Illusory contours over pathological retinal scotomas

Our visual percepts are not fully determined by physical stimulus inputs. Thus, in visual illusions such as the Kanizsa figure, inducers presented at the corners allow one to perceive the bounding contours of the figure in the absence of luminance-defined borders. We examined the discrimination of the curvature of these illusory contours that pass across retinal scotomas caused by macular degeneration. In contrast with previous studies with normal-sighted subjects that showed no perception of these illusory contours in the region of physiological scotomas at the optic nerve head, we demonstrated perfect discrimination of the curvature of the illusory contours over the pathological retinal scotoma. The illusion occurred despite the large scar around the macular lesion, strongly reducing discrimination of whether the inducer openings were acute or obtuse and suggesting that the coarse information in the inducers (low spatial frequency) sufficed. The result that subjective contours can pass through the pathological retinal scotoma suggests that the visual cortex, despite the loss of bottom-up input, can use low-spatial frequency information from the inducers to form a neural representation of new complex geometrical shapes inside the scotoma.     

Perceptual Centering Effects in Body Orientation

This study mathematically characterizes the results of DiZio and Lackner (Percept Psychphys 39(1): 39–46) on the perception of self-orientation during circular vection induced by an optokinetic stimulus. Using the hypothesis of perceptual centering, it is shown that five basic centering transformations can logically account for the full range of illusions reported by the subjects. All five of these transformations center the perceived orientations of body components, the rotating disk, and gravity : two align the perceived visual and inertial rotation axes, one centers the perceived axis of visual rotation in front of the head, and two straighten the perceived neck angle. These transformations generate a mathematical semigroup. Application of the semigroup to an actual stimulus condition generates an orbit of predicted illusions. The semigroup analysis of perceptual centering predicts all of the illusions observed in the experiments of DiZio and Lackner (Percept Psychphys 39(1): 39–46). Moreover, the structure of perceptual centering (1) provides a logical explanation for the occurrence of those misperceptions; and (2) predicts the complete set of perceptions that are expected to occur in a larger sample. In addition, our analysis predicts illusions in experimental conditions not yet investigated     

An adaptive model of sensory integration in a dynamic environment applied to human stance control

An adaptive estimator model of human spatial orientation is presented. The adaptive model dynamically weights sensory error signals. More specific, the model weights the difference between expected and actual sensory signals as a function of environmental conditions. The model does not require any changes in model parameters. Differences with existing models of spatial orientation are that: (1) environmental conditions are not specified but estimated, (2) the sensor noise characteristics are the only parameters supplied by the model designer, (3) history-dependent effects and mental resources can be modelled, and (4) vestibular thresholds are not included in the model; instead vestibular-related threshold effects are predicted by the model. The model was applied to human stance control and evaluated with results of a visually induced sway experiment. From these experiments it is known that the amplitude of visually induced sway reaches a saturation level as the stimulus level increases. This saturation level is higher when the support base is sway referenced. For subjects experiencing vestibular loss, these saturation effects do not occur. Unknown sensory noise characteristics were found by matching model predictions with these experimental results. Using only five model parameters, far more than five data points were successfully predicted. Model predictions showed that both the saturation levels are vestibular related since removal of the vestibular organs in the model removed the saturation effects, as was also shown in the experiments. It seems that the nature of these vestibular-related threshold effects is not physical, since in the model no threshold is included. The model results suggest that vestibular-related thresholds are the result of the processing of noisy sensory and motor output signals. Model analysis suggests that, especially for slow and small movements, the environment postural orientation can not be estimated optimally, which causes sensory illusions. The model also confirms the experimental finding that postural orientation is history dependent and can be shaped by instruction or mental knowledge. In addition the model predicts that: (1) vestibular-loss patients cannot handle sensory conflicting situations and will fall down, (2) during sinusoidal support-base translations vestibular function is needed to prevent falling, (3) loss of somatosensory information from the feet results in larger postural sway for sinusoidal support-base translations, and (4) loss of vestibular function results in falling for large support-base rotations with the eyes closed. These predictions are in agreement with experimental results. Received: 12 November 1999 / Accepted in revised form: 30 June 2000     

