| Challenge: | Existing studies have shown that surprisal theory ignores the possibility of similarity between words and treats them as distinct entities. |
| Approach: | They propose a new measure of comprehension effort called information value that accounts for communicative equivalences between possible continuations. |
| Outcome: | The proposed measure of comprehension effort is based on the diversity index of the diversity of communicative units. |
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| Challenge: | Novel metaphor comprehension involves complex semantic processes and linguistic creativity. |
| Approach: | They propose a cloze-style surprisal method that conditions on full-sentence context. |
| Outcome: | The proposed method shows that LM surprisal yields moderate correlations with scores/labels of metaphor novelty. |
On the Proper Treatment of Units in Surprisal Theory (2026.acl-long)
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| Challenge: | empirical work often leaves the notion of a unit underspecified . empirical work has sought to characterize the processing difficulty comprehenders experience . |
| Approach: | They propose a framework for reasoning about surprisal over arbitrary unit inventories . they argue that surprises should be explicit and treat tokenization as implementation detail . |
| Outcome: | The proposed framework disentangles the models' definitions and the regions of interest and treats tokenization as an implementation detail rather than a scientific primitive. |
The Linearity of the Effect of Surprisal on Reading Times across Languages (2023.findings-emnlp)
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| Challenge: | a large amount of insight into human language processing can be gleaned by studying word-by-word processing difficulty. |
| Approach: | They extend the study by examining eyetracking corpora of seven languages . they find evidence for superlinearity in some languages, but highly sensitive to language models . |
| Outcome: | The study extends existing studies on english to Danish, Dutch, English, German, Japanese, Mandarin, and Russian. |
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. |
On the Role of Context in Reading Time Prediction (2024.emnlp-main)
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| Challenge: | a new perspective on how readers integrate context during reading time prediction is presented . a recent study shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor. |
| Approach: | They propose a technique where they project surprisal onto the orthogonal complement of frequency. |
| Outcome: | The proposed method shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor. |
Not Every Metric is Equal: Cognitive Models for Predicting N400 and P600 Components During Reading Comprehension (2025.coling-main)
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| Challenge: | Several studies have focused on predicting the surprisal of a word and its reading time, but only recently, attention has been given to other components, such as P600. |
| Approach: | They propose to model reading times and ERP amplitudes using surprisal and entropy . they also propose a metric based on semantic similarity for N400 and P600 . |
| Outcome: | The proposed metric predicts reading times and ERP amplitudes in Mandarin Chinese. |
Towards A Scanpath-Conditioned Surprisal Theory: Modeling Reader Information States (2026.acl-long)
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| Challenge: | Standard surprisal is computed from the linear text prefix, but human reading is non-linear and memory constrained. |
| Approach: | They propose a formulation of surprisal conditioned on a reader-specific accessible information state given by the scanpath history and memory dynamics rather than by the written prefix alone. |
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Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension (2021.findings-acl)
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| Challenge: | a prior work using surprisal only considered within-sentence context, using n-grams, neural language models, or syntactic structure as conditioning context. |
| Approach: | They extend the surprisal approach to use broader topical context . they identify distinct patterns of neural activation for lexical surprised and topical surpresed . |
| Outcome: | The proposed method captures effects of local and topical contexts on processing . it shows that local and broad contextual cues recruit different brain regions . |
Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives (2023.emnlp-main)
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| Challenge: | 'information value' quantifies the predictability of an utterance relative to a set of plausible alternatives. |
| Approach: | They propose a method to obtain interpretable estimates of information value using neural text generators and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour. |
| Outcome: | The proposed method is able to obtain interpretable estimates of information value using neural text generators and exploits their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour. |
Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences (2024.findings-acl)
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| Challenge: | incorporating cognitive capacities increases predictive power of surprisal and entropy measures on reading data, whereas high performance in the psychometric tests is associated with lower sensitivity to predictability effects. |
| Approach: | They examine the predictive power (PP) of surprisal and entropy estimated from generative language models (LMs) on reading data from individuals who also completed a wide range of psychometric tests. |
| Outcome: | The LMs' predictive power is based on cognitive capacities and high performance in psychometric tests is associated with lower sensitivity to predictability effects. |