Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

Filling-in at the blind spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. It is known that during filling-in, nonlinear neural responses are observed in the early visual area that correlates with the perception, but the knowledge of underlying neural mechanism for filling-in at the blind spot is far from complete. In this work, we attempted to present a fresh perspective on the computational mechanism of filling-in process in the framework of hierarchical predictive coding, which provides a functional explanation for a range of neural responses in the cortex. We simulated a three-level hierarchical network and observe its response while stimulating the network with different bar stimulus across the blind spot. We find that the predictive-estimator neurons that represent blind spot in primary visual cortex exhibit elevated non-linear response when the bar stimulated both sides of the blind spot. Using generative model, we also show that these responses represent the filling-in completion. All these results are consistent with the finding of psychophysical and physiological studies. In this study, we also demonstrate that the tolerance in filling-in qualitatively matches with the experimental findings related to non-aligned bars. We discuss this phenomenon in the predictive coding paradigm and show that all our results could be explained by taking into account the efficient coding of natural images along with feedback and feed-forward connections that allow priors and predictions to co-evolve to arrive at the best prediction. These results suggest that the filling-in process could be a manifestation of the general computational principle of hierarchical predictive coding of natural images.     

Is neural filling-in necessary to explain the perceptual completion of motion and depth information?

Retinal activity is the first stage of visual perception. Retinal sampling is non-uniform and not continuous, yet visual experience is not characterized by holes and discontinuities in the world. How does the brain achieve this perceptual completion? Fifty years ago, it was suggested that visual perception involves a two-stage process of (i) edge detection followed by (ii) neural filling-in of surface properties. We examine whether this general hypothesis can account for the specific example of perceptual completion of a small target surrounded by dynamic dots (an ''artificial scotoma''), a phenomenon argued to provide insight into the mechanisms responsible for perception. We degrade the target''s borders using first blur and then depth continuity, and find that border degradation does not influence time to target disappearance. This indicates that important information for the continuity of target perception is conveyed at a coarse spatial scale. We suggest that target disappearance could result from adaptation that is not specific to borders, and question the need to hypothesize an active filling-in process to explain this phenomenon.     

Processing local signals into global patterns

Perceptual organization or grouping is one of the central issues in vision research. Recent reports in the neuroimaging literature suggest that perceptual organization is mediated by distributed visual areas that range from the primary visual cortex (V1) to higher visual areas, depending on the availability of grouping cues and on the weight of contribution of each visual area. Evidence suggests that grouping by proximity and collinearity, and also perhaps filling-in, involve V1, whereas grouping by similarity and symmetry seems to depend on activation of higher visual areas. Further studies should include deliberate controls for confounding factors such as attentional artifacts and radial orientation bias, to clarify how spatiotemporal information in visual areas is integrated to give rise to perceptual organization.     

Visual filling-in for computing perceptual surface properties

 The visual system is constantly confronted with the problem of integrating local signals into more global arrangements. This arises from the nature of early cell responses, whether they signal localized measures of luminance, motion, retinal position differences, or discontinuities. Consequently, from sparse, local measurements, the visual system must somehow generate the most likely hypothesis that is consistent with them. In this paper, we study the problem of determining achromatic surface properties, namely brightness. Mechanisms of brightness filling-in have been described by qualitative as well as quantitative models, such as by the one proposed by Cohen and Grossberg [Cohen and Grossberg (1984) Percept Psychophys 36: 428–456]. We demonstrate that filling-in from contrast estimates leads to a regularized solution for the computational problem of generating brightness representations from sparse estimates. This provides deeper insights into the nature of filling-in processes and the underlying objective function one wishes to compute. This particularly guided the proposal of a new modified version of filling-in, namely confidence-based filling-in which generates more robust brightness representations. Our investigation relates the modeling of perceptual data for biological vision to the mathematical frameworks of regularization theory and linear spatially variant diffusion. It therefore unifies different research directions that have so far coexisted in different scientific communities. Received: 11 March 1999 / Accepted in revised form: 14 March 2001     

