Brightness perception, illusory contours, and corticogeniculate feedback

Accentuation, Boolean maps and perception of (dis)similarity in a neural model of visual segmentation

We developed an interactive cortical circuit for visual segmentation that integrates bottom-up and top-down processing to segregate or group visual elements. A bottom-up pathway incorporates stimulus-driven saliency computation, top-down feature-based weighting by relevance and winner-take-all selection. A top-down pathway encompasses multiscale feedback projections, an object-based attention network and a visual segmentation network. Computer simulations have shown that a salient element in the stimulus guides spatial attention and further influences the decomposition of the nearby object into its parts, as postulated by the principle of accentuation. By contrast, when no single salient element is present, top-down feature-based attention highlights all locations occupied by the attended feature and the model forms a Boolean map, i.e., a spatial representation that makes the feature-based grouping explicit. The same distinction between bottom-up and top-down influences in perceptual organization can also be applied to texture perception. The model suggests that the principle of accentuation and feature-based similarity grouping are two manifestations of the same cortical circuit designed to detect similarities and dissimilarities of visual elements in a stimulus.

An interactive cortical architecture for perceptual organization by accentuation

Accentuation has been proposed as a general principle of perceptual organization. Here, we have developed a neurodynamic architecture to explain how accentuation affects boundary segmentation and shape perception. The model consists of bottom-up and top-down pathways. Bottom-up processing involves a set of feature maps that compute bottom-up salience of surfaces, boundaries, boundary completions, and junctions. Then, a feature-based winner-take-all network selects the most salient locations. Top-down processing includes an object-based attention stage that allows enhanced neural activity to propagate from the most salient locations to all connected locations, and a visual segmentation stage that employs inhibitory connections to segregate boundaries into distinct maps. The model was tested on a series of computer simulations showing how the position of the accent affects boundary segregation in the square-diamond and the pointing illusion. The model was also tested on a variety of texture segregation tasks, showing that its performance was comparable to that of human observers. The model suggests that there is an intermediate stage of visual processing between perceptual grouping and object recognition that helps the visual system choose between competing percepts of the ambiguous stimulus.

On the Spatiotemporal Nature of Vision, as Revealed by Covered Bridges and Puddles: A Dispatch from Vermont

Retinal painting, anorthoscopic perception and amodal completion are terms to describe visual phenomena that highlight the spatiotemporal integrative mechanisms that underlie primate vision. Although commonly studied using simplified lab-friendly stimuli presented on a computer screen, this is a report of observations made in a novel real-world context that highlight the rich contributions the mechanisms underlying these phenomena make to naturalistic vision.

A Canonical Laminar Neocortical Circuit Whose Bottom-Up, Horizontal, and Top-Down Pathways Control Attention, Learning, and Prediction

All perceptual and cognitive circuits in the human cerebral cortex are organized into layers. Specializations of a canonical laminar network of bottom-up, horizontal, and top-down pathways carry out multiple kinds of biological intelligence across different neocortical areas. This article describes what this canonical network is and notes that it can support processes as different as 3D vision and figure-ground perception; attentive category learning and decision-making; speech perception; and cognitive working memory (WM), planning, and prediction. These processes take place within and between multiple parallel cortical streams that obey computationally complementary laws. The interstream interactions that are needed to overcome these complementary deficiencies mix cell properties so thoroughly that some authors have noted the difficulty of determining what exactly constitutes a cortical stream and the differences between streams. The models summarized herein explain how these complementary properties arise, and how their interstream interactions overcome their computational deficiencies to support effective goal-oriented behaviors.

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface-shroud resonances support conscious seeing and action, whereas feature-category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure-ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive-emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

Neural dynamics of spreading attentional labels in mental contour tracing

Behavioral and neural data suggest that visual attention spreads along contour segments to bind them into a unified object representation. Such attentional labeling segregates the target contour from distractors in a process known as mental contour tracing. A recurrent competitive map is developed to simulate the dynamics of mental contour tracing. In the model, local excitation opposes global inhibition and enables enhanced activity to propagate on the path offered by the contour. The extent of local excitatory interactions is modulated by the output of the multi-scale contour detection network, which constrains the speed of activity spreading in a scale-dependent manner. Furthermore, an L-junction detection network enables tracing to switch direction at the L-junctions, but not at the X- or T-junctions, thereby preventing spillover to a distractor contour. Computer simulations reveal that the model exhibits a monotonic increase in tracing time as a function of the distance to be traced. Also, the speed of tracing increases with decreasing proximity to the distractor contour and with the reduced curvature of the contours. The proposed model demonstrated how an elaborated version of the winner-takes-all network can implement a complex cognitive operation such as contour tracing.

