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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