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The occipital lobe contains a substantial part of the neural machinery involved in visual perception. Mutations in the LAMC3 gene have recently been shown to cause complex bilateral occipital cortical gyration abnormalities. However, to what extent these structural changes impact visual behavior is not known. We recorded responses for two screening test batteries targeting visual function (Leuven - Perceptual Organization Screening Test, Cortical Vision Screening Test) and measured eye fixation performance in a visual attention experiment from a patient with homozygous LAMC3 gene mutation. Using voxel-based morphometry (VBM) we quantitatively assessed the extent of structural changes brought on by the genetic mutation by comparing mean cortical curvature, cortical thickness, and gray matter volume in 34 cortical areas between patient and an age-, sex-, and education-matched control group. Anatomical connectivity between these cortical areas was investigated by a structural covariance analysis. Visual screening-, and behavioral results revealed that the patient's impairments were predominantly in visuo-spatial attention. Consistent with this, VBM and structural connectivity results revealed significant structural changes in cortical regions subserving attentional functions. We conclude that the LAMC3 gene mutation affects cortical areas beyond the occipital lobe and primarily those visual functions that involve heavily distributed networks - such as visuo-spatial attention.

Expectations and prior knowledge strongly affect and even shape our visual perception. Specifically, valid expectations speed up perceptual decisions, and determine what we see in a noisy stimulus. Bayesian models have been remarkably successful to capture the behavioral effects of expectation. On the other hand several more mechanistic neural models have also been put forward, which will be referred as "predictive computation models" here. Both Bayesian and predictive computation models treat perception as a probabilistic inference process, and combine prior information and sensory input.Despite the well-established effects of expectation on recognition or decision-making, its effects on low-level visual processing, and the computational mechanisms underlying those effects remain elusive. Here we investigate how expectations affect early visual processing at the threshold level. Specifically, we measured temporal thresholds (shortest duration of presentation to achieve a certain success level) for detecting the spatial location of an intact image, which could be either a house or a face image.Task-irrelevant cues provided prior information, thus forming an expectation, about the category of the upcoming intact image. The validity of the cue was set to 100, 75 and 50% in different experimental sessions. In a separate session the cue was neutral * Corresponding author, buse.urgen@bilkent.edu.tr 1 and provided no information about the category of the upcoming intact image. Our behavioral results showed that valid expectations do not reduce temporal thresholds, rather violation of expectation increases the thresholds specifically when the expectation validity is high. Next, we implemented a recursive Bayesian model, in which the prior is first set using the validity of the specific experimental condition, but in subsequent iterations it is updated using the posterior of the previous iteration. Simulations using the model showed that the observed increase of the temporal thresholds in the unexpected trials is not due to a change in the internal parameters of the system (e.g. decision threshold or internal uncertainty). Rather, further processing is required for a successful detection when the expectation and actual input disagree. These results reveal some surprising behavioral effects of expectation at the threshold level, and show that a simple parsimonious computational model can successfully predict those effects. 4 plexity. However, in a dynamic, contextually rich environment with an often ambiguous 5 input, the visual system cannot process all sensory information accurately at once in 6 detail. To decrease the computational burden of this process higher level mechanisms 7 have been suggested to be involved in the information processing, which make our deci-8 sions become faster and more efficient (Summerfield and Egner, 2009; Summerfield and 9 De Lange, 2014). For instance, while we are searching for a painting in a room, we look 10 at the locations where the painting is more likely to be placed, i.e. the ...

To achieve fast feedback control of voluntary movements, the visual consequences of our motor commands need to be quickly identified and analyzed by the motor control processes in the brain. These processes work remarkably well even in complex visual environments and in the face of discrepancies between physical actuator and visually perceived effect, e.g. when moving a computer mouse on a visually crowded screen. Here we use an ambiguous situation in which a single cursor could be controlled by either the left or the right hand to determine the visual and cognitive factors that determine the assignment of a visual stimulus to the corresponding motor command. Our results demonstrate that the visuomotor system is exquisitely sensitive to the spatio-temporal correlation between cursor and hands, learning the appropriate mapping implicitly within several minutes. In contrast, spatial proximity between end effector and visual consequence has an immediate but only transient effect on the assignment process. Finally, an explicit instruction about which hand controls the cursor only has a minor influence when the instruction is presented first. These findings provide insight into the relative importance of the factors that determine the binding of visual information to the corresponding motor structures to enable fast feedback control.

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