Luminance gradient configuration determines perceived lightness in a simple geometric illusion

Asymmetric Brightness Effects With Dark Versus Light Glare-Like Stimuli

The glare effect is a brightness illusion that has captured the attention of the vision community since its discovery. However, its photometrical reversal, which we refer to here as photometrical reversed glare (PRG) stimuli, remained relatively unexplored. We presented three experiments that sought to examine the perceived brightness of a target area surrounded by luminance gradients in PRG stimuli and compare them with conventional glare effect configurations. Experiment 1 measured the brightness of the central target area of PRG stimuli through an adjustment task; the results showed that the target appeared brighter than similar, comparative areas not surrounded by luminance gradients. This finding was unexpected given the recent report that PRG stimuli cause pupil dilation. Meanwhile, Experiments 2 and 3 implemented a rating task to further test the findings in Experiment 1. Again, the study found a robust brightening illusion in the target area of PRG stimuli in a wide range of target and background luminance. The results are discussed in comparison with the brightness enhancement of the glare effect.

Visualization of Biomedical Data

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.