A functional and perceptual signature of the second visual area in primates

Dynamic skin behaviors in cephalopods

The coleoid cephalopods (cuttlefish, octopus, and squid) are a group of soft-bodied mollusks that exhibit a wealth of complex behaviors, including dynamic camouflage, object mimicry, skin-based visual communication, and dynamic body patterns during sleep. Many of these behaviors are visually driven and engage the animals' color changing skin, a pixelated display that is directly controlled by neurons projecting from the brain. Thus, cephalopod skin provides a direct readout of neural activity in the brain. During camouflage, cephalopods recreate on their skin an approximation of what they see, providing a window into perceptual processes in the brain. Additionally, cephalopods communicate their internal state during social encounters using innate skin patterns, and create waves of pigmentation on their skin during periods of arousal. Thus, by leveraging the visual displays of cephalopods, we can gain insight into how the external world is represented in the brain and how this representation is transformed into a recapitulation of the world on the skin. Here, we describe the rich skin behaviors of the coleoid cephalopods, what is known about cephalopod neuroanatomy, and how advancements in gene editing, machine learning, optical imaging, and electrophysiological tools may provide an opportunity to explore the neural bases of these fascinating behaviors.

Peripheral material perception

Humans can rapidly identify materials, such as wood or leather, even within a complex visual scene. Given a single image, one can easily identify the underlying "stuff," even though a given material can have highly variable appearance; fabric comes in unlimited variations of shape, pattern, color, and smoothness, yet we have little trouble categorizing it as fabric. What visual cues do we use to determine material identity? Prior research suggests that simple "texture" features of an image, such as the power spectrum, capture information about material properties and identity. Few studies, however, have tested richer and biologically motivated models of texture. We compared baseline material classification performance to performance with synthetic textures generated from the Portilla-Simoncelli model and several common image degradations. The textures retain statistical information but are otherwise random. We found that performance with textures and most degradations was well below baseline, suggesting insufficient information to support foveal material perception. Interestingly, modern research suggests that peripheral vision might use a statistical, texture-like representation. In a second set of experiments, we found that peripheral performance is more closely predicted by texture and other image degradations. These findings delineate the nature of peripheral material classification.

Developmentally stable representations of naturalistic image structure in macaque visual cortex

We studied visual development in macaque monkeys using texture stimuli, matched in local spectral content but varying in "naturalistic" structure. In adult monkeys, naturalistic textures preferentially drive neurons in areas V2 and V4, but not V1. We paired behavioral measurements of naturalness sensitivity with separately-obtained neuronal population recordings from neurons in areas V1, V2, V4, and inferotemporal cortex (IT). We made behavioral measurements from 16 weeks of age and physiological measurements as early as 20 weeks, and continued through 56 weeks. Behavioral sensitivity reached half of maximum at roughly 25 weeks of age. Neural sensitivities remained stable from the earliest ages tested. As in adults, neural sensitivity to naturalistic structure increased from V1 to V2 to V4. While sensitivities in V2 and IT were similar, the dimensionality of the IT representation was more similar to V4's than to V2's.

Primate V2 Receptive Fields Derived from Anatomically Identified Large-Scale V1 Inputs

In the primate visual system, visual object recognition involves a series of cortical areas arranged hierarchically along the ventral visual pathway. As information flows through this hierarchy, neurons become progressively tuned to more complex image features. The circuit mechanisms and computations underlying the increasing complexity of these receptive fields (RFs) remain unidentified. To understand how this complexity emerges in the secondary visual area (V2), we investigated the functional organization of inputs from the primary visual cortex (V1) to V2 by combining retrograde anatomical tracing of these inputs with functional imaging of feature maps in macaque monkey V1 and V2. We found that V1 neurons sending inputs to single V2 orientation columns have a broad range of preferred orientations, but are strongly biased towards the orientation represented at the injected V2 site. For each V2 site, we then constructed a feedforward model based on the linear combination of its anatomically-identified large-scale V1 inputs, and studied the response proprieties of the generated V2 RFs. We found that V2 RFs derived from the linear feedforward model were either elongated versions of V1 filters or had spatially complex structures. These modeled RFs predicted V2 neuron responses to oriented grating stimuli with high accuracy. Remarkably, this simple model also explained the greater selectivity to naturalistic textures of V2 cells compared to their V1 input cells. Our results demonstrate that simple linear combinations of feedforward inputs can account for the orientation selectivity and texture sensitivity of V2 RFs.

A novel approach to texture recognition combining deep learning orthogonal convolution with regional input features

Textures provide a powerful segmentation and object detection cue. Recent research has shown that deep convolutional nets like Visual Geometry Group (VGG) and ResNet perform well in non-stationary texture datasets. Non-stationary textures have local structures that change from one region of the image to the other. This is consistent with the view that deep convolutional networks are good at detecting local microstructures disguised as textures. However, stationary textures are textures that have statistical properties that are constant or slow varying over the entire region are not well detected by deep convolutional networks. This research demonstrates that simple seven-layer convolutional networks can obtain better results than deep networks using a novel convolutional technique called orthogonal convolution with pre-calculated regional features using grey level co-occurrence matrix. We obtained an average of 8.5% improvement in accuracy in texture recognition on the Outex dataset over GoogleNet, ResNet, VGG and AlexNet.

