Uncovering Visual-Semantic Psycholinguistic Properties from the Distributional Structure of Text Embedding Space

Sebastian Bruch; Si Wu

REVIEW 3 major objections 5 minor 36 references

Uncovering Visual-Semantic Psycholinguistic Properties from the Distributional Structure of Text Embedding Space

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper aims to establish that imageability and concreteness can be read off the geometry of text embeddings alone, using the peakedness of a word's nearest-neighbor neighborhood.

desk verdict A simple, cheap text-only imageability/concreteness estimator that clearly beats baselines, but the paper needs to fix a self-inclusion ambiguity and rule out frequency as the real driver. read the letter →

arxiv 2505.23029 v2 pith:RS4HUFEX submitted 2025-05-29 cs.CL

Si Wu , Sebastian Bruch This is my paper

classification cs.CL

keywords imageabilityconcretenesstextembeddingsneighborhoodstabilityunsupervisedestimationpsycholinguisticnormsimagecaptionsnearestneighborgeometry

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper seeks to show that two psycholinguistic properties—imageability, how readily a word evokes a mental image, and concreteness, how perceptible its referent is—can be estimated from text alone, without images or multimodal models. Its proposed Neighborhood Stability Measure (NSM) computes, for each word, the fraction of its nearest embedding neighbors whose own nearest neighbor stays inside the same neighbor set; a self-contained, sharply peaked neighborhood is taken to indicate a more imageable or concrete word. Across three caption corpora and three embedding models, NSM shows higher rank correlation with human imageability and concreteness ratings than the unsupervised baselines tested, while covering every word in the rating lexicons. If the claim holds, psycholinguistic norms could be expanded cheaply and at scale from existing caption collections.

What carries the argument

The central object is the Neighborhood Stability Measure (NSM), a distribution-free statistic over a collection of text embeddings. For a query word $q$ and radius $k$, it takes the $k$ approximate nearest neighbors $N_k(q)$ under cosine similarity and returns the fraction of those neighbors $u$ whose own nearest neighbor $N_1(u)$ is also in $N_k(q)$. That fraction is the $\alpha$-stability of the neighborhood (Definition 1), and the paper interprets a value near 1 as a sharp peak in the underlying distribution and a value near 0 as a flat, diffuse region. The machinery rests on approximate nearest neighbor search to make the computation scalable, on a precomputed nearest-neighbor table to reduce each query to $O(T)$, and on the natural-neighbor concept of mutual nearest neighbors, extended from pairs to sets.

What would settle it

Compute NSM on a caption collection after independently permuting the coordinates of each word's embedding so that cosine-similarity neighborhoods are destroyed while token frequency and vector norms are preserved; if the scrambled NSM still correlates with human imageability and concreteness ratings, the reported signal is not neighborhood geometry. A complementary check is to compare NSM within frequency-matched word bins: if the correlation disappears, frequency rather than visual semantics drives the effect.

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Extended reading notes

Core claim

On the authors' own terms, the discovery is that the visual-semantic character of a word leaves a geometric trace in its contexts, and that a distribution-free statistic can read that trace. Hypothesis 1 states that the distribution of contexts around an imageable or concrete word forms a sharper peak in semantic space. The authors operationalize peakedness through $\alpha$-stability: a neighborhood is $\alpha$-stable when a proportion $\alpha$ of its points have their nearest neighbor inside that neighborhood, and the Neighborhood Stability Measure (NSM) of a query word is the $\alpha$ of its $k$-nearest-neighbor set. The empirical claim is that this single number correlates more strongly with ground-truth ratings than existing unsupervised methods, often by a wide margin, and that it acts as a strong predictor in imageable/non-imageable and concrete/abstract classification, with reliability increasing for extreme NSM values.

Load-bearing premise

The load-bearing premise is that imageability and concreteness appear as the geometric concentration of a word's contexts in embedding space, and that this concentration is not confounded by word frequency, register, or lexical fixedness.

