EXPRESS: Can arrows change the subjective perception of space? Exploring symbolic attention repulsion

The attention repulsion effect (ARE) refers to distortions in the perception of space for areas nearby the focus of attention. For instance, when attending to the right-hand side of the visual field, objects in central vision may appear as though they are shifted to the left. The phenomenon is likely caused by changes in visual cell functioning. To date, research on the ARE has almost exclusively used exogenous manipulations of attention. In contrast, research exploring endogenous attention repulsion has been mixed, and no research has explored the effects of non-predictive arrow cues on this phenomenon. This gap in the literature is unexpected, as symbolic attention appears to be a unique form of attentional orienting compared to endogenous and exogenous attention. Therefore, the current study explored the effects of symbolic orienting on spatial repulsion and compared it to an exogenously generated ARE. Across four experiments, both exogenous and symbolic orienting resulted in AREs; however, the magnitude of the symbolic ARE was smaller than the exogenous ARE. This difference in magnitude persisted, even after testing both phenomena using stimulus timing parameters known to produce optimal effects in traditional attentional cueing paradigms. Therefore, compared to symbolic attention, it appears that exogenous manipulations may tightly constrict attention resources on the cued location, in turn potentially influencing the functioning of visual cells for enhanced perceptual processing.

S. Ramon Cajal. Nouvelles contributions à l’étude histologique de la rétine. Journal de l’Anatomie et de la Physiologie. XXXII année, 1896, № 5. Septembre—octobre. P. p. 481—543

In the development of rods and cones, the author notes 4 common and other main periods: the embryonic period, the period of unipolarity, the period of bipolarity and the period of the young state. The first period corresponds to the period of the germinal bodies of His and, to some extent, the period of mitosis, described, for example, by Koganeem and Shevich. The shape of the visual cells at this time is irregular, spherical. In newborn cats, rabbits and dogs, all these cells, apparently, have already passed this period, at least it is impossible to see mitosis at this time. In the second period, located in the beginning along the neighborhood with m. limit. externa, the cell stretches out and gives a long outgrowth, the end of which is the cell itself, gradually descending downward to the point at which it should be in adulthood. The body is ellipsoidal with a vertically standing long axis; but sometimes this form is changed through compression by the neighboring elements.

H. M. Reznikov. To the doctrine of the structure of the retina. -Diss. SPb. 1897 year

The work was carried out in the laboratory arranged by the author at the zemstvo emergency room. The author made his research according to the Golgi-Cajal method. At the beginning of the work, the author gives a short historical overview of the study of the structure of the retina, then describes the technique of the Golgi method in applying to the retina and finally gives the results of his own studies on birds. The author's conclusions are as follows: In day birds, the position of the nuclei in the outer nuclear, the layer is fixed only at the rods (near the outer border of the outer gossip-like layer). The cone nuclei lie in this layer at different heights. In night birds, cones and rods have the same base of legs and the same exact arrangement of nuclei as in mammals; fixed nuclei (at the inner border of m. limitans externa) have only cones. And so, the visual cells in night and day birds represent an inverse relationship in the sense of the position and fixation of the nuclei.

Infectious Uveitis, Mechanisms, and Immunopathology

It is important to distinguish infectious uveitis from a noninfectious one for early treatment. Bacteria, viruses, fungi, parasites are the causes of infectious uveitis. Bacterial diseases causing uveitis are mainly syphilis, tuberculosis, Lyme disease, and brucellosis. HIV, HSV, VZV, CMV, EBV, measles, and rubella are frequent viral causes. The most common fungal uveitis causes are Candidiasis, Aspergillosis, Cryptococcosis, and Histoplasmosis. It is now widely accepted that uveitis is not caused by direct infectious agents and that microorganisms alter immüne response leading to autoimmune and inflammatory diseases. The mechanism observed in immunopathogenesis is the destruction of retinal visual cells due to an irreversible CD4 T cell response. The Th1 cells reach the target retinal tissue from the peripheral lymphoid system for specific retinal autoantigens and cause changes in photoreceptors, leading to an increase of Th1 cytokines by an inflammatory reaction in the uveitic eyes, whereas the cytokines of Th2 increase in the later stages. In addition to Th1 cells, Th17, regulator T cells, cytokines, autoantibodies, and neutrophils are also discussed in immunopathogenesis.

Learning Visual Spatial Pooling by Strong PCA Dimension Reduction

In visual modeling, invariance properties of visual cells are often explained by a pooling mechanism, in which outputs of neurons with similar selectivities to some stimulus parameters are integrated so as to gain some extent of invariance to other parameters. For example, the classical energy model of phase-invariant V1 complex cells pools model simple cells preferring similar orientation but different phases. Prior studies, such as independent subspace analysis, have shown that phase-invariance properties of V1 complex cells can be learned from spatial statistics of natural inputs. However, those previous approaches assumed a squaring nonlinearity on the neural outputs to capture energy correlation; such nonlinearity is arguably unnatural from a neurobiological viewpoint but hard to change due to its tight integration into their formalisms. Moreover, they used somewhat complicated objective functions requiring expensive computations for optimization. In this study, we show that visual spatial pooling can be learned in a much simpler way using strong dimension reduction based on principal component analysis. This approach learns to ignore a large part of detailed spatial structure of the input and thereby estimates a linear pooling matrix. Using this framework, we demonstrate that pooling of model V1 simple cells learned in this way, even with nonlinearities other than squaring, can reproduce standard tuning properties of V1 complex cells. For further understanding, we analyze several variants of the pooling model and argue that a reasonable pooling can generally be obtained from any kind of linear transformation that retains several of the first principal components and suppresses the remaining ones. In particular, we show how the classic Wiener filtering theory leads to one such variant.