Efficient coding of natural images in the mouse visual cortex

How the activity of neurons gives rise to natural vision remains a matter of intense investigation. The mid-level visual areas along the ventral stream are selective to a common class of natural images-textures-but a circuit-level understanding of this selectivity and its link to perception remains unclear. We addressed these questions in mice, first showing that they can perceptually discriminate between textures and statistically simpler spectrally matched stimuli, and between texture types. Then, at the neural level, we found that the secondary visual area (LM) exhibited a higher degree of selectivity for textures compared to the primary visual area (V1). Furthermore, textures were represented in distinct neural activity subspaces whose relative distances were found to correlate with the statistical similarity of the images and the mice's ability to discriminate between them. Notably, these dependencies were more pronounced in LM, where the texture-related subspaces were smaller than in V1, resulting in superior stimulus decoding capabilities. Together, our results demonstrate texture vision in mice, finding a linking framework between stimulus statistics, neural representations, and perceptual sensitivity-a distinct hallmark of efficient coding computations.

Foundations of visual form selectivity for neurons in macaque V1 and V2

We have measured the visually evoked activity of single neurons recorded in areas V1 and V2 of awake, fixating macaque monkeys, and captured their responses with a common computational model. We used a stimulus set composed of "droplets" of localized contrast, band-limited in orientation and spatial frequency; each brief stimulus contained a random superposition of droplets presented in and near the mapped receptive field. We accounted for neuronal responses with a 2-layer linear-nonlinear model, representing each receptive field by a combination of orientation- and scale-selective filters. We fit the data by jointly optimizing the model parameters to enforce sparsity and to prevent overfitting. We visualized and interpreted the fits in terms of an "afferent field" of nonlinearly combined inputs, dispersed in the 4 dimensions of space and spatial frequency. The resulting fits generally give a good account of the responses of neurons in both V1 and V2, capturing an average of 40% of the explainable variance in neuronal firing. Moreover, the resulting models predict neuronal responses to image families outside the test set, such as gratings of different orientations and spatial frequencies. Our results offer a common framework for understanding processing in the early visual cortex, and also demonstrate the ways in which the distributions of neuronal responses in V1 and V2 are similar but not identical.

A mechanism for deviance detection and contextual routing in the thalamus: a review and theoretical proposal

Predictive processing theories conceptualize neocortical feedback as conveying expectations and contextual attention signals derived from internal cortical models, playing an essential role in the perception and interpretation of sensory information. However, few predictive processing frameworks outline concrete mechanistic roles for the corticothalamic (CT) feedback from layer 6 (L6), despite the fact that the number of CT axons is an order of magnitude greater than that of feedforward thalamocortical (TC) axons. Here we review the functional architecture of CT circuits and propose a mechanism through which L6 could regulate thalamic firing modes (burst, tonic) to detect unexpected inputs. Using simulations in a model of a TC cell, we show how the CT feedback could support prediction-based input discrimination in TC cells by promoting burst firing. This type of CT control can enable the thalamic circuit to implement spatial and context selective attention mechanisms. The proposed mechanism generates specific experimentally testable hypotheses. We suggest that the L6 CT feedback allows the thalamus to detect deviance from predictions of internal cortical models, thereby supporting contextual attention and routing operations, a far more powerful role than traditionally assumed.

Neuronal and behavioral responses to naturalistic texture images in macaque monkeys

The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and thus unlikely to reflect a critical aspect of the formation of perceptual decisions.

Significance statement

As visual signals propagate along the cortical hierarchy, they encode increasingly complex aspects of the sensory environment and likely have a more direct relationship with perceptual experience. We replicate and extend previous results from anesthetized monkeys differentiating the selectivity of neurons along the first step in cortical vision from area V1 to V2. However, our results further complicate efforts to establish neural signatures that reveal the relationship between perception and the neuronal activity of sensory populations. We find that choice-correlated activity in V1 and V2 is unstable across different observers and tasks, and also untethered from neuronal sensitivity and other features of nonsensory response modulation.

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.

A CODE model bridging crowding in sparse and dense displays

Visual crowding is arguably the strongest limitation imposed on extrafoveal vision, and is a relatively well-understood phenomenon. However, most investigations and theories are based on sparse displays consisting of a target and at most a handful of flanker objects. Recent findings suggest that the laws thought to govern crowding may not hold for densely cluttered displays, and that grouping and nearest neighbour effects may be more important. Here we present a computational model that accounts for crowding effects in both sparse and dense displays. The model is an adaptation and extension of an earlier model that has previously successfully accounted for spatial clustering, numerosity and object-based attention phenomena. Our model combines grouping by proximity and similarity with a nearest neighbour rule, and defines crowding as the extent to which target and flankers fail to segment. We show that when the model is optimized for explaining crowding phenomena in classic, sparse displays, it also does a good job in capturing novel crowding patterns in dense displays, in both existing and new data sets. The model thus ties together different principles governing crowding, specifically Bouma's law, grouping, and nearest neighbour similarity effects.

High-performing neural network models of visual cortex benefit from high latent dimensionality

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the latent dimensionality of their natural image representations. A popular view holds that optimal DNNs compress their representations onto low-dimensional subspaces to achieve invariance and robustness, which suggests that better models of visual cortex should have lower dimensional geometries. Surprisingly, we found a strong trend in the opposite direction-neural networks with high-dimensional image subspaces tended to have better generalization performance when predicting cortical responses to held-out stimuli in both monkey electrophysiology and human fMRI data. Moreover, we found that high dimensionality was associated with better performance when learning new categories of stimuli, suggesting that higher dimensional representations are better suited to generalize beyond their training domains. These findings suggest a general principle whereby high-dimensional geometry confers computational benefits to DNN models of visual cortex.