Ensemble cortical responses to rival visual stimuli: Effect of monocular transient

Binocular rivalry is a fascinating perceptual phenomenon that has been characterized extensively at the psychophysical level. However, the underlying neural mechanism remains poorly understood. In particular, the role of the early visual pathway remains controversial. In this study, we used voltage-sensitive dye imaging to measure the spatiotemporal activity patterns in cat area 18 evoked by dichoptic orthogonal grating stimuli. We found that after several seconds of monocular stimulation with an oriented grating, an orthogonal stimulus to the other eye evoked a reversal of the cortical response pattern, which may contribute to flash suppression in perception. Furthermore, after several seconds of rival binocular stimulation with unequal contrasts, transient increase in the contrast of the weak stimulus evoked a long-lasting cortical response. This transient-triggered response could contribute to the perceptual switch during binocular rivalry. Together, these results point to a significant contribution of early visual cortex to transient-triggered switch in perceptual dominance.     

Experience-dependent plasticity in adult visual cortex

Experience-dependent plasticity is a prominent feature of the mammalian visual cortex. Although such neural changes are most evident during development, adult cortical circuits can be modified by a variety of manipulations, such as perceptual learning and visual deprivation. Elucidating the underlying mechanisms at the cellular and synaptic levels is an essential step in understanding neural plasticity in the mature animal. Although developmental and adult plasticity share many common features, notable differences may be attributed to developmental cortical changes at multiple levels. These range from shifts in the molecular profiles of cortical neurons to changes in the spatiotemporal dynamics of network activity. In this review, we will discuss recent progress and remaining challenges in understanding adult visual plasticity, focusing on the primary visual cortex.     

Adult visual cortical plasticity

The visual cortex has the capacity for experience-dependent change, or cortical plasticity, that is retained throughout life. Plasticity is invoked for encoding information during perceptual learning, by internally representing the regularities of the visual environment, which is useful for facilitating intermediate-level vision-contour integration and surface segmentation. The same mechanisms have adaptive value for functional recovery after CNS damage, such as that associated with stroke or neurodegenerative disease. A common feature to plasticity in primary visual cortex (V1) is an association field that links contour elements across the visual field. The circuitry underlying the association field includes a plexus of long-range horizontal connections formed by cortical pyramidal cells. These connections undergo rapid and exuberant sprouting and pruning in response to removal of sensory input, which can account for the topographic reorganization following retinal lesions. Similar alterations in cortical circuitry may be involved in perceptual learning, and the changes observed in V1 may be representative of how learned information is encoded throughout the cerebral cortex.     

The effects of task and saliency on latencies for colour and motion processing

In human visual perception, there is evidence that different visual attributes, such as colour, form and motion, have different neural-processing latencies. Specifically, recent studies have suggested that colour changes are processed faster than motion changes. We propose that the processing latencies should not be considered as fixed quantities for different attributes, but instead depend upon attribute salience and the observer's task. We asked observers to respond to high- and low-salience colour and motion changes in three different tasks. The tasks varied from having a strong motor component to having a strong perceptual component. Increasing salience led to shorter processing times in all three tasks. We also found an interaction between task and attribute: motion was processed more quickly in reaction-time tasks, whereas colour was processed more quickly in more perceptual tasks. Our results caution against making direct comparisons between latencies for processing different visual attributes without equating salience or considering task effects. More-salient attributes are processed faster than less-salient ones, and attributes that are critical for the task are also processed more quickly.     