Looking at Op Art from a computational viewpoint

Arts history tells an exciting story about repeated attempts to represent features that are crucial for the understanding of our environment and which, at the same time, go beyond the inherently two-dimensional nature of a flat painting surface: depth and motion. In the twentieth century, Op artists such as Bridget Riley began to experiment with simple black and white patterns that do not represent motion in an artistic way but actually create vivid dynamic illusions in static pictures. The cause of motion illusions in such paintings is still a matter of debate. The role of involuntary eye movements in this phenomenon is studied here with a computational approach. The possible consequences of shifting the retinal image of synthetic wave gratings, dubbed as 'riloids', were analysed by a two-dimensional array of motion detectors (2DMD model), which generates response maps representing the spatial distribution of motion signals generated by such a stimulus. For a two-frame sequence reflecting a saccadic displacement, these motion signal maps contain extended patches in which local directions change only little. These directions, however, do not usually precisely correspond to the direction of pattern displacement that can be expected from the geometry of the curved gratings as an instance of the so-called 'aperture problem'. The patchy structure of the simulated motion detector response to the displacement of riloids resembles the motion illusion, which is not perceived as a coherent shift of the whole pattern but as a wobbling and jazzing of ill-defined regions. Although other explanations are not excluded, this might support the view that the puzzle of Op Art motion illusions could potentially have an almost trivial solution in terms of small involuntary eye movement leading to image shifts that are picked up by well-known motion detectors in the early visual system. This view can have further consequences for our understanding of how the human visual system usually compensates for eye movements, in order to let us perceive a stable world despite continuous image shifts generated by gaze instability.     

The central performance drop in texture segmentation: a simulation based on a spatial filter model

 This article presents a computational model of early visual information processing that attempts to account for the central performance drop (CPD) in texture segmentation. CPD is the finding that detection performance on short stimulus displays of line textures using orientation differences to set off the target is not maximal at the foveal center but in parafoveal areas. A comparison between a simulation and psychophysical experimental data supported the assumption that the CPD may be explained by properties of spatial frequency channels whose band-pass filter characteristics are not constant over the retina but differ with eccentricity in a defined manner. The model provided satisfactory predictions of experimental data based on densely or widely spaced line elements in texture fields. It is concluded that preattentive texture analysis might be performed by a relatively small number of simple spatial filters. Received: 14 November 1996 / Accepted in revised form: 3 June 1997     

What are lightness illusions and why do we see them?

Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition—and as a direct consequence of their experience—the networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation—although assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.     

Modeling spatial integration in the ocular following response using a probabilistic framework

The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in ocular following response (OFR), is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may understand this response as an ideal observer in a probabilistic framework by using Bayesian theory [Weiss, Y., Simoncelli, E.P., Adelson, E.H., 2002. Motion illusions as optimal percepts. Nature Neuroscience, 5(6), 598-604, doi:10.1038/nn858] which we previously proved to be successfully adapted to model the OFR for different levels of noise with full field gratings. More recent experiments of OFR have used disk gratings and bipartite stimuli which are optimized to study the dynamics of center-surround integration. We quantified two main characteristics of the spatial integration of motion: (i) a finite optimal stimulus size for driving OFR, surrounded by an antagonistic modulation and (ii) a direction selective suppressive effect of the surround on the contrast gain control of the central stimuli [Barthélemy, F.V., Vanzetta, I., Masson, G.S., 2006. Behavioral receptive field for ocular following in humans: dynamics of spatial summation and center-surround interactions. Journal of Neurophysiology, (95), 3712-3726, doi:10.1152/jn.00112.2006]. Herein, we extended the ideal observer model to simulate the spatial integration of the different local motion cues within a probabilistic representation. We present analytical results which show that the hypothesis of independence of local measures can describe the spatial integration of the motion signal. Within this framework, we successfully accounted for the contrast gain control mechanisms observed in the behavioral data for center-surround stimuli. However, another inhibitory mechanism had to be added to account for suppressive effects of the surround.     