Normalization by orientation-tuned surround in human V1-V3

An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a multi-stage computation. The first stage is linear filtering. The second stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of the linear filters in quadrature phase. The third stage is normalization, in which a local population of complex cells mutually inhibit one another. Because the population includes cells tuned to a range of orientations and spatial frequencies, the result is that the responses are effectively normalized by the local stimulus contrast. Here, using evidence from human functional MRI, we show that the classical model fails to account for the relative responses to two classes of stimuli: straight, parallel, band-passed contours (gratings), and curved, band-passed contours (snakes). The snakes elicit fMRI responses that are about twice as large as the gratings, yet a traditional divisive normalization model predicts responses that are about the same. Motivated by these observations and others from the literature, we implement a divisive normalization model in which cells matched in orientation tuning ("tuned normalization") preferentially inhibit each other. We first show that this model accounts for differential responses to these two classes of stimuli. We then show that the model successfully generalizes to other band-pass textures, both in V1 and in extrastriate cortex (V2 and V3). We conclude that even in primary visual cortex, complex features of images such as the degree of heterogeneity, can have large effects on neural responses.

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system

The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model's object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer's disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the in-silico setup that simulates neuroplasticity improves the model's biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.

EasyEyes - A new method for accurate fixation in online vision testing

Online methods allow testing of larger, more diverse populations, with much less effort than in-lab testing. However, many psychophysical measurements, including visual crowding, require accurate eye fixation, which is classically achieved by testing only experienced observers who have learned to fixate reliably, or by using a gaze tracker to restrict testing to moments when fixation is accurate. Alas, both approaches are impractical online as online observers tend to be inexperienced, and online gaze tracking, using the built-in webcam, has a low precision (±4 deg). EasyEyes open-source software reliably measures peripheral thresholds online with accurate fixation achieved in a novel way, without gaze tracking. It tells observers to use the cursor to track a moving crosshair. At a random time during successful tracking, a brief target is presented in the periphery. The observer responds by identifying the target. To evaluate EasyEyes fixation accuracy and thresholds, we tested 12 naive observers in three ways in a counterbalanced order: first, in the laboratory, using gaze-contingent stimulus presentation; second, in the laboratory, using EasyEyes while independently monitoring gaze using EyeLink 1000; third, online at home, using EasyEyes. We find that crowding thresholds are consistent and individual differences are conserved. The small root mean square (RMS) fixation error (0.6 deg) during target presentation eliminates the need for gaze tracking. Thus, this method enables fixation-dependent measurements online, for easy testing of larger and more diverse populations.

Compact deep neural network models of visual cortex

A powerful approach to understanding the computations carried out in visual cortex is to develop models that predict neural responses to arbitrary images. Deep neural network (DNN) models have worked remarkably well at predicting neural responses [1, 2, 3], yet their underlying computations remain buried in millions of parameters. Have we simply replaced one complicated system in vivo with another in silico ? Here, we train a data-driven deep ensemble model that predicts macaque V4 responses ∼50% more accurately than currently-used task-driven DNN models. We then compress this deep ensemble to identify compact models that have 5,000x fewer parameters yet equivalent accuracy as the deep ensemble. We verified that the stimulus preferences of the compact models matched those of the real V4 neurons by measuring V4 responses to both 'maximizing' and adversarial images generated using compact models. We then analyzed the inner workings of the compact models and discovered a common circuit motif: Compact models share a similar set of filters in early stages of processing but then specialize by heavily consolidating this shared representation with a precise readout. This suggests that a V4 neuron's stimulus preference is determined entirely by its consolidation step. To demonstrate this, we investigated the compression step of a dot-detecting compact model and found a set of simple computations that may be carried out by dot-selective V4 neurons. Overall, our work demonstrates that the DNN models currently used in computational neuroscience are needlessly large; our approach provides a new way forward for obtaining explainable, high-accuracy models of visual cortical neurons.

Exploring the role of texture features in deep convolutional neural networks: Insights from Portilla-Simoncelli statistics

It is well-understood that the performance of Deep Convolutional Neural Networks (DCNNs) in image recognition tasks is influenced not only by shape but also by texture information. Despite this, understanding the internal representations of DCNNs remains a challenging task. This study employs a simplified version of the Portilla-Simoncelli Statistics, termed "minPS," to explore how texture information is represented in a pre-trained VGG network. Using minPS features extracted from texture images, we perform a sparse regression on the activations across various channels in VGG layers. Our findings reveal that channels in the early to middle layers of the VGG network can be effectively described by minPS features. Additionally, we observe that the explanatory power of minPS sub-groups evolves as one ascends the network hierarchy. Specifically, sub-groups termed Linear Cross Scale (LCS) and Energy Cross Scale (ECS) exhibit weak explanatory power for VGG channels. To investigate the relationship further, we compare the original texture images with their synthesized counterparts, generated using VGG, in terms of minPS features. Our results indicate that the absence of certain minPS features suggests their non-utilization in VGG's internal representations.