Editorial extensions

If this is right

NSM gives 100% coverage of the rating lexicons: any word that can be embedded receives a score, removing the vocabulary-mismatch bottleneck that leaves the HML baseline able to predict only about 13% of imageability words and 2.7% of concreteness words.
The estimate is reusable: an embedding collection and its nearest-neighbor index are built once, and scoring a new word then costs one neighborhood query plus table lookups, in contrast to the roughly 120 GPU-hours reported for the image-generation baseline.
The signal depends on the text domain: moving from caption collections to general text collections such as MS MARCO or Quora drops NSM's correlations substantially, indicating that the visual-semantic property is encoded most strongly in visual-descriptive regions of semantic space.
Embedding dimensionality and collection size matter: smaller 384-dimensional embeddings and larger caption collections give stronger correlations, consistent with distance concentration in high dimensions.
Extreme NSM values carry more meaning: when words with mid-range scores are excluded, classification AUC rises, so the measure can be used as a confidence-graded predictor.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because NSM is defined for any query vector, the same statistic could be applied to phrases and sentences; the authors state they have not tested this, and a human-rating study for sentence-level imageability would be the direct test.
The paper does not control for word frequency or register when attributing neighborhood sharpness to visual semantics; a frequency-matched or residual analysis would show whether NSM adds signal beyond the caption-frequency baselines it reports.
NSM could double as a diagnostic for embedding spaces: a model whose neighborhoods do not sharpen for concrete words may have anisotropic or poorly calibrated geometry, independent of the psycholinguistic prediction task.
The caption-corpus requirement suggests a path to cross-lingual norms only where comparable visual-descriptive text exists, so languages without large caption collections would need translated or synthetically generated captions.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

NSM is a genuinely new and simple measure, and the empirical showing is better than the baselines by a clear margin. The paper deserves a serious referee, but not without two fixes: state the self-exclusion rule in Algorithm 1, and add a frequency- or diversity-matched analysis.

The core idea is crisp: for a word, embed its contexts, look at the k nearest neighbors, and count how many of those have their own nearest neighbor inside that set. That extends Zhu et al.'s natural neighbors from pointwise to set-level stability, and applying it to imageability/concreteness is new. The experiments are broad: three caption datasets, three embedding models, and sensible baselines including HML and AVGCLIP. The coverage table is a nice touch, showing HML only covers 2.7% of words.

The soft spots are real but fixable. Algorithm 1 as written never says whether the query q is excluded from the nearest-neighbor search. If q is in the collection, q is its own nearest neighbor, and many other points might have q as nearest neighbor, which could make NSM artificially high. The paper needs to state the exclusion rule.

More substantively, the paper reports FREQ as a baseline but never partials it out. Since FREQ correlates 0.26-0.35 with the ratings, and NSM correlates 0.58-0.66, it's possible that NSM is largely a frequency or contextual-diversity detector. The paper's hypothesis is about peakedness, but the evaluation doesn't discriminate between geometric sharpness and plain lexical statistics. The Discussion concedes that 'why NSM performs better on image-caption datasets' is unanswered; that is exactly where the mechanism needs defense. A matched-pairs analysis or partial correlation would settle it.

Minor: Table 1 has no error bars. The 10-trial averaging is good, but reporting variance or significance would help.

I recommend engaging with this paper. It's a solid empirical contribution with reproducible code. The conceptual claim is underdetermined, but the method works, and the authors are honest about the limits. A referee should ask for the self-exclusion clarification and a frequency-controlled experiment. With those, it's a clean paper.

For reading group, I'd say maybe—the confound discussion is worth a conversation. I'd cite it if I needed a cheap imageability estimator.

Referee Report

3 major / 5 minor

Summary. The paper proposes the Neighborhood Stability Measure (NSM), an unsupervised, distribution-free statistic computed from text-only caption embeddings, intended to quantify the 'sharpness' of the neighborhood of a word in semantic space. The authors hypothesize that this sharpness correlates with psycholinguistic imageability and concreteness ratings. They evaluate NSM on COCO, CC3M, and CC12M captions embedded with three sentence transformer models, comparing against frequency counts, the visual-concreteness method of Hessel et al. (2018), and the text-to-image method of Wu and Smith (2023). The main results report higher Spearman correlations with MRC imageability and Brysbaert et al. concreteness than these baselines, as well as strong AUC in threshold-based classification. The paper also reports degraded performance on non-caption text collections and an analysis of the effect of radius k.

Significance. If the result holds, the paper offers a cheap, single-modal, unsupervised estimator for two psycholinguistic variables, with full vocabulary coverage and no need for generative image sampling. The proposed measure is simple, interpretable, and built on standard ANN machinery; the authors provide code and data. The empirical claim that text-embedding geometry alone captures imageability/concreteness is interesting and would be a useful contribution to computational psycholinguistics. However, the current manuscript contains a formal degeneracy in the definition of NSM and leaves the main mechanistic claim underdetermined by not controlling for frequency or contextual diversity.

major comments (3)