Predicting Illusory Contours Without Extracting Special Image Features

Boundary completion is one of the desired properties of a robust object boundary detection model, since in real-word images the object boundaries are commonly not fully and clearly seen. An extreme example of boundary completion occurs in images with illusory contours, where the visual system completes boundaries in locations without intensity gradient. Most illusory contour models extract special image features, such as L and T junctions, while the task is known to be a difficult issue in real-world images. The proposed model uses a functional optimization approach, in which a cost value is assigned to any boundary arrangement to find the arrangement with minimal cost. The functional accounts for basic object properties, such as alignment with the image, object boundary continuity, and boundary simplicity. The encoding of these properties in the functional does not require special features extraction, since the alignment with the image only requires extraction of the image edges. The boundary arrangement is represented by a border ownership map, holding object boundary segments in discrete locations and directions. The model finds multiple possible image interpretations, which are ranked according to the probability that they are supposed to be perceived. This is achieved by using a novel approach to represent the different image interpretations by multiple functional local minima. The model is successfully applied to objects with real and illusory contours. In the case of Kanizsa illusion the model predicts both illusory and real (pacman) image interpretations. The model is a proof of concept and is currently restricted to synthetic gray-scale images with solid regions.

Feature-Linking Model for Image Enhancement

Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We present a feature-linking model (FLM) that uses the timing of spikes to encode information. The first spiking time of FLM is applied to image enhancement, and the processing mechanisms are consistent with the human visual system. The enhancement algorithm achieves boosting the details while preserving the information of the input image. Experiments are conducted to demonstrate the effectiveness of the proposed method. Results show that the proposed method is effective.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine.

Specificity of V1-V2 orientation networks in the primate visual cortex

The computation of texture and shape involves integration of features of various orientations. Orientation networks within V1 tend to involve cells which share similar orientation selectivity. However, emergent properties in V2 require the integration of multiple orientations. We now show that, unlike interactions within V1, V1-V2 orientation interactions are much less synchronized and are not necessarily orientation dependent. We find V1-V2 orientation networks are of two types: a more tightly synchronized, orientation-preserving network and a less synchronized orientation-diverse network. We suggest that such diversity of V1-V2 interactions underlies the spatial and functional integration required for computation of higher order contour and shape in V2.

A Neurocomputational account of the role of contour facilitation in brightness perception

A new filling-in model is proposed in order to account for challenging brightness illusions, where inducing background elements are spatially separated from the gray target such as dungeon, cube and grating illusions, bullseye display and ring patterns. This model implements the simple idea that neural response to low-contrast contour is enhanced (facilitated) by the presence of collinear or parallel high-contrast contours in its wider neighborhood. Contour facilitation is achieved via dendritic inhibition, which enables the computation of maximum function among inputs to the node. Recurrent application of maximum function leads to the propagation of the neural signal along collinear or parallel contour segments. When a strong global-contour signal is accompanied with a weak local-contour signal at the same location, conditions are met to produce brightness assimilation within the Filling-in Layer. Computer simulations showed that the model correctly predicts brightness appearance in all of the aforementioned illusions as well as in White's effect, Benary's cross, Todorović's illusion, checkerboard contrast, contrast-contrast illusion and various variations of the White's effect. The proposed model offers new insights on how geometric factors (contour colinearity or parallelism), together with contrast magnitude contribute to the brightness perception.

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.

Computing with a canonical neural circuits model with pool normalization and modulating feedback

Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interaction with an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. Among those, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model.