Model metamers reveal divergent invariances between biological and artificial neural networks

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human-model discrepancy. The human recognizability of a model's metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

Spatiotemporal cortical dynamics for visual scene processing as revealed by EEG decoding

The human visual system rapidly recognizes the categories and global properties of complex natural scenes. The present study investigated the spatiotemporal dynamics of neural signals involved in visual scene processing using electroencephalography (EEG) decoding. We recorded visual evoked potentials from 11 human observers for 232 natural scenes, each of which belonged to one of 13 natural scene categories (e.g., a bedroom or open country) and had three global properties (naturalness, openness, and roughness). We trained a deep convolutional classification model of the natural scene categories and global properties using EEGNet. Having confirmed that the model successfully classified natural scene categories and the three global properties, we applied Grad-CAM to the EEGNet model to visualize the EEG channels and time points that contributed to the classification. The analysis showed that EEG signals in the occipital electrodes at short latencies (approximately 80 ~ ms) contributed to the classifications, whereas those in the frontal electrodes at relatively long latencies (200 ~ ms) contributed to the classification of naturalness and the individual scene category. These results suggest that different global properties are encoded in different cortical areas and with different timings, and that the combination of the EEGNet model and Grad-CAM can be a tool to investigate both temporal and spatial distribution of natural scene processing in the human brain.

Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density

Purpose

Anatomical "noise" is an important limitation of full-field digital mammography. Understanding its impact on clinical judgments is made difficult by the complexity of breast parenchyma, which results in image texture not fully captured by the power spectrum. While the number of possible parameters for characterizing anatomical noise is quite large, a specific set of local texture statistics has been shown to be visually salient, and human sensitivity to these statistics corresponds to their informativeness in natural scenes.

Approach

We evaluate these local texture statistics in addition to standard power-spectral measures to determine whether they have additional explanatory value for radiologists' breast density judgments. We analyzed an image database consisting of 111 disease-free mammographic screening exams (4 views each) acquired at the University of Pittsburgh Medical Center. Each exam had a breast density score assigned by the examining radiologist. Power-spectral descriptors and local image statistics were extracted from images of breast parenchyma. Model-selection criteria and accuracy were used to assess the explanatory and predictive value of local image statistics for breast density judgments.

Results

The model selection criteria show that adding local texture statistics to descriptors of the power spectra produce better explanatory and predictive models of radiologists' judgments of breast density. Thus, local texture statistics capture, in some form, non-Gaussian aspects of texture that radiologists are using.

Conclusions

Since these local texture statistics are expected to be impacted by imaging factors like modality, dose, and image processing, they suggest avenues for understanding and optimizing observer performance.

Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks: A Review

Artificial neural networks (ANNs) that are heavily inspired by the human brain now achieve human-level performance across multiple task domains. ANNs have thus drawn attention in neuroscience, raising the possibility of providing a framework for understanding the information encoded in the human brain. However, the correspondence between ANNs and the brain cannot be measured directly. They differ in outputs and substrates, neurons vastly outnumber their ANN analogs (i.e., nodes), and the key algorithm responsible for most of modern ANN training (i.e., backpropagation) is likely absent from the brain. Neuroscientists have thus taken a variety of approaches to examine the similarity between the brain and ANNs at multiple levels of their information hierarchy. This review provides an overview of the currently available approaches and their limitations for evaluating brain-ANN correspondence.

Generalizing biological surround suppression based on center surround similarity via deep neural network models

Sensory perception is dramatically influenced by the context. Models of contextual neural surround effects in vision have mostly accounted for Primary Visual Cortex (V1) data, via nonlinear computations such as divisive normalization. However, surround effects are not well understood within a hierarchy, for neurons with more complex stimulus selectivity beyond V1. We utilized feedforward deep convolutional neural networks and developed a gradient-based technique to visualize the most suppressive and excitatory surround. We found that deep neural networks exhibited a key signature of surround effects in V1, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found that in some neurons, especially in late layers, when the center stimulus was altered, the most suppressive surround surprisingly can follow the change. Through the visualization approach, we generalized previous understanding of surround effects to more complex stimuli, in ways that have not been revealed in visual cortices. In contrast, the suppression based on center surround similarity was not observed in an untrained network. We identified further successes and mismatches of the feedforward CNNs to the biology. Our results provide a testable hypothesis of surround effects in higher visual cortices, and the visualization approach could be adopted in future biological experimental designs.

A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex

Midlevel features, such as contour and texture, provide a computational link between low- and high-level visual representations. Although the nature of midlevel representations in the brain is not fully understood, past work has suggested a texture statistics model, called the P-S model (Portilla and Simoncelli, 2000), is a candidate for predicting neural responses in areas V1-V4 as well as human behavioral data. However, it is not currently known how well this model accounts for the responses of higher visual cortex to natural scene images. To examine this, we constructed single-voxel encoding models based on P-S statistics and fit the models to fMRI data from human subjects (both sexes) from the Natural Scenes Dataset (Allen et al., 2022). We demonstrate that the texture statistics encoding model can predict the held-out responses of individual voxels in early retinotopic areas and higher-level category-selective areas. The ability of the model to reliably predict signal in higher visual cortex suggests that the representation of texture statistics features is widespread throughout the brain. Furthermore, using variance partitioning analyses, we identify which features are most uniquely predictive of brain responses and show that the contributions of higher-order texture features increase from early areas to higher areas on the ventral and lateral surfaces. We also demonstrate that patterns of sensitivity to texture statistics can be used to recover broad organizational axes within visual cortex, including dimensions that capture semantic image content. These results provide a key step forward in characterizing how midlevel feature representations emerge hierarchically across the visual system.SIGNIFICANCE STATEMENT Intermediate visual features, like texture, play an important role in cortical computations and may contribute to tasks like object and scene recognition. Here, we used a texture model proposed in past work to construct encoding models that predict the responses of neural populations in human visual cortex (measured with fMRI) to natural scene stimuli. We show that responses of neural populations at multiple levels of the visual system can be predicted by this model, and that the model is able to reveal an increase in the complexity of feature representations from early retinotopic cortex to higher areas of ventral and lateral visual cortex. These results support the idea that texture-like representations may play a broad underlying role in visual processing.