Expectation Suppression in Early Visual Cortex Depends on Task Set

Stimulus expectation can modulate neural responses in early sensory cortical regions, with expected stimuli often leading to a reduced neural response. However, it is unclear whether this expectation suppression is an automatic phenomenon or is instead dependent on the type of task a subject is engaged in. To investigate this, human subjects were presented with visual grating stimuli in the periphery that were either predictable or non-predictable while they performed three tasks that differently engaged cognitive resources. In two of the tasks, the predictable stimulus was task-irrelevant and spatial attention was engaged at fixation, with a high load on either perceptual or working memory resources. In the third task, the predictable stimulus was task-relevant, and therefore spatially attended. We observed that expectation suppression is dependent on the cognitive resources engaged by a subjects’ current task. When the grating was task-irrelevant, expectation suppression for predictable items was visible in retinotopically specific areas of early visual cortex (V1-V3) during the perceptual task, but it was abolished when working memory was loaded. When the grating was task-relevant and spatially attended, there was no significant effect of expectation in early visual cortex. These results suggest that expectation suppression is not an automatic phenomenon, but dependent on attentional state and type of available cognitive resources.     

On the role of medial geometry in human vision.

A key challenge underlying theories of vision is how the spatially restricted, retinotopically represented feature analysis can be integrated to form abstract, coordinate-free object models. A resolution likely depends on the use of intermediate-level representations which can on the one hand be populated by local features and on the other hand be used as atomic units underlying the formation of, and interaction with, object hypotheses. The precise structure of this intermediate representation derives from the varied requirements of a range of visual tasks which motivate a significant role for incorporating a geometry of visual form. The need to integrate input from features capturing surface properties such as texture, shading, motion, color, etc., as well as from features capturing surface discontinuities such as silhouettes, T-junctions, etc., implies a geometry which captures both regional and boundary aspects. Curves, as a geometric model of boundaries, have been extensively used as an intermediate representation in computational, perceptual, and physiological studies, while the use of the medial axis (MA) has been popular mainly in computer vision as a geometric region-based model of the interior of closed boundaries. We extend the traditional model of the MA to represent images, where each MA segment represents a region of the image which we call a visual fragment. We present a unified theory of perceptual grouping and object recognition where through various sequences of transformations of the MA representation, visual fragments are grouped in various configurations to form object hypotheses, and are related to stored models. The mechanisms underlying both the computation and the transformation of the MA is a lateral wave propagation model. Recent psychophysical experiments depicting contrast sensitivity map peaks at the medial axes of stimuli, and experiments on perceptual filling-in, and brightness induction and modulation, are consistent with both the use of an MA representation and a propagation-based scheme. Also, recent neurophysiological recordings in V1 correlate with the MA hypothesis and a horizontal propagation scheme. This evidence supports a geometric computational paradigm for processing sensory data where both dynamic in-plane propagation and feedforward-feedback connections play an integral role.     

A neural model of surface perception: lightness, anchoring, and filling-in

A neural model is proposed of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models clarify how the brain can compute relative contrast. The anchored Filling-In Lightness Model (aFILM) clarifies how the brain 'anchors' lightness percepts to determine an absolute lightness scale that uses the full dynamic range of neurons. The model quantitatively simulates lightness anchoring properties (Articulation, Insulation, Configuration, Area Effect) and other lightness data (discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, Craik-O'Brien-Cornsweet illusion). The model clarifies how retinal processing stages achieve light adaptation and spatial contrast adaptation, and how cortical processing stages fill-in surface lightness using long-range horizontal connections that are gated by boundary signals. The new filling-in mechanism runs 1000 times faster than diffusion mechanisms of previous filling-in models.     

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.     

Neural plasticity in high-level visual cortex underlying object perceptual learning

With intensive training, human can achieve impressive behavioral improvement on various perceptual tasks. This phenomenon, termed perceptual learning, has long been considered as a hallmark of the plasticity of sensory neural system. Not surprisingly, high-level vision, such as object perception, can also be improved by perceptual learning. Here we review recent psychophysical, electrophysiological, and neuroimaging studies investigating the effects of training on object selective cortex, such as monkey inferior temporal cortex and human lateral occipital area. Evidences show that learning leads to an increase in object selectivity at the single neuron level and/or the neuronal population level. These findings indicate that high-level visual cortex in humans is highly plastic and visual experience can strongly shape neural functions of these areas. At the end of the review, we discuss several important future directions in this area.     

