Contour, color and shape analysis beyond the striate cortex

Mapping Activity and Functional Organisation of the Motor and Visual Pathways Using ADC-fMRI in the Human Brain

In contrast to blood-oxygenation level-dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the apparent diffusion coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC-fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function. To demonstrate the specificity and value of ADC-fMRI, both ADC- and BOLD-fMRI data were collected at 3 T in human subjects during visual stimulation and motor tasks. The first aim of this study was to identify an acquisition design for ADC that minimises BOLD contributions. By examining the timings in responses, we report that ADC 0/1 timeseries (acquired with b values of 0 and 1 ms/μm 2 $$ {\upmu \mathrm{m}}^2 $$ ) exhibit residual vascular contamination, while ADC 0.2/1 timeseries (with b values of 0.2 and 1 ms/μm 2 $$ {\upmu \mathrm{m}}^2 $$ ) show minimal BOLD influence and higher sensitivity to neuromorphological coupling. Second, a general linear model was employed to identify activation clusters for ADC 0.2/1 and BOLD, from which the average ADC and BOLD responses were calculated. The negative ADC response exhibited a significantly reduced delay relative to the task onset and offset as compared to BOLD. This early onset further supports the notion that ADC is sensitive to neuromorphological rather than neurovascular coupling. Remarkably, in the group-level analysis, positive BOLD activation clusters were detected in the visual and motor cortices, while the negative ADC clusters mainly highlighted pathways in white matter connected to the motor cortex. In the averaged individual level analysis, negative ADC activation clusters were also present in the visual cortex. This finding confirmed the reliability of negative ADC as an indicator of brain function, even in regions with lower vascularisation such as white matter. Finally, we established that ADC-fMRI time courses yield the expected functional organisation of the visual system, including both grey and white matter regions of interest. Functional connectivity matrices were used to perform hierarchical clustering of brain regions, where ADC-fMRI successfully reproduced the expected structure of the dorsal and ventral visual pathways. This organisation was not replicated with the b = 0.2 ms/μm 2 $$ {\upmu \mathrm{m}}^2 $$ diffusion-weighted time courses, which can be seen as a proxy for BOLD (via T2-weighting). These findings underscore the robustness of ADC time courses in functional MRI studies, offering complementary insights into BOLD-fMRI regarding brain function and connectivity patterns.

Orientation selectivity mapping in the visual cortex

The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.

Impaired Experience-Dependent Theta Oscillation Synchronization and Inter-Areal Synaptic Connectivity in the Visual Cortex of Fmr1 KO Mice

Fragile X syndrome (FX) is the most prevalent inheritable form of autism spectrum disorder (ASD), characterized by hypersensitivity, difficulty in habituating to new sensory stimuli, and intellectual disability. Individuals with FX often experience visual perception and learning deficits. Visual experience leads to the emergence of the familiarity-evoked theta band oscillations in the primary visual cortex (V1) and the lateromedial area (LM) of mice. These theta oscillations in V1 and LM are synchronized with each other, providing a mechanism of sensory multi-areal binding. However, how this multi-areal binding and the corresponding theta oscillations are altered in FX is not known. Using iDISCO whole brain clearing with light-sheet microscopy, we quantified immediate early gene Fos expression in V1 and LM, identifying deficits in experience-dependent neural activity in FX mice. We performed simultaneous in vivo recordings with silicon probes in V1 and LM of awake mice and channelrhodopsin-2-assisted circuit mapping (CRACM) in acute brain slices to examine the neural activity and strength of long-range synaptic connections between V1 and LM in both wildtype (WT) and Fmr1 knockout (KO) mice, the model of FX, before and after visual experience. Our findings reveal synchronized familiarity-evoked theta oscillations in V1 and LM, the increased strength of V1→LM functional and synaptic connections, which correlated with the corresponding changes of presynaptic short-term plasticity in WT mice. The LM oscillations were attenuated in FX mice and correlated with impaired functional and synaptic connectivity and short-term plasticity in the feedforward (FF) V1→LM and feedback (FB) LM→V1 pathways. Finally, using 4Pi single-molecule localization microscopy (SMLM) in thick brain tissue, we identified experience-dependent changes in the density and shape of dendritic spines in layer 5 pyramidal cells of WT mice, which correlated with the functional synaptic measurements. Interestingly, there was an increased dendritic spine density and length in naïve FX mice that failed to respond to experience. Our study provides the first comprehensive characterization of the role of visual experience in triggering inter-areal neural synchrony and shaping synaptic connectivity in WT and FX mice.

Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas

To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.

Mapping the dynamics of visual feature coding: Insights into perception and integration

The basic computations performed in the human early visual cortex are the foundation for visual perception. While we know a lot about these computations, a key missing piece is how the coding of visual features relates to our perception of the environment. To investigate visual feature coding, interactions, and their relationship to human perception, we investigated neural responses and perceptual similarity judgements to a large set of visual stimuli that varied parametrically along four feature dimensions. We measured neural responses using electroencephalography (N = 16) to 256 grating stimuli that varied in orientation, spatial frequency, contrast, and colour. We then mapped the response profiles of the neural coding of each visual feature and their interactions, and related these to independently obtained behavioural judgements of stimulus similarity. The results confirmed fundamental principles of feature coding in the visual system, such that all four features were processed simultaneously but differed in their dynamics, and there was distinctive conjunction coding for different combinations of features in the neural responses. Importantly, modelling of the behaviour revealed that every stimulus feature contributed to perceptual judgements, despite the untargeted nature of the behavioural task. Further, the relationship between neural coding and behaviour was evident from initial processing stages, signifying that the fundamental features, not just their interactions, contribute to perception. This study highlights the importance of understanding how feature coding progresses through the visual hierarchy and the relationship between different stages of processing and perception.

