Papers with human sentence processing difficulty
Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly (2026.eacl-short)
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| Challenge: | Recent work has observed an inverse scaling relationship between Transformers’ per-word estimated probability and the predictive power of their surprisal estimates on reading times. |
| Approach: | They conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs on two functional magnetic resonance imaging datasets. |
| Outcome: | Recent work shows that surprisal from larger Transformer-based models is less predictive of reading times, resolving the inconclusive results and indicating that this trend is not specific to latency-based measures. |
Linear Recency Bias During Training Improves Transformers’ Fit to Reading Times (2025.coling-main)
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| Challenge: | Recent research has shown a strong fit between surprisal values from Transformers and reading times. |
| Approach: | They evaluate a Transformer model that uses a recency bias added to attention scores to improve the fit to human reading times. |
| Outcome: | The proposed model improves on a Transformer that includes a recency bias added to attention scores. |
A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing (2023.findings-emnlp)
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| Challenge: | Theories of human sentence processing can be divided into two broad categories: expectation-based theories and memory-based ones. |
| Approach: | They propose to integrate expectations and retrieval from working memory into a unified cognitive model that can capture syntactic and semantic interference effects observed in human experiments. |
| Outcome: | The proposed model captures syntactic and semantic interference effects observed in human experiments. |