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Neural recordings from macaque area MT reveal a novel mechanism for detecting moving objects during self-motion, involving neurons with incongruent tuning for depth from motion parallax and binocular disparity cues.

Perception is proposed to be a dynamic motor-sensory closed-loop process in which information flows through the environment and the brain in continuous loops, converging towards steady-state percepts.

Judgments of how much time elapsed between two events in a story are predicted by changes in fMRI activity patterns.

Default mode network and visual cortex are connected via two parallel pathways that differentially respond to the processing of visual scenes and semantic information about objects, reflecting domain-specific organisation.

Behavioral and computational results show that the perception of our body as our own depends on Bayesian probabilistic reasoning that take into account the variations in sensory uncertainty when integrating visual and somatosensory cues.

Empirical experiments and computational modeling reveal a stochastic world model on gravity for stability inference that represents gravity’s vertical direction as a Gaussian distribution.

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

Fixational eye movements transform the spatial scene into temporal modulations on the retina, which, together with the known sensitivities of retinal neurons, provide a comprehensive account of human spatial sensitivity.

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