As observers gained experience in the VR task, sensitivity to monocular and binocular cues increased. Surprisingly, the addition of motion parallax signals appeared to cause observers to rely almost exclusively on monocular cues. As expected, sensitivity was greater when monocular and binocular cues were presented together than in isolation. We tested a large cohort of observers who reported having no prior VR experience and found that binocular cue sensitivity was substantially weaker than monocular cue sensitivity. Here we evaluated the impact of experience on motion-in-depth (MID) sensitivity in a virtual reality (VR) environment. For example, the appearance of weak binocular sensitivity may relate to extensive prior experience with two-dimensional (2D) displays in which binocular cues are not informative. However, laboratory assessments may reflect factors beyond inherent perceptual sensitivity. In the laboratory, sensitivity to different three-dimensional (3D) motion cues varies across observers and is often weak for binocular cues. The visual system exploits multiple signals, including monocular and binocular cues, to determine the motion of objects through depth. This difference results in more effective use of available sensory information in the processing of 3-D motion than orientation and may reflect the temporal urgency of avoiding and intercepting moving objects. However, they also reveal a fundamental difference in how left- and right-eye perspective signals are represented for 3-D orientation versus motion perception. These results indicate that the integration of perspective and stereoscopic cues is a shared computational strategy across 3-D processing domains. Instead, 3-D motion sensitivity was best explained by a model in which stereoscopic cues were integrated with left- and right-eye perspective cues whose representations were at least partially independent. Importantly, sensitivity to combined cue stimuli was greater than predicted by the canonical model, which previous studies found to account for the perception of 3-D orientation in both humans and monkeys. The monkeys exhibited idiosyncratic differences in their biases and sensitivities for each cue, including left- and right-eye perspective cues, suggesting that the signals undergo at least partially separate neural processing.
We measured the sensitivity of male macaque monkeys to 3-D motion signaled by left-eye perspective cues, right-eye perspective cues, stereoscopic cues, and all three cues combined. Here, we show that this model fails to account for 3-D motion perception. The canonical model describing the integration of these cues assumes that perspective signals sensed by the left and right eyes are indiscriminately pooled into a single representation that contributes to perception. Robust 3-D visual perception is achieved by integrating stereoscopic and perspective cues. For instance, our framework leads to the conjecture that the development of the nervous system might be correlated with the occurrence of local thermal changes in embryo-fetal tissues. Hidden, unexpected multidisciplinary relationships can be found when mathematics copes with neural phenomena, leading to novel answers for everlasting neuroscientific questions. Further, the physical concepts of soft-matter polymers and nematic colloids might shed new light on neurulation in mammalian embryos.
Presheaves from category theory permit the tackling of nervous phase spaces in terms of the theory of infinity categories, highlighting an approach based on equivalence rather than equality. The multisynaptic ascending fibers connecting the peripheral receptors to the neocortical areas can be assessed in terms of knot the-ory/braid groups. The Monge's theorem might contribute to our visual ability of depth perception and that the brain connectome can be tackled in terms of tunnelling nanotubes. We aim to elucidate whether underrated notions from geometry, topology, group theory and category theory can assess neuronal issues and provide experimentally testable hypotheses. For example, geometric constraints are powerful enough to define cellular distribution and drive the embryonal development of the central nervous system. The massive amount of available neurodata suggests the existence of a mathematical backbone underlying neuronal oscillatory activities.
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