Stereopsis is the ability to estimate distance based on the different views seen in the two eyes [1-5]. It is an important model perceptual system in neuroscience and a major area of machine vision. Mammalian, avian, and almost all machine stereo algorithms look for similarities between the luminance-defined images in the two eyes, using a series of computations to produce a map showing how depth varies across the scene [3, 4, 6-14]. Stereopsis has also evolved in at least one invertebrate, the praying mantis [15-17]. Mantis stereopsis is presumed to be simpler than vertebrates' [15, 18], but little is currently known about the underlying computations. Here, we show that mantis stereopsis uses a fundamentally different computational algorithm from vertebrate stereopsis-rather than comparing luminance in the two eyes' images directly, mantis stereopsis looks for regions of the images where luminance is changing. Thus, while there is no evidence that mantis stereopsis works at all with static images, it successfully reveals the distance to a moving target even in complex visual scenes with targets that are perfectly camouflaged against the background in terms of texture. Strikingly, these insects outperform human observers at judging stereoscopic distance when the pattern of luminance in the two eyes does not match. Insect stereopsis has thus evolved to be computationally efficient while being robust to poor image resolution and to discrepancies in the pattern of luminance between the two eyes. VIDEO ABSTRACT.

Detecting motion is essential for animals to perform a wide variety of functions. In order to do so, animals could exploit motion cues, including both first-order cues—such as luminance correlation over time—and second-order cues, by correlating higher-order visual statistics. Since first-order motion cues are typically sufficient for motion detection, it is unclear why sensitivity to second-order motion has evolved in animals, including insects. Here, we investigate the role of second-order motion in prey capture by praying mantises. We show that prey detection uses second-order motion cues to detect figure motion. We further present a model of prey detection based on second-order motion sensitivity, resulting from a layer of position detectors feeding into a second layer of elementary-motion detectors. Mantis stereopsis, in contrast, does not require figure motion and is explained by a simpler model that uses only the first layer in both eyes. Second-order motion cues thus enable prey motion to be detected, even when perfectly matching the average background luminance and independent of the elementary motion of any parts of the prey. Subsequent to prey detection, processes such as stereopsis could work to determine the distance to the prey. We thus demonstrate how second-order motion mechanisms enable ecologically relevant behavior such as detecting camouflaged targets for other visual functions including stereopsis and target tracking.

No abstract

No abstract

No abstract