Color constancy algorithms play a crucial role in computer vision, and their performance needs to be accurately evaluated. However, recent years have seen scant systematic research on the correlation between human visual perception and objective distance measures for quantifying the performance of such algorithms. In this study, therefore, the authors systematically assessed the performance of 34 existing distance measures by psychophysical studies. Six classical color constancy algorithms and two recent algorithms were adopted to process over 110 images within 4 categories (Indoor, Human, Street, and Nature), and the influence of color space on the performance of distance measures was explored. Visual assessments obtained from 48 subjects were used to analyze the consistency between predictions of distance measures and human visual responses. It was found that the two most commonly used distance measures, the recovery angle error and the reproduction angle error in normalized RGB color space, exhibited high correlation with visual judgments, producing correlation coefficients of approximately 0.86. Meanwhile, significant performance variations among distance measures across different color spaces were also observed. Distance measures in uniform color spaces exhibited excellent consistency with human perception, yielding correlation coefficients of approximately 0.88. In addition, it was found that specific scenes also influenced the accuracy of distance measures. Our study highlights the importance of selecting appropriate color spaces for evaluating color constancy algorithms and offers more insights for the optimization of distance measures in the future.