Dynamics of subjective contour formation in the early visual cortex

Perceptual and attentional impairments of conscious access involve distinct neural mechanisms despite equal task performance

This study investigates failures in conscious access resulting from either weak sensory input (perceptual impairments) or unattended input (attentional impairments). Participants viewed a Kanizsa stimulus with or without an illusory triangle within a rapid serial visual presentation of distractor stimuli. We designed a novel Kanizsa stimulus that contained additional ancillary features of different complexity (local contrast and collinearity) that were independently manipulated. Perceptual performance on the Kanizsa stimulus (presence vs. absence of an illusion) was equated between the perceptual (masking) and attentional (attentional blink) manipulation to circumvent common confounds related to conditional differences in task performance. We trained and tested classifiers on electroencephalogram (EEG) data to reflect the processing of specific stimulus features, with increasing levels of complexity. We show that late stages of processing (~200-250 ms), reflecting the integration of complex stimulus features (collinearity, illusory triangle), were impaired by masking but spared by the attentional blink. In contrast, decoding of local contrast (the spatial arrangement of stimulus features) was observed early in time (~80 ms) and was left largely unaffected by either manipulation. These results replicate previous work showing that feedforward processing is largely preserved under both perceptual and attentional impairments. Crucially, however, under matched levels of performance, only attentional impairments left the processing of more complex visual features relatively intact, likely related to spared lateral and local feedback processes during inattention. These findings reveal distinct neural mechanisms associated with perceptual and attentional impairments and thus contribute to a comprehensive understanding of distinct neural stages leading to conscious access.

A detailed theory of thalamic and cortical microcircuits for predictive visual inference

Understanding cortical microcircuitry requires theoretical models that can tease apart their computational logic from biological details. Although Bayesian inference serves as an abstract framework of cortical computation, precisely mapping concrete instantiations of computational models to biology under real-world tasks is necessary to produce falsifiable neural models. On the basis of a recent generative model, recursive cortical networks, that demonstrated excellent performance on vision benchmarks, we derive a theoretical cortical microcircuit by placing the requirements of the computational model within biological constraints. The derived model suggests precise algorithmic roles for the columnar and laminar feed-forward, feedback, and lateral connections, the thalamic pathway, blobs and interblobs, and the innate lineage-specific interlaminar connectivity within cortical columns. The model also explains several visual phenomena, including the subjective contour effect and neon-color spreading effect, with circuit-level precision. Our model and methodology provides a path forward in understanding cortical and thalamic computations.

Electroencephalographic Responses to the Number of Objects in Partially Occluded and Uncovered Scenes

Perceptual completion is ubiquitous when estimating properties such as the shape, size, or number of objects in partially occluded scenes. Behavioral experiments showed that the number of hidden objects is underestimated in partially occluded scenes compared with an estimation based on the density of visible objects and the amount of occlusion. It is still unknown at which processing level this (under)estimation of the number of hidden objects occurs. We studied this question using a passive viewing task in which observers viewed a game board that was initially partially occluded and later was uncovered to reveal its hidden parts. We simultaneously measured the electroencephalographic responses to the partially occluded board presentation and its uncovering. We hypothesized that if the underestimation is a result of early sensory processing, it would be observed in the activities of P1 and N1, whereas if it is because of higher level processes such as expectancy, it would be reflected in P3 activities. Our data showed that P1 amplitude increased with numerosity in both occluded and uncovered states, indicating a link between P1 and simple stimulus features. The N1 amplitude was highest when both the initially visible and uncovered areas of the board were completely filled with game pieces, suggesting that the N1 component is sensitive to the overall Gestalt. Finally, we observed that P3 activity was reduced when the density of game pieces in the uncovered parts matched the initially visible parts, implying a relationship between the P3 component and expectation mismatch. Overall, our results suggest that inferences about the number of hidden items are reflected in high-level processing.

Intermodulation frequency components in steady-state visual evoked potentials: Generation, characteristics and applications

The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.

Frontotemporal network contribution to occluded face processing

Primates are known for their exceptional ability to recognize faces. However, we still have much to learn about how their brains process faces when they are partially hidden. When we cover parts of a face, it affects how our brains respond, even though we still perceive the face as a whole. This suggests that complex brain networks are at work in understanding partially hidden faces. To explore this further, we studied two brain regions, the ventrolateral prefrontal cortex (vlPFC) and the inferior temporal cortex (ITC), while showing primate images of faces with parts occluded. We found that vlPFC neurons were more active when faces were partially covered, while ITC neurons preferred fully visible faces. Interestingly, the ITC seemed to process occluded faces in a separate phase after the vlPFC responded. Our research revealed a coordinated effort between these brain regions based on the level of facial obstruction. Specifically, the vlPFC seemed to play a crucial role, driving the representation of occluded faces in the later phase of ITC processing. Importantly, we also found that the brain processes occluded faces differently from those that are fully visible, suggesting specialized mechanisms for handling these situations. These findings highlight the importance of feedback from the vlPFC in understanding occluded faces in the ITC region of the brain. Understanding these neural processes not only enhances our understanding of how primates perceive faces but also provides insights into broader aspects of visual cognition.

Perception of illusory contours in children and adults: An eye-tracking study

The eye-tracking study investigated the perception of subjective Kanizsa and Ehrenstein figures in adults and in children aged 3-4, 5-6, 7-8, and 9-11 years of age. More specifically, the distribution of looking at the inner stimulus part versus the inducing elements was measured for illusory figures, figures with real contours, and control displays. It was hypothesized that longer looking at the inner area of the illusory figures indicates global contour interpolation, whereas longer looking at the inducing elements indicates a local processing mode. According to the results, participants of all ages looked longer at the illusory Kanizsa and Ehrenstein contours than at the figures' inducing elements. However, performance was lowest in the children aged 3-4 years and increased during the preschool period. Moreover, the illusory contour displays elicited comparable visual responses as did the real contour displays. The use of the control displays that contained no contour information ensured that the participants' looking behavior was not driven by a spontaneous tendency to attend to the inner stimulus parts. The study confirms the view that sensitivity to illusory contours emerges very early in life.