A recruitment through coherence theory of working memory

The interactions between prefrontal cortex and other areas during working memory have been studied for decades. Here we outline a conceptual framework describing interactions between these areas during working memory, and review evidence for key elements of this model. We specifically suggest that a top-down signal sent from prefrontal to sensory areas drives oscillations in these areas. Spike timing within sensory areas becomes locked to these working-memory-driven oscillations, and the phase of spiking conveys information about the representation available within these areas. Downstream areas receiving these phase-locked spikes from sensory areas can recover this information via a combination of coherent oscillations and gating of input efficacy based on the phase of their local oscillations. Although the conceptual framework is based on prefrontal interactions with sensory areas during working memory, we also discuss the broader implications of this framework for flexible communication between brain areas in general.

Variability in training unlocks generalization in visual perceptual learning through invariant representations

Stimulus and location specificity are long considered hallmarks of visual perceptual learning. This renders visual perceptual learning distinct from other forms of learning, where generalization can be more easily attained, and therefore unsuitable for practical applications, where generalization is key. Based on the hypotheses derived from the structure of the visual system, we test here whether stimulus variability can unlock generalization in perceptual learning. We train subjects in orientation discrimination, while we vary the amount of variability in a task-irrelevant feature, spatial frequency. We find that, independently of task difficulty, this manipulation enables generalization of learning to new stimuli and locations, while not negatively affecting the overall amount of learning on the task. We then use deep neural networks to investigate how variability unlocks generalization. We find that networks develop invariance to the task-irrelevant feature when trained with variable inputs. The degree of learned invariance strongly predicts generalization. A reliance on invariant representations can explain variability-induced generalization in visual perceptual learning. This suggests new targets for understanding the neural basis of perceptual learning in the higher-order visual cortex and presents an easy-to-implement modification of common training paradigms that may benefit practical applications.

The Paton prize lecture 2021: A colourful experience leading to a reassessment of colour vision and its theories

In this lecture, given in honour of Sir William Paton, a brilliant scientist and one of Britain's great patrons of biology, I give a personal account of the fundamental issues in colour vision that I have tackled since 1973, when I discovered a cortical zone lying outside the primary visual cortex that is rich in cells with chromatic properties. I do not provide an exhaustive review of colour vision but summarise how my views on colour vision and theories surrounding it have changed in light of that discovery.

Crossmodal correspondences between basic tastes and visual design features: A narrative historical review

People tend to associate abstract visual features with basic taste qualities. This narrative historical review critically evaluates the literature on these associations, often referred to as crossmodal correspondences, between basic tastes and visual design features such as color hue and shape curvilinearity. The patterns, discrepancies, and evolution in the development of the research are highlighted while the mappings that have been reported to date are summarized. The review also reflects on issues of cross-cultural validity and deviations in the matching patterns that are observed when correspondences are assessed with actual tastants versus with verbal stimuli. The various theories that have been proposed to account for different classes of crossmodal correspondence are discussed, among which the statistical and affective (or emotional-mediation) accounts currently appear most promising. Several critical research questions for the future are presented to address the gaps that have been identified in the literature and help validate the popular theories on the origin and operations of visual-taste correspondences.

Training with optic flow stimuli promotes recovery in cortical blindness

BACKGROUND

Cortical blindness is a form of severe vision loss that is caused by damage to the primary visual cortex (V1) or its afferents. This condition has devastating effects on quality of life and independence. While there are few treatments currently available, accumulating evidence shows that certain visual functions can be restored with appropriate perceptual training: Stimulus sensitivity can be increased within portions of the blind visual field. However, this increased sensitivity often remains highly specific to the trained stimulus, limiting the overall improvement in visual function.

OBJECTIVE

Recent advances in the field of perceptual learning show that such specificity can be overcome with training paradigms that leverage the properties of higher-level visual cortical structures, which have greater capacity to generalize across stimulus positions and features. This targeting can be accomplished by using more complex training stimuli that elicit robust responses in these visual structures.

METHODS

We trained cortically blind subjects with a complex optic flow motion stimulus that was presented in a location of their blind field. Participants were instructed to train with the stimulus at home for approximately 30 minutes per day. Once performance plateaued, the stimulus was moved deeper into the blind field. A battery of pre- and post-training measures, with careful eye tracking, was performed to quantify the improvements.

RESULTS

We show that 1) optic flow motion discrimination can be relearned in cortically blind fields; 2) training with an optic flow stimulus can lead to improvements that transfer to different tasks and untrained locations; and 3) such training leads to a significant expansion of the visual field. The observed expansion of the visual field was present even when eye movements were carefully controlled. Finally, we show that regular training is critical for improved visual function, as sporadic training reduced the benefits of training, even when the total numbers of training sessions were equated.

CONCLUSIONS

These findings are consistent with the hypothesis that complex training stimuli can improve outcomes in cortical blindness, provided that patients adhere to a regular training regimen. Nevertheless, such interventions remain limited in their ability to restore functional vision.

Psychological and physiological evidence for an initial 'Rough Sketch' calculation of personal space

Personal space has been defined as “the area individuals maintain around themselves into which others cannot intrude without arousing discomfort”. However, the precise relationship between discomfort (or arousal) responses as a function of distance from an observer remains incompletely understood. Also the mechanisms involved in recognizing conspecifics and distinguishing them from other objects within personal space have not been identified. Accordingly, here we measured personal space preferences in response to real humans and human-like avatars (in virtual reality), using well-validated “stop distance” procedures. Based on threshold measurements of personal space, we examined within-subject variations in discomfort-related responses across multiple distances (spanning inside and outside each individual’s personal space boundary), as reflected by psychological (ratings) and physiological (skin conductance) responses to both humans and avatars. We found that the discomfort-by-distance functions for both humans and avatars were closely fit by a power law. These results suggest that the brain computation of visually-defined personal space begins with a ‘rough sketch’ stage, which generates responses to a broad range of human-like stimuli, in addition to humans. Analogous processing mechanisms may underlie other brain functions which respond similarly to both real and simulated human body parts.

The extrastriate symmetry response is robust to variation in visual memory load

An Event Related Potential response to visual symmetry, known as the Sustained Posterior Negativity (SPN), is generated whether symmetry is task relevant or not, and whether symmetry is attended or not. However, no study has yet examined interference from concurrent memory tasks. To answer this fundamental question, we investigated whether the SPN is robust to variation in Visual Working Memory (VWM) load. In Experiment 1 (N = 24), each trial involved a sample display, a probe and a test display. Sample and test displays contained either four colors or four black shapes, and the probe was either a symmetrical or random pattern. We compared a memory task and a passive viewing task. In the memory task, participants held color or shape information in VWM when the probe was presented. In the passive viewing task, there were no memory demands. Contrary to our predictions, there was no evidence that VWM interfered with the symmetry response. Instead, there was a general SPN enhancement during both color and shape memory tasks compared to passive viewing. In Experiment 2 (N = 24), we used symmetrical patterns themselves as sample and test to maximize interference. Again, the SPN was enhanced in the memory task compared to passive viewing. We conclude that the visual symmetry response is not impaired by concurrent VWM tasks, even when these tasks involve remembering symmetry itself. It seems that the SPN is not only attention-proof, but also memory-proof. This adds to evidence that symmetry perception is robust and automatic.

Deep saliency models learn low-, mid-, and high-level features to predict scene attention

Deep saliency models represent the current state-of-the-art for predicting where humans look in real-world scenes. However, for deep saliency models to inform cognitive theories of attention, we need to know how deep saliency models prioritize different scene features to predict where people look. Here we open the black box of three prominent deep saliency models (MSI-Net, DeepGaze II, and SAM-ResNet) using an approach that models the association between attention, deep saliency model output, and low-, mid-, and high-level scene features. Specifically, we measured the association between each deep saliency model and low-level image saliency, mid-level contour symmetry and junctions, and high-level meaning by applying a mixed effects modeling approach to a large eye movement dataset. We found that all three deep saliency models were most strongly associated with high-level and low-level features, but exhibited qualitatively different feature weightings and interaction patterns. These findings suggest that prominent deep saliency models are primarily learning image features associated with high-level scene meaning and low-level image saliency and highlight the importance of moving beyond simply benchmarking performance.

Modulations of depth responses in the human brain by object context: Does biological relevance matter?

Depth sensitivity has been shown to be modulated by object context (plausibility). It is possible that it is behavioural relevance rather than object plausibility per se which drives this effect. Here, we manipulated the biological relevance of objects (face or a non-face) and tested whether object relevance affects behavioural sensitivity and neural responses to depth-position. In a first experiment, we presented human observers with disparity-defined faces and non-faces, and observers were asked to judge the depth position of the target under signal-noise and clear (fine) task conditions. In the second experiment, we concurrently measured behavioural and fMRI responses to depth. We found that behavioural performance varied across stimulus conditions such that they were significantly worse for the upright face than the inverted face and the random shape in the SNR task, but worse for the random shape than the upright face in the feature task. Pattern analysis of fMRI responses revealed that activity of FFA was distinctly different during depth judgments of the upright face versus the other two stimuli, with its responses (and to a stronger extent, those of V3) appearing functionally-relevant to behavioural performance. We speculate that FFA is not only involved in object analysis, but exerts considerable influence on stereoscopic mechanisms as early as in V3 based on a broader appreciation of the stimulus' behavioural relevance.Significance StatementWe asked how disparity sensitivity is modulated by object (biological) relevance using behavioural and neuroimaging paradigms. We show that behavioural sensitivity to depth-position changes in biological (face) vs non-biological (random surface) contexts, and that these changes are task-dependent. Imaging results highlight a potentially key role of the fusiform region for governing the modulation of stereo encoding by object relevance. These findings highlight powerful interactions between object recognition mechanisms and stereoencoding, such that the utility of disparity information may be up/down weighed depending on the biological relevance of the object.

