The fruit fly estimates visual motion by incorporating ON-OFF asymmetric processing that only improves performance when stimuli have light-dark asymmetries matched to natural scenes.

Human tilt estimation in natural scenes is predicted by an image-computable Bayes optimal model that is grounded in the statistics of natural images and scenes.

Visual sensitivity to correlation patterns in rats matches that previously measured in humans, as well as predictions from efficient coding theory based on the statistics of natural images.

Environmental heterogeneity may contribute to the high levels of genetic variation in glucosinolate genes found in Arabidopsis thaliana.

An optimization principle allows natural-image data to predict human sensitivity to synthetic, un-natural textures with multiple gray levels.

The visual message conveyed by retinal neurons to the brain when signaling natural scenes resembles the individual receptive fields only when viewed in context of the neuronal population.

Psychophysical measures of human sensitivity to visual patterns reveal that the brain preferentially processes those features of stimuli that are more variable in the natural world.

Peripheral appearance models emphasising pooling processes that depend on retinal eccentricity will instead need to explore input-dependent grouping and segmentation.

New computational models provide insights into how the insect brain estimates the speed and direction of movement.

Longitudinal transcriptomic analysis of unvaccinated patients reveals that early NK cell cytotoxicity activation distinguishes mild from severe COVID-19, supporting the relevance of concerted innate–adaptive immune responses against the virus.