Receptive field focus of visual area V4 neurons determines responses to illusory surfaces

Illusory figures demonstrate the visual system's ability to infer surfaces under conditions of fragmented sensory input. To investigate the role of midlevel visual area V4 in visual surface completion, we used multielectrode arrays to measure spiking responses to two types of visual stimuli: Kanizsa patterns that induce the perception of an illusory surface and physically similar control stimuli that do not. Neurons in V4 exhibited stronger and sometimes rhythmic spiking responses for the illusion-promoting configurations compared with controls. Moreover, this elevated response depended on the precise alignment of the neuron's peak visual field sensitivity (receptive field focus) with the illusory surface itself. Neurons whose receptive field focus was over adjacent inducing elements, less than 1.5° away, did not show response enhancement to the illusion. Neither receptive field sizes nor fixational eye movements could account for this effect, which was present in both single-unit signals and multiunit activity. These results suggest that the active perceptual completion of surfaces and shapes, which is a fundamental problem in natural visual experience, draws upon the selective enhancement of activity within a distinct subpopulation of neurons in cortical area V4.

Cortical synchronization and perceptual framing

How does the brain group together different parts of an object into a coherent visual object representation? Different parts of an object may be processed by the brain at different rates and may thus become desynchronized. Perceptual framing is a process that resynchronizes cortical activities corresponding to the same retinal object. A neural network model is presented that is able to rapidly resynchronize desynchronized neural activities. The model provides a link between perceptual and brain data. Model properties quantitatively simulate perceptual framing data, including psychophysical data about temporal order judgments and the reduction of threshold contrast as a function of stimulus length. Such a model has earlier been used to explain data about illusory contour formation, texture segregation, shape-from-shading, 3-D vision, and cortical receptive fields. The model hereby shows how many data may be understood as manifestations of a cortical grouping process that can rapidly resynchronize image parts that belong together in visual object representations. The model exhibits better synchronization in the presence of noise than without noise, a type of stochastic resonance, and synchronizes robustly when cells that represent different stimulus orientations compete. These properties arise when fast long-range cooperation and slow short-range competition interact via nonlinear feedback interactions with cells that obey shunting equations.

Neural dynamics of object-based multifocal visual spatial attention and priming: object cueing, useful-field-of-view, and crowding

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.

Is the Kanizsa illusion triggered by the simultaneous contrast mechanism?

Current illusory contour models do not predict the disappearance of the Kanizsa illusion due to specific spatial luminance distributions within the inducers. We suggest that these stimulus conditions are characterized by an insufficient amount of induced brightness. Our model's core assumption is that contour edge detection of the Kanizsa illusion and the simultaneous contrast (brightness induction) effect are triggered by the same mechanism. The simultaneous contrast can immunize the occlusion detection mechanism against spatial and temporal noise. Our model contains physiologically inspired building blocks that detect the oriented contour edges, complete the illusory contours, and enhance them. The model succeeds in predicting the appearance and the disappearance of many different Kanizsa illusion variants.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing.

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

Under 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.

Dopaminergic and non-dopaminergic value systems in conditioning and outcome-specific revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal VAlues Triggers Option Revaluations) neural model. MOTIVATOR describes cognitive-emotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.

Spikes, synchrony, and attentive learning by laminar thalamocortical circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single-cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current source densities and local field potentials; and single-cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, learning, and consciousness. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

CONFIGR: a vision-based model for long-range figure completion

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.

Texture segregation by visual cortex: Perceptual grouping, attention, and learning

A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits the Ben-Shahar and Zucker [Ben-Shahar, O. & Zucker, S. (2004). Sensitivity to curvatures in orientation-based texture segmentation. Vision Research, 44, 257-277] human psychophysical data on orientation-based textures. Surface-based attentional shrouds improve texture learning and classification: Brodatz texture classification rate varies from 95.1% to 98.6% with correct attention, and from 74.1% to 75.5% without attention. Object boundary output of the model in response to photographic images is compared to computer vision algorithms and human segmentations.

A neural model of 3D shape-from-texture: Multiple-scale filtering, boundary grouping, and surface filling-in

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.

Can subthreshold summation be observed with the Ehrenstein illusion?