A distinct population of heterogeneously color-tuned neurons in macaque visual cortex

Color is a key feature of natural environments that higher mammals routinely use to detect food, avoid predators, and interpret social signals. The distribution of color signals in natural scenes is widely variable, ranging from uniform patches to highly nonuniform regions in which different colors lie in close proximity. Whether individual neurons are tuned to this high degree of variability of color signals is unknown. Here, we identified a distinct population of cells in macaque visual cortex (area V4) that have a heterogeneous receptive field (RF) structure in which individual subfields are tuned to different colors even though the full RF is only weakly tuned. This spatial heterogeneity in color tuning indicates a higher degree of complexity of color-encoding mechanisms in visual cortex than previously believed to efficiently extract chromatic information from the environment.

Mouse visual cortex areas represent perceptual and semantic features of learned visual categories

Associative memories are stored in distributed networks extending across multiple brain regions. However, it is unclear to what extent sensory cortical areas are part of these networks. Using a paradigm for visual category learning in mice, we investigated whether perceptual and semantic features of learned category associations are already represented at the first stages of visual information processing in the neocortex. Mice learned categorizing visual stimuli, discriminating between categories and generalizing within categories. Inactivation experiments showed that categorization performance was contingent on neuronal activity in the visual cortex. Long-term calcium imaging in nine areas of the visual cortex identified changes in feature tuning and category tuning that occurred during this learning process, most prominently in the postrhinal area (POR). These results provide evidence for the view that associative memories form a brain-wide distributed network, with learning in early stages shaping perceptual representations and supporting semantic content downstream.

Depth-dependent functional MRI responses to chromatic and achromatic stimuli throughout V1 and V2

In the primate visual system, form (shape, location) and color information are processed in separate but interacting pathways. Recent access to high-resolution neuroimaging has facilitated the exploration of the structure of these pathways at the mesoscopic level in the human visual cortex. We used 7T fMRI to observe selective activation of the primary visual cortex to chromatic versus achromatic stimuli in five participants across two scanning sessions. Achromatic checkerboards with low spatial frequency and high temporal frequency targeted the color-insensitive magnocellular pathway. Chromatic checkerboards with higher spatial frequency and low temporal frequency targeted the color-selective parvocellular pathway. This work resulted in three main findings. First, responses driven by chromatic stimuli had a laminar profile biased towards superficial layers of V1, as compared to responses driven by achromatic stimuli. Second, we found stronger preference for chromatic stimuli in parafoveal V1 compared with peripheral V1. Finally, we found alternating, stimulus-selective bands stemming from the V1 border into V2 and V3. Similar alternating patterns have been previously found in both NHP and human extrastriate cortex. Together, our findings confirm the utility of fMRI for revealing details of mesoscopic neural architecture in human cortex.

Effect of Stimulus Orientation on Visual Function in Children with Refractive Amblyopia

Purpose

We investigated and characterized the patterns of meridional anisotropies in newly diagnosed refractive amblyopes using pattern onset–offset visual evoked potentials (POVEPs) and psychophysical grating acuity (GA).

Methods

Twenty-five refractive amblyopes were recruited and compared with non-amblyopic controls from our previous study. Monocular POVEPs were recorded in response to sinewave 4 cycles per degree (cpd) grating stimuli oriented along each individual participants' principal astigmatic meridians, which were approximately horizontal (meridian 1) and vertical (meridian 2). Binocular POVEPs in response to the same stimuli, but oriented at 45°, 90°, 135°, and 180°, were recorded. Psychophysical GAs were assessed along the same meridians using a two-alternative non-forced-choice technique. The C3 amplitudes and peak latencies of the POVEPs and GAs were compared across meridians for both groups (refractive amblyopes and controls) using linear mixed models (monocular) and ANOVA (binocular), and post hoc analysis was conducted to determine if meridional anisotropies in this cohort of amblyopes were related to low (≤1.50 diopters [D]), moderate (1.75–2.75 D) and high (≥3.00 D) astigmatism.

Results

In the newly diagnosed refractive amblyopes, there were no significant meridional anisotropies across all outcome measures, but the post hoc analysis demonstrated that C3 amplitude was significantly higher in those with low (P = 0.02) and moderate (P = 0.004) astigmatism compared to those with high astigmatism. Refractive amblyopes had poorer GA and C3 amplitudes compared to controls by approximately two lines on the logMAR chart (monocular: P = 0.013; binocular: P = 0.014) and approximately 6 µV (monocular: P = 0.009; binocular: P = 0.027), respectively.

Conclusions

Deleterious effects of high astigmatism was evident in newly diagnosed refractive amblyopes, but the neural deficits do not seem to be orientation-specific for the stimulus parameters investigated.

Invariant Synapse Density and Neuronal Connectivity Scaling in Primate Neocortical Evolution

Synapses are involved in the communication of information from one neuron to another. However, a systematic analysis of synapse density in the neocortex from a diversity of species is lacking, limiting what can be understood about the evolution of this fundamental aspect of brain structure. To address this, we quantified synapse density in supragranular layers II-III and infragranular layers V-VI from primary visual cortex and inferior temporal cortex in a sample of 25 species of primates, including humans. We found that synapse densities were relatively constant across these levels of the cortical visual processing hierarchy and did not significantly differ with brain mass, varying by only 1.9-fold across species. We also found that neuron densities decreased in relation to brain enlargement. Consequently, these data show that the number of synapses per neuron significantly rises as a function of brain expansion in these neocortical areas of primates. Humans displayed the highest number of synapses per neuron, but these values were generally within expectations based on brain size. The metabolic and biophysical constraints that regulate uniformity of synapse density, therefore, likely underlie a key principle of neuronal connectivity scaling in primate neocortical evolution.

Center bias outperforms image salience but not semantics in accounting for attention during scene viewing

How do we determine where to focus our attention in real-world scenes? Image saliency theory proposes that our attention is 'pulled' to scene regions that differ in low-level image features. However, models that formalize image saliency theory often contain significant scene-independent spatial biases. In the present studies, three different viewing tasks were used to evaluate whether image saliency models account for variance in scene fixation density based primarily on scene-dependent, low-level feature contrast, or on their scene-independent spatial biases. For comparison, fixation density was also compared to semantic feature maps (Meaning Maps; Henderson & Hayes, Nature Human Behaviour, 1, 743-747, 2017) that were generated using human ratings of isolated scene patches. The squared correlations (R2) between scene fixation density and each image saliency model's center bias, each full image saliency model, and meaning maps were computed. The results showed that in tasks that produced observer center bias, the image saliency models on average explained 23% less variance in scene fixation density than their center biases alone. In comparison, meaning maps explained on average 10% more variance than center bias alone. We conclude that image saliency theory generalizes poorly to real-world scenes.

The role of prefrontal cortex in the control of feature attention in area V4

When searching for an object in a cluttered scene, we can use our memory of the target object features to guide our search, and the responses of neurons in multiple cortical visual areas are enhanced when their receptive field contains a stimulus sharing target object features. Here we tested the role of the ventral prearcuate region (VPA) of prefrontal cortex in the control of feature attention in cortical visual area V4. VPA was unilaterally inactivated in monkeys performing a free-viewing visual search for a target stimulus in an array of stimuli, impairing monkeys' ability to find the target in the array in the affected hemifield, but leaving intact their ability to make saccades to targets presented alone. Simultaneous recordings in V4 revealed that the effects of feature attention on V4 responses were eliminated or greatly reduced while leaving the effects of spatial attention on responses intact. Altogether, the results suggest that feedback from VPA modulates processing in visual cortex during attention to object features.

Attention to colors induces surround suppression at category boundaries

We investigated how attention to a visual feature modulates representations of other features. The feature-similarity gain model predicts a graded modulation, whereas an alternative model asserts an inhibitory surround in feature space. Although evidence for both types of modulations can be found, a consensus has not emerged in the literature. Here, we aimed to reconcile these different views by systematically measuring how attention modulates color perception. Based on previous literature, we also predicted that color categories would impact attentional modulation. Our results showed that both surround suppression and feature-similarity gain modulate perception of colors but they operate on different similarity scales. Furthermore, the region of the suppressive surround coincided with the color category boundary, suggesting a categorical sharpening effect. We implemented a neural population coding model to explain the observed behavioral effects, which revealed a hitherto unknown connection between neural tuning shift and surround suppression.

Precision and False Perceptual Inference

Accurate perceptual inference fundamentally depends upon accurate beliefs about the reliability of sensory data. In this paper, we describe a Bayes optimal and biologically plausible scheme that refines these beliefs through a gradient descent on variational free energy. To illustrate this, we simulate belief updating during visual foraging and show that changes in estimated sensory precision (i.e., confidence in visual data) are highly sensitive to prior beliefs about the contents of a visual scene. In brief, confident prior beliefs induce an increase in estimated precision when consistent with sensory evidence, but a decrease when they conflict. Prior beliefs held with low confidence are rapidly updated to posterior beliefs, determined by sensory data. These induce much smaller changes in beliefs about sensory precision. We argue that pathologies of scene construction may be due to abnormal priors, and show that these can induce a reduction in estimated sensory precision. Having previously associated this precision with cholinergic signaling, we note that several neurodegenerative conditions are associated with visual disturbances and cholinergic deficits; notably, the synucleinopathies. On relating the message passing in our model to the functional anatomy of the ventral visual stream, we find that simulated neuronal loss in temporal lobe regions induces confident, inaccurate, empirical prior beliefs at lower levels in the visual hierarchy. This provides a plausible, if speculative, computational mechanism for the loss of cholinergic signaling and the visual disturbances associated with temporal lobe Lewy body pathology. This may be seen as an illustration of the sorts of hypotheses that may be expressed within this computational framework.

Retina-Inspired Filter

This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.

Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian learning mechanisms in the cerebral cortex

The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aim of the current study was to compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain. Forty participants were presented with both paradigms during EEG recording. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. This therefore indicates that both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.

Computational Neuropsychology and Bayesian Inference

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Working memory, attention, and salience in active inference

The psychological concepts of working memory and attention are widely used in the cognitive and neuroscientific literatures. Perhaps because of the interdisciplinary appeal of these concepts, the same terms are often used to mean very different things. Drawing on recent advances in theoretical neurobiology, this paper tries to highlight the correspondence between these established psychological constructs and the formal processes implicit in mathematical descriptions of brain function. Here, we consider attention and salience from the perspective offered by active inference. Using variational principles and simulations, we use active inference to demonstrate how attention and salience can be disambiguated in terms of message passing between populations of neurons in cortical and subcortical structures. In brief, we suggest that salience is something that is afforded to actions that realise epistemic affordance, while attention per se is afforded to precise sensory evidence - or beliefs about the causes of sensations.