Human time perception and its illusions

Why does a clock sometimes appear stopped? Is it possible to perceive the world in slow motion during a car accident? Can action and effect be reversed? Time perception is surprisingly prone to measurable distortions and illusions. The past few years have introduced remarkable progress in identifying and quantifying temporal illusions of duration, temporal order, and simultaneity. For example, perceived durations can be distorted by saccades, by an oddball in a sequence, or by stimulus complexity or magnitude. Temporal order judgments of actions and sensations can be reversed by the exposure to delayed motor consequences, and simultaneity judgments can be manipulated by repeated exposure to nonsimultaneous stimuli. The confederacy of recently discovered illusions points to the underlying neural mechanisms of time perception.     

Features of perception of length of segments under conditions of ponzo and Müller-Lyer illusions in schizophrenia

In order to better appreciate the neurophysiologic mechanisms of perception of length under conditions of geometrical visual illusions, we studied sensitivity of mentally healthy subjects and schizophrenic patients to Ponzo and Müller-Lyer illusion. Patients with schizophrenia estimated length of segments of Müller-Lyer figure less precisely. Accuracy of perception of length of segments in Ponzo figure was ambiguously connected with the duration of the disease. Persons suffering from schizophrenia for a short time were less inclined to Ponzo illusion than mentally healthy subjects. On the contrary, patients who suffered from schizophrenia for a long time were more sensitive to this illusion. Ponzo illusion can be used as a marker of schizophrenia which is found out during the specific period of development of the disease. High sensitivity of patients with schizophrenia to Müller-Lyer and Ponzo illusions supports a hypothesis about the role of the global analysis of an image during processing of its low-frequency component in formation of the illusions under study.     

A possible explanation of the low-level brightness–contrast illusions in the light of an extended classical receptive field model of retinal ganglion cells

The low-level brightness–contrast illusions constitute a special class within visual illusions. Speculations exist that these illusions may be processed through the filtering action of the retinal ganglion cells without necessitating much intervention from higher order processes of visual perception. Concept of the classical receptive field of the ganglion cell, derived from early physiological studies, prompted the idea that a Difference of Gaussian (DoG) model might explain the low-level illusions. In spite of its many successes, the DoG model fails to explain some of these illusions. It has been shown in this paper that it is possible to simulate those illusions with a model that takes into cognizance the role of the extended classical receptive field     

Dynamic decorrelation as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.     

Orientation enhancement in early visual processing can explain time course of brightness contrast and White’s illusion

Dynamics of orientation tuning in V1 indicates that computational model of V1 should not only comprise of bank of static spatially oriented filters but also include the contribution for dynamical response facilitation or suppression along orientation. Time evolution of orientation response in V1 can emerge due to time- dependent excitation and lateral inhibition in the orientation domain. Lateral inhibition in the orientation domain suggests that Ernst Mach’s proposition can be applied for the enhancement of initial orientation distribution that is generated due to interaction of visual stimulus with spatially oriented filters and subcortical temporal filter. Oriented spatial filtering that appears much early ( $<$ 70 ms) in the sequence of visual information processing can account for many of the brightness illusions observed at steady state. It is therefore expected that time evolution of orientation response might be reflecting in the brightness percept over time. Our numerical study suggests that only spatio-temporal filtering at early phase can explain experimentally observed temporal dynamics of brightness contrast illusion. But, enhancement of orientation response at early phase of visual processing is the key mechanism that can guide visual system to predict the brightness by “Max-rule” or “Winner Takes All” (WTA) estimation and thus producing White’s illusions at any exposure.     

Distortions of length perception: anisotropy of the visual field and geometric illusions

A combined influence of stimulus orientation and structure on the judgement of length was tested in psychophysical experiments. The subjects adjusted the test part of a stimulus to be equal in length to the reference part. The V-shaped stimuli (three dots, or the Oppel-Kundt figure, or one dot and two Müller-Layer wings) were generated on the monitor. In the Oppel-Kundt and Müller-Layer figures, the filled part was considered as a reference and the empty part as a test. In session of the experiments, values of errors measured as functions of orientation of the parts of the stimuli. We assume that experiments with the three-dot stimuli yielded pure characteristics of visual field anisotropy, while those with the Oppel-Kundt and Müller-Layer figures showed a combined effect of both anisotropy and illusions. The data demonstrated that illusions and anisotropy are to be interpreted as independent factors, which converge to an algebraic summation in a simultaneous manifestation.     

