# Hues and Cues: Human vs. CLIP

arXiv: https://arxiv.org/abs/2509.02305 · HTML: https://mlsanity.com/paper/2509.02305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)

## Built on by (most cited first, 1 of 1)

- [Color Names in Vision-Language Models](https://mlsanity.com/paper/2509.22524/) (2025)

## Abstract (arXiv)

Playing games is inherently human, and a lot of games are created to challenge different human characteristics. However, these tasks are often left out when evaluating the human-like nature of artificial models. The objective of this work is proposing a new approach to evaluate artificial models via board games. To this effect, we test the color perception and color naming capabilities of CLIP by playing the board game Hues & Cues and assess its alignment with humans. Our experiments show that CLIP is generally well aligned with human observers, but our approach brings to light certain cultural biases and inconsistencies when dealing with different abstraction levels that are hard to identify with other testing strategies. Our findings indicate that assessing models with different tasks like board games can make certain deficiencies in the models stand out in ways that are difficult to test with the commonly used benchmarks.