Subthreshold summation between physical target lines and illusory contours induced by edges such as those produced in the Kanizsa illusion has been reported in previous studies. Here, we investigated the ability of line-induced illusory contours, using Ehrenstein figures, to produce similar subthreshold summation. In the first experiment, three stimulus conditions were presented. The target line was superimposed on the illusory contour of a four-arm Ehrenstein figure, or the target was presented between two dots (which replaced the arms of the Ehrenstein figure), or the target was presented on an otherwise blank screen (control). Detection of the target line was significantly worse when presented on the illusory contour (on the Ehrenstein figure) than when presented between two dots. This result was consistent for both curved and straight target lines, as well as for a 100 ms presentation duration and unlimited presentation duration. Performance was worst in the control condition. The results for the three stimulus conditions were replicated in a second experiment in which an eight-arm Ehrenstein figure was used to produce a stronger and less ambiguous illusory contour. In the third experiment, the target was either superimposed on the illusory contour, or was located across the central gap (illusory surface) of the Ehrenstein figure, collinear with two arms of the figure. As in the first two experiments, the target was either presented on the Ehrenstein figure, or between dots, or on a blank screen. Detection was better in the dot condition than in the Ehrenstein condition, regardless of whether the target was presented on the illusory contour or collinear with the arms of the Ehrenstein figure. These three experiments demonstrate the ability of reduced spatial uncertainty to facilitate the detection of a target line, but do not provide any evidence for subthreshold summation between a physical target line and the illusory contours produced by an Ehrenstein figure. The incongruence of these results with previous findings on Kanizsa figures is discussed.

Coding the presence of visual objects in a recurrent neural network of visual cortex

Before we can recognize a visual object, our visual system has to segregate it from its background. This requires a fast mechanism for establishing the presence and location of objects independently of their identity. Recently, border-ownership neurons were recorded in monkey visual cortex which might be involved in this task [Zhou, H., Friedmann, H., von der Heydt, R., 2000. Coding of border ownership in monkey visual cortex. J. Neurosci. 20 (17), 6594-6611]. In order to explain the basic mechanisms required for fast coding of object presence, we have developed a neural network model of visual cortex consisting of three stages. Feed-forward and lateral connections support coding of Gestalt properties, including similarity, good continuation, and convexity. Neurons of the highest area respond to the presence of an object and encode its position, invariant of its form. Feedback connections to the lowest area facilitate orientation detectors activated by contours belonging to potential objects, and thus generate the experimentally observed border-ownership property. This feedback control acts fast and significantly improves the figure-ground segregation required for the consecutive task of object recognition.

Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates

What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that proposes how cognitive, emotional, timing, and motor processes that involve brain regions such as the prefrontal and temporal cortex, amygdala, hippocampus, and cerebellum may interact to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes, notably a combination of underaroused emotional depression in the amygdala and related affective brain regions, learning of hyperspecific recognition categories in the temporal and prefrontal cortices, and breakdowns of adaptively timed attentional and motor circuits in the hippocampal system and cerebellum. The model clarifies how malfunctions in a subset of these mechanisms can, through a systemwide vicious circle of environmentally mediated feedback, cause and maintain problems with them all.

Looking back: corticothalamic feedback and early visual processing

Although once regarded as a simple sensory relay on the way to the cortex, it is increasingly apparent that the thalamus has a role in the ongoing moment-by-moment processing of sensory input and in cognition. This involves extensive corticofugal feedback connections and the interplay of these with the local thalamic circuitry and the other converging inputs. Here, using the feline visual system as the primary model, some of the latest developments in this field are reviewed and placed in the perspective of an integrated view of system function. Cortical feedback mediated by ionotropic and metabotropic glutamate receptors, and effects mediated by the neuromodulator nitric oxide, all have a role in integrating the thalamic mechanism into the cortical circuit. The essential point is that the perspective of higher-level sensory mechanisms shifts and modulates the thalamic circuitry in ways that optimize abstraction of a meaningful representation of the external world. This review is part of the TINS special issue on The Neural Substrates of Cognition.

Pattern reversal visual evoked responses of V1/V2 and V5/MT as revealed by MEG combined with probabilistic cytoarchitectonic maps

