Datasets:

MrPatoVera
/

lexical-consensus-data

license: cc-by-4.0
pretty_name: Visual similarity, human similarity and artificial word learning (CIFAR)
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - cognitive-science
  - word-learning
  - representational-alignment
  - dinov2
  - things
  - imagenet-hsj
  - lewis-frank
  - arxiv:2611.14116
size_categories:
  - 1K<n<10K
configs:
  - config_name: pairs_c100
    data_files: pairs_c100/pairs_c100.parquet
  - config_name: learner_c100
    data_files: learner_c100/learner_c100.parquet
  - config_name: xtmem_aggregates
    data_files: xtmem_aggregates/xtmem_aggregates.parquet
  - config_name: word_norms
    data_files: word_norms/word_norms.parquet

Visual similarity, human similarity and artificial word learning (CIFAR)

Companion dataset for Visual Similarity in Adult Word Extension and Artificial Word Learning (Vera, 2026). Author: Patricio M. Vera (ORCID 0009-0004-5384-5952), Simbionte.org. Code: github.com/patriciomvera/lexical-consensus. Preprint: arXiv:2611.14116 (pdf, doi:10.48550/arXiv.2611.14116). CIFAR-10H companion (CC BY-NC-SA 4.0): MrPatoVera/lexical-consensus-data-cifar10h. Collection: Lexical Consensus.

Summary

Tabular companion to analyses that relate a frozen self-supervised visual encoder (DINOv2-small) to human similarity and word-extension behaviour, and to an artificial prototype learner over the same space. This repository holds the CC BY 4.0 configs. A separate repository, MrPatoVera/lexical-consensus-data-cifar10h, holds the CIFAR-10H-derived tables under CC BY-NC-SA 4.0.

Config	Unit	Rows	What it is
`pairs_c100`	CIFAR-100 class pair	4,950	DINOv2 / CVCL / WordNet / THINGS / HSJ distances
`learner_c100`	Sampled two-category word	100	Prototype-learner naming accuracy + distances
`xtmem_aggregates`	Image × teaching condition	96	Aggregated adult extension (Lewis & Frank 2018)
`word_norms`	Word	95	Glasgow AoA + our DINOv2 dispersion

Auxiliary files (not HF configs): maps/map_things_v1.json, maps/map_hsj_v1.json, and recipe/ (rebuild instructions for XTMEM aggregates).

How it was built (recipe)

Encoder. DINOv2-small CLS embeddings (384-d), L2-normalised. Default Hugging Face image processor: RGB, bicubic resize of the shorter side to 256, 224×224 centre crop, ImageNet normalisation. Pair distance = cosine distance between class centroids (all CIFAR-100 training images per class).

Learner. Prototype (normalised centroid) in 3-way, 5-shot episodes; naming accuracy on held-out images. The two native contrast classes (contrast1, contrast2) are sampled with the same deterministic seed schedule used in the DINOv2 dissociation experiment and reused for the CVCL re-run; they are episode metadata for both encoders, not CVCL-only.

Human similarity.

THINGS SPoSE 66-d distances via a versioned CIFAR→THINGS map (map_things_v1.json).
ImageNet-HSJ: d_hsj is defined only when hsj_n ≥ 10; raw means and counts are also shipped. Trial-level HSJ data are not shipped (rebuild from OSF cn2s3).
Glasgow Norms age of acquisition (aoa_glasgow). Word-frequency (zipf from wordfreq) is not redistributed (author request not to repackage).
dispersion in word_norms is our own derived value: within-class coherence of DINOv2 embeddings for that CIFAR label (not a Glasgow Norms field).

Adult word extension (XTMEM / Lewis & Frank 2018). Only aggregates are shipped: one row per image × harmonized teaching condition over target-domain trials (in_target = 1). Seven raw condition spellings are unified to four canonical labels (one, three_subordinate, three_basic, three_superordinate), matching the paper's exploratory figure. That yields 96 units (= paper \XUnits).

Suppression rule (formal). Any aggregate cell with n_participants < 5 is omitted from xtmem_aggregates. In the full Lewis & Frank sample no cell falls below this threshold at the published grain.

Why n_participants is 520 in every cell. Each person-session runs 12 trials = 4 teaching conditions × 3 categories. Every test image belongs to one category domain, so with in_target = 1 every session contributes exactly one observation to every image × condition cell. Unique people = 520 (paper HFourPersons); unique sessions = 600 (56 people repeated across experiments). Column n_sessions makes this transparent. The constant is a property of the crossed design, not a broadcast of a global total.

Per-participant / per-trial tables (h4_long.csv, h4_first_session.csv) are not redistributed — see recipe/ to rebuild locally from OSF yekhj.

CVCL. Distances and accuracies from the CVCL vision encoder (MIT-licensed model; weights not included). Same episode design as DINOv2 for the 100 pairs.

What we tried that did not ship

Redistributing per-participant XTMEM derivatives — rejected to keep the paper, the code repository and this dataset consistent, and because stimulus-image provenance is undocumented in the upstream GitHub tree.
Packing wordfreq zipf values — rejected at the author's request.
Shipping CIFAR / ImageNet / THINGS / XTMEM images or embedding caches — out of scope; large and not ours to rehost.
A single shared license covering CIFAR-10H — impossible without forcing NC-SA onto CC BY material; CIFAR-10H tables live in the sister repository.
Publishing hsj_trials.csv.gz (~511k rows) — optional; omitted (rebuild from OSF cn2s3).

Limitations

THINGS / HSJ coverage is partial (see covered_* columns). Class mappings involve judgements; scout / strict / origmap variants are included where they exist.
XTMEM aggregates do not support re-analysis of person-level random effects; use the recipe and the original OSF deposit for that.
No images. Learner results are for frozen encoders, CIFAR, two-category words and shallow learners. Post hoc paper analyses are exploratory.
Research use; not a production model-evaluation suite.

Sources and licenses

Source	License	What enters this repo
Lewis & Frank 2018 (OSF yekhj)	CC BY 4.0	Aggregates only
THINGS (OSF f5rn6)	CC BY 4.0	Distances + map
ImageNet-HSJ (OSF cn2s3)	CC BY 4.0	Distances + map
Glasgow Norms	CC BY 4.0	`aoa_glasgow`
CVCL model	MIT	Distances / accuracies only
Our distances, learner outputs, and `dispersion`	CC BY 4.0 (this dataset)	All remaining columns

Third-party values remain under their own licenses. Attribution in any redistribution must preserve the chain above.

Citation

@misc{vera2026visualsimilarity,
  title         = {Visual Similarity in Adult Word Extension and Artificial Word Learning},
  author        = {Vera, Patricio M.},
  year          = {2026},
  eprint        = {2611.14116},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi           = {10.48550/arXiv.2611.14116},
  url           = {https://arxiv.org/abs/2611.14116},
  note          = {Affiliation: Simbionte.org. ORCID: 0009-0004-5384-5952.
                   Code: https://github.com/patriciomvera/lexical-consensus}
}

Files

pairs_c100/pairs_c100.parquet
learner_c100/learner_c100.parquet
xtmem_aggregates/xtmem_aggregates.parquet
xtmem_aggregates/condition_label_map.json
word_norms/word_norms.parquet
maps/map_things_v1.json
maps/map_hsj_v1.json
recipe/