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Color vision models: Some simulations, a general n-dimensional model, and the colourvision R package.

Felipe M Gawryszewski¹

¹Departamento de Zoologia Instituto de Ciências Biológicas Universidade de Brasília Brasília Brazil.

Ecology and Evolution

|September 26, 2018

Summary

Color vision models are useful in ecology but can yield errors in common measurements. This study offers a guide, unified framework, and R package to improve color vision modeling accuracy and flexibility.

Keywords:

chromaticity diagram color hexagon color space color triangle color vision model receptor noise‐limited model

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Area of Science:

Ecology and evolution
Sensory biology
Vision science

Background:

Color vision models enable objective assessment of color perception, crucial for ecological and evolutionary studies.
Existing models, including the Chittka color hexagon and receptor noise-limited models, are widely applied but can produce spurious results under certain measurement conditions.
The assumptions underlying these models are largely consistent, suggesting potential for unification and generalization.

Purpose of the Study:

To provide a comprehensive guide to color vision modeling, highlighting potential pitfalls in common applications.
To present a unified framework that generalizes and extends existing color vision models.
To develop an R package for accessible and accurate implementation of color vision models.

Main Methods:

Simulations were used to evaluate established color vision models (Chittka, Endler & Mielke, receptor noise-limited models).
A unified framework was developed by analyzing the shared assumptions of existing models.
An R package was created to facilitate the application of the generalized color vision model.

Main Results:

Color vision models yield similar results when their specific requirements are met, but can generate spurious outputs in common scenarios.
Log-transformed models with relative photoreceptor outputs are susceptible to errors with low stimulus photon catch relative to background.
Chromatic backgrounds and achromatic low-reflectance stimuli can lead to unrealistic model predictions.

Conclusions:

Existing color vision models, despite differences, share fundamental assumptions that allow for a unified approach.
The new formulation accommodates varying photoreceptor types, allows user-defined models, and enables adjustable chromaticity diagram scaling.
This work enhances the reliability and applicability of color vision models in scientific research, particularly in ecology and evolution.