Predictive coding: A Possible Explanation of Filling-in at the blind spot

Filling-in at the blind-spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. Though there are enough evidence to conclude that some kind of neural computation is involved in filling-in at the blind spot especially in the early visual cortex, the knowledge of the actual computational mechanism is far from complete. We have investigated the bar experiments and the associated filling-in phenomenon in the light of the hierarchical predictive coding framework, where the blind-spot was represented by the absence of early feed-forward connection. We recorded the responses of predictive estimator neurons at the blind-spot region in the V1 area of our three level (LGN-V1-V2) model network. These responses are in agreement with the results of earlier physiological studies and using the generative model we also showed that these response profiles indeed represent the filling-in completion. These demonstrate that predictive coding framework could account for the filling-in phenomena observed in several psychophysical and physiological experiments involving bar stimuli. These results suggest that the filling-in could naturally arise from the computational principle of hierarchical predictive coding (HPC) of natural images.Comment: 23 pages, 9 figure

Highly accurate retinotopic maps of the physiological blind spot in human visual cortex

The physiological blind spot is a naturally occurring scotoma corresponding with the optic disc in the retina of each eye. Even during monocular viewing, observers are usually oblivious to the scotoma, in part because the visual system extrapolates information from the surrounding area. Unfortunately, studying this visual field region with neuroimaging has proven difficult, as it occupies only a small part of retinotopic cortex. Here, we used functional magnetic resonance imaging and a novel data-driven method for mapping the retinotopic organization in and around the blind spot representation in V1. Our approach allowed for highly accurate reconstructions of the extent of an observer’s blind spot, and out-performed conventional model-based analyses. This method opens exciting opportunities to study the plasticity of receptive fields after visual field loss, and our data add to evidence suggesting that the neural circuitry responsible for impressions of perceptual completion across the physiological blind spot most likely involves regions of extrastriate cortex—beyond V1

Neural Models of Seeing and Thinking

Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

The integration of bottom-up and top-down signals in human perception in health and disease

To extract a meaningful visual experience from the information falling on the retina, the visual system must integrate signals from multiple levels. Bottom-up signals provide input relating to local features while top-down signals provide contextual feedback and reflect internal states of the organism. In this thesis I will explore the nature and neural basis of this integration in two key areas. I will examine perceptual filling-in of artificial scotomas to investigate the bottom-up signals causing changes in perception when filling-in takes place. I will then examine how this perceptual filling-in is modified by top-down signals reflecting attention and working memory. I will also investigate hemianopic completion, an unusual form of filling-in, which may reflect a breakdown in top-down feedback from higher visual areas. The second part of the thesis will explore a different form of top-down control of visual processing. While the effects of cognitive mechanisms such as attention on visual processing are well-characterised, other types of top-down signal such as reward outcome are less well explored. I will therefore study whether signals relating to reward can influence visual processing. To address these questions, I will employ a range of methodologies including functional MRI, magnetoencephalography and behavioural testing in healthy participants and patients with cortical damage. I will demonstrate that perceptual filling-in of artificial scotomas is largely a bottom-up process but that higher cognitive functions can modulate the phenomenon. I will also show that reward modulates activity in higher visual areas in the absence of concurrent visual stimulation and that receiving reward leads to enhanced activity in primary visual cortex on the next trial. These findings reveal that integration occurs across multiple levels even for processes rooted in early retinotopic regions, and that higher cognitive processes such as reward can influence the earliest stages of cortical visual processing

A Neural Model of Surface Perception: Lightness, Anchoring, and Filling-in

This article develops a neural model of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models have clarified how the brain can compute the relative contrast of images from variably illuminate scenes. How the brain determines an absolute lightness scale that "anchors" percepts of surface lightness to us the full dynamic range of neurons remains an unsolved problem. Lightness anchoring properties include articulation, insulation, configuration, and are effects. The model quantatively simulates these and other lightness data such as discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, and the Craik-O'Brien-Cornsweet illusion. The model also clarifies the functional significance for lightness perception of anatomical and neurophysiological data, including gain control at retinal photoreceptors, and spatioal contrast adaptation at the negative feedback circuit between the inner segment of photoreceptors and interacting horizontal cells. The model retina can hereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A later model cortical processing stages, boundary representations gate the filling-in of surface lightness via long-range horizontal connections. Variants of this filling-in mechanism run 100-1000 times faster than diffusion mechanisms of previous biological filling-in models, and shows how filling-in can occur at realistic speeds. A new anchoring mechanism called the Blurred-Highest-Luminance-As-White (BHLAW) rule helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural images under variable lighting conditions.Air Force Office of Scientific Research (F49620-01-1-0397); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-01-1-0624

