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During natural visual behavior in mice, orienting towards a target is driven by head movements, during which the eyes stabilize and shift the visual input.

Virtual reality experiments show that motorists slow down when driving in fog, but they speed up when visibility is reduced equally at all distances.

The medial and lateral subdivisions of the entorhinal cortex are important for deciding "where to go from here" and "what to do to this object" in a visual context, respectively.

Humans use a near-optimal eye movement strategy to efficiently extract information about high-level visual categories.

Context-based object recognition causally relies on both scene- and object-selective cortex, with scene-selective cortex generating expectations (at 160-200 ms after onset) that disambiguate object representations in object-selective cortex (at 260-300 ms after onset).

Widespread color-opponency in mouse V1 enhances object decoding in the sky, highlighting the evolutionary importance of color processing in non-primate species.

Rendering compound vision at high speed with high precision, enabling rapid, data-driven exploration of the compound eye design space.

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.

Deep network features exhibit a robust correlation with brain activity in scene-selective cortex, but are not sufficient to explain human scene categorization behavior, which is strongly shaped by information about the function (possibility for action) of the scene.

The visual information walkers use for path selection during locomotion was revealed by analysis of a three-dimensional numerical representation of the natural terrain.