From Stereogram to Surface: How the Brain Sees the World in Depth

When we look at a scene, how do we consciously see surfaces infused with lightness and color at the correct depths? Random Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate such conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so even across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces. A neural model predicts how the layered circuits of visual cortex generate these 3D surface percepts using interactions between visual boundary and surface representations that obey complementary computational rules.Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (EIA-01-30851, SBE-0354378); Office of Naval Research (N00014-01-1-0624

How does binocular rivalry emerge from cortical mechanisms of 3-D vision?

AbstractUnder natural viewing conditions, a single depthful percept of the world is consciously seen. When dissimilar images are presented to corresponding regions of the two eyes, binocular rivalry may occur, during which the brain consciously perceives alternating percepts through time. How do the same brain mechanisms that generate a single depthful percept of the world also cause perceptual bistability, notably binocular rivalry? What properties of brain representations correspond to consciously seen percepts? A laminar cortical model of how cortical areas V1, V2, and V4 generate depthful percepts is developed to explain and quantitatively simulate binocular rivalry data. The model proposes how mechanisms of cortical development, perceptual grouping, and figure-ground perception lead to single and rivalrous percepts. Quantitative model simulations of perceptual grouping circuits demonstrate influences of contrast changes that are synchronized with switches in the dominant eye percept, gamma distribution of dominant phase durations, piecemeal percepts, and coexistence of eye-based and stimulus-based rivalry. The model as a whole also qualitatively explains data about the involvement of multiple brain regions in rivalry, the effects of object attention on switching between superimposed transparent surfaces, monocular rivalry, Marroquin patterns, the spread of suppression during binocular rivalry, binocular summation, fusion of dichoptically presented orthogonal gratings, general suppression during binocular rivalry, and pattern rivalry. These data explanations follow from model brain mechanisms that assure non-rivalrous conscious percepts

How does binocular rivalry emerge from cortical mechanisms of 3-D vision?

AbstractUnder natural viewing conditions, a single depthful percept of the world is consciously seen. When dissimilar images are presented to corresponding regions of the two eyes, binocular rivalry may occur, during which the brain consciously perceives alternating percepts through time. How do the same brain mechanisms that generate a single depthful percept of the world also cause perceptual bistability, notably binocular rivalry? What properties of brain representations correspond to consciously seen percepts? A laminar cortical model of how cortical areas V1, V2, and V4 generate depthful percepts is developed to explain and quantitatively simulate binocular rivalry data. The model proposes how mechanisms of cortical development, perceptual grouping, and figure-ground perception lead to single and rivalrous percepts. Quantitative model simulations of perceptual grouping circuits demonstrate influences of contrast changes that are synchronized with switches in the dominant eye percept, gamma distribution of dominant phase durations, piecemeal percepts, and coexistence of eye-based and stimulus-based rivalry. The model as a whole also qualitatively explains data about the involvement of multiple brain regions in rivalry, the effects of object attention on switching between superimposed transparent surfaces, monocular rivalry, Marroquin patterns, the spread of suppression during binocular rivalry, binocular summation, fusion of dichoptically presented orthogonal gratings, general suppression during binocular rivalry, and pattern rivalry. These data explanations follow from model brain mechanisms that assure non-rivalrous conscious percepts

A border-ownership model based on computational electromagnetism

The mathematical relation between a vector electric field and its corresponding scalar potential field is useful to formulate computational problems of lower/middle-order visual processing, specifically related to the assignment of borders to the side of the object: so-called border ownership (BO). BO coding is a key process for extracting the objects from the background, allowing one to organize a cluttered scene. We propose that the problem is solvable simultaneously by application of a theorem of electromagnetism, i.e., “conservative vector fields have zero rotation, or “curl.” We hypothesize that (i) the BO signal is definable as a vector electric field with arrowheads pointing to the inner side of perceived objects, and (ii) its corresponding scalar field carries information related to perceived order in depth of occluding/occluded objects. A simple model was developed based on this computational theory. Model results qualitatively agree with object-side selectivity of BO-coding neurons, and with perceptions of object order. The model update rule can be reproduced as a plausible neural network that presents new interpretations of existing physiological results. Results of this study also suggest that T-junction detectors are unnecessary to calculate depth order

How visual illusions illuminate complementary brain processes: illusory depth from brightness and apparent motion of illusory contours

Neural models of perception clarify how visual illusions arise from adaptive neural processes. Illusions also provide important insights into how adaptive neural processes work. This article focuses on two illusions that illustrate a fundamental property of global brain organization; namely, that advanced brains are organized into parallel cortical processing streams with computationally complementary properties. That is, in order to process certain combinations of properties, each cortical stream cannot process complementary properties. Interactions between these streams, across multiple processing stages, overcome their complementary deficiencies to compute effective representations of the world, and to thereby achieve the property of complementary consistency. The two illusions concern how illusory depth can vary with brightness, and how apparent motion of illusory contours can occur. Illusory depth from brightness arises from the complementary properties of boundary and surface processes, notably boundary completion and surface-filling in, within the parvocellular form processing cortical stream. This illusion depends upon how surface contour signals from the V2 thin stripes to the V2 interstripes ensure complementary consistency of a unified boundary/surface percept. Apparent motion of illusory contours arises from the complementary computations of form and motion processing across the parvocellular and magnocellular cortical processing streams. This illusion depends upon how illusory contours help to complete boundary representations for object recognition, how apparent motion signals can help to form continuous trajectories for target tracking and prediction, and how formotion interactions from V2-to-MT enable completed object representations to be continuously tracked even when they move behind intermittently occluding objects through time

Logic and phenomenology of incompleteness in illusory figures: new cases and hypotheses

Why is it relevant to analyze the role of incompleteness in illusory figure formation? Incompleteness probes the general problems of organization of the visual world and object segregation. The organization problem is one of the most important problems in visual neuroscience; namely: How and why are a very large numebr of unorganized elements of the retinal image combined, reduced, grouped and segregated to create visual objects? Within the problem of organizaiton, illusory figures are often considered to be one of the best examples to understand how and why the visual system segregates objects with a particular shape, color, and depth stratification. Understanding the role played by incompleteness in inducing illusory figures can thus be useful for understanding the principles of organization (the How) of perceptual forms and the more general logic of perception (the Why). To this purpose, incompletenss is here studied by analyzing its underlying organization principles and its inner logic

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