentence‐level processing: sentence surprisal and sentence relevance. Using multilingual large language models (LLMs), we compute sentence surprisal through three methods, chain rule, next sentence prediction, and negative log‐likelihood, and apply a “memory‐aware” approach to calculate sentence‐level semantic relevance based on convolution operations. The sentence‐level metrics developed are tested and compared to validate whether they can predict the reading speed of sentences, and, further, we explore how sentence‐level metrics take effects on human processing and comprehending sentences as a whole across languages. The results show that sentence‐level metrics are highly capable of predicting sentence reading speed. Our results also indicate that these computational sentence‐level metrics are exceptionally effective at predicting and explaining the processing difficulties encountered by readers in processing sentences as a whole across a variety of languages. The proposed sentence‐level metrics offer significant interpretability and achieve high accuracy in predicting human sentence reading speed, as they capture unique aspects of comprehension difficulty beyond word‐level measures. These metrics serve as valuable computational tools for investigating human sentence processing and advancing our understanding of naturalistic reading. Their strong performance and generalization capabilities highlight their potential to drive progress at the intersection of LLMs and cognitive science
Cognition · Comprehension · Computational model · Inverted sentence · Linguistics · Natural language processing · Psycholinguistics · Sentence · Sentence processing · Computer Science · Neurobiology of Language and Bilingualism · Psychology · Text Readability and Simplification · Topic Modeling · Artificial Intelligence
Information‐Processing and Cognition
Generalized Additive Models
The effect of word predictability on reading time is logarithmic
Transformers
Neural mechanisms of language comprehension
Data from eye-tracking corpora as evidence for theories of syntactic processing complexity
Composition in Distributional Models of Semantics
What do we mean by prediction in language comprehension?
Eye Movements as Reflections of Comprehension Processes in Reading
The influence of referential processing on sentence complexity
Incrementality and Prediction in Human Sentence Processing
Over-reliance on English hinders cognitive science
A probabilistic earley parser as a psycholinguistic model
Linguistic complexity
An Activation‐Based Model of Sentence Processing as Skilled Memory Retrieval
Computational principles of working memory in sentence comprehension
Swift
Lossy‐Context Surprisal
Expectation-based syntactic comprehension
An interpretable measure of semantic similarity for predicting eye movements in reading
Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficulty
Discourse coherence modulates use of predictive processing during sentence comprehension
Attention-aware semantic relevance predicting Chinese sentence reading
Speed and time, texts and sentences
Berts of a feather
Expectation-based Comprehension
Two decades of structure building
The Effect of Repeated Readings on Reading Rate, Speech Pauses, and Word Recognition Accuracy
Information-theoretical Complexity Metrics
Anticipatory Processes in Sentence Processing
Eye movements in reading and information processing
Argument-Head Distance and Processing Complexity
| Highly cited | No |
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