MooneyMaker: A Python package to create ambiguous two-tone images

TL;DR

MooneyMaker is an open-source Python package that automates the creation of ambiguous two-tone Mooney images, enabling more consistent and efficient visual perception research through various generation techniques.

Contribution

It introduces a versatile, automated tool for generating Mooney images with multiple methods, improving consistency and reproducibility in visual perception studies.

Findings

Lower initial recognizability correlates with higher post-template recognition.

The package allows direct comparison of different generation techniques.

Standardization improves reproducibility in Mooney image research.

Abstract

Mooney images are high-contrast, two-tone visual stimuli, created by thresholding photographic images. They allow researchers to separate image content from image understanding, making them valuable for studying visual perception. An ideal Mooney image for this purpose achieves a specific balance: it initially appears unrecognizable but becomes fully interpretable to the observer after seeing the original template. Researchers traditionally created these stimuli manually using subjective criteria, which is labor-intensive and can introduce inconsistencies across studies. Automated generation techniques now offer an alternative to this manual approach. Here, we present MooneyMaker, an open-source Python package that automates the generation of ambiguous Mooney images using several complementary approaches. Users can choose between various generation techniques that range from approaches based on image statistics to deep learning models. These models strategically alter edge information to increase initial ambiguity. The package lets users create two-tone images with multiple methods and directly compare the results visually. In an experiment, we validate MooneyMaker by generating Mooney images using different techniques and assess their recognizability for human observers before and after disambiguating them by presenting the template images. Our results reveal that techniques with lower initial recognizability are associated with higher post-template recognition (i.e. a larger disambiguation effect). To help vision scientists build effective databases of Mooney stimuli, we provide practical guidelines for technique selection. By standardizing the generation process, MooneyMaker supports more consistent and reproducible visual perception research.