Music in Our Ears: The Biological Bases of Musical Timbre Perception

Timbre is the attribute of sound that allows humans and other animals to distinguish among different sound sources. Studies based on psychophysical judgments of musical timbre, ecological analyses of sound''s physical characteristics as well as machine learning approaches have all suggested that timbre is a multifaceted attribute that invokes both spectral and temporal sound features. Here, we explored the neural underpinnings of musical timbre. We used a neuro-computational framework based on spectro-temporal receptive fields, recorded from over a thousand neurons in the mammalian primary auditory cortex as well as from simulated cortical neurons, augmented with a nonlinear classifier. The model was able to perform robust instrument classification irrespective of pitch and playing style, with an accuracy of 98.7%. Using the same front end, the model was also able to reproduce perceptual distance judgments between timbres as perceived by human listeners. The study demonstrates that joint spectro-temporal features, such as those observed in the mammalian primary auditory cortex, are critical to provide the rich-enough representation necessary to account for perceptual judgments of timbre by human listeners, as well as recognition of musical instruments.     

Marker correspondence, not processing latency, determines temporal binding of visual attributes

BACKGROUND: When simultaneous visual events appear to occur at different times, the discrepancy has generally been ascribed to time differences in neural transmission or cortical processing that lead to asynchronous awareness of the events. RESULTS: We found, however, that an apparent delay of changes in motion direction relative to synchronous color changes occurs only for rapid alternations, and this delay is not accompanied by a difference in reaction time. We also found that perceptual asynchrony depends on the temporal structure of the stimuli (transitions [first-order temporal change] versus turning points [second-order temporal change]) rather than the attribute type (color versus motion). CONCLUSIONS: We propose that the perception of the relative time of events is based on the relationship of representations of temporal pattern that we term time markers. We conclude that the perceptual asynchrony effects studied here do not reflect differential neural delays for different attributes; rather, they arise from a faulty correspondence match between color transitions and position transitions (motion), which in turn results from a difficulty in detecting turning points (direction reversals) and a preference for matching markers of the same type.     

Binaral Rivalry in the Presence of Visual Perceptual and Semantic Influences

When two different odorants are presented simultaneously to the two nostrils, we experience alternations in olfactory percepts, a phenomenon called binaral rivalry. Little is known about the nature of such alternations. Here we investigate this issue by subjecting unstable and stable olfactory percepts to the influences of visual perceptual or semantic cues as participants engage in simultaneous samplings of either two different odorants (binaral) or a single odorant and water (mononaral), one to each nostril. We show that alternations of olfactory percepts in the binaral setting persist in the presence of visual perceptual and semantic modulations. We also show that perceptual cues have a stronger effect than semantic cues in the binaral case, whereas their effects are comparable in the mononaral setting. Our findings provide evidence that an inherent, stimulus-driven process underlies binaral rivalry despite its general susceptibility to top-down influences.     

Perceptual learning and decision-making in human medial frontal cortex

The dominant view that perceptual learning is accompanied by changes in early sensory representations has recently been challenged. Here we tested the idea that perceptual learning can be accounted for by reinforcement learning involving changes in higher decision-making areas. We trained subjects on an orientation discrimination task involving feedback over 4 days, acquiring fMRI data on the first and last day. Behavioral improvements were well explained by a reinforcement learning model in which learning leads to enhanced readout of sensory information, thereby establishing noise-robust representations of decision variables. We find stimulus orientation encoded in early visual and higher cortical regions such as lateral parietal cortex and anterior cingulate cortex (ACC). However, only activity patterns in the ACC tracked changes in decision variables during learning. These results provide strong evidence for perceptual learning-related changes in higher order areas and suggest that perceptual and reward learning are based on a common neurobiological mechanism.     

Predicting Cortical Dark/Bright Asymmetries from Natural Image Statistics and Early Visual Transforms

The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features -- such as visual contrast, spatial scale, and depth -- differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries.