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Freezing is an effective defense strategy for some prey, because their predators rely on visual motion to distinguish objects from their surroundings. An object moving over a background progressively covers (deletes) and uncovers (accretes) background texture while simultaneously producing discontinuities in the optic flow field. These events unambiguously specify kinetic occlusion and can produce a crisp edge, depth perception, and figure-ground segmentation between identically textured surfaces - percepts which all disappear without motion. Given two abutting regions of uniform random texture with different motion velocities, one region appears to be situated farther away and behind the other (i.e., the ground) if its texture is accreted or deleted at the boundary between the regions, irrespective of region and boundary velocities. Consequently, a region with moving texture appears farther away than a stationary region if the boundary is stationary, but it appears closer (i.e., the figure) if the boundary is moving coherently with the moving texture. A computational model of visual areas V1 and V2 shows how interactions between orientation- and direction-selective cells first create a motion-defined boundary and then signal kinetic occlusion at that boundary. Activation of model occlusion detectors tuned to a particular velocity results in the model assigning the adjacent surface with a matching velocity to the far depth. A weak speed-depth bias brings faster-moving texture regions forward in depth in the absence of occlusion (shearing motion). These processes together reproduce human psychophysical reports of depth ordering for key cases of kinetic occlusion displays.

How are spatial and object attention coordinated to achieve rapid object learning and recognition during eye movement search? How do prefrontal priming and parietal spatial mechanisms interact to determine the reaction time costs of intra-object attention shifts, inter-object attention shifts, and shifts between visible objects and covertly cued locations? What factors underlie individual differences in the timing and frequency of such attentional shifts? How do transient and sustained spatial attentional mechanisms work and interact? How can volition, mediated via the basal ganglia, influence the span of spatial attention? A neural model is developed of how spatial attention in the where cortical stream coordinates view-invariant object category learning in the what cortical stream under free viewing conditions. The model simulates psychological data about the dynamics of covert attention priming and switching requiring multifocal attention without eye movements. The model predicts how “attentional shrouds” are formed when surface representations in cortical area V4 resonate with spatial attention in posterior parietal cortex (PPC) and prefrontal cortex (PFC), while shrouds compete among themselves for dominance. Winning shrouds support invariant object category learning, and active surface-shroud resonances support conscious surface perception and recognition. Attentive competition between multiple objects and cues simulates reaction-time data from the two-object cueing paradigm. The relative strength of sustained surface-driven and fast-transient motion-driven spatial attention controls individual differences in reaction time for invalid cues. Competition between surface-driven attentional shrouds controls individual differences in detection rate of peripheral targets in useful-field-of-view tasks. The model proposes how the strength of competition can be mediated, though learning or momentary changes in volition, by the basal ganglia. A new explanation of crowding shows how the cortical magnification factor, among other variables, can cause multiple object surfaces to share a single surface-shroud resonance, thereby preventing recognition of the individual objects.

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the ''early vision'' stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once the pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.

A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are transformed into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive grouping network that coherently binds texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a smooth 3D surface percept. The model quantitatively simulates challenging psychophysical data about perception of prolate ellipsoids [Todd, J., & Akerstrom, R. (1987). Perception of three-dimensional form from patterns of optical texture. Journal of Experimental Psychology: Human Perception and Performance, 13(2), 242–255]. In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. Simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are also presented.

Heavily occluded objects are more difficult for classification algorithms to identify correctly than unoccluded objects. This effect is rare and thus hard to measure with datasets like ImageNet and PASCAL VOC, however, owing to biases in human-generated image pose selection. We introduce a dataset that emphasizes occlusion and additions to a standard convolutional neural network aimed at increasing invariance to occlusion. An unmodified convolutional neural network trained and tested on the new dataset rapidly degrades to chance-level accuracy as occlusion increases. Training with occluded data slows this decline but still yields poor performance with high occlusion. Integrating novel preprocessing stages to segment the input and inpaint occlusions is an effective mitigation. A convolutional network so modified is nearly as effective with more than 81% of pixels occluded as it is with no occlusion. Such a network is also more accurate on unoccluded images than an otherwise identical network that has been trained with only unoccluded images. These results depend on successful segmentation. The occlusions in our dataset are deliberately easy to segment from the figure and background. Achieving similar results on a more challenging dataset would require finding a method to split figure, background, and occluding pixels in the input.

The perception of a glossy surface in a static monochromatic image can occur when a bright highlight is embedded in a compatible context of shading and a bounding contour. Some images naturally give rise to the impression that a surface has a uniform reflectance, characteristic of a shiny object, even though the highlight may only cover a small portion of the surface. Nonetheless, an observer's impression of gloss may be partial and nonuniform at image regions outside of a highlight. A rating scale and small probe points indicating image locations were used to investigate the differential perception of gloss within a single object. Gloss ratings given by observers were not uniform across a surface, but decreased as a function of distance from a highlight. When, by design, the distance from a highlight was uncoupled from the luminance value at corresponding probe points, the decrease in rated gloss correlated more with distance than with luminance change. Experiments also indicated that gloss ratings may change as a function of estimated surface distance, rather than as a function of image distance. Surface continuity affected gloss ratings, suggesting that surface and gloss processing are closely related.

Visible surfaces in a natural environment often have multiple components of reflectance, including a diffuse component, by which light is scattered isotropically in all possible directions, and a specular component, by which light is reflected anisotropically within a limited range of directions. The research described in the present article was designed to investigate how these different components of reflectance influence the perception of lightness. Human observers were presented with shaded images of smoothly curved surfaces and asked to compare the relative lightness of different surface regions whose diffuse and specular components of luminance were independently manipulated. The results revealed that observers are able to discount the presence of specular highlights so that the relative lightness among different regions is determined almost entirely by the diffuse component of surface reflectance.

We examined the allocation of attention in depth using a multi-element tracking paradigm. Observers were required to track a predefined subset of from two to eight elements in displays containing up to sixteen identical moving elements. We first show that depth cues, such as binocular disparity and occlusion through T-junctions, improve performance in a multi-element tracking task in the case where element boundaries are allowed to intersect in the depiction of motion in a single frontoparallel plane. We also show that the allocation of attention across two perceptually distinguishable planar surfaces, either frontoparallel or receding at a slanting angle and defined by coplanar elements, is easier than allocation of attention within a single surface. The same result was not found when attention was required to be deployed across items of two-color populations rather than across items of a single color. Our results suggest that, when surface information does not suffice to distinguish between targets and distractors that are embedded in these surfaces, division of attention across two surfaces aids in tracking moving targets. A final experiment with populations of elements moving within distinct volumes produced similar results, suggesting that spatial separation in three dimensions, rather than confinement to surfaces as such, may explain the improved performance for the two-surface case.

This article further develops the FACADE neural model of 3-D vision and figure-ground perception to quantitatively explain properties of the McCollough effect (ME). The model proposes that many ME data result from visual system mechanisms whose primary function is to adaptively align, through learning, boundary and surface representations that are positionally shifted due to the process of binocular fusion. For example, binocular boundary representations are shifted by binocular fusion relative to monocular surface representations, yet the boundaries must become positionally aligned with the surfaces to control binocular surface capture and filling-in. The model also includes perceptual reset mechanisms that use habituative transmitters in opponent processing circuits. Thus the model shows how ME data may arise from a combination of mechanisms that have a clear functional role in biological vision. Simulation results with a single set of parameters quantitatively fit data from 13 experiments that probe the nature of achromatic/chromatic and monocular/binocular interactions during induction of the ME. The model proposes how perceptual learning, opponent processing, and habituation at both monocular and binocular surface representations are involved, including early thalamocortical sites. In particular, it explains the anomalous ME utilizing these multiple processing sites. Alternative models of the ME are also summarized and compared with the present model.

The visual cortex has a laminar organization whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood. A neural model proposes how the laminar circuits of V1 and V2 generate perceptual groupings that maintain sensitivity to the contrasts and spatial organization of scenic cues. The model can decisively choose which groupings cohere and survive, even while balanced excitatory and inhibitory interactions preserve contrast-sensitive measures of local boundary likelihood or strength. In the model, excitatory inputs from lateral geniculate nucleus (LGN) activate layers 4 and 6 of V1. Layer 6 activates an on-center off-surround network of inputs to layer 4. Together these layer 4 inputs preserve analog sensitivity to LGN input contrasts. Layer 4 cells excite pyramidal cells in layer 2/3, which activate monosynaptic long-range horizontal excitatory connections between layer 2/3 pyramidal cells, and short-range disynaptic inhibitory connections mediated by smooth stellate cells. These interactions support inward perceptual grouping between two or more boundary inducers, but not outward grouping from a single inducer. These boundary signals feed back to layer 4 via the layer 6-to-4 on-center off-surround network. This folded feedback joins cells in different layers into functional columns while selecting winning groupings. Layer 6 in V1 also sends top-down signals to LGN using an on-center off-surround network, which suppresses LGN cells that do not receive feedback, while selecting, enhancing, and synchronizing activity of those that do. The model is used to simulate psychophysical and neurophysiological data about perceptual grouping, including various Gestalt grouping laws.

The influence of monocular occlusion cues on the perceived direction of motion of barber pole patterns is examined. Unlike previous studies that have emphasized the importance of binocular disparity, we find that monocular cues strongly influence the perceived motion direction and can even override binocular depth cues. The difference in motion bias for occluders with and without disparity cues is relatively small. Additionally, although ‘T-junctions’ aligned with occluders are particularly important, they are not strictly necessary for creating a change in motion perception. Finally, the amount of motion bias differs for several stimulus configurations, suggesting that the extrinsic:intrinsic classification of terminators is not all-or-none. © 1998 Elsevier Science Ltd. All rights reserved.

The motion after-effect (MAE) can be elicited by adapting observers to global motion of randomly distributed dots before they view a display containing dots moving in random directions, but no global motion. Experiments by others have shown that if the adaptation stimulus contains two directions of motion, the MAE points opposite to the vector sum of the adapting directions. The present study investigated whether such vector addition in the MAE could also occur if the two directions of motion were presented to separate eyes. Observers were adapted to different, but not opposite, directions of motion in the two eyes. Either the left eye, the right eye, or both eyes were tested. Observers reported the direction of perceived motion during the test. When they saw the test stimulus with both eyes, observers reported seeing motion in the direction opposite that of the vector sum of the adaptation directions. In the monocular test conditions observers reported MAE directions opposite to the corresponding monocular adaptation directions. In a second experiment we verified that subjects had interocular transfer of the MAE. Together these results are consistent with a model in which (1) addition of adaptation directions occurs at a binocular site; (2) directional adaptation occurs at a monocular site; and (3) monocular adaptation is able to change the threshold for obtaining an MAE at the binocular site, thus acting like binocular adaptation in interocular transfer of the MAE.

This paper is about a phenomenon that combines a delicate beauty with a profoundsignificance for understanding visual perception. The neon-like glow of a color that escapesthe boundaries of a real figure and fills the surrounding area until it is halted by theboundaries of an illusory figure has an ethereal quality unlike any other brightness and coloreffect. It also has substantial implications regarding the way in which our visual system usesseemingly incomplete stimuli to generate meaningful percepts, segregate objects from theirbackgrounds, and provide them with color and depth.As it so often happens in science, the effect was discovered and then independentlyrediscovered in the span of a few years. In 1971, Dario Varin of the University of Milanpublished a monograph on Ochromatic contrast and diffusionO whose front cover isreproduced in figure 1. A circular transparent veil can be seen to extend over four sets ofconcentric rings partly composed of blue arcs. It has a subtle bluish tinge and is luminescent,as if it emitted a weak light, or as if it were a colored spot of light cast by some off-screenlamp. This veil is illusory, in the sense that a point-by-point description of the reflectancespectrum of the white background would not reveal any changes corresponding to it.

Receptive field sizes of neurons in early primate visual areas increase with eccentricity, as does temporal processing speed. The fovea is evidently specialized for slow, fine movements while the periphery is suited for fast, coarse movements. In either the fovea or periphery discrete flashes can produce motion percepts. Grossberg and Rudd (1989) used traveling Gaussian activity profiles to model long-range apparent motion percepts. We propose a neural model constrained by physiological data to explain how signals from retinal ganglion cells to V1 affect the perception of motion as a function of eccentricity. Our model incorporates cortical magnification, receptive field overlap and scatter, and spatial and temporal response characteristics of retinal ganglion cells for cortical processing of motion. Consistent with the finding of Baker and Braddick (1985), in our model the maximum flash distance that is perceived as an apparent motion (Dmax) increases linearly as a function of eccentricity. Baker and Braddick (1985) made qualitative predictions about the functional significance of both stimulus and visual system parameters that constrain motion perception, such as an increase in the range of detectable motions as a function of eccentricity and the likely role of higher visual processes in determining Dmax. We generate corresponding quantitative predictions for those functional dependencies for individual aspects of motion processing. Simulation results indicate that the early visual pathway can explain the qualitative linear increase of Dmax data without reliance on extrastriate areas, but that those higher visual areas may serve as a modulatory influence on the exact Dmax increase.

Humans are capable of rapidly determining whether regions in a visual scene appear as figures in the foreground or as background, yet how figure-ground segregation occurs in the primate visual system is unknown. Figures in the environment are perceived to own their borders, and recent neurophysiology has demonstrated that certain cells in primate visual area V2 have border-ownership selectivity. We present a dynamic model based on physiological data that indicates areas V1, V2, and V4 act as an interareal network to determine border-ownership. Our model predicts that competition between curvature- sensitive cells in V4 that have on-surround receptive fields of different sizes can determine likely figure locations and rapidly propagate the information interareally to V2 border-ownership cells that receive contrast information from V1. In the model border-ownership is an emergent property produced by the dynamic interactions between V1, V2, and V4, one which could not be determined by any single cortical area alone.

A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms. The model builds upon previous work on filling-in models that produce brightness profiles through the interaction of boundary and feature signals. Boundary computations that are sensitive to luminance steps and to continuous luminance gradients are presented. A new interpretation of feature signals through the explicit representation of contrast-driven and luminance-driven information is provided and directly addresses the issue of brightness "anchoring". Computer simulations illustrate the model's competencies.

An observer traversing an environment actively relocates gaze to fixate objects. Evidence suggests that gaze is frequently directed toward the center of an object considered as target but more likely toward the edges of an object that appears as an obstacle. We suggest that this difference in gaze might be motivated by specific patterns of optic flow that are generated by either fixating the center or edge of an object. To support our suggestion we derive an analytical model that shows: Tangentially fixating the outer surface of an obstacle leads to strong flow discontinuities that can be used for flow-based segmentation. Fixation of the target center while gaze and heading are locked without head-, body-, or eye-rotations gives rise to a symmetric expansion flow with its center at the point being approached, which facilitates steering toward a target. We conclude that gaze control incorporates ecological constraints to improve the robustness of steering and collision avoidance by actively generating flows appropriate to solve the task.

The researchers at Boston University (BU)'s Neuromorphics Laboratory, part of the National Science Foundation (NSF)-sponsored Center of Excellence for Learning in Education, Science, and Technology (CELEST), are working in collaboration with the engineers and scientists at Hewlett-Packard (HP) to implement neural models of intelligent processes for the next generation of dense, low-power, computer hardware that will use memristive technology to bring data closer to the processor where computation occurs. The HP and BU teams are jointly designing an optimal infrastructure, simulation, and software platform to build an artificial brain. The resulting Cog Ex Machina (Cog) software platform has been successfully used to implement a large-scale, multicomponent brain system that is able to simulate some key rat behavioral results in a virtual environment and has been applied to control robotic platforms as they learn to interact with their environment.

Humans accurately judge their direction of heading when translating in a rigid environment, unless independently moving objects (IMOs) cross the observer's focus of expansion (FoE). Studies show that an IMO on a laterally moving path that maintains a fixed distance with respect to the observer (non-approaching; C. S. Royden & E. C. Hildreth, 1996) biases human heading estimates differently from an IMO on a lateral path that gets closer to the observer (approaching; W. H. Warren & J. A. Saunders, 1995). C. S. Royden (2002) argued that differential motion operators in primate brain area MT explained both data sets, concluding that differential motion was critical to human heading estimation. However, neurophysiological studies show that motion pooling cells, but not differential motion cells, in MT project to heading-sensitive cells in MST (V. K. Berezovskii & R. T. Born, 2000). It is difficult to reconcile differential motion heading models with these neurophysiological data. We generate motion sequences that mimic those viewed by human subjects. Model MT pools over V1; units in model MST perform distance-weighted template matching and compete in a recurrent heading representation layer. Our model produces heading biases of the same direction and magnitude as humans through a peak shift in model MSTd without using differential motion operators, maintaining consistency with known primate neurophysiology.

An analysis of the reset of visual cortical circuits responsible for the binding or segmentation of visual features into coherent visual forms yields a model that explains properties of visual persistence. The reset mechanisms prevent massive smearing of visual percepts in response to rapidly moving images. The model simulates relationships among psychophysical data showing inverse relations of persistence to flash luminance and duration, greater persistence of illusory contours than real contours, a U-shaped temporal function for persistence of illusory contours, a reduction of persistence due to adaptation with a stimulus of like orientation, an increase of persistence with spatial separation of a masking stimulus. The model suggests that a combination of habituative, opponent, and endstopping mechanisms prevent smearing and limit persistence. Earlier work with the model has analyzed data about boundary formation, texture segregation, shape-from-shading, and figure-ground separation. Thus, several types of data support each model mechanism and new predictions are made.

Motion transparency occurs when multiple coherent motions are perceived in one spatial location. Imagine, for instance, looking out of the window of a bus on a bright day, where the world outside the window is passing by and movements of passengers inside the bus are reflected in the window. The overlay of both motions at the window leads to motion transparency, which is challenging to process. Noisy and ambiguous motion signals can be reduced using a competition mechanism for all encoded motions in one spatial location. Such a competition, however, leads to the suppression of multiple peak responses that encode different motions, as only the strongest response tends to survive. As a solution, we suggest a local center-surround competition for population-encoded motion directions and speeds. Similar motions are supported, and dissimilar ones are separated, by representing them as multiple activations, which occurs in the case of motion transparency. Psychophysical findings, such as motion attraction and repulsion for motion transparency displays, can be explained by this local competition. Besides this local competition mechanism, we show that feedback signals improve the processing of motion transparency. A discrimination task for transparent versus opaque motion is simulated, where motion transparency is generated by superimposing large field motion patterns of either varying size or varying coherence of motion. The model’s perceptual thresholds with and without feedback are calculated. We demonstrate that initially weak peak responses can be enhanced and stabilized through modulatory feedback signals from higher stages of processing.

The watercolor effect (WCE) is a filling-in phenomenon in a region demarcated by two thin abutting lines. The perceived chromaticity of the region is similar to that of the interior line. We develop a series of achromatic WCE stimuli to induce lightness changes analogous to the induced chromaticity in the chromatic version of the WCE. We use a variation of the paired-comparison paradigm to quantify the induced lightness of the filled-in regions to regions with real luminance variations. The luminance of the inner line is fixed, while the luminance of the outer line varies across stimuli. Data from seven subjects (five naive) confirm that an achromatic WCE exists. Moreover, outer lines with both high and low luminances can generate a WCE with an inner line of a moderate luminance. All subjects show a single peak of the effect strength for both polarity conditions, which is never at the extreme luminance levels. Most subjects show an inverted U curve for effect strength as a function of the contrast of the outer lines against the background. Results suggest that the contrast difference between the outer line and the inner line affects the existence and the strength of the achromatic WCE in a nonlinear way.

According to Hering's color theory, certain hues (red vs green and blue vs yellow) are mutually exclusive as components of a single color; consequently a color cannot be perceived as reddish-green or bluish-yellow. The goal of our study is to test this key postulate of the opponent color theory. Using the method of adjustment, our observers determine the boundaries of chromatic zones in a red–green continuum. We demonstrate on two distinct stimulus sets, one formed using a chromatic grid and neon spreading and the other based on solid colored regions, that the chromatic contrast of a purple surround over a red figure results in perception of 'forbidden' reddish-green colors. The observed phenomenon can be understood as resulting from the construction of a virtual filter, a process that bypasses photoreceptor summation and permits forbidden color combinations. Showing that opponent hue combinations, previously reported only under artificial image stabilization, can be present in normal viewing conditions offers new approaches for the experimental study of the dimensionality and structure of perceptual color space.

How do human observers perceive a coherent pattern of motion from a disparate set of local motion measures? Our research has examined how ambiguous motion signals along straight contours are spatially integrated to obtain a globally coherent perception of motion. Observers viewed displays containing a large number of apertures, with each aperture containing one or more contours whose orientations and velocities could be independently specified. The total pattern of the contour trajectories across the individual apertures was manipulated to produce globally coherent motions, such as rotations, expansions, or translations. For displays containing only straight contours extending to the circumferences of the apertures, observers' reports of global motion direction were biased whenever the sampling of contour orientations was asymmetric relative to the direction of motion. Performance was improved by the presence of identifiable features, such as line ends or crossings, whose trajectories could be tracked over time. The reports of our observers were consistent with a pooling process involving a vector average of measures of the component of velocity normal to contour orientation, rather than with the predictions of the intersection-of-constraints analysis in velocity space.

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3-dimensional virtual reality environment to determine the position of objects on the basis of motion discontinuities and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles so that the goal acts as an attractor of heading and obstacles act as repellers. In addition, the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas middle temporal, medial superior temporal, and posterior parietal cortex can be used to guide steering. The model quantitatively simulates human psychophysical data about visually guided steering, obstacle avoidance, and route selection.

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT(-)/MSTv and MT(+)/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model's retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT(+) interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT(-) interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.

Visually-based navigation is a key competence during spatial cognition. Animals avoid obstacles and approach goals in novel cluttered environments using optic flow to compute heading with respect to the environment. Most navigation models try either explain data, or to demonstrate navigational competence in real-world environments without regard to behavioral and neural substrates. The current article develops a model that does both. The ViSTARS neural model describes interactions among neurons in the primate magnocellular pathway, including V1, MT(+), and MSTd. Model outputs are quantitatively similar to human heading data in response to complex natural scenes. The model estimates heading to within 1.5 degrees in random dot or photo-realistically rendered scenes, and within 3 degrees in video streams from driving in real-world environments. Simulated rotations of less than 1 degrees /s do not affect heading estimates, but faster simulated rotation rates do, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.