[Section 4.2, Definition 1 and Algorithm 1] As written, NSM is identically 1 for every query, regardless of the data. In Definition 1, N1(u) is defined as the nearest neighbor of u in X (Equation 1). Since u itself belongs to X and δ(u,u) is maximal for any reasonable similarity (e.g., cosine similarity), the nearest neighbor of u is u itself. Therefore, for every u in Nk(q), the condition N1(u) ∈ Nk(q) holds trivially, making α = 1. The paper never states that the point itself is excluded from the nearest-neighbor search. This is a load-bearing issue: the reported correlations in Table 1 and the AUC results in Figures 3 and 4 cannot be reproduced from the stated algorithm. The fix is straightforward—define N1(u) as the nearest neighbor in X \ {u}, or explicitly use the second nearest neighbor—but it must be stated and ideally verified in the revised manuscript.
[Section 5.1 and Table 1; Section 6] The manuscript never controls for word frequency or contextual diversity, so the central claim that embedding geometry specifically encodes visual-semantic properties is underdetermined. The FREQ baselines in Table 1 already show moderate correlations (up to 0.34 and 0.35), and NSM is a local geometric quantity computed from the same caption collections; it may largely be a proxy for frequency or contextual diversity, which are themselves correlated with imageability and concreteness. To support Hypothesis 1, the authors should report partial Spearman correlations controlling for log frequency, or match words across frequency bins and show NSM retains its discriminative power. The paper's own Discussion concedes that 'why NSM performs better on image-caption datasets' is left unanswered; this is exactly the point where the visual-semantic interpretation needs defense.
[Table 1 and Table 2] The comparison with HML is not apples-to-apples. HML's correlation is computed only on the 12.9% of imageability words and 2.7% of concreteness words for which it can produce a score, whereas NSM and the other baselines are evaluated on the full rating vocabularies. Because Spearman's correlation is sensitive to the set of points, the reported superiority of NSM over HML could be partly an artifact of different evaluation subsets. The authors should either report HML on a common subset of words or explicitly discuss how the coverage disparity affects the comparison.

minor comments (5)

[Abstract] There are missing spaces in the typeset text, e.g., 'NSMcorrelates' and 'NSMis', which should be corrected.
[Section 4.2, Algorithm 1] The algorithm should specify that k must be smaller than the collection size |X|, since otherwise Nk(q) contains all points and the stability value is trivially defined.
[Section 4.2, input notation] The Algorithm 1 input line says 'embedding collection X ∈ R^d' but X is a set of vectors in R^d; use X ⊂ R^d consistently.
[Section 4.1, Figure 1] The tSNE visualization is illustrative only; its conclusions depend on tSNE hyperparameters and projection randomness. This is fine, but the text should not overstate what the figure demonstrates.
[Section 5.1, Methods] The phrase 'this results in a near exact nearest neighbor search' is informal; specify the approximation quality or report the recall of the ANN index on a sample.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: NSM is a purely geometric, unsupervised statistic validated against external human ratings; no load-bearing step reduces to its inputs.

full rationale

The paper's chain is: Hypothesis 1 links imageability/concreteness to the peakedness of a word's neighborhood in semantic embedding space; Algorithm 1 defines NSM as a distribution-free geometric statistic (the fraction of a query's k-nearest neighbors whose own nearest neighbor lies in the same set); and Section 5 measures Spearman correlation between NSM and external human ratings (MRC and Brysbaert et al.). The only tunable quantity is the radius k, and it is chosen on a validation split (Section 5.1: 'We randomly split each ratings dataset into non-overlapping validation (20%) and test (80%) subsets. If a method requires hyperparameter tuning, we do so on the validation split'), which is standard and not circular. NSM does not incorporate the ground-truth ratings in its definition, so the correlation is an empirical finding rather than a tautology. Self-citations are not load-bearing: Bruch (2024) is cited for ANN-search background and dimensionality-concentration effects, and Wu and Smith (2023) is cited as a baseline and for dataset comparisons; neither supplies the paper's central claim. No uniqueness theorem or ansatz is imported from the authors' prior work. The manuscript itself flag an admitted explanatory gap in Sections 6 and 7: 'We have also left unanswered the question of why NSM performs better on image-caption datasets' and whether its performance can be improved on general text collections. That acknowledgment is a limitation and a potential confound about visual-caption domain dependence, not a circular step. The skeptic's frequency-confound concern—that NSM may partly track word frequency or contextual diversity because Table 1 reports FREQ but does not partial it out—is a substantive correctness/validity threat, not a circularity, since the statistic is computed from geometry alone before any ratings are consulted. Overall, the central claim is empirically testable and tested against external benchmarks, so circularity is effectively absent.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central contribution rests on domain assumptions about caption text and embedding geometry, plus one unstated algorithmic convention (self-exclusion in nearest neighbor computation). The only fitted quantity is the neighborhood radius k, with ANN index parameters as minor implementation choices. No new physical or geometric entities are introduced.

