---
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"](https://arxiv.org/abs/2505.17117) 
 (Shani et al., 2025), which quantitatively compares human and large language model (LLM) conceptual representations using information-theoretic tools.

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**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.

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**Languages**

English 🇺🇸

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**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.


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**Usage**


```py
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')
```

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**Citation**

If you use this dataset, please cite:

```bibtex
@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}
}
```