It's all in the timing: delayed feedback in autism may weaken predictive mechanisms during contour integration

Humans rely on predictive and integrative mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. Although initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through automatic conveyance of statistical probabilities based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP)-the IC-effect-that occurs over lateral occipital scalp during the timeframe of the visual N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6- to 17-yr-old children with ASD (n = 32) or NT development (n = 53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21 ms later in ASD, even though initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared with NT children. This delay in the feedback-dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by visual feedback.NEW & NOTEWORTHY Children with autism often present with an atypical visual perceptual style that emphasizes parts or details over the whole. Using electroencephalography (EEG), this study identifies delays in the visual feedback from higher-order sensory brain areas to primary sensory regions. Because this type of visual feedback is thought to carry information about prior sensory experiences, individuals with autism may have difficulty efficiently using prior experience or putting together parts into a whole to help make sense of incoming new visual information. This provides empirical neural evidence to support theories of disrupted sensory perception mechanisms in autism.

Mind-brain identity theory confirmed?

Presented here is a novel graphical, structural, and functional model of the embodied mind. Despite strictly adhering to a physicalistic and reductionist approach, this model successfully resolves the apparent contradiction between the thesis regarding the causal closure of the physical realm and the widely held common-sense belief that the mental realm can influence physical behavior. Furthermore, it substantiates the theory of mind-brain identity while shedding light on its neural foundation. Consciousness, viewed as an epiphenomenon in certain respects, simultaneously possesses causal potency. These two aspects operate concurrently through distinct brain processes. Within the paper, particular emphasis is placed on the significance of qualia and emotions, accompanied by an explanation of their phenomenal nature grounded in the perceptual theory of emotions. The proposed model elucidates how autonomous agents can deliberate on various action scenarios and consciously select the most optimal ones for themselves, considering their knowledge of the world, motivations, preferences, and emotions.

Amodal completion across the brain: The impact of structure and knowledge

This study investigates the phenomenon of amodal completion within the context of naturalistic objects, employing a repetition suppression paradigm to disentangle the influence of structure and knowledge cues on how objects are completed. The research focuses on early visual cortex (EVC) and lateral occipital complex (LOC), shedding light on how these brain regions respond to different completion scenarios. In LOC, we observed suppressed responses to structure and knowledge-compatible stimuli, providing evidence that both cues influence neural processing in higher-level visual areas. However, in EVC, we did not find evidence for differential responses to completions compatible or incompatible with either structural or knowledge-based expectations. Together, our findings suggest that the interplay between structure and knowledge cues in amodal completion predominantly impacts higher-level visual processing, with less pronounced effects on the early visual cortex. This study contributes to our understanding of the complex mechanisms underlying visual perception and highlights the distinct roles played by different brain regions in amodal completion.

Pattern completion and disruption characterize contextual modulation in the visual cortex

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli - where the center and surround share the same orientation - are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in vivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.

Brightness illusions drive a neuronal response in the primary visual cortex under top-down modulation

Brightness illusions are a powerful tool in studying vision, yet their neural correlates are poorly understood. Based on a human paradigm, we presented illusory drifting gratings to mice. Primary visual cortex (V1) neurons responded to illusory gratings, matching their direction selectivity for real gratings, and they tracked the spatial phase offset between illusory and real gratings. Illusion responses were delayed compared to real gratings, in line with the theory that processing illusions requires feedback from higher visual areas (HVAs). We provide support for this theory by showing a reduced V1 response to illusions, but not real gratings, following HVAs optogenetic inhibition. Finally, we used the pupil response (PR) as an indirect perceptual report and showed that the mouse PR matches the human PR to perceived luminance changes. Our findings resolve debates over whether V1 neurons are involved in processing illusions and highlight the involvement of feedback from HVAs.

Paying attention to natural scenes in area V1

Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesized that attentional and contextual signals interact in V1 in a manner that primarily benefits the representation of natural stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes, but not by synthetic stimuli lacking higher-order statistical regularities. Population analysis revealed that neuronal responses converged to a low-dimensional subspace only for natural stimuli. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the natural-scene subspace, indicating an alignment between the attentional and natural stimulus variance. These results suggest that attentional and contextual signals interact in V1 in a manner optimized for natural vision.

It's all in the timing: Delayed feedback in autism may weaken predictive mechanisms during contour integration

Humans rely on predictive mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. While initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through conveyance of statistical predictions based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP) - the IC-effect - that occurs over lateral occipital scalp during the timeframe of the N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6-17-year-old children with ASD (n=32) or NT development (n=53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21ms later in ASD, even though timing of initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared to NT children. This delay in the feedback dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by statistical prediction mechanisms.

Reconstructing visual illusory experiences from human brain activity

Visual illusions provide valuable insights into the brain's interpretation of the world given sensory inputs. However, the precise manner in which brain activity translates into illusory experiences remains largely unknown. Here, we leverage a brain decoding technique combined with deep neural network (DNN) representations to reconstruct illusory percepts as images from brain activity. The reconstruction model was trained on natural images to establish a link between brain activity and perceptual features and then tested on two types of illusions: illusory lines and neon color spreading. Reconstructions revealed lines and colors consistent with illusory experiences, which varied across the source visual cortical areas. This framework offers a way to materialize subjective experiences, shedding light on the brain's internal representations of the world.

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.

Neural networks underlying visual illusions: An activation likelihood estimation meta-analysis

Visual illusions have long been used to study visual perception and contextual integration. Neuroimaging studies employ illusions to identify the brain regions involved in visual perception and how they interact. We conducted an Activation Likelihood Estimation (ALE) meta-analysis and meta-analytic connectivity modeling on fMRI studies using static and motion illusions to reveal the neural signatures of illusory processing and to investigate the degree to which different areas are commonly recruited in perceptual inference. The resulting networks encompass ventral and dorsal regions, including the inferior and middle occipital cortices bilaterally in both types of illusions. The static and motion illusion networks selectively included the right posterior parietal cortex and the ventral premotor cortex respectively. Overall, these results describe a network of areas crucially involved in perceptual inference relying on feed-back and feed-forward interactions between areas of the ventral and dorsal visual pathways. The same network is proposed to be involved in hallucinogenic symptoms characteristic of schizophrenia and other disorders, with crucial implications in the use of illusions as biomarkers.

Recurrent pattern completion drives the neocortical representation of sensory inference

When sensory information is incomplete or ambiguous, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanism of sensory inference is not known. Illusory contours (ICs) are key tools to study sensory inference because they contain edges or objects that are implied only by their spatial context. Using cellular resolution, mesoscale two-photon calcium imaging and multi-Neuropixels recordings in the mouse visual cortex, we identified a sparse subset of neurons in the primary visual cortex (V1) and higher visual areas that respond emergently to ICs. We found that these highly selective 'IC-encoders' mediate the neural representation of IC inference. Strikingly, selective activation of these neurons using two-photon holographic optogenetics was sufficient to recreate IC representation in the rest of the V1 network, in the absence of any visual stimulus. This outlines a model in which primary sensory cortex facilitates sensory inference by selectively strengthening input patterns that match prior expectations through local, recurrent circuitry. Our data thus suggest a clear computational purpose for recurrence in the generation of holistic percepts under sensory ambiguity. More generally, selective reinforcement of top-down predictions by pattern-completing recurrent circuits in lower sensory cortices may constitute a key step in sensory inference.

Challenging deep learning models with image distortion based on the abutting grating illusion

Even state-of-the-art deep learning models lack fundamental abilities compared with humans. While many image distortions have been proposed to compare deep learning with humans, they depend on mathematical transformations instead of human cognitive functions. Here, we propose an image distortion based on the abutting grating illusion, which is a phenomenon discovered in humans and animals. The distortion generates illusory contour perception using line gratings abutting each other. We applied the method to MNIST, high-resolution MNIST, and "16-class-ImageNet" silhouettes. Many models, including models trained from scratch and 109 models pretrained with ImageNet or various data augmentation techniques, were tested. Our results show that abutting grating distortion is challenging even for state-of-the-art deep learning models. We discovered that DeepAugment models outperformed other pretrained models. Visualization of early layers indicates that better-performing models exhibit the endstopping property, which is consistent with neuroscience discoveries. Twenty-four human subjects classified distorted samples to validate the distortion.

Contextual drive of neuronal responses in mouse V1 in the absence of feedforward input

Neurons in the primary visual cortex (V1) respond to stimuli in their receptive field (RF), which is defined by the feedforward input from the retina. However, V1 neurons are also sensitive to contextual information outside their RF, even if the RF itself is unstimulated. Here, we examined the cortical circuits for V1 contextual responses to gray disks superimposed on different backgrounds. Contextual responses began late and were strongest in the feedback-recipient layers of V1. They differed between the three main classes of inhibitory neurons, with particularly strong contextual drive of VIP neurons, indicating a contribution of disinhibitory circuits to contextual drive. Contextual drive was strongest when the gray disk was perceived as figure, occluding its background, rather than a hole. Our results link contextual drive in V1 to perceptual organization and provide previously unknown insight into how recurrent processing shapes the response of sensory neurons to facilitate figure perception.

Independent repetition suppression in macaque area V2 and inferotemporal cortex

When a complexly structured natural image is presented twice in succession, first as adapter and then as test, neurons in area TE of macaque inferotemporal cortex exhibit repetition suppression, responding less strongly to the second presentation than to the first. This phenomenon, which has been studied primarily in TE, might plausibly be argued to arise in TE because TE neurons respond selectively to complex images and thus carry information adequate for determining whether an image is or is not a repeat. However, the idea has never been put to a direct test. To resolve this issue, we monitored neuronal responses to sequences of complex natural images under identical conditions in areas V2 and TE. We found that repetition suppression occurs in both areas. Moreover, in each area, suppression takes the form of a dynamic alteration whereby the initial peak of excitation is followed by a trough and then a rebound of firing rate. To assess whether repetition suppression in either area is transmitted from the other area, we analyzed the timing of the phenomenon and its degree of spatial generalization. Suppression occurs at shorter latency in V2 than in TE. Therefore it is not simply fed back from TE. Suppression occurs in TE but not in V2 under conditions in which the test and adapter are presented in different visual field quadrants. Therefore it is not simply fed forward from V2. We conclude that repetition suppression occurs independently in V2 and TE.NEW & NOTEWORTHY When a complexly structured natural image is presented twice in rapid succession, neurons in inferotemporal area TE exhibit repetition suppression, responding less strongly to the second than to the first presentation. We have explored whether this phenomenon is confined to high-order areas where neurons respond selectively to such images and thus carry information relevant to recognizing a repeat. We have found surprisingly that repetition suppression occurs even in low-order visual area V2.