free parameters (2)

radius k = tuned on validation split; optimal values not reported, Figure 5 suggests 2^10 to 2^12
Controls neighborhood size; NSM values and correlations are sensitive to k (Figure 5). Tuned on ground-truth validation ratings per collection.
ANN index parameters (nprobe, number of clusters) = nprobe=128, num_clusters=8*sqrt(|X|)
Chosen by hand for approximate nearest neighbor search. The authors state this makes the search near exact, so it is not central to the hypothesis but is part of the method.

assumptions (4)

domain assumption The text portion of image-caption datasets contains sufficient distributional signals to estimate imageability and concreteness without the visual modality.
This is the paper's stated supposition in the abstract and Section 1: 'we work on the supposition that text itself in an image-caption dataset offers sufficient signals.'
domain assumption A finite collection of text embeddings is a faithful surrogate for the unknown semantic-space distribution around a word.
Section 4.1 states 'Testing the hypothesis above directly is therefore out of the question' and uses the vector collection as a proxy for the true distribution.
ad hoc to paper The nearest neighbor of a point in a set is defined excluding the point itself, even though Definition 1 does not state this.
Without this exclusion every u in N_k(q) has itself as its nearest neighbor and NSM is trivially 1 for all queries. The paper never states the self-exclusion rule.
domain assumption Cosine similarity between sentence embeddings reflects semantic-context similarity relevant to human concreteness judgments.
The embedding models are trained for semantic similarity, and the hypothesis assumes neighborhoods in this geometry correspond to the context distribution of words.

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Cite this review

Pith. "Pith review of Uncovering Visual-Semantic Psycholinguistic Properties from the Distributional Structure of Text Embedding Space." pith.science (2026). https://pith.science/paper/RS4HUFEX

@misc{pith2026250523029,
  author       = {Pith},
  title        = {Pith review of: Uncovering Visual-Semantic Psycholinguistic Properties from the Distributional Structure of Text Embedding Space},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RS4HUFEX}},
  note         = {Machine review of arXiv:2505.23029}
}

read the original abstract

Imageability (potential of text to evoke a mental image) and concreteness (perceptibility of text) are two psycholinguistic properties that link visual and semantic spaces. It is little surprise that computational methods that estimate them do so using parallel visual and semantic spaces, such as collections of image-caption pairs or multi-modal models. In this paper, we work on the supposition that text itself in an image-caption dataset offers sufficient signals to accurately estimate these properties. We hypothesize, in particular, that the peakedness of the neighborhood of a word in the semantic embedding space reflects its degree of imageability and concreteness. We then propose an unsupervised, distribution-free measure, which we call Neighborhood Stability Measure (NSM), that quantifies the sharpness of peaks. Extensive experiments show that NSM correlates more strongly with ground-truth ratings than existing unsupervised methods, and is a strong predictor of these properties for classification. Our code and data are available on GitHub (https://github.com/Artificial-Memory-Lab/imageability).

Figures

Figures reproduced from arXiv: 2505.23029 by the authors.

**Figure 1.** Visualization of the neighborhoods of a set of imageable words (in blue) and non-imageable words (in red) in the embedding collection of ALLMINILM(CC3M). We use tSNE (van der Maaten and Hinton, 2008) for dimensionality reduction. Neighborhoods of non-imageable words have a wider spread and overlap with each other, whereas imageable words have comparatively more separable neighborhoods. than a non-imageable word. In… view at source ↗

**Figure 2.** Illustration of a stable neighborhood (encir [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Performance in classification tasks. The [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Classification by NSMALLMINILM(CC12M) . Words whose NSM scores are in [0, ω] are negative (i.e., non-imageable or abstract), and [1 − ω, 1] positive, repeated for ω ∈ {0.35, 0.4, 0.45, 0.5}. There is more confidence in predictions if the NSM score is very small or ver…

**Figure 5.** Figure 5: Effect of the NSM radius, k, on the correlation with imageability and concreteness, for select embedding collections. A value between 2 10 and 2 12 yields peak performance for reasonably large collections. supplementary section, we examine the effect k has on the corr…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Sebastian Bruch. 2024. Foundations of Vector Retrieval. Springer Nature Switzerland

work page 2024

[4] [4]

Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior Research Methods, 46:904--911

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