Texture brightness filling-in

The masking procedure by Paradiso and Nakayama (1991) (Vision Research, 31, (1221–1236) was used to investigate brightness filling-in within textures made of line elements: a texture stimulus was masked by a second stimulus containing a square contour. When a uniform texture was presented, the texture region inside the masking square appeared darkened and a small number of texture elements were perceived with a degenerated shape, appearing as dim dots or shorter line elements; it is as if the line element expanded from a bright point to fill the entire region defined by its contour. If the texture stimulus was a texture patch segregating from the surrounding texture by an orientation gradient and this patch was inside the square mask, darkening was not as strong as in the previous condition, and masked line elements preserved their elongated shape. Brightness spreading was measured in two experiments using dichoptic presentations. Experiment 1 used an adjustment task and showed that the brightness of texture line elements spread from equiluminant borders between segregating textures. Experiment 2 used a matching task and demonstrated that spreading was blocked by segregation borders dependent on the orientation gradient between texture line elements. The selectivity for line orientation began 40–80 msec after texture onset and maximal spreading occurred at ∼120 msec. These findings may indicate that two processes subserve filling-in within textures: the first spreads isotropically the mean stimulus luminance at an initial processing stage of image analysis; at a later stage, the second spreads a texture flow (both brightness and shape of line elements) directed along the orientation of texture line elements. The texture flow mechanism fills in with a texture surface the region bounded by segregation contours

Quasi-Modal Encounters Of The Third Kind: The Filling-In Of Visual Detail

Although Pessoa et al. imply that many aspects of the filling-in debate may be displaced by a regard for active vision, they remain loyal to naive neural reductionist explanations of certain pieces of psychophysical evidence. Alternative interpretations are provided for two specific examples and a new category of filling-in (of visual detail) is proposed

The analytic edge - image reconstruction from edge data via the Cauchy Integral

A novel image reconstruction algorithm from edges (image gradients) follows from the Sokhostki-Plemelj Theorem of complex analysis, an elaboration of the standard Cauchy (Singular) Integral. This algorithm demonstrates the use of Singular Integral Equation methods to image processing, extending the more common use of Partial Differential Equations (e.g. based on variants of the Diffusion or Poisson equations). The Cauchy Integral approach has a deep connection to and sheds light on the (linear and non-linear) diffusion equation, the retinex algorithm and energy-based image regularization. It extends the commonly understood local definition of an edge to a global, complex analytic structure - the analytic edge - the contrast weighted kernel of the Cauchy Integral. Superposition of the set of analytic edges provides a "filled-in" image which is the piece-wise analytic image corresponding to the edge (gradient data) supplied. This is a fully parallel operation which avoids the time penalty associated with iterative solutions and thus is compatible with the short time (about 150 milliseconds) that is biologically available for the brain to construct a perceptual image from edge data. Although this algorithm produces an exact reconstruction of a filled-in image from the gradients of that image, slight modifications of it produce images which correspond to perceptual reports of human observers when presented with a wide range of "visual contrast illusion" images

Binocular interactions in human vision

Early visual processing is subject to binocular interactions because cells in striate cortex show binocular responses and ocular dominance (Hubel & Weisel, 1968). The work presented in this thesis suggests that these physiological interactions can be revealed in psychophysical experiments using normal human observers. In the region corresponding to the blind spot, where binocular interactions differ from areas of the visual field which are represented by two eyes, monocular contrast sensitivity is increased. This finding can be partially explained by an absence of normal binocular interactions in this location (Chapter 2). A hemianopic patient was studied in an attempt to discover whether the effect in normal observers was mediated by either a mechanism in striate cortex or via a subcortical pathway. However, the results were unable to distinguish between these two explanations (Chapter 3).In a visual search task, no difference in reaction time was observed for targets presented to the region corresponding to the blind spot compared with targets presented to adjacent binocularly represented areas of the visual field. Since performance was unaffected by the monocularity of the region corresponding to the blind, pop-out for orientation may be mediated beyond striate cortex where cells are binocularly balanced (Chapter 5). Further support for this contention was provided by studies of orientation pop-out in central vision which found that dichoptic presentation of stimuli did not affect the degree of pop-out obtained and that in general, visual search for a target based solely on eye of origin is impossible (Chapter 6). However, a task that measured orientation difference sensitivity more directly than the search experiments, found that thresholds were higher for dichoptically presented stimuli. This suggests the involvement of neurons that receive a weighted input from each eye. A model of orientation difference coding can account for the results by assuming that the range of inhibition across which orientation differences are coded is narrower for dichoptic stimuli leading to a greater resolvable orientation difference (Chapter 7)