Layer-specific, retinotopically-diffuse modulation in human visual cortex in response to viewing emotionally expressive faces

Viewing faces that are perceived as emotionally expressive evokes enhanced neural responses in multiple brain regions, a phenomenon thought to depend critically on the amygdala. This emotion-related modulation is evident even in primary visual cortex (V1), providing a potential neural substrate by which emotionally salient stimuli can affect perception. How does emotional valence information, computed in the amygdala, reach V1? Here we use high-resolution functional MRI to investigate the layer profile and retinotopic distribution of neural activity specific to emotional facial expressions. Across three experiments, human participants viewed centrally presented face stimuli varying in emotional expression and performed a gender judgment task. We found that facial valence sensitivity was evident only in superficial cortical layers and was not restricted to the retinotopic location of the stimuli, consistent with diffuse feedback-like projections from the amygdala. Together, our results provide a feedback mechanism by which the amygdala directly modulates activity at the earliest stage of visual processing.

Laminar functional magnetic resonance imaging in vision research

Magnetic resonance imaging (MRI) scanners at ultra-high magnetic fields have become available to use in humans, thus enabling researchers to investigate the human brain in detail. By increasing the spatial resolution, ultra-high field MR allows both structural and functional characterization of cortical layers. Techniques that can differentiate cortical layers, such as histological studies and electrode-based measurements have made critical contributions to the understanding of brain function, but these techniques are invasive and thus mainly available in animal models. There are likely to be differences in the organization of circuits between humans and even our closest evolutionary neighbors. Thus research on the human brain is essential. Ultra-high field MRI can observe differences between cortical layers, but is non-invasive and can be used in humans. Extensive previous literature has shown that neuronal connections between brain areas that transmit feedback and feedforward information terminate in different layers of the cortex. Layer-specific functional MRI (fMRI) allows the identification of layer-specific hemodynamic responses, distinguishing feedback and feedforward pathways. This capability has been particularly important for understanding visual processing, as it has allowed researchers to test hypotheses concerning feedback and feedforward information in visual cortical areas. In this review, we provide a general overview of successful ultra-high field MRI applications in vision research as examples of future research.

Amodal completion and relationalism

Amodal completion is usually characterized as the representation of those parts of the perceived object that we get no sensory stimulation from. In the case of the visual sense modality, for example, amodal completion is the representation of occluded parts of objects we see. I argue that relationalism about perception, the view that perceptual experience is constituted by the relation to the perceived object, cannot give a coherent account of amodal completion. The relationalist has two options: construe the perceptual relation as the relation to the entire perceived object or as the relation to the unoccluded parts of the perceived object. I argue that neither of these options are viable.

The complexity of simple counting: ERP findings reveal early perceptual and late numerical processes in different arrangements

The counting process can only be fully understood when taking into account the visual characteristics of the sets counted. Comparing behavioral data as well as event-related brain potentials (ERPs) evoked by different task-irrelevant arrangements of dots during an exact enumeration task, we aimed to investigate the effect of illusory contour detection on the counting process while other grouping cues like proximity were controlled and dot sparsity did not provide a cue to the numerosity of sets. Adult participants (N = 37) enumerated dots (8-12) in irregular and two different types of regular arrangements which differed in the shape of their illusory dot lattices. Enumeration speed was affected by both arrangement and magnitude. The type of arrangement influenced an early ERP negativity peaking at about 270 ms after stimulus onset, whereas numerosity only affected later ERP components (> 300 ms). We also observed that without perceptual cues, magnitude was constructed at a later stage of cognitive processing. We suggest that chunking is a prerequisite for more fluent counting which influences automatic processing (< 300 ms) during enumeration. We conclude that the procedure of exact enumeration depends on the interaction of several perceptual and numerical processes that are influenced by magnitude and arrangement.

Filled/non-filled pairs: An empirical challenge to the integrated information theory of consciousness

Perceptual filling-in for vision is the insertion of visual properties (e.g., color, contour, luminance, or motion) into one's visual field, when those properties have no corresponding retinal input. This paper introduces and provides preliminary empirical support for filled/non-filled pairs, pairs of images that appear identical, yet differ by amount of filling-in. It is argued that such image pairs are important to the experimental testing of theories of consciousness. We review recent experimental research and conclude that filling-in involves brain activity with relatively high integrated information (Φ) compared to veridical visual perceptions. We then present filled/non-filled pairs as an empirical challenge to the integrated information theory of consciousness, which predicts that phenomenologically identical experiences depend on brain processes with identical Φ.

Feedback from lateral occipital cortex to V1/V2 triggers object completion: Evidence from functional magnetic resonance imaging and dynamic causal modeling

Illusory figures demonstrate the visual system's ability to integrate disparate parts into coherent wholes. We probed this object integration process by either presenting an integrated diamond shape or a comparable ungrouped configuration that did not render a complete object. Two tasks were used that either required localization of a target dot (relative to the presented configuration) or discrimination of the dot's luminance. The results showed that only when the configuration was task relevant (in the localization task), performance benefited from the presentation of an integrated object. Concurrent functional magnetic resonance imaging was performed and analyzed using dynamic causal modeling to investigate the (causal) relationship between regions that are associated with illusory figure completion. We found object‐specific feedback connections between the lateral occipital cortex (LOC) and early visual cortex (V1/V2). These modulatory connections persisted across task demands and hemispheres. Our results thus provide direct evidence that interactions between mid‐level and early visual processing regions engage in illusory figure perception. These data suggest that LOC first integrates inputs from multiple neurons in lower‐level cortices, generating a global shape representation while more fine‐graded object details are then determined via feedback to early visual areas, independently of the current task demands.

Gestalts at threshold could reveal Gestalts as predictions

We review and revisit the predictive processing inspired “Gestalts as predictions” hypothesis. The study of Gestalt phenomena at and below threshold can help clarify the role of higher-order object selective areas and feedback connections in mid-level vision. In two psychophysical experiments assessing manipulations of contrast and configurality we showed that: (1) Gestalt phenomena are robust against saliency manipulations across the psychometric function even below threshold (with the accuracy gains and higher saliency associated with Gestalts being present even around chance performance); and (2) peak differences between Gestalt and control conditions happened around the time where responses to Gestalts are starting to saturate (mimicking the differential contrast response profile of striate vs. extra-striate visual neurons). In addition, Gestalts are associated with steeper psychometric functions in all experiments. We propose that these results reflect the differential engagement of object-selective areas in Gestalt phenomena and of information- or percept-based processing, as opposed to energy- or stimulus-based processing, more generally. In addition, the presence of nonlinearities in the psychometric functions suggest differential top-down modulation of the early visual cortex. We treat this as a proof of principle study, illustrating that classic psychophysics can help assess possible involvement of hierarchical predictive processing in Gestalt phenomena.