How do visual form and motion processes cooperate to compute object motion when each process separately is insufficient? Consider, for example, a deer moving behind a bush. Here the partially occluded fragments of motion signals available to an observer must be coherently grouped into the motion of a single object. A 3D FORMOTION model comprises five important functional interactions involving the brain's form and motion systems that address such situations. Because the model's stages are analogous to areas of the primate visual system, we refer to the stages by corresponding anatomical names. In one of these functional interactions, 3D boundary representations, in which figures are separated from their backgrounds, are formed in cortical area V2. These depth-selective V2 boundaries select motion signals at the appropriate depths in MT via V2-to-MT signals. In another, motion signals in MT disambiguate locally incomplete or ambiguous boundary signals in V2 via MT-to-V1-to-V2 feedback. The third functional property concerns resolution of the aperture problem along straight moving contours by propagating the influence of unambiguous motion signals generated at contour terminators or corners. Here, sparse 'feature tracking signals' from, for example, line ends are amplified to overwhelm numerically superior ambiguous motion signals along line segment interiors. In the fourth, a spatially anisotropic motion grouping process takes place across perceptual space via MT-MST feedback to integrate veridical feature-tracking and ambiguous motion signals to determine a global object motion percept. The fifth property uses the MT-MST feedback loop to convey an attentional priming signal from higher brain areas back to V1 and V2. The model's use of mechanisms such as divisive normalization, endstopping, cross-orientation inhibition, and long-range cooperation is described. Simulated data include: the degree of motion coherence of rotating shapes observed through apertures, the coherent vs. element motion percepts separated in depth during the chopsticks illusion, and the rigid vs. nonrigid appearance of rotating ellipses.

A variety of mechanisms have been proposed to explain illusory contour formation. However, since prior studies have focused on a single mechanism, there is no clear consensus regarding contour formation mechanisms. Here we developed a novel vivid dynamic display we call the chomping pacman that allows measurement of minute differences in contour clarity. This illusion is shown to be more vivid than traditional static displays. Using the method of adjustment, we are able to identify three mechanisms of contour formation: extrapolation, interpolation, and figural. We posit that these three mechanisms combine to form the general illusory contour percept in traditional static Kanizsa displays and likely contribute to real-world contour perception.

A neural model is developed of how motion integration and segmentation processes, both within and across apertures, compute global motion percepts. Figure–ground properties, such as occlusion, influence which motion signals determine the percept. For visible apertures, a line's terminators do not specify true line motion. For invisible apertures, a line's intrinsic terminators create veridical feature-tracking signals. Sparse feature-tracking signals can be amplified before they propagate across position and are integrated with ambiguous motion signals within line interiors. This integration process determines the global percept. It is the result of several processing stages: directional transient cells respond to image transients and input to a directional short-range filter that selectively boosts feature-tracking signals with the help of competitive signals. Then, a long-range filter inputs to directional cells that pool signals over multiple orientations, opposite contrast polarities, and depths. This all happens no later than cortical area MT. The directional cells activate a directional grouping network, proposed to occur within cortical area MST, within which directions compete to determine a local winner. Enhanced feature-tracking signals typically win over ambiguous motion signals. Model MST cells that encode the winning direction feed back to model MT cells, where they boost directionally consistent cell activities and suppress inconsistent activities over the spatial region to which they project. This feedback accomplishes directional and depthful motion capture within that region. Model simulations include the barberpole illusion, motion capture, the spotted barberpole, the triple barberpole, the occluded translating square illusion, motion transparency and the chopsticks illusion. Qualitative explanations of illusory contours from translating terminators and plaid adaptation are also given.

Smooth pursuit eye movements (SPEMs) are eye rotations that are used to maintain fixation on a moving target. Such rotations complicate the interpretation of the retinal image, because they nullify the retinal motion of the target, while generating retinal motion of stationary objects in the background. This poses a problem for the oculomotor system, which must track the stabilized target image while suppressing the optokinetic reflex, which would move the eye in the direction of the retinal background motion (opposite to the direction in which the target is moving). Similarly, the perceptual system must estimate the actual direction and speed of moving objects in spite of the confounding effects of the eye rotation. This paper proposes a neural model to account for the ability of primates to accomplish these tasks. The model simulates the neurophysiological properties of cell types found in the superior temporal sulcus of the macaque monkey, specifically the medial superior temporal (MST) region. These cells process signals related to target motion, background motion, and receive an efference copy of eye velocity during pursuit movements. The model focuses on the interactions between cells in the ventral and dorsal subdivisions of MST, which are hypothesized to process target velocity and background motion, respectively. The model explains how these signals can be combined to explain behavioral data about pursuit maintenance and perceptual data from human studies, including the Aubert-Fleischl phenomenon and the Filehne Illusion, thereby clarifying the functional significance of neurophysiological data about these MST cell properties. It is suggested that the connectivity used in the model may represent a general strategy used by the brain in analyzing the visual world.

Cells in the dorsal medial superior temporal cortex (MSTd) process optic flow generated by self-motion during visually guided navigation. A neural model shows how interactions between well-known neural mechanisms (log polar cortical magnification, Gaussian motion-sensitive receptive fields, spatial pooling of motion-sensitive signals and subtractive extraretinal eye movement signals) lead to emergent properties that quantitatively simulate neurophysiological data about MSTd cell properties and psychophysical data about human navigation. Model cells match MSTd neuron responses to optic flow stimuli placed in different parts of the visual field, including position invariance, tuning curves, preferred spiral directions, direction reversals, average response curves and preferred locations for stimulus motion centers. The model shows how the preferred motion direction of the most active MSTd cells can explain human judgments of self-motion direction (heading), without using complex heading templates. The model explains when extraretinal eye movement signals are needed for accurate heading perception, and when retinal input is sufficient, and how heading judgments depend on scene layouts and rotation rates.