Neural Correlates and Perceived Attractiveness of Male and Female Shoulder-to-Hip Ratio in Men and Women: An EEG Study

While there are studies regarding the neural correlates of human facial attractiveness, there are few investigations considering neural responses for body form attractiveness. The most prominent physical feature defining men's attractiveness is their physical fitness and upper body strength. Shoulder-to-hip ratio (SHR), a sexually dimorphic trait in humans, is an indicator of men's attractiveness for both men and women. The current study is the first to report on the neurophysiological responses to male and female body forms varying in SHR in healthy heterosexual men and women observers. Electroencephalographic (EEG) signals were acquired while participants completed an oddball task as well as a subsequent attractiveness judgement task. Behavioral results showed larger SHRs were considered more attractive than smaller SHRs, regardless of stimuli and participants' sex. The electrophysiological results for both the oddball task and the explicit judgement of attractiveness showed that brain activity related to male SHR body stimuli differed depending on the specific ratios, both at early and late processing stages. For female avatars, SHR did not modulate neural activity. Collectively the data implicate posterior brain regions in the perception of body forms that differ in attractiveness vis-a-vis variation of SHR, and frontal brain regions when such perceptions are rated explicitly.

Texture statistics involved in specular highlight exclusion for object lightness perception

The human visual system estimates the physical properties of objects, such as their lightness. Previous studies on the lightness perception of glossy three-dimensional objects have suggested that specular highlights are detected and excluded in lightness perception. However, only a few studies have attempted to elucidate the mechanisms underlying this exclusion. This study aimed to elucidate the image features that contribute to the highlight exclusion of lightness perception. We used Portilla-Simoncelli texture statistics (PS statistics), an image feature set similar to the representation in the early visual cortex, to explore their relationships with highlight exclusion for lightness perception. In experiment 1, computer graphics images of bumpy plastic plates with various physical parameters were used as stimuli, and the lightness perception on them was measured using a lightness matching task. We then calculated the highlight exclusion index, which represented the degree of highlight exclusion. Finally, we evaluated the correlation between the highlight exclusion index and the four PS statistic subsets. In experiment 2, an image synthesis algorithm was used to create images in which either the PS statistic subset was manipulated. The highlight exclusion indexes of the synthesized images were then measured. The results revealed that the PS statistic subset consisting of lowest-order image features, such as moment statistics of luminance, acts as a necessary condition for highlight exclusion, whereas the other three subsets consisting of higher order features are not crucial. These results suggest that the low-order image features are the most important among the features in PS statistics for highlight exclusion, even though image features higher order than those in PS statistics must be directly involved.

Mice and primates use distinct strategies for visual segmentation

The rodent visual system has attracted great interest in recent years due to its experimental tractability, but the fundamental mechanisms used by the mouse to represent the visual world remain unclear. In the primate, researchers have argued from both behavioral and neural evidence that a key step in visual representation is 'figure-ground segmentation', the delineation of figures as distinct from backgrounds. To determine if mice also show behavioral and neural signatures of figure-ground segmentation, we trained mice on a figure-ground segmentation task where figures were defined by gratings and naturalistic textures moving counterphase to the background. Unlike primates, mice were severely limited in their ability to segment figure from ground using the opponent motion cue, with segmentation behavior strongly dependent on the specific carrier pattern. Remarkably, when mice were forced to localize naturalistic patterns defined by opponent motion, they adopted a strategy of brute force memorization of texture patterns. In contrast, primates, including humans, macaques, and mouse lemurs, could readily segment figures independent of carrier pattern using the opponent motion cue. Consistent with mouse behavior, neural responses to the same stimuli recorded in mouse visual areas V1, RL, and LM also did not support texture-invariant segmentation of figures using opponent motion. Modeling revealed that the texture dependence of both the mouse's behavior and neural responses could be explained by a feedforward neural network lacking explicit segmentation capabilities. These findings reveal a fundamental limitation in the ability of mice to segment visual objects compared to primates.

Discrimination of textures with spatial correlations and multiple gray levels

Analysis of visual texture is important for many key steps in early vision. We study visual sensitivity to image statistics in three families of textures that include multiple gray levels and correlations in two spatial dimensions. Sensitivities to positive and negative correlations are approximately independent of correlation sign, and signals from different kinds of correlations combine quadratically. We build a computational model, fully constrained by prior studies of sensitivity to uncorrelated textures and black-and-white textures with spatial correlations. The model accounts for many features of the new data, including sign-independence, quadratic combination, and the dependence on gray-level distribution.

