license: wtfpl
task_categories:
- token-classification
language:
- en
tags:
- cognition
- concepts
- clusters
- categories
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}
}