An element-arrangement pattern is composed of two types of elements arranged differently in different regions of a pattern. Rapid texture segregation depends on spontaneously discriminating the difference in the arrangement of the elements. Five experiments investigated the perceived segregation of patterns composed of two types of squares arranged in vertical stripes in the top and bottom regions and in a checkerboard arrangement in the middle region. The squares were either equal in luminance and different in hue or equal in hue and different in luminance. The rated similarities of the two hues in a pattern failed to predict perceived segregation. For a given background luminance, the perceived segregation was predicted by the square-root of the sum of the squares of the differences in the outputs of the L - M + S and L + M - S opponent channels, where L, M, and S were the cone contrasts of the long-, medium-, and short-wavelength receptors. The perceived similarity of the two hues in a pattern was not affected by the background luminance but was a function of cone excitations instead. For patterns differing in hue and equal in luminance, perceived segregation was an inverse function of the background luminance. A white background decreased the perceived segregation, but a black background did not. The effect of background luminance was not on the discrimination of the individual hues. The two hues making up a texture pattern were clearly distinguishable on a white background. A white background interfered with the discrimination of the vertical and diagonal columns of squares that distinguished the texture regions. For patterns differing in luminance and equal in hue, black and white backgrounds decreased the perceived segregation. The results indicate that adapting to an achromatic luminance distant from the luminance of the squares increased the Weber threshold for discriminating luminance differences, but did not increase the Weber threshold for discriminating hue differences. The experiments also revealed that luminance was the primary factor affecting perceived segregation and that perceived brightness is secondary. The results are consistent with the hypothesis that perceived segregation in element-arrangemcnt patterns is primarily a function of the differences in the outputs of relatively early filtering mechanisms that encode pattern differences prior to the specification of the element shapes and their properties.

A neural model of motion perception simulates psychophysical data concerning first-order and second-order motion stimuli, including the reversal of perceived motion direction with distance from the stimulus (Γ display), and data about directional judgments as a function of relative spatial phase or spatial and temporal frequency. Many other second-order motion percepts that have been ascribed to a second non-Fourier processing stream can also be explained in the model by interactions between ON and OFF cells within a single, neurobiologically interpreted magnocellular processing stream. Yet other percepts may be traced to interactions between form and motion processing streams, rather than to processing within multiple motion processing streams. The model hereby explains why monkeys with lesions of the parvocellular layers, but not of the magnocellular layers, of the lateral geniculate nucleus (LGN) are capable of detecting the correct direction of second-order motion, why most cells in area MT are sensitive to both first-order and second-order motion, and why after 2-amino-4-phosphonobutyrate injection selectively blocks retinal ON bipolar cells, cortical cells are sensitive only to the motion of a moving bright bar’s trailing edge. Magnocellular LGN cells show relatively transient responses, whereas parvocellular LGN cells show relatively sustained responses. Correspondingly, the model bases its directional estimates on the outputs of model ON and OFF transient cells that are organized in opponent circuits wherein antagonistic rebounds occur in response to stimulus offset. Center–surround interactions convert these ON and OFF outputs into responses of lightening and darkening cells that are sensitive both to direct inputs and to rebound responses in their receptive field centers and surrounds. The total pattern of activity increments and decrements is used by subsequent processing stages (spatially short-range filters, competitive interactions, spatially long-range filters, and directional grouping cells) to determine the perceived direction of motion.

When an expansion flow field of moving dots is overlapped by planar motion, observers perceive an illusory displacement of the focus of expansion (FOE) in the direction of the planar motion (Duffy and Wurtz, Vision Research, 1993;33:1481‐1490). The illusion may be a consequence of induced motion, wherein an induced component of motion relative to planar dots is added to the motions of expansion dots to produce the FOE shift. While such a process could be mediated by local ‘center-surround’ receptive fields, the effect could also be due to a higher level process which detects and subtracts large-field planar motion from the flow field. We probed the mechanisms underlying this illusion by adding varying amounts of rotation to the expansion stimulus, and by varying the speed and size of the planar motion field. The introduction of rotation into the stimulus produces an illusory shift in a direction perpendicular to the planar motion. Larger FOE shifts were perceived for greater speeds and sizes of planar motion fields, although the speed effect saturated at high speeds. While the illusion appears to share a common mechanism with center-surround induced motion, our results also point to involvement of a more global mechanism that subtracts coherent planar motion from the flow field. Such a process might help to maintain visual stability during eye movements. © 1998 Elsevier Science Ltd. All rights reserved.

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size‐speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-tuned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1iMT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception. © 1998 Elsevier Science Ltd. All rights reserved.

An element-arrangement pattern is composed of two types of elements that differ in the ways in which they are arranged in different regions of the pattern. We report experiments on the perceived segregation of chromatic element-arrangement patterns composed of equal-size red and blue squares as the luminances of the surround, the interspaces and the background (surround plus interspaces) are varied. Perceived segregation was markedly reduced by increasing the luminance of the interspaces. Perceived segregation was approximately constant for constant ratios of interspace luminance to square luminance and increased with the contrast ratio of the squares. Unlike achromatic element-arrangement patterns composed of squares differing in lightness [Beck et al (1991). Vision Research, 32, 719–743] perceived segregation did not decrease when the luminance of the interspaces was below that of the squares. Similar results were obtained for red and yellow, red and green, green and yellow, green and blue, and blue and yellow squares. Perceived segregation based on edge alignment was not interfered with by high intensity interspaces. Stereoscopic cues that caused the squares composing the element-arrangement pattern to be seen in front of the interspaces did not greatly improve perceived segregation. One explanation of the results is in terms of inhibitory interactions among achromatic and chromatic cortical cells tuned to spatial frequency and orientation. Alternately, the results may be explained in terms of how the luminance of the interspaces affects the grouping of the squares for encoding surface representations. Neither explanation accounts fully for the data and both mechanisms may be involved.