Sensitivity to naturalistic texture relies primarily on high spatial frequencies

Natural images contain information at multiple spatial scales. Though we understand how early visual mechanisms split multiscale images into distinct spatial frequency channels, we do not know how the outputs of these channels are processed further by mid-level visual mechanisms. We have recently developed a texture discrimination task that uses synthetic, multi-scale, "naturalistic" textures to isolate these mid-level mechanisms. Here, we use three experimental manipulations (image blur, image rescaling, and eccentric viewing) to show that perceptual sensitivity to naturalistic structure is strongly dependent on features at high object spatial frequencies (measured in cycles/image). As a result, sensitivity depends on a texture acuity limit, a property of the visual system that sets the highest retinal spatial frequency (measured in cycles/degree) at which observers can detect naturalistic features. Analysis of the texture images using a model observer analysis shows that naturalistic image features at high object spatial frequencies carry more task-relevant information than those at low object spatial frequencies. That is, the dependence of sensitivity on high object spatial frequencies is a property of the texture images, rather than a property of the visual system. Accordingly, we find human observers' ability to extract naturalistic information (their efficiency) is similar for all object spatial frequencies. We conclude that the mid-level mechanisms that underlie perceptual sensitivity effectively extract information from all image features below the texture acuity limit, regardless of their retinal and object spatial frequency.

Spatial frequency representation in V2 and V4 of macaque monkey

Spatial frequency (SF) is an important attribute in the visual scene and is a defining feature of visual processing channels. However, there remain many unsolved questions about how extrastriate areas in primate visual cortex code this fundamental information. Here, using intrinsic signal optical imaging in visual areas of V2 and V4 of macaque monkeys, we quantify the relationship between SF maps and (1) visual topography and (2) color and orientation maps. We find that in orientation regions, low to high SF is mapped orthogonally to orientation; in color regions, which are reported to contain orthogonal axes of color and lightness, low SFs tend to be represented more frequently than high SFs. This supports a population-based SF fluctuation related to the 'color/orientation' organizations. We propose a generalized hypercolumn model across cortical areas, comprised of two orthogonal parameters with additional parameters.

Efficient processing of natural scenes in visual cortex

Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.

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.

Improving the accuracy of single-trial fMRI response estimates using GLMsingle

Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.

Ensemble perception without phenomenal awareness of elements

Humans efficiently recognize complex scenes by grouping multiple features and objects into ensembles. It has been suggested that ensemble processing does not require, or even impairs, conscious discrimination of individual element properties. The present study examined whether ensemble perception requires phenomenal awareness of elements. We asked observers to judge the mean orientation of a line-based texture pattern whose central region was made invisible by backward masks. Masks were composed of either a Mondrian pattern (Exp. 1) or of an annular contour (Exp. 2) which, unlike the Mondrian, did not overlap spatially with elements in the central region. In the Mondrian-mask experiment, perceived mean orientation was determined only by visible elements outside the central region. However, in the annular-mask experiment, perceived mean orientation matched the mean orientation of all elements, including invisible elements within the central region. Results suggest that the visual system can compute spatial ensembles even without phenomenal awareness of stimuli.

The representation of shape and texture in category-selective regions of ventral-temporal cortex

Neuroimaging studies using univariate and multivariate approaches have shown that the fusiform face area (FFA) and parahippocampal place area (PPA) respond selectively to images of faces and places. The aim of this study was to determine the extent to which this selectivity to faces or places is based on the shape or texture properties of the images. Faces and houses were filtered to manipulate their texture properties, while preserving the shape properties (spatial envelope) of the images. In Experiment 1, multivariate pattern analysis (MVPA) showed that patterns of fMRI response to faces and houses in FFA and PPA were predicted by the shape properties, but not by the texture properties of the image. In Experiment 2, a univariate analysis (fMR‐adaptation) showed that responses in the FFA and PPA were sensitive to changes in both the shape and texture properties of the image. These findings can be explained by the spatial scale of the representation of images in the FFA and PPA. At a coarser scale (revealed by MVPA), the neural selectivity to faces and houses is sensitive to variation in the shape properties of the image. However, at a finer scale (revealed by fMR‐adaptation), the neural selectivity is sensitive to the texture properties of the image. By combining these neuroimaging paradigms, our results provide insights into the spatial scale of the neural representation of faces and places in the ventral‐temporal cortex.

Functional recursion of orientation cues in figure-ground separation

Visual texture is an important cue to figure-ground organization. While processing of texture differences is a prerequisite for the use of this cue to extract figure-ground organization, these stages are distinct processes. One potential indicator of this distinction is the possibility that texture statistics play a different role in the figure vs. in the ground. To determine whether this is the case, we probed figure-ground processing with a family of local image statistics that specified textures that varied in the strength and spatial scale of structure, and the extent to which features are oriented. For image statistics that generated approximately isotropic textures, the threshold for identification of figure-ground structure was determined by the difference in correlation strength in figure vs. ground, independent of whether the correlations were present in figure, ground, or both. However, for image statistics with strong orientation content, thresholds were up to two times higher for correlations in the ground, vs. the figure. This held equally for texture-defined objects with convex or concave boundaries, indicating that these threshold differences are driven by border ownership, not boundary shape. Similar threshold differences were found for presentation times ranging from 125 to 500 ms. These findings identify a qualitative difference in how texture is used for figure-ground analysis, vs. texture discrimination. Additionally, it reveals a functional recursion: texture differences are needed to identify tentative boundaries and consequent scene organization into figure and ground, but then scene organization modifies sensitivity to texture differences according to the figure-ground assignment.