Attention to Color Sharpens Neural Population Tuning via Feedback Processing in the Human Visual Cortex Hierarchy

Attention can facilitate the selection of elementary object features such as color, orientation, or motion. This is referred to as feature-based attention and it is commonly attributed to a modulation of the gain and tuning of feature-selective units in visual cortex. Although gain mechanisms are well characterized, little is known about the cortical processes underlying the sharpening of feature selectivity. Here, we show with high-resolution magnetoencephalography in human observers (men and women) that sharpened selectivity for a particular color arises from feedback processing in the human visual cortex hierarchy. To assess color selectivity, we analyze the response to a color probe that varies in color distance from an attended color target. We find that attention causes an initial gain enhancement in anterior ventral extrastriate cortex that is coarsely selective for the target color and transitions within ∼100 ms into a sharper tuned profile in more posterior ventral occipital cortex. We conclude that attention sharpens selectivity over time by attenuating the response at lower levels of the cortical hierarchy to color values neighboring the target in color space. These observations support computational models proposing that attention tunes feature selectivity in visual cortex through backward-propagating attenuation of units less tuned to the target.SIGNIFICANCE STATEMENT Whether searching for your car, a particular item of clothing, or just obeying traffic lights, in everyday life, we must select items based on color. But how does attention allow us to select a specific color? Here, we use high spatiotemporal resolution neuromagnetic recordings to examine how color selectivity emerges in the human brain. We find that color selectivity evolves as a coarse to fine process from higher to lower levels within the visual cortex hierarchy. Our observations support computational models proposing that feature selectivity increases over time by attenuating the responses of less-selective cells in lower-level brain areas. These data emphasize that color perception involves multiple areas across a hierarchy of regions, interacting with each other in a complex, recursive manner.

Discrete capacity limits and neuroanatomical correlates of visual short-term memory for objects and spatial locations

Working memory is responsible for keeping information in mind when it is no longer in view, linking perception with higher cognitive functions. Despite such crucial role, short-term maintenance of visual information is severely limited. Research suggests that capacity limits in visual short-term memory (VSTM) are correlated with sustained activity in distinct brain areas. Here, we investigated whether variability in the structure of the brain is reflected in individual differences of behavioral capacity estimates for spatial and object VSTM. Behavioral capacity estimates were calculated separately for spatial and object information using a novel adaptive staircase procedure and were found to be unrelated, supporting domain-specific VSTM capacity limits. Voxel-based morphometry (VBM) analyses revealed dissociable neuroanatomical correlates of spatial versus object VSTM. Interindividual variability in spatial VSTM was reflected in the gray matter density of the inferior parietal lobule. In contrast, object VSTM was reflected in the gray matter density of the left insula. These dissociable findings highlight the importance of considering domain-specific estimates of VSTM capacity and point to the crucial brain regions that limit VSTM capacity for different types of visual information. Hum Brain Mapp 38:767-778, 2017. © 2016 Wiley Periodicals, Inc.

Smooth versus Textured Surfaces: Feature-Based Category Selectivity in Human Visual Cortex

In fMRI studies, human lateral occipital (LO) cortex is thought to respond selectively to images of objects, compared with nonobjects. However, it remains unresolved whether all objects evoke equivalent levels of activity in LO, and, if not, which image features produce stronger activation. Here, we used an unbiased parametric texture model to predict preferred versus nonpreferred stimuli in LO. Observation and psychophysical results showed that predicted preferred stimuli (both objects and nonobjects) had smooth (rather than textured) surfaces. These predictions were confirmed using fMRI, for objects and nonobjects. Similar preferences were also found in the fusiform face area (FFA). Consistent with this: (1) FFA and LO responded more strongly to nonfreckled (smooth) faces, compared with otherwise identical freckled (textured) faces; and (2) strong functional connections were found between LO and FFA. Thus, LO and FFA may be part of an information-processing stream distinguished by feature-based category selectivity (smooth > textured).

Representation of Perceptual Color Space in Macaque Posterior Inferior Temporal Cortex (the V4 Complex)

The lateral geniculate nucleus is thought to represent color using two populations of cone-opponent neurons [L vs M; S vs (L + M)], which establish the cardinal directions in color space (reddish vs cyan; lavender vs lime). How is this representation transformed to bring about color perception? Prior work implicates populations of glob cells in posterior inferior temporal cortex (PIT; the V4 complex), but the correspondence between the neural representation of color in PIT/V4 complex and the organization of perceptual color space is unclear. We compared color-tuning data for populations of glob cells and interglob cells to predictions obtained using models that varied in the color-tuning narrowness of the cells, and the color preference distribution across the populations. Glob cells were best accounted for by simulated neurons that have nonlinear (narrow) tuning and, as a population, represent a color space designed to be perceptually uniform (CIELUV). Multidimensional scaling and representational similarity analyses showed that the color space representations in both glob and interglob populations were correlated with the organization of CIELUV space, but glob cells showed a stronger correlation. Hue could be classified invariant to luminance with high accuracy given glob responses and above-chance accuracy given interglob responses. Luminance could be read out invariant to changes in hue in both populations, but interglob cells tended to prefer stimuli having luminance contrast, regardless of hue, whereas glob cells typically retained hue tuning as luminance contrast was modulated. The combined luminance/hue sensitivity of glob cells is predicted for neurons that can distinguish two colors of the same hue at different luminance levels (orange/brown).