Visual search data are given a unified quantitative explanation by a model of how spatial maps in the parietal cortex and object recognition categories in the inferotemporal cortex deploy attentional resources as they reciprocally interact with visual representations in the prestriate cortex. The model visual representations are organized into multiple boundary and surface representations. Visual search in the model is initiated by organizing multiple items that lie within a given boundary or surface representation into a candidate search grouping. These items are compared with object recognition categories to test for matches or mismatches. Mismatches can trigger deeper searches and recursive selection of new groupings until a target object is identified. The model provides an alternative to Feature Integration and Guided Search models.

Illusory contours can be induced along directions approximately collinear to edges or approximately perpendicular to the ends of lines. Using a rating scale procedure we explored the relation between the two types of inducers by systematically varying the thickness of inducing elements to result in varying amounts of “edge-like” or “line-like” induction. Inducers for our illusory figures consisted of concentric rings with arcs missing. Observers judged the clarity and brightness of illusory figures as the number of arcs, their thicknesses, and spacings were parametrically varied. Degree of clarity and amount of induced brightness were both found to be inverted-U functions of the number of arcs. These results mandate that any valid model of illusory contour formation must account for interference effects between parallel lines or between those neural units responsible for completion of boundary signals in directions perpendicular to the ends of thin lines. Line width was found to have an effect on both clarity and brightness, a finding inconsistent with those models which employ only completion perpendicular to inducer orientation.

A neural network model of global motion segmentation by visual cortex is described. Called the motion boundary contour system (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively reorganized into a coherent global motion signal, Unlike many previous researchers, we analyze how a coherent motion signal is imparted to all regions of a moving figure, not only to regions at which unambiguous motion signals exist. The model hereby suggests a solution to the global aperture problem. The motion BCS describes how preprocessing of motion signals by a motion oriented contrast (MOC) filter is joined to long-range cooperative grouping mechanisms in a motion cooperative-competitive (MOCC) loop to control phenomena such as motion capture. The motion BCS is computed in parallel with the static BCS of Grossberg and Mingolla (1985a, 1985b, 1987). Homologous properties of the motion BCS and the static BCS, specialized to process motion directions and static orientations, respectively, support a unified explanation of many data about static form perception and motion form perception that have heretofore been unexplained or treated separately. Predictions about microscopic computational differences of the parallel cortical streams V1 → MT and V1 → V2 → MT are made— notably, the magnocellular thick stripe and parvocellular interstripe streams. It is shown how the motion BCS can compute motion directions that may be synthesized from multiple orientations with opposite directions of contrast. Interactions of model simple cells, complex cells, hyper-complex cells, and bipole cells are described, with special emphasis given to new functional roles in direction disambiguation for endstopping at multiple processing stages and to the dynamic interplay of spatially short-range and long-range interactions.

Twenty subjects viewed displays of square and trapezoidal luminous dot figures rotating about a vertical axis in both polar and parallel projections. The frequency of reported oscillation of the rotating figures was as high for the squares as for the trapezoids, provided that the squares contained static slant information in the form of internal texture gradients. Previously reported oscillation effects using shapes other than trapezoids have generally been unreliable, with a subject as likely to see rotation as oscillation for a given cycle, and then only in parallel projection. As a consequence, linear perspective information in trapezoidal shape has been taken to be the indispensable element for obtaining a strong oscillation effect. The authors argue that in the construction of the rotating object, a required condition for reliably producing the effect is a violation ofsome optical constraint regarding slant information, not necessarily one involving linear perspective.

Recent results towards development of a neural network architecture for general-purpose preattentive vision are summarized. The architecture contains two parallel subsystems, the boundary contour system (BCS) and the feature contour system (FCS), which interact together to generate a representation of form-and-color-and-depth. Emergent boundary segmentation within the BCS and featural filling-in within the FCS are herein emphasized within a monocular setting. Applications to the analysis of boundaries, textures, and smooth surfaces are described, as is a model for invariant brightness perception under variable illumination conditions. The theory shows how suitably defined parallel and hierarchical interactions overcome computational uncertainties that necessarily exist at early processing stages. Some of the psychophysical and neurophysiological data supporting the theory's predictions are mentioned.

A real-time visual processing theory is used to analyse real and illusory contour formation, contour and brightness interactions, neon color spreading, complementary color induction, and filling-in of discounted illuminants and scotomas. The theory also physically interprets and generalizes Land's retinex theory. These phenomena are traced to adaptive processes that overcome limitations of visual uptake to synthesize informative visual representations of the external world. Two parallel contour sensitive processes interact to generate the theory's brightness, color, and form estimates. A boundary contour process is sensitive to orientation and amount of contrast but not to direction of contrast in scenic edges. It synthesizes boundaries sensitive to the global configuration of scenic elements. A feature contour process is insensitive to orientation but sensitive to both amount of contrast and to direction of contrast in scenic edges. It triggers a diffusive filling-in of featural quality within perceptual domains whose boundaries are determined by completed boundary contours. The boundary contour process is hypothesized to include cortical interactions initiated by hypercolumns in Area 17 of the visual cortex. The feature contour process is hypothesized to include cortical interactions initiated by the cytochrome oxydase staining blobs in Area 17. Relevant data from striate and prestriate visual cortex, including data that support two predictions, are reviewed. Implications for other perceptual theories and axioms of geometry are discussed.