Modelling stereokinetic phenomena by a minimum relative motion assumption: the tilted disk, the ellipsoid and the tilted bar

The stereokinetic phenomena of the tilted disk and of the ellipsoid are visual illusions of depth elicited by a flat figure with elliptic contour rotating at uniform speed in the frontal plane of an observer. Strictly related to the appearance of the ellipsoid is the stereokinetic phenomenon of the tilted bar, elicited by a line segment of constant length rotating at uniform speed in the frontal plane. We present a mathematical model of these phenomena, based on an assumption of minimization by the Visual System of the differences between the lengths of the velocity vectors of the stimulus (minimum relative motion assumption): the "rigidity hypothesis" is able to explain the appearance of the tilted disk but not the appearance of the ellipsoid and of the tilted bar. The theoretical results obtained by our modelling are in good agreement with the experimental observations.     

The plasticity of correspondence: After-effects,illusions and horopter shifts in depth perception

Retinal disparity is the cue for stereoscopic depth perception. Disparity detection begins with cortical single units driven binocularly from the two eyes. A previous paper (Nelson, 1975) has shown that inhibitory and facilitatory interactions are essential to insure successful disparity detection, particularly with repeating stimulus patterns, and that such a system will display all the appropriate properties of sensory fusion. This paper shows that most depth illusions occur as by-products of the same domain interactions. Such illusion effects fall into two classes: those caused by shifts in the distribution of activity along the appropriate sensory domain (here, the disparity domain) and those caused by changes in the average activity level within the domain. Profile shifts cause depth contrast illusions. The most important profile level change is an activity lowering due to disparity domain inhibition. This adversely affects fusional range (Panum's area). It is postulated that all domain interactions persist following cessation of stimulation. Persistent profile shifts cause depth after-effect illusions; persistent profile lowering is responsible for threshold elevation after-effects.Sensory fusion, the coding errors seen in illusions, the induced effect, and widespread failure to perceive depth from disparity input show that retinal correspondence is not stable in the normal individual. Yet horopter research has attempted to specify one set of retinal points as corresponding. Not surprisingly, horopter research shows systematic shifts in retinal correspondence linked to eye position. Small, simple, tonic modulations of the domain interactions responsible for so many other stereopsis system properties provide a satisfactory cortical mechanism for horopter changes.     

On the discriminability of hROIs, human visually selected regions-of-interest

Many studies have tried to answer an important question: is it possible to predict human visually selected regions-of-interest (hROIs)? hROIs are defined as the loci of eye fixations and they can be analyzed by their spatial distribution over the visual stimulus and their temporal ordering. We used a simplified set of geometrical spatial kernels and linear filter models as bottom-up conspicuity operators that produce algorithmically selected regions-of-interest, aROIs. As a direct approach we measured the ability of these aROIs to predict human scanpaths. The level of prediction is measured by two similarity indices: S _p for spatial similarity and S _s for temporal ordering similarity. At the same time we assessed the discriminability of the hROI loci, in terms of conspicuity, with respect to non-selected (not of interest) regions of an image. We prove that this discrimination is possible and further correlates with the positional similarity index S _p . Other human scanpath experimental conditions are presented in parsing diagrams and discussed. A general top–down/bottom–up scanpath model is finally formulated.     