Predictive coding feedback results in perceived illusory contours in a recurrent neural network

Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perception. One difference from human vision is that they do not appear to perceive illusory contours (e.g. Kanizsa squares) in the same way humans do. Physiological evidence from visual cortex suggests that the perception of illusory contours could involve feedback connections. Would recurrent feedback neural networks perceive illusory contours like humans? In this work we equip a deep feedforward convolutional network with brain-inspired recurrent dynamics. The network was first pretrained with an unsupervised reconstruction objective on a natural image dataset, to expose it to natural object contour statistics. Then, a classification decision head was added and the model was finetuned on a form discrimination task: squares vs. randomly oriented inducer shapes (no illusory contour). Finally, the model was tested with the unfamiliar "illusory contour" configuration: inducer shapes oriented to form an illusory square. Compared with feedforward baselines, the iterative "predictive coding" feedback resulted in more illusory contours being classified as physical squares. The perception of the illusory contour was measurable in the luminance profile of the image reconstructions produced by the model, demonstrating that the model really "sees" the illusion. Ablation studies revealed that natural image pretraining and feedback error correction are both critical to the perception of the illusion. Finally we validated our conclusions in a deeper network (VGG): adding the same predictive coding feedback dynamics again leads to the perception of illusory contours.

Is prediction nothing more than multi-scale pattern completion of the future?

While the notion of the brain as a prediction machine has been extremely influential and productive in cognitive science, there are competing accounts of how best to model and understand the predictive capabilities of brains. One prominent framework is of a "Bayesian brain" that explicitly generates predictions and uses resultant errors to guide adaptation. We suggest that the prediction-generation component of this framework may involve little more than a pattern completion process. We first describe pattern completion in the domain of visual perception, highlighting its temporal extension, and show how this can entail a form of prediction in time. Next, we describe the forward momentum of entrained dynamical systems as a model for the emergence of predictive processing in non-predictive systems. Then, we apply this reasoning to the domain of language, where explicitly predictive models are perhaps most popular. Here, we demonstrate how a connectionist model, TRACE, exhibits hallmarks of predictive processing without any representations of predictions or errors. Finally, we present a novel neural network model, inspired by reservoir computing models, that is entirely unsupervised and memoryless, but nonetheless exhibits prediction-like behavior in its pursuit of homeostasis. These explorations demonstrate that brain-like systems can get prediction "for free," without the need to posit formal logical representations with Bayesian probabilities or an inference machine that holds them in working memory.

Dogs (Canis lupus familiaris) are susceptible to the Kanizsa's triangle illusion

The ability to complete partially missing contours is widespread across the animal kingdom, but whether this extends to dogs is still unknown. To address this gap in knowledge, we assessed dogs' susceptibility to one of the most common contour illusions, the Kanizsa's triangle. Six dogs were trained to discriminate a triangle from other geometrical figures using a two-alternative conditioned discrimination task. Once the learning criterion was reached, dogs were presented with the Kanizsa's triangle and a control stimulus, where inducers were rotated around their centre, so as to disrupt what would be perceived as a triangle by a human observer. As a group, dogs chose the illusory triangle significantly more often than control stimuli. At the individual level, susceptibility to the illusion was shown by five out of six dogs. This is the first study where dogs as a group show susceptibility to a visual illusion in the same manner as humans. Moreover, the analyses revealed a negative effect of age on susceptibility, an effect that was also found in humans. Altogether, this suggests that the underling perceptual mechanisms are similar between dogs and humans, and in sharp contrast with other categories of visual illusions to which the susceptibility of dogs has been previously assessed.

Interference of Illusory Contour Perception by a Distractor

The visual system is capable of recognizing objects when object information is widely separated in space, as revealed by the Kanizsa-type illusory contours (ICs). Attentional involvement in perception of ICs is an important topic, and the present study examined whether and how the processing of ICs is interfered with by a distractor. Discrimination between thin and short deformations of an illusory circle was investigated in the absence or presence of a central dynamic patch, with difficulty of discrimination varied in three levels (easy, medium, and hard). Reaction time (RT) was significantly shorter in the absence compared to the presence of the distractor in the easy and medium conditions. Correct rate (CR) was significantly higher in the absence compared to the presence of the distractor in the easy condition, and the magnitude of the difference between CRs of distracted and non-distracted responses significantly reduced as task difficulty increased. These results suggested that perception of ICs is more likely to be vulnerable to distraction when more attentional resources remain available. The present finding supports that attention is engaged in perception of ICs and that distraction of IC processing is associated with perceptual load.

