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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
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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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