The Role of Top-Down Focused Spatial Attention in Preattentive Salience Coding and Salience-based Attentional Capture

Selective visual attention requires an efficient coordination between top-down and bottom-up attention control mechanisms. This study investigated the behavioral and neural effects of top-down focused spatial attention on the coding of highly salient distractors and their tendency to capture attention. Combining spatial cueing with an irrelevant distractor paradigm revealed bottom-up based attentional capture only when attention was distributed across the whole search display, including the distractor location. Top-down focusing spatial attention on the target location abolished attentional capture of a salient distractor outside the current attentional focus. Functional data indicated that the missing capture effect was not based on diminished bottom-up salience signals at unattended distractor locations. Irrespectively of whether salient distractors occurred at attended or unattended locations, their presence enhanced BOLD signals at their respective spatial representation in early visual areas as well as in inferior frontal, superior parietal, and medial parietal cortex. Importantly, activity in these regions reflected the presence of a salient distractor rather than attentional capture per se. Moreover, successfully inhibiting attentional capture of a salient distractor at an unattended location further increased neural responses in medial parietal regions known to be involved in controlling spatial attentional shifts. Consequently, data provide evidence that top-down focused spatial attention prevents automatic attentional capture by supporting attentional control processes counteracting a spatial bias toward a salient distractor.

Sparse coding generates curvature selectivity in V4 neurons

The cortical area V4 produces a representation of curvature as the intermediate-level representation of an object's shape. We investigated whether sparse coding is the principle driving the generation of the spatial properties of the receptive field in V4 that exhibit curvature selectivity. To investigate the role of sparseness in the construction of curvature representations, we applied component analysis with a sparseness constraint to the activity of model V2 neurons that were responding to shapes derived from natural images. Our simulation results showed that single basis functions with medium degrees of sparseness (0.7-0.8) produced curvature selectivity, and their population activity produced acute curvature bias. The results support the hypothesis that sparseness plays an essential role in the construction of curvature selectivity in V4.

The Rapid Capture of Attention by Rewarded Objects

When a stimulus is associated with a reward, it becomes prioritized, and the allocation of attention to that stimulus increases. For low-level features, such as color, this reward-based allocation of attention can manifest early in time and as a faster and stronger shift of attention to targets with that color, as reflected by the N2pc (a parieto-occipital electrophysiological component peaking at ∼250 msec). It is unknown, however, if reward associations can similarly modulate attentional shifts to complex objects or object categories, or if reward-related modulation of attentional allocation to such stimuli would occur later in time or through a different mechanism. Here, we used magnetoencephalographic recordings in 24 participants to investigate how object categories with a reward association would modulate the shift of attention. On each trial, two colored squares were presented, one in a target color and the other in a distractor color, each with an embedded object. Participants searched for the target-colored square and performed a corner discrimination task. The embedded objects were from either a rewarded or non-rewarded category, and if a rewarded-category object were present within the target-colored square, participants could earn extra money for correct performance. We observed that when the target color contained an object from a rewarded versus a non-rewarded category, the neural shift of attention to the target was faster and of greater magnitude, although the rewarded objects were not relevant for correct task performance. These results suggest that reward associations of complex objects can rapidly modulate attentional allocation to a target.

Modality-specificity of Selective Attention Networks

Objective: To establish the modality specificity and generality of selective attention networks.

Method: Forty-eight young adults completed a battery of four auditory and visual selective attention tests based upon the Attention Network framework: the visual and auditory Attention Network Tests (vANT, aANT), the Test of Everyday Attention (TEA), and the Test of Attention in Listening (TAiL). These provided independent measures for auditory and visual alerting, orienting, and conflict resolution networks. The measures were subjected to an exploratory factor analysis to assess underlying attention constructs.

Results: The analysis yielded a four-component solution. The first component comprised of a range of measures from the TEA and was labeled “general attention.” The third component was labeled “auditory attention,” as it only contained measures from the TAiL using pitch as the attended stimulus feature. The second and fourth components were labeled as “spatial orienting” and “spatial conflict,” respectively—they were comprised of orienting and conflict resolution measures from the vANT, aANT, and TAiL attend-location task—all tasks based upon spatial judgments (e.g., the direction of a target arrow or sound location).

Conclusions: These results do not support our a-priori hypothesis that attention networks are either modality specific or supramodal. Auditory attention separated into selectively attending to spatial and non-spatial features, with the auditory spatial attention loading onto the same factor as visual spatial attention, suggesting spatial attention is supramodal. However, since our study did not include a non-spatial measure of visual attention, further research will be required to ascertain whether non-spatial attention is modality-specific.

Figure-ground organization and the emergence of proto-objects in the visual cortex

A long history of studies of perception has shown that the visual system organizes the incoming information early on, interpreting the 2D image in terms of a 3D world and producing a structure that provides perceptual continuity and enables object-based attention. Recordings from monkey visual cortex show that many neurons, especially in area V2, are selective for border ownership. These neurons are edge selective and have ordinary classical receptive fields (CRF), but in addition their responses are modulated (enhanced or suppressed) depending on the location of a 'figure' relative to the edge in their receptive field. Each neuron has a fixed preference for location on one side or the other. This selectivity is derived from the image context far beyond the CRF. This paper reviews evidence indicating that border ownership selectivity reflects the formation of early object representations ('proto-objects'). The evidence includes experiments showing (1) reversal of border ownership signals with change of perceived object structure, (2) border ownership specific enhancement of responses in object-based selective attention, (3) persistence of border ownership signals in accordance with continuity of object perception, and (4) remapping of border ownership signals across saccades and object movements. Findings 1 and 2 can be explained by hypothetical grouping circuits that sum contour feature signals in search of objectness, and, via recurrent projections, enhance the corresponding low-level feature signals. Findings 3 and 4 might be explained by assuming that the activity of grouping circuits persists and can be remapped. Grouping, persistence, and remapping are fundamental operations of vision. Finding these operations manifest in low-level visual areas challenges traditional views of visual processing. New computational models need to be developed for a comprehensive understanding of the function of the visual cortex.