Amodal completion instead of predictive coding can explain activity suppression of early visual cortex during illusory shape perception

A set of recent neuroimaging studies observed that the perception of an illusory shape can elicit both positive and negative feedback modulations in different parts of the early visual cortex. When three Pac-Men shapes were aligned in such a way that they created an illusory triangle (i.e., the Kanizsa illusion), neural activity in early visual cortex was enhanced in those neurons that had receptive fields that overlapped with the illusory shape but suppressed in neurons whose receptive field overlapped with the Pac-Men inducers. These results were interpreted as congruent with the predictive coding framework, in which neurons in early visual cortex enhance or suppress their activity depending on whether the top-down predictions match the bottom-up sensory inputs. However, there are several plausible alternative explanations for the activity modulations. Here we tested a recent proposal (Moors, 2015) that the activity suppression in early visual cortex during illusory shape perception reflects neural adaptation to perceptually stable input. Namely, the inducers appear perceptually stable during the illusory shape condition (discs on which a triangle is superimposed), but not during the control condition (discs that change into Pac-Men). We examined this hypothesis by manipulating the perceptual stability of inducers. When the inducers could be perceptually interpreted as persistent circles, we replicated the up- and downregulation pattern shown in previous studies. However, when the inducers could not be perceived as persistent circles, we still observed enhanced activity in neurons representing the illusory shape but the suppression of activity in neurons representing the inducers was absent. Thus our results support the hypothesis that the activity suppression in neurons representing the inducers during the Kanizsa illusion is better explained by neural adaptation to perceptually stable input than by reduced prediction error.

Brain network mechanisms of visual shape completion

Visual shape completion recovers object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the process’s underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate patterns were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 36 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across 95% of observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal dorsal attention, default mode, and cingulo-opercular networks. Each parcel’s task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10⁻⁹), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modelling task activation differences in the secondary visual network. Dorsal attention and frontoparietal connections could also model activations in the remaining networks. Taken together, these results suggest that shape completion relies upon a sparsely distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks.

The Generation and Modulation of Distinct Gamma Oscillations with Local, Horizontal, and Feedback Connections in the Primary Visual Cortex: A Model Study on Large-Scale Networks

Gamma oscillation (GAMMA) in the local field potential (LFP) is a synchronized activity commonly found in many brain regions, and it has been thought as a functional signature of network connectivity in the brain, which plays important roles in information processing. Studies have shown that the response property of GAMMA is related to neural interaction through local recurrent connections (RC), feed-forward (FF), and feedback (FB) connections. However, the relationship between GAMMA and long-range horizontal connections (HC) in the brain remains unclear. Here, we aimed to understand this question in a large-scale network model for the primary visual cortex (V1). We created a computational model composed of multiple excitatory and inhibitory units with biologically plausible connectivity patterns for RC, FF, FB, and HC in V1; then, we quantitated GAMMA in network models at different strength levels of HC and other connection types. Surprisingly, we found that HC and FB, the two types of large-scale connections, play very different roles in generating and modulating GAMMA. While both FB and HC modulate a fast gamma oscillation (around 50-60 Hz) generated by FF and RC, HC generates a new GAMMA oscillating around 30 Hz, whose power and peak frequency can also be modulated by FB. Furthermore, response properties of the two GAMMAs in a network with both HC and FB are different in a way that is highly consistent with a recent experimental finding for distinct GAMMAs in macaque V1. The results suggest that distinct GAMMAs are signatures for neural connections in different spatial scales and they might be related to different functions for information integration. Our study, for the first time, pinpoints the underlying circuits for distinct GAMMAs in a mechanistic model for macaque V1, which might provide a new framework to study multiple gamma oscillations in other cortical regions.

From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence

Despite the recent progress in AI powered by deep learning in solving narrow tasks, we are not close to human intelligence in its flexibility, versatility, and efficiency. Efficient learning and effective generalization come from inductive biases, and building Artificial General Intelligence (AGI) is an exercise in finding the right set of inductive biases that make fast learning possible while being general enough to be widely applicable in tasks that humans excel at. To make progress in AGI, we argue that we can look at the human brain for such inductive biases and principles of generalization. To that effect, we propose a strategy to gain insights from the brain by simultaneously looking at the world it acts upon and the computational framework to support efficient learning and generalization. We present a neuroscience-inspired generative model of vision as a case study for such approach and discuss some open problems about the path to AGI.

Feedback contribution to surface motion perception in the human early visual cortex

Human visual surface perception has neural correlates in early visual cortex, but the role of feedback during surface segmentation in human early visual cortex remains unknown. Feedback projections preferentially enter superficial and deep anatomical layers, which provides a hypothesis for the cortical depth distribution of fMRI activity related to feedback. Using ultra-high field fMRI, we report a depth distribution of activation in line with feedback during the (illusory) perception of surface motion. Our results fit with a signal re-entering in superficial depths of V1, followed by a feedforward sweep of the re-entered information through V2 and V3. The magnitude and sign of the BOLD response strongly depended on the presence of texture in the background, and was additionally modulated by the presence of illusory motion perception compatible with feedback. In summary, the present study demonstrates the potential of depth-resolved fMRI in tackling biomechanical questions on perception.

A neural network trained for prediction mimics diverse features of biological neurons and perception

Recent work has shown that convolutional neural networks (CNNs) trained on image recognition tasks can serve as valuable models for predicting neural responses in primate visual cortex. However, these models typically require biologically-infeasible levels of labeled training data, so this similarity must at least arise via different paths. In addition, most popular CNNs are solely feedforward, lacking a notion of time and recurrence, whereas neurons in visual cortex produce complex time-varying responses, even to static inputs. Towards addressing these inconsistencies with biology, here we study the emergent properties of a recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the resulting model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single-unit response dynamics to complex perceptual motion illusions, even when subjected to highly impoverished stimuli. These results suggest potentially deep connections between recurrent predictive neural network models and computations in the brain, providing new leads that can enrich both fields.

