Papers with naturalistic reading
The Impact of Token Granularity on the Predictive Power of Language Model Surprisal (2025.acl-long)
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| Challenge: | Word-by-word language model surprisal is often used to model the incremental processing of human readers, but has been overlooked in cognitive modeling due to the granularity of subword tokens. |
| Approach: | They propose to manipulate token granularity to account for processing difficulty of naturalistic text and garden-path constructions. |
| Outcome: | The proposed model can account for the processing difficulty of naturalistic text and garden-path constructions by using tokens defined by a vocabulary size of 8,000. |
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns (2022.acl-long)
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| Challenge: | Existing work to predict gaze patterns during naturalistic reading has not been conducted on general text characteristics. |
| Approach: | They propose to use two eye-tracking corpora of naturalistic reading and two language models to test their performance. |
| Outcome: | The proposed models predict eye-tracking measures during naturalistic reading and language processing. |
Dual Alignment Between Language Model Layers and Human Sentence Processing (2026.acl-long)
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| Challenge: | Existing studies have demonstrated both the successes and limitations of accurate predictability estimation by modern LMs in cognitive modeling. |
| Approach: | They propose to use internal layers to better estimate human cognitive effort observed in syntactic ambiguity processing in English. |
| Outcome: | The proposed models can be modeled using surprisal from early layers of large language models (LLMs) this raises the question whether such advantages extend to more syntactically challenging constructions, where surprised estimates underestimate human cognitive effort. |