Pattern reversal stimulation provides an established tool for assessing the integrity of the visual pathway and for studying early visual processing. Numerous magnetoencephalographic (MEG) and electroencephalographic (EEG) studies have revealed a three-phasic waveform of the averaged pattern reversal visual evoked potential/magnetic field, with components N75(m), P100(m), and N145(m). However, the anatomical assignment of these components to distinct cortical generators is still a matter of debate, which has inter alia connected with considerable interindividual variations of the human striate and extrastriate cortex. The anatomical variability can be compensated for by means of probabilistic cytoarchitectonic maps, which are three-dimensional maps obtained by an observer-independent statistical mapping in a sample of ten postmortem brains. Transformed onto a subject's brain under consideration, these maps provide the probability with which a given voxel of the subject's brain belongs to a particular cytoarchitectonic area. We optimize the spatial selectivity of the probability maps for V1 and V2 with a probability threshold which optimizes the self- vs. cross-overlap in the population of postmortem brains used for deriving the probabilistic cytoarchitectonic maps. For the first time, we use probabilistic cytoarchitectonic maps of visual cortical areas in order to anatomically identify active cortical generators underlying pattern reversal visual evoked magnetic fields as revealed by MEG. The generators are determined with magnetic field tomography (MFT), which reconstructs the current source density in each voxel. In all seven subjects, our approach reveals generators in V1/V2 (with a greater overlap with V1) and in V5 unilaterally (right V5 in three subjects, left V5 in four subjects) and consistent time courses of their stimulus-locked activations, with three peak activations in V1/V2 (contributing to C1m/N75m, P100m, and N145m) and two peak activations in V5 (contributing to P100m and N145m). The reverberating V1/V2 and V5 activations demonstrate the effect of recurrent activation mechanisms including V1 and extrastriate areas and/or corticofugal feedback loops. Our results demonstrate that the combined investigation of MEG signals with MFT and probabilistic cytoarchitectonic maps significantly improves the anatomical identification of active brain areas.

Gradient representation and perception in the early visual system--a novel account of Mach band formation

Recent evidence suggests that object surfaces and their properties are represented at early stages in the visual system of primates. Most likely invariant surface properties are extracted to endow primates with robust object recognition capabilities. In real-world scenes, luminance gradients are often superimposed on surfaces. We argue that gradients should also be represented in the visual system, since they encode highly variable information, such as shading, focal blur, and penumbral blur. We present a neuronal architecture which was designed and optimized for segregating and representing luminance gradients in real-world images. Our architecture in addition provides a novel theory for Mach bands, whereby corresponding psychophysical data are predicted consistently.

Smooth Gradient Representations as a Unifying Account of Chevreul's Illusion, Mach Bands, and a Variant of the Ehrenstein Disk

Recent evidence suggests that the primate visual system generates representations for object surfaces (where we consider representations for the surface attribute brightness). Object recognition can be expected to perform robustly if those representations are invariant despite environmental changes (e.g., in illumination). In real-world scenes, it happens, however, that surfaces are often overlaid by luminance gradients, which we define as smooth variations in intensity. Luminance gradients encode highly variable information, which may represent surface properties (curvature), nonsurface properties (e.g., specular highlights, cast shadows, illumination inhomogeneities), or information about depth relationships (cast shadows, blur). We argue, on grounds of the unpredictable nature of luminance gradients, that the visual system should establish corresponding representations, in addition to surface representations. We accordingly present a neuronal architecture, the so-called gradient system, which clarifies how spatially accurate gradient representations can be obtained by relying on only high-resolution retinal responses. Although the gradient system was designed and optimized for segregating, and generating, representations of luminance gradients with real-world luminance images, it is capable of quantitatively predicting psychophysical data on both Mach bands and Chevreul's illusion. It furthermore accounts qualitatively for a modified Ehrenstein disk.

Some facilitatory effects of lorazepam on dynamic visual binding

RATIONALE

The benzodiazepine lorazepam enhances the potential for inhibitory gamma-aminobutyric acid (GABAA) synapses in the cortex to stabilize postsynaptic, excitatory activity by synchronizing discharge rates at frequencies of around 40 Hz. Treatment with lorazepam also affects contour integration processes, suggesting that GABAA-mediated synchronization plays a role in visuospatial organization. This conclusion is supported by other physiological studies that link visual feature integration with neuronal synchronization.

OBJECTIVES

One experiment was conducted to assess variations in dynamic figural priming as a result of lorazepam administration.

METHODS

Observers were presented a modified version of a figural priming paradigm designed to investigate the effects of dynamic synchronization on visual feature integration. The priming paradigm consisted of premask crosses presented in a square arrangement within the same phase of a multiphase premask matrix oscillating at 40 Hz. Observers responded to a subsequently presented target square. The modification consisted of line elements presented at various distances relative to the unspecified extension of the lines making up the premask crosses. It was expected that priming effects would be enhanced for lines terminating close to the unspecified extension but only following administration of lorazepam.

RESULTS

As anticipated, priming was enhanced substantially when the premask crosses flickered around static lines that terminated adjacent to the unspecified extension between the premask crosses. This effect was maximal following treatment with lorazepam.