Evaluating the neurophysiological evidence for predictive processing as a model of perception

For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

Tracking the completion of parts into whole objects: Retinotopic activation in response to illusory figures in the lateral occipital complex

Illusory figures demonstrate the visual system's ability to integrate separate parts into coherent, whole objects. The present study was performed to track the neuronal object construction process in human observers, by incrementally manipulating the grouping strength within a given configuration until the emergence of a whole-object representation. Two tasks were employed: First, in the spatial localization task, object completion could facilitate performance and was task-relevant, whereas it was irrelevant in the second, luminance discrimination task. Concurrent functional magnetic resonance imaging (fMRI) used spatial localizers to locate brain regions representing task-critical illusory-figure parts to investigate whether the step-wise object construction process would modulate neural activity in these localized brain regions. The results revealed that both V1 and the lateral occipital complex (LOC, with sub-regions LO1 and LO2) were involved in Kanizsa figure processing. However, completion-specific activations were found predominantly in LOC, where neural activity exhibited a modulation in accord with the configuration's grouping strength, whether or not the configuration was relevant to performing the task at hand. Moreover, right LOC activations were confined to LO2 and responded primarily to surface and shape completions, whereas left LOC exhibited activations in both LO1 and LO2 and was related to encoding shape structures with more detail. Together, these results demonstrate that various grouping properties within a visual scene are integrated automatically in LOC, with sub-regions located in different hemispheres specializing in the component sub-processes that render completed objects.

Everything is subjective under water surface, too: visual illusions in fish

The study of visual illusions has captured the attention of comparative psychologists since the last century, given the unquestionable advantage of investigating complex perceptual mechanisms with relatively simple visual patterns. To date, the observation of animal behavior in the presence of visual illusions has been largely confined to mammal and bird studies. Recently, there has been increasing interest in investigating fish, too. The attention has been particularly focused on guppies, redtail splitfin and bamboo sharks. Overall, the tested species were shown to experience a human-like perception of different illusory phenomena involving size, number, motion, brightness estimation and illusory contours. However, in some cases, no illusory effects, or evidence for a reverse illusion, were also reported. Here, we review the current state of the art in this field. We conclude that a wider investigation of visual illusions in fish is fundamental to form a broader comprehension of perceptual systems of vertebrates. Furthermore, we believe that this type of investigation could help us to address general important issues in perceptual studies, such as the role of ecology in shaping perceptual systems, the existence of interindividual variability in the visual perception of nonhuman species and the role of cortical activity in the emergence of visual illusions.

Studying Cortical Plasticity in Ophthalmic and Neurological Disorders: From Stimulus-Driven to Cortical Circuitry Modeling Approaches

Unsolved questions in computational visual neuroscience research are whether and how neurons and their connecting cortical networks can adapt when normal vision is compromised by a neurodevelopmental disorder or damage to the visual system. This question on neuroplasticity is particularly relevant in the context of rehabilitation therapies that attempt to overcome limitations or damage, through either perceptual training or retinal and cortical implants. Studies on cortical neuroplasticity have generally made the assumption that neuronal population properties and the resulting visual field maps are stable in healthy observers. Consequently, differences in the estimates of these properties between patients and healthy observers have been taken as a straightforward indication for neuroplasticity. However, recent studies imply that the modeled neuronal properties and the cortical visual maps vary substantially within healthy participants, e.g., in response to specific stimuli or under the influence of cognitive factors such as attention. Although notable advances have been made to improve the reliability of stimulus-driven approaches, the reliance on the visual input remains a challenge for the interpretability of the obtained results. Therefore, we argue that there is an important role in the study of cortical neuroplasticity for approaches that assess intracortical signal processing and circuitry models that can link visual cortex anatomy, function, and dynamics.

Steady-state visual evoked potentials reveal enhanced neural responses to illusory surfaces during a concurrent visual attention task

Under natural viewing conditions, visual stimuli are often obscured by occluding surfaces. To aid object recognition, the visual system actively reconstructs the missing information, as exemplified in the classic Kanizsa illusion, a phenomenon termed "modal completion". Single-cell recordings in monkeys have shown that neurons in early visual cortex respond to illusory contours, but it has proven difficult to measure the neural correlates of modal completion in humans. We used electroencephalography (EEG) to measure steady-state visual-evoked potentials (SSVEPs) from disks with quarter segments removed to induce an illusory shape (or rotated to eliminate the illusory square in control trials). Opposing pairs of inducers were tagged with one of two flicker frequencies (2.5 or 4 Hz). During stimulus presentations, participants performed an attention task at fixation that required them to judge the orientation of a briefly flashed central bar while ignoring congruent (same orientation) or incongruent (different orientation) flanker bars that appeared on or off the illusory surface. Importantly, the occurrence of any illusory shape was never task relevant. Frequency-based analyses revealed that SSVEP amplitudes were reliably enhanced for trials in which an illusory square appeared, relative to control trials, at 4, 5 and 8 Hz and at an intermodulation frequency of 13 Hz. Participants' reaction times in the flanker task were significantly slower for incongruent versus congruent trials, and this distractor interference effect occurred only in the presence of an illusory surface and not in the control condition. Our results reveal a robust neural correlate of modal completion in the human visual system and provide evidence that visual completion can affect attentional control processes as deployed in a flanker task.

Neuroimaging Findings on Amodal Completion: A Review

Amodal completion is the phenomenon of perceiving completed objects even though physically they are partially occluded. In this review, we provide an extensive overview of the results obtained from a variety of neuroimaging studies on the neural correlates of amodal completion. We discuss whether low-level and high-level cortical areas are implicated in amodal completion; provide an overview of how amodal completion unfolds over time while dissociating feedforward, recurrent, and feedback processes; and discuss how amodal completion is represented at the neuronal level. The involvement of low-level visual areas such as V1 and V2 is not yet clear, while several high-level structures such as the lateral occipital complex and fusiform face area seem invariant to occlusion of objects and faces, respectively, and several motor areas seem to code for object permanence. The variety of results on the timing of amodal completion hints to a mixture of feedforward, recurrent, and feedback processes. We discuss whether the invisible parts of the occluded object are represented as if they were visible, contrary to a high-level representation. While plenty of questions on amodal completion remain, this review presents an overview of the neuroimaging findings reported to date, summarizes several insights from computational models, and connects research of other perceptual completion processes such as modal completion. In all, it is suggested that amodal completion is the solution to deal with various types of incomplete retinal information, and highly depends on stimulus complexity and saliency, and therefore also give rise to a variety of observed neural patterns.

