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](https://orcid.org/0009-0004-5384-5952)), [Simbionte.org](https://simbionte.org).
> Code: [github.com/patriciomvera/lexical-consensus](https://github.com/patriciomvera/lexical-consensus).
> Preprint: [arXiv:2611.14116](https://arxiv.org/abs/2611.14116) ([pdf](https://arxiv.org/pdf/2611.14116), [doi:10.48550/arXiv.2611.14116](https://doi.org/10.48550/arXiv.2611.14116)).
> CIFAR-10H companion (CC BY-NC-SA 4.0): [`MrPatoVera/lexical-consensus-data-cifar10h`](https://huggingface.co/datasets/MrPatoVera/lexical-consensus-data-cifar10h).
> Collection: [Lexical Consensus](https://huggingface.co/collections/MrPatoVera/lexical-consensus-6af31e7f9870b8e83e668c49).

## 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`](https://huggingface.co/datasets/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` &lt; **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](https://github.com/patriciomvera/lexical-consensus) 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

```bibtex
@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/
```

	---
	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](https://orcid.org/0009-0004-5384-5952)), [Simbionte.org](https://simbionte.org).
	> Code: [github.com/patriciomvera/lexical-consensus](https://github.com/patriciomvera/lexical-consensus).
	> Preprint: [arXiv:2611.14116](https://arxiv.org/abs/2611.14116) ([pdf](https://arxiv.org/pdf/2611.14116), [doi:10.48550/arXiv.2611.14116](https://doi.org/10.48550/arXiv.2611.14116)).
	> CIFAR-10H companion (CC BY-NC-SA 4.0): [`MrPatoVera/lexical-consensus-data-cifar10h`](https://huggingface.co/datasets/MrPatoVera/lexical-consensus-data-cifar10h).
	> Collection: [Lexical Consensus](https://huggingface.co/collections/MrPatoVera/lexical-consensus-6af31e7f9870b8e83e668c49).

	## 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`](https://huggingface.co/datasets/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](https://github.com/patriciomvera/lexical-consensus) 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

	```bibtex
	@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/
	```