CONCLUSIONS

This finding supports the idea that GABAA-enhanced inhibitory synchronization mediates continuity coding during early visual processing.

Functional neuroimaging findings on the human perception of illusory contours

Illusory contours (IC) have attracted a considerable interest in recent years to derive models of how sensory information is processed and integrated within the visual system. In addition to various findings from neuropsychology, neurophysiology, and psychophysics, several recent studies have used functional neuroimaging to identify the cerebral substrates underlying human perception of IC (in particular Kanizsa figures). In this paper, we review the results from more than 20 neuroimaging studies on IC perception and highlight the great diversity of findings across these studies. We then provide a detailed discussion about the localization ('where' debate) and the timing ('when' debate) of IC processing as suggested by functional neuroimaging. Cortical responses involving visual areas as early as V1/V2 and latencies as rapid as 100 ms have been reported in several studies. Particular issues concerning the role of the right hemisphere and the retinotopic encoding of IC are also discussed. These different findings are tentatively brought together to propose different hypothetical cortical mechanisms that might be responsible for the visual formation of IC. Several remaining questions on IC processing that could potentially be explored with functional neuroimaging techniques are finally emphasized.

Focused attention is not enough to activate discontinuities in lines, but scrutiny is

We distinguish between the roles played by spatial attention and conscious intention in terms of their impact on the processing of segmentation signals, like discontinuities in lines, associated with the act of scrutinizing. We showed previously that the processing of discontinuities in lines can be activated. This is evidenced by an impairment in the detection of a gap between parallel elements when it follows a gap between collinear elements in the same location and orientation. This effect is no longer observed if attention is divided between two gaps in the first stimulus. The results from this study show that focusing attention on a gap between collinear elements is not enough to observe a modulation, consistently with the need to integrate, rather than to separate, collinear elements in usual conditions. The modulation is sensitive to the conscious expectations of subjects, suggesting that an intention can trigger modulations that spatial attention cannot.

Recovering real-world images from single-scale boundaries with a novel filling-in architecture

Filling-in models were successful in predicting psychophysical data for brightness perception. Nevertheless, their suitability for real-world image processing has never been examined. A unified architecture for both predicting psychophysical data and real-world image processing would constitute a powerful theory for early visual information processing. As a first contribution of the present paper, we identified three principal problems with current filling-in architectures, which hamper the goal of having such a unified architecture. To overcome these problems we propose an advance to filling-in theory, called BEATS filling-in, which is based on a novel nonlinear diffusion operator. BEATS filling-in furthermore introduces novel boundary structures. We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in. As a second contribution we propose a novel mechanism for encoding luminance information in contrast responses ('multiplex contrasts'), which is based on recent neurophysiological findings. Again, by simulations, we show that 'multiplex contrasts' at a single, high-resolution filter scale are sufficient for recovering absolute luminance levels. Hence, 'multiplex contrasts' represent a novel theory addressing how the brain encodes and decodes luminance information.

Voluntary attention modulates the brightness of overlapping transparent surfaces

A new class of brightness illusions is introduced that cannot be entirely accounted for by bottom-up models of neuronal processing. In these new illusions, brightness can be modulated by the location of voluntary attention in the absence of eye movements. These effects may arise from top-down or mid-level mechanisms that determine how 3D surfaces and transparent layers are constructed, which in turn influence perceived brightness. Attention is not the only factor that influences perceived brightness in overlapping transparent surfaces. For example, grouping procedures may favor the minimal number of transparent layers necessary to account for the geometry of the stimulus, causing surfaces on a common layer to change brightness together. Attentional modulation of brightness places constraints on possible future models of filling-in, transparent surface formation, brightness perception, and attentional processing.