Selective representations of texture and motion in mouse higher visual areas

The mouse visual cortex contains interconnected higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine the representations of complex visual stimuli by L2/3 neurons across mouse higher visual areas, measured using large-field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how receptive field models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.

Texture-like representation of objects in human visual cortex

Humans are exquisitely sensitive to the spatial arrangement of visual features in objects and scenes, but not in visual textures. Category-selective regions in the visual cortex are widely believed to underlie object perception, suggesting such regions should distinguish natural images of objects from synthesized images containing similar visual features in scrambled arrangements. Contrarily, we demonstrate that representations in category-selective cortex do not discriminate natural images from feature-matched scrambles but can discriminate images of different categories, suggesting a texture-like encoding. We find similar insensitivity to feature arrangement in Imagenet-trained deep convolutional neural networks. This suggests the need to reconceptualize the role of category-selective cortex as representing a basis set of complex texture-like features, useful for a myriad of behaviors.

The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of the visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real-world objects, that is, by representing visual textures. To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal to noise, as all observer models could predict human performance in image categorization tasks. How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for natural object discrimination is available. Thus, our results suggest that the role of the human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.

Numerosity tuning in human association cortices and local image contrast representations in early visual cortex

Human early visual cortex response amplitudes monotonically increase with numerosity (object number), regardless of object size and spacing. However, numerosity is typically considered a high-level visual or cognitive feature, while early visual responses follow image contrast in the spatial frequency domain. We find that, at fixed contrast, aggregate Fourier power (at all orientations and spatial frequencies) follows numerosity closely but nonlinearly with little effect of object size, spacing or shape. This would allow straightforward numerosity estimation from spatial frequency domain image representations. Using 7T fMRI, we show monotonic responses originate in primary visual cortex (V1) at the stimulus’s retinotopic location. Responses here and in neural network models follow aggregate Fourier power more closely than numerosity. Truly numerosity tuned responses emerge after lateral occipital cortex and are independent of retinotopic location. We propose numerosity’s straightforward perception and neural responses may result from the pervasive spatial frequency analyses of early visual processing.

The authors show that spatial frequency domain Fourier power closely but nonlinearly follows numerosity, simplifying computing numerosity from early visual responses. Monotonic early visual cortex and neural network responses follow Fourier power, while later tuned responses follow numerosity.

Processing of visual statistics of naturalistic videos in macaque visual areas V1 and V4

Natural scenes are characterized by diverse image statistics, including various parameters of the luminance histogram, outputs of Gabor-like filters, and pairwise correlations between the filter outputs of different positions, orientations, and scales (Portilla–Simoncelli statistics). Some of these statistics capture the response properties of visual neurons. However, it remains unclear to what extent such statistics can explain neural responses to natural scenes and how neurons that are tuned to these statistics are distributed across the cortex. Using two-photon calcium imaging and an encoding-model approach, we addressed these issues in macaque visual areas V1 and V4. For each imaged neuron, we constructed an encoding model to mimic its responses to naturalistic videos. By extracting Portilla–Simoncelli statistics through outputs of both filters and filter correlations, and by computing an optimally weighted sum of these outputs, the model successfully reproduced responses in a subpopulation of neurons. We evaluated the selectivities of these neurons by quantifying the contributions of each statistic to visual responses. Neurons whose responses were mainly determined by Gabor-like filter outputs (low-level statistics) were abundant at most imaging sites in V1. In V4, the relative contribution of higher order statistics, such as cross-scale correlation, was increased. Preferred image statistics varied markedly across V4 sites, and the response similarity of two neurons at individual imaging sites gradually declined with increasing cortical distance. The results indicate that natural scene analysis progresses from V1 to V4, and neurons sharing preferred image statistics are locally clustered in V4.

The Role of Specular Reflections and Illumination in the Perception of Thickness in Solid Transparent Objects

Specular reflections and refractive distortions are complex image properties of solid transparent objects, but despite this complexity, we readily perceive the 3D shapes of these objects (e.g., glass and clear plastic). We have found in past work that relevant sources of scene complexity have differential effects on 3D shape perception, with specular reflections increasing perceived thickness, and refractive distortions decreasing perceived thickness. In an object with both elements, such as glass, the two optical properties may complement each other to support reliable perception of 3D shape. We investigated the relative dominance of specular reflection and refractive distortions in the perception of shape. Surprisingly, the ratio of specular reflection to refractive component was almost equal to that of ordinary glass and ice, which promote correct percepts of 3D shape. The results were also explained by the variance in local RMS contrast in stimulus images but may depend on overall luminance and contrast of the surrounding light field.

Attention impedes neural representation of interpolated orientation during perceptual completion

One of the most remarkable functional feats accomplished by visual system is the interpolation of missing retinal inputs based on surrounding information, a process known as perceptual completion. Perceptual completion enables the active construction of coherent, vivid percepts from spatially discontinuous visual information that is prevalent in real-life visual scenes. Despite mounting evidence linking sensory activity enhancement and perceptual completion, surprisingly little is known about whether and how attention, a fundamental modulator of sensory activities, affects perceptual completion. Using EEG-based time-resolved inverted encoding model (IEM), we reconstructed the moment-to-moment representation of the illusory grating that resulted from spatially interpolating the orientation of surrounding inducers. We found that, despite manipulation of observers' attentional focus, the illusory grating representation unfolded in time in a similar manner. Critically, attention to the surrounding inducers simultaneously attenuated the illusory grating representation and delayed its temporal development. Our findings disclosed, for the first time, the suppressive role of selective attention in perceptual completion and were suggestive of a fast, automatic neural machinery that implements the interpolation of missing visual information.

