item
stringlengths 3
19
| category
stringclasses 24
values | typicality
float64 1
10
| subdataset
stringclasses 3
values |
---|---|---|---|
Dog | Animal | 10 | McCloskey1978 |
Horse | Animal | 9.83 | McCloskey1978 |
Cow | Animal | 9.75 | McCloskey1978 |
Sparrow | Animal | 7.5 | McCloskey1978 |
Cobra | Animal | 6.75 | McCloskey1978 |
Trout | Animal | 6.66 | McCloskey1978 |
Lizard | Animal | 6.5 | McCloskey1978 |
Unicorn | Animal | 6.14 | McCloskey1978 |
Lobster | Animal | 6.13 | McCloskey1978 |
Jellyfish | Animal | 5.92 | McCloskey1978 |
Woman | Animal | 5.54 | McCloskey1978 |
Worm | Animal | 5.3 | McCloskey1978 |
Tadpole | Animal | 5.21 | McCloskey1978 |
Spider | Animal | 5.16 | McCloskey1978 |
Mosquito | Animal | 4.92 | McCloskey1978 |
Amoeba | Animal | 4.21 | McCloskey1978 |
Poet | Animal | 4.04 | McCloskey1978 |
Sea Anemone | Animal | 3.92 | McCloskey1978 |
Hydra | Animal | 3.77 | McCloskey1978 |
Euglena | Animal | 3.6 | McCloskey1978 |
Sponge | Animal | 3.58 | McCloskey1978 |
Cocoon | Animal | 3.46 | McCloskey1978 |
Egg | Animal | 3.38 | McCloskey1978 |
Bacterium | Animal | 3.29 | McCloskey1978 |
Yeast | Animal | 2.5 | McCloskey1978 |
Fungus | Animal | 2.3 | McCloskey1978 |
Virus | Animal | 2.3 | McCloskey1978 |
Tree | Animal | 1.33 | McCloskey1978 |
Tulip | Animal | 1.33 | McCloskey1978 |
Car | Animal | 1 | McCloskey1978 |
Robin | Bird | 10 | McCloskey1978 |
Sparrow | Bird | 9.96 | McCloskey1978 |
Eagle | Bird | 9.58 | McCloskey1978 |
Owl | Bird | 8.71 | McCloskey1978 |
Partridge | Bird | 8.42 | McCloskey1978 |
Vulture | Bird | 8.38 | McCloskey1978 |
Goose | Bird | 8.29 | McCloskey1978 |
Duck | Bird | 8.25 | McCloskey1978 |
Condor | Bird | 8.23 | McCloskey1978 |
Pheasant | Bird | 8.17 | McCloskey1978 |
Buzzard | Bird | 8.08 | McCloskey1978 |
Rooster | Bird | 7.96 | McCloskey1978 |
Turkey | Bird | 7.92 | McCloskey1978 |
Quail | Bird | 7.88 | McCloskey1978 |
Chicken | Bird | 7.75 | McCloskey1978 |
Albatross | Bird | 7.52 | McCloskey1978 |
Loon | Bird | 7.43 | McCloskey1978 |
Pelican | Bird | 7.29 | McCloskey1978 |
Ostrich | Bird | 7.25 | McCloskey1978 |
Dodo | Bird | 7.13 | McCloskey1978 |
Penguin | Bird | 6.96 | McCloskey1978 |
Pterodactyl | Bird | 4.96 | McCloskey1978 |
Bat | Bird | 3.63 | McCloskey1978 |
Chicken Egg | Bird | 2.96 | McCloskey1978 |
Flying Squirrel | Bird | 2.63 | McCloskey1978 |
Butterfly | Bird | 2.38 | McCloskey1978 |
Vampire | Bird | 2.29 | McCloskey1978 |
Bee | Bird | 2.04 | McCloskey1978 |
Locust | Bird | 1.83 | McCloskey1978 |
Saw | Carpenter's Tool | 9.83 | McCloskey1978 |
Sandpaper | Carpenter's Tool | 8.54 | McCloskey1978 |
Nails | Carpenter's Tool | 8.46 | McCloskey1978 |
Workbench | Carpenter's Tool | 8.13 | McCloskey1978 |
Vise | Carpenter's Tool | 7.88 | McCloskey1978 |
Varnish | Carpenter's Tool | 5.71 | McCloskey1978 |
Soldering Iron | Carpenter's Tool | 5.5 | McCloskey1978 |
Crowbar | Carpenter's Tool | 5.08 | McCloskey1978 |
Sledge Hammer | Carpenter's Tool | 4.74 | McCloskey1978 |
Calculator | Carpenter's Tool | 3.29 | McCloskey1978 |
Dress | Clothing | 9.92 | McCloskey1978 |
Shirt | Clothing | 9.92 | McCloskey1978 |
Necktie | Clothing | 9.04 | McCloskey1978 |
Socks | Clothing | 8.92 | McCloskey1978 |
Shoes | Clothing | 8.79 | McCloskey1978 |
Cuff Links | Clothing | 6.79 | McCloskey1978 |