The hierarchy of directional interactions in visual motion processing

It is well known that context influences our perception of visual motion direction. For example, spatial and temporal context manipulations can be used to induce two well-known motion illusions: direction repulsion and the direction after-effect (DAE). Both result in inaccurate perception of direction when a moving pattern is either superimposed on (direction repulsion), or presented following adaptation to (DAE), another pattern moving in a different direction. Remarkable similarities in tuning characteristics suggest that common processes underlie the two illusions. What is not clear, however, is whether the processes driving the two illusions are expressions of the same or different neural substrates. Here we report two experiments demonstrating that direction repulsion and the DAE are, in fact, expressions of different neural substrates. Our strategy was to use each of the illusions to create a distorted perceptual representation upon which the mechanisms generating the other illusion could potentially operate. We found that the processes mediating direction repulsion did indeed access the distorted perceptual representation induced by the DAE. Conversely, the DAE was unaffected by direction repulsion. Thus parallels in perceptual phenomenology do not necessarily imply common neural substrates. Our results also demonstrate that the neural processes driving the DAE occur at an earlier stage of motion processing than those underlying direction repulsion.     

On the contours of apparent motion: A new perspective on visual space-time

Previous results on the perception of motion indicate that perceived motion paths cannot be explained solely in terms of simple feature-specific analyzers. This is particularly true of apparent (phi) motion. In this paper we develop a dynamic network, with simple filtering and summation properties, which can predict the geometric paths of apparent motion in various spatio-temporal configurations. The network assumptions predict a non-Euclidean metric for the visual space-time of motion perception and we consider the implications of such distortions for various visual displays, including illusions.     

Energy model for contrast detection: spatiotemporal characteristics of threshold vision

A model for contrast detection of spatiotemporal stimuli is proposed which consists of a spatiotemporal linear filter, an energy device and a threshold device. Assuming the existence of independent intrinsic noise, the probability of stimulus detection was approximated by a Weibull function of the response energy. With this assumption, the stimulus energy is a constant at fixed detection probability. This energy model for contrast detection satisfactorily accounted for the elliptical threshold contours of line pairs at stimulus separations within the range 2–30 min and at stimulus onset asynchronies within the range 20–140 ms. The threshold contour at a large stimulus onset asynchrony (300 ms) was in the form of a rounded square. This finding was explained by assuming that the probability of seeing the line pair was determined by the joint probability that at least one stimulus had been detected. With the energy model, the temporal and spatial autocorrelation functions of the response to a flashed line were evaluated. The autocorrelation functions thus determined were used to predict the temporal contrast sensitivity function to a flickering line stimulus and the spatial contrast sensitivity function to flashed gratings, which were in agreement with the experimental data. The data obtained were fitted adequately by an impulse response approximated by a spatiotemporal Gabor-like function. Received: 08 December 1997 / Accepted in revised form: 26 January 1999     

A first- and second-order motion energy analysis of peripheral motion illusions leads to further evidence of "feature blur" in peripheral vision

Background

Anatomical and physiological differences between the central and peripheral visual systems are well documented. Recent findings have suggested that vision in the periphery is not just a scaled version of foveal vision, but rather is relatively poor at representing spatial and temporal phase and other visual features. Shapiro, Lu, Huang, Knight, and Ennis (2010) have recently examined a motion stimulus (the “curveball illusion”) in which the shift from foveal to peripheral viewing results in a dramatic spatial/temporal discontinuity. Here, we apply a similar analysis to a range of other spatial/temporal configurations that create perceptual conflict between foveal and peripheral vision.

Methodology/Principal Findings

To elucidate how the differences between foveal and peripheral vision affect super-threshold vision, we created a series of complex visual displays that contain opposing sources of motion information. The displays (referred to as the peripheral escalator illusion, peripheral acceleration and deceleration illusions, rotating reversals illusion, and disappearing squares illusion) create dramatically different perceptions when viewed foveally versus peripherally. We compute the first-order and second-order directional motion energy available in the displays using a three-dimensional Fourier analysis in the (x, y, t) space. The peripheral escalator, acceleration and deceleration illusions and rotating reversals illusion all show a similar trend: in the fovea, the first-order motion energy and second-order motion energy can be perceptually separated from each other; in the periphery, the perception seems to correspond to a combination of the multiple sources of motion information. The disappearing squares illusion shows that the ability to assemble the features of Kanisza squares becomes slower in the periphery.

Conclusions/Significance

The results lead us to hypothesize “feature blur” in the periphery (i.e., the peripheral visual system combines features that the foveal visual system can separate). Feature blur is of general importance because humans are frequently bringing the information in the periphery to the fovea and vice versa.