Kinetic occlusion produces discontinuities in the optic flow field, whose perception requires the detection of an unexpected onset or offset of otherwise predictably moving or stationary contrast patches. Many cells in primate visual cortex are directionally selective for moving contrasts, and recent reports suggest that this selectivity arises through the inhibition of contrast signals moving in the cells’ null direction, as in the rabbit retina. This nulling inhibition circuit (Barlow-Levick) is here extended to also detect motion onsets and offsets. The selectivity of extended circuit units, measured as a peak evidence accumulation response to motion onset/offset compared to the peak response to constant motion, is analyzed as a function of stimulus speed. Model onset cells are quiet during constant motion, but model offset cells activate during constant motion at slow speeds. Consequently, model offset cell speed tuning is biased towards higher speeds than onset cell tuning, similarly to the speed tuning of cells in the middle temporal area when exposed to speed ramps. Given a population of neurons with different preferred speeds, this asymmetry addresses a behavioral paradox—why human subjects in a simple reaction time task respond more slowly to motion offsets than onsets for low speeds, even though monkey neuron firing rates react more quickly to the offset of a preferred stimulus than to its onset.

Observers judged the slants and tilts of numerous regions within shaded images of ellipsoid surfaces that varied in shape, orientation, surface reflectance, and direction of illumination. The perceived three-dimensional structure of each surface was calculated from these judgments. Much of the error in observers' responses resulted from a tendency to perceive surfaces whose axes were aligned with the display screen. The presence of specular highlights or cast shadows, in contrast, had no effect on performance. The results of the experiment indicate that several assumptions of certain formal models for perception of shape from shading are not psychologically valid. The most notable of these assumptions are that the visual system initially assumes that all surfaces have Lambertian reflectance and that illuminant direction must be known before shape detection can proceed. These assumptions are often accompanied by a third assumption that surface orientation is detected locally, and global shape determined by smoothing over local surface orientation estimates. The present experiment indicates that an alternative approach offered by Koenderink and van Doorn may be more psychologically accurate, as it avoids all three assumptions.

A real-time visual processing theory is used to analyze and explain a wide variety of perceptual grouping and segmentation phenomena, including the grouping of textured images, randomly defined images, and images built up from periodic scenic elements. The theory explains how “local” feature processing and “emergent” features work together to segment a scene, how segmentations may arise across image regions that do not contain any luminance differences, how segmentations may override local image properties in favor of global statistical factors, and why segmentations that powerfully influence object recognition may be barely visible or totally invisible. Network interactions within a Boundary Contour (BC) System, a Feature Contour (FC) System, and an Object Recognition (OR) System are used to explain these phenomena. The BC System is defined by a hierarchy of orientationally tuned interactions, which can be divided into two successive subsystems called the OC filter and the CC loop. The OC filter contains two successive stages of oriented receptive fields which are sensitive to different properties of image contrasts. The OC filter generates inputs to the CC loop, which contains successive stages of spatially short-range competitive interactions and spatially long-range cooperative interactions. Feedback between the competitive and cooperative stages synthesizes a global context-sensitive segmentation from among the many possible groupings of local featural elements. The properties of the BC System provide a unified explanation of several ostensibly different Gestalt rules. The BC System also suggests explanations and predictions concerning the architecture of the striate and prestriate visual cortices. The BC System embodies new ideas concerning the founda-tions of geometry, on-line statistical decision theory, and the resolution of uncertainty in quan-tum measurement systems. Computer simulations establish the formal competence of the BC System as a perceptual grouping system. The properties of the BC System are compared with probabilistic and artificial intelligence models of segmentation. The total network suggests a new approach to the design of computer vision systems, and promises to provide a universal set of rules for perceptual grouping of scenic edges, textures, and smoothly shaded regions.

A neural network model is developed to explain how visual thalamocortical interactions give rise to boundary percepts such as illusory contours and surface percepts such as filled-in brightnesses. Top-down feedback interactions are needed in addition to bottom-up feed-forward interactions to simulate these data. One feedback loop is modeled between lateral geniculate nucleus (LGN) and cortical area V1, and another within cortical areas V1 and V2. The first feedback loop realizes a matching process which enhances LGN cell activities that are consistent with those of active cortical cells, and suppresses LGN activities that are not. This corticogeniculate feedback, being endstopped and oriented, also enhances LGN ON cell activations at the ends of thin dark lines, thereby leading to enhanced cortical brightness percepts when the lines group into closed illusory contours. The second feedback loop generates boundary representations, including illusory contours, that coherently bind distributed cortical features together. Brightness percepts form within the surface representations through a diffusive filling-in process that is contained by resistive gating signals from the boundary representations. The model is used to simulate illusory contours and surface brightnesses induced by Ehrenstein disks, Kanizsa squares, Glass patterns, and cafe wall patterns in single contrast, reverse contrast, and mixed contrast configurations. These examples illustrate how boundary and surface mechanisms can generate percepts that are highly context-sensitive, including how illusory contours can be amodally recognized without being seen, how model simple cells in V1 respond preferentially to luminance discontinuities using inputs from both LGN ON and OFF cells, how model bipole cells in V2 with two colinear receptive fields can help to complete curved illusory contours, how short-range simple cell groupings and long-range bipole cell groupings can sometimes generate different outcomes, and how model double-opponent, filling-in and boundary segmentation mechanisms in V4 interact to generate surface brightness percepts in which filling-in of enhanced brightness and darkness can occur before the net brightness distribution is computed by double-opponent interactions.