In scene-selective occipital cortex and within 200 ms of processing, visual inputs are sorted according to their typical spatial position within a scene.

Well-controlled psychological experiments show that there is little overlap in how humans and convolutional networks classify adversarial images, highlighting the problem of using CNNs as models of human vision.

Digital reconstruction of environment combined with eye and head-tracking enabled the process of prey-detection and capture to be seen from the freely moving mouse’s point-of-view and shows the exact visual-field and retinal location mice use when chasing prey and the advantage.

A Plant Cell Atlas that shows the location and organization of molecules at cellular and tissue levels could have far-reaching consequences for plant sciences.

Motor performance benefits from rewarding contexts via a non-uniform reduction of response latencies and increase in feedback gains across the feedback loops involved in upper limb motor control.

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

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.

Each stage of the scientific life cycle stands to benefit from the introduction of data science techniques.

A novel Bayesian method of modeling retinotopic maps is more accurate than traditional voxel-wise methods and can be used to automatically derive high-quality maps.

Late cataract-treated individuals learn to recalibrate vision for action in the months to years after sight restoration surgery, demonstrating that this ability can develop even in the absence of early pattern vision.