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", which allows feedback signals to not directly affect activity that is fed forward.

Coding of chromatic spatial contrast by macaque V1 neurons

Color perception relies on comparisons between adjacent lights, but how the brain performs these comparisons is poorly understood. To elucidate the underlying neural mechanisms, we recorded spiking responses of individual V1 neurons in macaque monkeys to pairs of stimuli within the classical receptive field (RF). We estimated the spatial-chromatic RF of each neuron and then presented customized colored edges using a novel closed-loop technique. We found that many double-opponent (DO) cells, which have spatially and chromatically opponent RFs, responded to chromatic contrast as a weighted sum, akin to how other V1 cells responded to luminance contrast. Yet other neurons integrated chromatic signals non-linearly, confirming that linear signal integration is not an obligate property of V1 neurons. The functional similarity of cone-opponent DO cells and cone non-opponent simple cells suggests that these two groups may share a common underlying neural circuitry, promotes the construction of image-computable models for full-color image representation, and sheds new light on V1 complex cells.

Inference via sparse coding in a hierarchical vision model

Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology. But how the level of sparsity contributes to performance on visual tasks is not well understood. In this work, sparse coding has been integrated into an existing hierarchical V2 model (Hosoya & Hyvärinen, 2015), but replacing its independent component analysis (ICA) with an explicit sparse coding in which the degree of sparsity can be controlled. After training, the sparse coding basis functions with a higher degree of sparsity resembled qualitatively different structures, such as curves and corners. The contributions of the models were assessed with image classification tasks, specifically tasks associated with mid-level vision including figure–ground classification, texture classification, and angle prediction between two line stimuli. In addition, the models were assessed in comparison with a texture sensitivity measure that has been reported in V2 (Freeman et al., 2013) and a deleted-region inference task. The results from the experiments show that although sparse coding performed worse than ICA at classifying images, only sparse coding was able to better match the texture sensitivity level of V2 and infer deleted image regions, both by increasing the degree of sparsity in sparse coding. Greater degrees of sparsity allowed for inference over larger deleted image regions. The mechanism that allows for this inference capability in sparse coding is described in this article.

Visual discrimination of optical material properties: A large-scale study

Complex visual processing involved in perceiving the object materials can be better elucidated by taking a variety of research approaches. Sharing stimulus and response data is an effective strategy to make the results of different studies directly comparable and can assist researchers with different backgrounds to jump into the field. Here, we constructed a database containing several sets of material images annotated with visual discrimination performance. We created the material images using physically based computer graphics techniques and conducted psychophysical experiments with them in both laboratory and crowdsourcing settings. The observer's task was to discriminate materials on one of six dimensions (gloss contrast, gloss distinctness of image, translucent vs. opaque, metal vs. plastic, metal vs. glass, and glossy vs. painted). The illumination consistency and object geometry were also varied. We used a nonverbal procedure (an oddity task) applicable for diverse use cases, such as cross-cultural, cross-species, clinical, or developmental studies. Results showed that the material discrimination depended on the illuminations and geometries and that the ability to discriminate the spatial consistency of specular highlights in glossiness perception showed larger individual differences than in other tasks. In addition, analysis of visual features showed that the parameters of higher order color texture statistics can partially, but not completely, explain task performance. The results obtained through crowdsourcing were highly correlated with those obtained in the laboratory, suggesting that our database can be used even when the experimental conditions are not strictly controlled in the laboratory. Several projects using our dataset are underway.

Estimating null and potent modes of feedforward communication in a computational model of cortical activity

Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.

Photorealistic Reconstruction of Visual Texture From EEG Signals

Recent advances in brain decoding have made it possible to classify image categories based on neural activity. Increasing numbers of studies have further attempted to reconstruct the image itself. However, because images of objects and scenes inherently involve spatial layout information, the reconstruction usually requires retinotopically organized neural data with high spatial resolution, such as fMRI signals. In contrast, spatial layout does not matter in the perception of "texture," which is known to be represented as spatially global image statistics in the visual cortex. This property of "texture" enables us to reconstruct the perceived image from EEG signals, which have a low spatial resolution. Here, we propose an MVAE-based approach for reconstructing texture images from visual evoked potentials measured from observers viewing natural textures such as the textures of various surfaces and object ensembles. This approach allowed us to reconstruct images that perceptually resemble the original textures with a photographic appearance. The present approach can be used as a method for decoding the highly detailed "impression" of sensory stimuli from brain activity.

Symmetry Processing in the Macaque Visual Cortex

Symmetry is a highly salient feature of the natural world that is perceived by many species. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. Macaque could constitute a good animal model to explore the underlying neural mechanisms, but a previous comparative study concluded that functional magnetic resonance imaging responses to mirror symmetry in this species were weaker than those observed in humans. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation and reflection symmetry embedded in regular textures. Highly consistent responses to symmetry were found in a large network of areas (notably in areas V3 and V4), in line with what was reported in humans under identical experimental conditions. Our results suggest that the cortical networks that process symmetry in humans and macaques are potentially more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing.