REVIEW 3 major objections 4 minor 1 references
Do Language Models Agree with Human Perceptions of Suspense in Stories?
T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash
Pith's one-line read Language models can recognize suspense as a category but cannot reproduce human judgments of how much suspense a story passage contains or how suspense rises and falls across a text.
desk verdict A clear, testable dissociation claim from the abstract — LMs pass binary suspense detection but fail graded magnitude and trajectory — deserves peer review, but the key comparison hinges on scale calibration and the methods were not readable in this rendering. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is an LM-as-participant replication design. The authors take four established human studies of suspense perception—their stories, segment boundaries, and rating tasks—and replace the human responses with ratings produced by LMs, so that model judgments can be compared directly with the human judgments the original studies recorded. The second mechanism is adversarial permutation: story segments are reordered to test whether LMs and humans are sensitive to the same ordering of narrative events. Together these mechanisms let the paper separate recognition of suspense as a topic from perception of its magnitude and trajectory.
What would settle it
Take the same stories and segment boundaries used in the paper, but replace numeric ratings with a ranking task: ask human readers and LMs to order the segments from least to most suspenseful, then compare the rank orders. If LMs reproduce the human rank order across many stories, the claim that LMs cannot estimate the relative amount of suspense is falsified; if their rank orders diverge, the claim is supported.
Extended reading notes
Core claim
The paper's central claim is that current language models have only a superficial grasp of narrative suspense. Given the same story segments that human participants rated, LMs correctly identify which passages are designed to induce suspense, but they cannot accurately estimate the relative amount of suspense within a text sequence compared with human judgments, and they fail to reproduce the human perception of suspense rising and falling across multiple segments. The divergence is systematic: when the authors adversarially permute the order of story text, LM suspense responses move in ways human perceptions would not. The paper concludes that LMs can superficially identify and track certai
Load-bearing premise
The load-bearing premise is that the number a language model produces for 'how suspenseful is this?' sits on the same measuring scale as the human ratings from the original studies, so a gap between the two numbers is interpreted as a gap in suspense perception rather than a difference in how models use rating scales.
Editorial extensions
If this is right
- If the claim is right, LMs can serve as coarse binary filters for 'is this text intended to be suspenseful' but not as continuous annotators of suspense intensity.
- Automated systems that use LM affect ratings to predict reader engagement, locate a story's climax, or evaluate pacing will systematically misplace where and how much suspense a reader would feel.
- Because the deficit appears in graded magnitude and trajectory rather than in category detection, benchmarks of LM affective understanding should separate classification accuracy from agreement with human continuous ratings.
- The permutation experiments imply that LM suspense recognition depends on local textual cues more than on global narrative order, so order-sensitive story understanding remains a gap.
- The limitation appears across both open-weight and closed-source LMs tested, making it a property of current LM behavior rather than a quirk of one model family.
Reading between the lines
- The paper's negative result could partly reflect how LMs use numeric rating scales—central tendency, range compression, or anchor wording—rather than a true absence of human-like suspense perception. A calibration pass that matches LM score distributions to the original human rating distributions would separate these explanations.
- A natural extension is to replace absolute Likert ratings with forced-choice ranking: if LMs can rank story segments by suspense as humans do, then the reported deficit lies in scale use rather than perception; if ranking also fails, the deficit is perceptual.
- The permutation results point to a broader diagnostic for narrative understanding: models that genuinely track story structure should show human-like changes in suspense when the resolution is moved before the buildup, and the paper's method makes that test straightforward.
- For applied systems, the result implies that any pipeline using LM affect scores to edit stories, generate reader-engagement predictions, or summarize narrative tension will inherit misplaced suspense peaks, so human validation remains necessary at the point of use.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper replicates four published psychological studies of narrative suspense by substituting human participants with large language models (open-weight and closed-source). The central claim is a dissociation: LMs can discriminate whether a text is intended to induce suspense in humans, but they cannot accurately estimate the relative amount of suspense within a text sequence, nor can they reproduce human-rated rise-and-fall trajectories across segments. Adversarial permutation of story order is used to probe why LM suspense judgments diverge from human judgments, and the authors conclude that LMs do not process suspense the way human readers do.
Significance. If the dissociation holds, it would be a useful empirical boundary for affective narrative understanding in LLMs, and the design is commendable: replicating four external studies with multiple models reduces the risk of benchmark-specific artifacts, and the binary-vs-graded contrast provides an internal control. The permutation experiments are a plausible way to probe order sensitivity. The paper also has the virtue of making a falsifiable negative claim about model capabilities. However, verification is currently blocked both by a corrupted full-text rendering and by the absence, in the readable portions, of any calibration-invariant analysis linking LM outputs to the original human Likert scales. The central claim is therefore plausible but not yet established.
major comments (3)
- [Abstract and Results tables] The headline claim 'LMs cannot accurately estimate the relative amount of suspense' is a quantitative comparison between LM outputs and human Likert ratings from the four replicated studies. The visible comparison block (columns 'Human Study Result' vs 'LM Result') shows raw values but no rank-based or calibration-invariant agreement scores. LLM Likert responses are known to exhibit central-tendency bias, range compression, and anchor sensitivity; a low raw Pearson correlation could occur even when the LM rank-orders segments identically to humans. Please report Spearman/Kendall correlations and a monotonic-calibration robustness check (e.g., isotonic regression mapping LM scores to human ratings) and show that the graded-magnitude dissociation remains.
- [Conclusion] The final inference that LMs 'do not process suspense in the same way as human readers' goes beyond the behavioral evidence reported. Divergent ratings under scrambled text could arise from lower-level surface statistics, recency effects, or prompt-induced local coherence biases, rather than a difference in suspense-specific processing. The permutation results would be more convincing if tied to a priori predictions about what features each model class would use; otherwise the conclusion should be hedged to 'their judgments depend on different aspects of text order/structure.'
- [Limitations section] The supplied manuscript text is largely corrupted mojibake, and the Limitations section is not readable. If the four seminal studies' stimulus texts are published in accessible sources, they are very likely present in the pretraining corpora of the evaluated open-weight and closed-source LMs. The abstract does not describe a contamination check. The authors should explicitly state whether the exact stimuli (or near-duplicates) appeared in pretraining data and discuss the direction of bias: contamination would inflate binary classification success and would obscure any clean interpretation of the graded-magnitude failure. This must be legible in the final text.
minor comments (4)
- [Throughout] The full text appears to be encoded incorrectly; several paragraphs, including the Limitations and Conclusion sections, are mojibake. This must be fixed before any substantive review.
- [Abstract] The phrase 'to identify what cause human and LM perceptions of suspense to diverge' needs grammatical revision: 'what causes' or 'the causes of divergence'.
- [Tables] The repeated table rows (e.g., 'Reading Time', 'MC' and 'SAT' rows) are visually cluttered and not fully labeled. Provide clear column definitions and mark which statistics are LM-generated versus taken from the original studies.
- [References] The text should name the four seminal studies in the Abstract or Introduction; due to corruption I could not verify they are identified in the body. If they are not, please add them.
Circularity Check
No significant circularity: the central comparison is an empirical benchmark against external human data, not a derivation from fitted inputs or self-citation.
full rationale
The paper's claims are empirical: it replicates four published psychological studies of human suspense perception by substituting LM responses for human responses. The target quantities—human suspense ratings—come from external prior work, not from the LMs or from parameters fitted in this paper. The reported dissociation (binary suspense-intent classification succeeds while graded relative-suspense and trajectory judgments diverge from human ratings) is a comparison of measured LM outputs against external human benchmarks. No equation in the abstract or in the readable fragments defines the human rating in terms of LM outputs, and no fitted parameter is renamed as a prediction. The visible table fragments simply juxtapose 'Human Study Result' and 'LM Result' columns, which is an experimental contrast, not a circular construction. The reader-identified concern about Likert-scale commensurability is a validity or robustness threat—whether raw LM scores are directly comparable to human Likert ratings—not a circularity, because the human data were not generated from the LM data. If the full text contains self-citations, none are visible in the supplied material as load-bearing justifications for the central empirical claim. Therefore, under the rule that circularity must be exhibited by quote and specific reduction, no circular step is identified.
Assumptions & free parameters
assumptions (4)
- domain assumption The four replicated psychological studies provide valid ground-truth measurements of human suspense perception.
- domain assumption LM scalar ratings elicited by prompts are commensurable with human Likert ratings on the original scales.
- domain assumption The tested open-weight and closed-source LMs are representative of 'LMs' as a class.
- ad hoc to paper Behavioral divergence on scrambled text licenses claims about differences in underlying processing.
Cite this review
Pith. "Pith review of Do Language Models Agree with Human Perceptions of Suspense in Stories?." pith.science (2026). https://pith.science/paper/OEB3ZKGE
@misc{pith2026250815794,
author = {Pith},
title = {Pith review of: Do Language Models Agree with Human Perceptions of Suspense in Stories?},
year = {2026},
howpublished = {\url{https://pith.science/paper/OEB3ZKGE}},
note = {Machine review of arXiv:2508.15794}
}
read the original abstract
Suspense is an affective response to narrative text that is believed to involve complex cognitive processes in humans. Several psychological models have been developed to describe this phenomenon and the circumstances under which text might trigger it. We replicate four seminal psychological studies of human perceptions of suspense, substituting human responses with those of different open-weight and closed-source LMs. We conclude that while LMs can distinguish whether a text is intended to induce suspense in people, LMs cannot accurately estimate the relative amount of suspense within a text sequence as compared to human judgments, nor can LMs properly capture the human perception for the rise and fall of suspense across multiple text segments. We probe the abilities of LM suspense understanding by adversarially permuting the story text to identify what cause human and LM perceptions of suspense to diverge. We conclude that, while LMs can superficially identify and track certain facets of suspense, they do not process suspense in the same way as human readers.
Reference graph
Works this paper leans on
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work page Pith review arXiv 2025
Reviewed August 5, 2026 · model on record in the stance chip above.
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