# Relationships among lightness illusions uncovered by analyses of individual differences

## TL;DR

This study explores how different lightness illusions are related by analyzing individual differences and using factor analysis to uncover underlying patterns.

## Contribution

The study introduces a novel approach to evaluating lightness illusion models by identifying underlying factors through factor analysis.

## Key findings

- Factor analysis revealed that lightness illusions can be grouped into distinct categories like assimilation and contrast.
- Three models based on early visual processes showed biases toward specific illusion factors.
- The study suggests that evaluating models based on underlying factors provides better insights than counting individual illusions.

## Abstract

Computational models that explain lightness/brightness illusions have been proposed. These models have been assessed using a simplistic criterion: the number of illusions each model can correctly predict from the test set. This simple method of evaluation assumes that each illusion is independent; however, because the independence and similarity among lightness illusions have not been well established, potential interdependencies among the illusions in the test set could distort the evaluation of models. Moreover, evaluating models with a single value obscures where the model's strengths and weaknesses lie. We collected the magnitudes of various lightness illusions through two online experiments and applied exploratory factor analyses. Both experiments identified some underlying factors in these illusions, suggesting that they can be classified into a few distinct groups. Experiment 1 identified three common factors; assimilation, contrast, and White's effect. Experiment 2, with a different illusion set, identified two factors—assimilation and contrast. We then examined three well-known models that are based on early visual processes, using the outcomes of the experiments. The examination of these models revealed biases in the models toward specific factors or sets of illusions, which suggested their limitations. This study clarified that correlations of illusion magnitudes provide valuable insights into both illusions and models and highlighted the need to assess models based on their ability to account for underlying factors rather than individual illusions.