A laminar cortical model for 3D perception of slanted and curved surfaces and of 2D images: development, attention, and bistability

A model of laminar visual cortical dynamics proposes how 3D boundary and surface representations arise from viewing slanted and curved 3D objects and 2D images. The 3D boundary representations emerge from non-classical receptive field interactions within intracortical and intercortical feedback circuits. Such non-classical interactions within cortical areas V1 and V2 contextually disambiguate classical receptive field responses to ambiguous visual cues using cells that are sensitive to colinear contours, angles, and disparity gradients. Remarkably, these cell types can all be explained as variants of a unified perceptual grouping circuit whose most familiar example is a 2D colinear bipole cell. Model simulations show how this circuit can develop cell selectivity to colinear contours and angles, how slanted surfaces can activate 3D boundary representations that are sensitive to angles and disparity gradients, how 3D filling-in occurs across slanted surfaces, how a 2D Necker cube image can be represented in 3D, and how bistable 3D Necker cube percepts occur. The model also explains data about slant aftereffects and 3D neon color spreading. It shows how chemical transmitters that habituate, or depress, in an activity-dependent way can help to control development and also to trigger bistable 3D percepts and slant aftereffects. Attention can influence which of these percepts is perceived by propagating selectively along object boundaries.

Using afterimages to test neural mechanisms for perceptual filling-in

Many theories of visual perception propose that brightness information spreads from edges to define the perceived intensity of the interior of visual surfaces. Several theories of visual perception have hypothesized that this filling-in process is similar to a diffusion of information where the signals coding brightness spread to nearest neighbors. This paper shows that diffusive mechanisms fail to account for the characteristics of certain afterimage percepts that seem to be dependent on the filling-in process. A psychophysical experiment tests a key property of diffusion-based filling-in mechanisms and finds data that rejects this class of models. A non-diffusive based filling-in mechanism is proposed and is shown to act much like the diffusive based mechanism in many instances, but also produces afterimage percepts that match the experimental data.

Illusory contours and surfaces without amodal completion and depth stratification

Cognitive and figural cues were studied in modified Ehrenstein figures made from letters of the alphabet instead of radial lines. Capital letters with and without terminators (L, J vs O, D) were used, oriented towards or away from the central gap. Three groups, of 14 subjects each, estimated the magnitude of either (i) the illusory contour, (ii) brightness enhancement, or (iii) apparent depth. Strong illusory contour formation and brightness enhancement, but no depth stratification, were perceived in figures devoid of apparent occlusion and amodal completion. These results demonstrate that the Ehrenstein illusion can arise from line ends--with no need for perceptual completion, showing that illusory boundaries and surfaces can be dissociated from apparent depth. Results support a bottom-up explanation in terms of end-stopped neurons in the visual cortex. Conversely, top-down processes appear to be responsible for depth stratification.

Anomalous induction of brightness and surface qualities: a new illusion due to radial lines and chromatic rings

When a chromatic (eg light-blue) annulus surrounds the central gap of an Ehrenstein figure so as to connect the inner ends of the radial lines, a striking new lightness effect emerges: the central white disk has both a self-luminous quality (brighter than in the regular Ehrenstein figure) and a surface quality (dense, paste-like). Self-luminous and surface qualities do not ordinarily appear co-extensively: hence, the brightness induction is called anomalous. In experiment 1, subjects separately scaled self-luminous and surface properties, and in experiment 2, brightness was nulled by physically darkening the central gap. Experiments 3 and 4 were designed to evaluate the importance of chromatic versus achromatic properties of the annulus; other aspects of the annulus (width or the inclusion of a thin black ring inside or outside the chromatic annulus) were tested in experiments 5-7. In experiments 8-12, subjects rated the brightness of modified Ehrenstein figures varying the radial lines (number, length, width, contrast, arrangement). Variation of these parameters generally affected brightness enhancement in the Ehrenstein figure and anomalous brightness induction in a similar manner, but was stronger for the latter effect. On the basis of these results, anomalous brightness induction is attributed to a surface induction process triggered by an interaction between illusory brightness enhancement (due to the radial lines) and border ownership (due to the blue annulus).

Perceptual binding of sensory events: the inclusive characteristics model

A conceptual model of a perceptual system is proposed, in which each neural level forms characteristics inclusive of the data held in the underlying level. As a result, the stimulus field can be expressed as a hierarchically ordered set of overlying sensory characteristics: from sensory features to higher inclusive characteristics binding sensory data to form whole images and scenes. Specific patterns of electrical activity reflecting inclusive characteristics are transmitted via reverse projections from the upper neural levels to the lower. This forms a downward excitation transmission cascade, stimulating those neural structures whose signals correspond to the higher inclusive characteristics of the given perceptual act. It is demonstrated that these mechanisms are in good agreement with experimental data obtained from psychological and neurophysiological studies and may support the binding of sensory events at all perceptual levels.