Visual perception depends strongly on spatial context. A classic example is the tilt illusion where the perceived orientation of a central stimulus differs from its physical orientation when surrounded by tilted spatial contexts. Here we show that such contextual modulation of orientation perception exhibits trait-like interindividual diversity that correlates with interindividual differences in effective connectivity within human primary visual cortex. We found that the degree to which spatial contexts induced illusory orientation perception, namely, the magnitude of the tilt illusion, varied across healthy human adults in a trait-like fashion independent of stimulus size or contrast. Parallel to contextual modulation of orientation perception, the presence of spatial contexts affected effective connectivity within human primary visual cortex between peripheral and foveal representations that responded to spatial context and central stimulus, respectively. Importantly, this effective connectivity from peripheral to foveal primary visual cortex correlated with interindividual differences in the magnitude of the tilt illusion. Moreover, this correlation with illusion perception was observed for effective connectivity under tilted contextual stimulation but not for that under iso-oriented contextual stimulation, suggesting that it reflected the impact of orientation-dependent intra-areal connections. Our findings revealed an interindividual correlation between intra-areal connectivity within primary visual cortex and contextual influence on orientation perception. This neurophysiological-perceptual link provides empirical evidence for theoretical proposals that intra-areal connections in early visual cortices are involved in contextual modulation of visual perception.
The effect of point cloud denoising is very important for the subsequent surface fitting and modeling design of the 3D scanning process. How to extract feature points quickly and accurately has become a research hotspot. However, the key to point cloud denoising lies in singular values and outliers. Therefore, this paper proposes a denoising model coupled with multi-feature parameters, discusses the influence degree of each feature point parameter separately, and uses the swarm intelligence algorithm to solve a set of optimal parameter weights to determine the point cloud denoising model, and to achieve the optimal denoising effect of 3D scattered point cloud. The simulation results show that the swarm intelligence algorithm used is faster and less time-consuming than the existing differential evolution algorithm. At the same time, the point cloud denoising model proposed in this paper has better performance than radius filtering and statistical filtering. denoising effect.
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