Buttons | Clothing | 6.17 | McCloskey1978 |
Bracelet | Clothing | 5.83 | McCloskey1978 |
Necklace | Clothing | 5.79 | McCloskey1978 |
Handkerchief | Clothing | 5.71 | McCloskey1978 |
Wig | Clothing | 5.33 | McCloskey1978 |
Watch | Clothing | 5.25 | McCloskey1978 |
Handbag | Clothing | 4.96 | McCloskey1978 |
Eyeglasses | Clothing | 4.83 | McCloskey1978 |
Cane | Clothing | 4.67 | McCloskey1978 |
Corsage | Clothing | 4.29 | McCloskey1978 |
Wallet | Clothing | 4.08 | McCloskey1978 |
Makeup | Clothing | 3.96 | McCloskey1978 |
Medal | Clothing | 3.96 | McCloskey1978 |
Umbrella | Clothing | 3.83 | McCloskey1978 |
Fingernail Polish | Clothing | 3.67 | McCloskey1978 |
Contact Lens | Clothing | 3.58 | McCloskey1978 |
Briefcase | Clothing | 3.21 | McCloskey1978 |
Dentures | Clothing | 3.08 | McCloskey1978 |
Suitcase | Clothing | 2.92 | McCloskey1978 |
Beard | Clothing | 2.71 | McCloskey1978 |
Hearing Aid | Clothing | 2.67 | McCloskey1978 |
Hairbrush | Clothing | 2.17 | McCloskey1978 |
Bandaid | Clothing | 1.79 | McCloskey1978 |
Cancer | Disease | 9.75 | McCloskey1978 |
Tuberculosis | Disease | 9.67 | McCloskey1978 |
Dataset Summary
This dataset provides digitized versions of classic human categorization benchmarks from seminal cognitive psychology studies by Rosch (1973, 1975) and McCloskey & Glucksberg (1978). These datasets capture human judgments about semantic categories and typicality, offering high-fidelity insights into how humans organize conceptual knowledge.
This dataset was released as part of the study "From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning" (Shani et al., 2025), which quantitatively compares human and large language model (LLM) conceptual representations using information-theoretic tools.
Supported Tasks and Leaderboards
Conceptual Alignment: Evaluating how well model-derived clusters match human semantic categories.
Typicality Modeling: Assessing the alignment between human-rated item typicality and model-internal semantic distances.
Rate-Distortion Evaluation: Benchmarking conceptual representations with an information-theoretic framework balancing complexity and semantic fidelity.
Languages
English 🇺🇸
Dataset Structure
Each row in the dataset corresponds to an item (e.g., “robin”, “sofa”) and includes:
item: the concept/item name.
category: the human-assigned semantic category (e.g., "bird", "furniture").
typicality_score: human-rated typicality of the item for its category.
subdataset: the paper that introduced this datapoint (options: [Rosch1973, Rosch1975, McCloskey1978]).
The three subdatasets include:
Rosch1973: 48 items in 8 categories with typicality rankings.
Rosch1975: 552 items in 10 categories with typicality rankings.
McCloskey1978: 449 items in 18 categories with typicality rankings.
Usage
from datasets import load_dataset
# Load all splits
ds = load_dataset("CShani/human-concepts")['train']
# Load a specific sub-dataset
rosch75 = ds.filter(lambda x: x['subdataset'] == 'Rosch1975')
Citation
If you use this dataset, please cite:
@article{shani2025fromtokens,
title={From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning},
author={Shani, Chen and Jurafsky, Dan and LeCun, Yann and Shwartz-Ziv, Ravid},
journal={arXiv preprint arXiv:2505.17117},
year={2025}
}
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