Stimulus complexity shapes response correlations in primary visual cortex

Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure, and arise from structured internal dynamics. However, most theories of visual perception treat contributions of neurons to the representation of stimuli independently and focus on mean responses. Here, we argue that, in a functional model of visual perception, featuring probabilistic inference over a hierarchy of features, inferences about high-level features modulate inferences about low-level features ultimately introducing structured internal dynamics and patterns in SCCs. Specifically, high-level inferences for complex stimuli establish the local context in which neurons in the primary visual cortex (V1) interpret stimuli. Since the local context differentially affects multiple neurons, this conjecture predicts specific modulations in the fine structure of SCCs as stimulus identity and, more importantly, stimulus complexity varies. We designed experiments with natural and synthetic stimuli to measure the fine structure of SCCs in V1 of awake behaving macaques and assessed their dependence on stimulus identity and stimulus statistics. We show that the fine structure of SCCs is specific to the identity of natural stimuli and changes in SCCs are independent of changes in response mean. Critically, we demonstrate that stimulus specificity of SCCs in V1 can be directly manipulated by altering the amount of high-order structure in synthetic stimuli. Finally, we show that simple phenomenological models of V1 activity cannot account for the observed SCC patterns and conclude that the stimulus dependence of SCCs is a natural consequence of structured internal dynamics in a hierarchical probabilistic model of natural images.

Sounds enhance visual completion processes

Everyday vision includes the detection of stimuli, figure-ground segregation, as well as object localization and recognition. Such processes must often surmount impoverished or noisy conditions; borders are perceived despite occlusion or absent contrast gradients. These illusory contours (ICs) are an example of so-called mid-level vision, with an event-related potential (ERP) correlate at ∼100-150 ms post-stimulus onset and originating within lateral-occipital cortices (the IC_effect). Presently, visual completion processes supporting IC perception are considered exclusively visual; any influence from other sensory modalities is currently unknown. It is now well-established that multisensory processes can influence both low-level vision (e.g. detection) as well as higher-level object recognition. By contrast, it is unknown if mid-level vision exhibits multisensory benefits and, if so, through what mechanisms. We hypothesized that sounds would impact the IC_effect. We recorded 128-channel ERPs from 17 healthy, sighted participants who viewed ICs or no-contour (NC) counterparts either in the presence or absence of task-irrelevant sounds. The IC_effect was enhanced by sounds and resulted in the recruitment of a distinct configuration of active brain areas over the 70-170 ms post-stimulus period. IC-related source-level activity within the lateral occipital cortex (LOC), inferior parietal lobe (IPL), as well as primary visual cortex (V1) were enhanced by sounds. Moreover, the activity in these regions was correlated when sounds were present, but not when absent. Results from a control experiment, which employed amodal variants of the stimuli, suggested that sounds impact the perceived brightness of the IC rather than shape formation per se. We provide the first demonstration that multisensory processes augment mid-level vision and everyday visual completion processes, and that one of the mechanisms is brightness enhancement. These results have important implications for the design of treatments and/or visual aids for low-vision patients.

Prior object-knowledge sharpens properties of early visual feature-detectors

Early stages of visual processing are carried out by neural circuits activated by simple and specific features, such as the orientation of an edge. A fundamental question in human vision is how the brain organises such intrinsically local information into meaningful properties of objects. Classic models of visual processing emphasise a one-directional flow of information from early feature-detectors to higher-level information-processing. By contrast to this view, and in line with predictive-coding models of perception, here, we provide evidence from human vision that high-level object representations dynamically interact with the earliest stages of cortical visual processing. In two experiments, we used ambiguous stimuli that, depending on the observer’s prior object-knowledge, can be perceived as either coherent objects or as a collection of meaningless patches. By manipulating object knowledge we were able to determine its impact on processing of low-level features while keeping sensory stimulation identical. Both studies demonstrate that perception of local features is facilitated in a manner consistent with an observer’s high-level object representation (i.e., with no effect on object-inconsistent features). Our results cannot be ascribed to attentional influences. Rather, they suggest that high-level object representations interact with and sharpen early feature-detectors, optimising their performance for the current perceptual context.

Brain mechanisms for perceiving illusory lines in humans

Illusory contours (ICs) are perceptions of visual borders despite absent contrast gradients. The psychophysical and neurobiological mechanisms of IC processes have been studied across species and diverse brain imaging/mapping techniques. Nonetheless, debate continues regarding whether IC sensitivity results from a (presumably) feedforward process within low-level visual cortices (V1/V2) or instead are processed first within higher-order brain regions, such as lateral occipital cortices (LOC). Studies in animal models, which generally favour a feedforward mechanism within V1/V2, have typically involved stimuli inducing IC lines. By contrast, studies in humans generally favour a mechanism where IC sensitivity is mediated by LOC and have typically involved stimuli inducing IC forms or shapes. Thus, the particular stimulus features used may strongly contribute to the model of IC sensitivity supported. To address this, we recorded visual evoked potentials (VEPs) while presenting human observers with an array of 10 inducers within the central 5°, two of which could be oriented to induce an IC line on a given trial. VEPs were analysed using an electrical neuroimaging framework. Sensitivity to the presence vs. absence of centrally-presented IC lines was first apparent at ∼200 ms post-stimulus onset and was evident as topographic differences across conditions. We also localized these differences to the LOC. The timing and localization of these effects are consistent with a model of IC sensitivity commencing within higher-level visual cortices. We propose that prior observations of effects within lower-tier cortices (V1/V2) are the result of feedback from IC sensitivity that originates instead within higher-tier cortices (LOC).