Feedforward and feedback connections and their relation to the cytox modules of V2 in Cebus monkeys

To study the circuitry related to the ventral stream of visual information processing and its relation to the cytochrome oxidase (CytOx) modules in visual area V2, we injected anterograde and retrograde cholera toxin subunit B (CTb) tracer into nine sites in area V4 in five Cebus apella monkeys. The injection site locations ranged from 2° to 10° eccentricity in the lower visual field representation of V4. Alternate cortical sections, cut tangentially to the pial surface or in the coronal plane, were stained for CTb immunocytochemistry or for CytOx histochemistry or for Nissl. Our results indicate that the V4-projecting cells and terminal-like labeling were located in interstripes and thin CytOx-rich stripes and avoided the CytOx-rich thick stripes in V2. The feedforward projecting cell bodies in V2 were primarily located in the supragranular layers and sparsely located in the infragranular layers, whereas the feedback projections (i.e., the terminal-like labels) were located in the supra- and infragranular layers. V4 injections of CTb resulted in labeling of the thin stripes and interstripes of V2 and provided an efficient method of distinguishing the V2 modules that were related to the ventral stream from the CytOx-rich thick stripes, related to the dorsal stream. In V2, there was a significant heterogeneity in the distribution of projections: feedforward projections were located in CytOx-rich thin stripes and in the CytOx-poor interstripes, whereas the feedback projections were more abundant in the thin stripes than in the interstripes. J. Comp. Neurol. 522:3091–3105, 2014.

Shape analysis of the human brain: a brief survey

The survey outlines and compares popular computational techniques for quantitative description of shapes of major structural parts of the human brain, including medial axis and skeletal analysis, geodesic distances, Procrustes analysis, deformable models, spherical harmonics, and deformation morphometry, as well as other less widely used techniques. Their advantages, drawbacks, and emerging trends, as well as results of applications, in particular, for computer-aided diagnostics, are discussed.

Cortical activation to object shape and speed of motion during the first year

A great deal is known about the functional organization of cortical networks that mediate visual object processing in the adult. The current research is part of a growing effort to identify the functional maturation of these pathways in the developing brain. The current research used near-infrared spectroscopy to investigate functional activation of the infant cortex during the processing of featural information (shape) and spatiotemporal information (speed of motion) during the first year of life. Our investigation focused on two areas that were implicated in previous studies: anterior temporal cortex and posterior parietal cortex. Neuroimaging data were collected with 207 infants across three age groups: 3-6 months (Experiment 1), 7-8 months (Experiment 2), and 10-12 months (Experiments 3 and 4). The neuroimaging data revealed age-related changes in patterns of activation to shape and speed information, mostly involving posterior parietal areas, some of which were predicted and others that were not. We suggest that these changes reflect age-related differences in the perceptual and/or cognitive processes engaged during the task.

Dynamic integration of task-relevant visual features in posterior parietal cortex

The primate visual system consists of multiple hierarchically organized cortical areas, each specialized for processing distinct aspects of the visual scene. For example, color and form are encoded in ventral pathway areas such as V4 and inferior temporal cortex, while motion is preferentially processed in dorsal pathway areas such as the middle temporal area. Such representations often need to be integrated perceptually to solve tasks that depend on multiple features. We tested the hypothesis that the lateral intraparietal area (LIP) integrates disparate task-relevant visual features by recording from LIP neurons in monkeys trained to identify target stimuli composed of conjunctions of color and motion features. We show that LIP neurons exhibit integrative representations of both color and motion features when they are task relevant and task-dependent shifts of both direction and color tuning. This suggests that LIP plays a role in flexibly integrating task-relevant sensory signals.

A feed-forward spiking model of shape-coding by IT cells

The ability to recognize a shape is linked to figure-ground (FG) organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across FG reversals. Here we present a network structure which explains both shape-coding by simulated IT cells and suppression of responses to FG reversed stimuli. In our model FG segregation is achieved before shape discrimination, which is itself evidenced by the difference in spiking onsets of a pair of output cells. The studied example also includes feature extraction and illustrates a classification of binary images depending on the dominance of vertical or horizontal borders.

Guided Search 2.0 A revised model of visual search

An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.

Ventral and dorsal visual stream contributions to the perception of object shape and object location

Growing evidence suggests that the functional specialization of the two cortical visual pathways may not be as distinct as originally proposed. Here, we explore possible contributions of the dorsal "where/how" visual stream to shape perception and, conversely, contributions of the ventral "what" visual stream to location perception in human adults. Participants performed a shape detection task and a location detection task while undergoing fMRI. For shape detection, comparable BOLD activation in the ventral and dorsal visual streams was observed, and the magnitude of this activation was correlated with behavioral performance. For location detection, cortical activation was significantly stronger in the dorsal than ventral visual pathway and did not correlate with the behavioral outcome. This asymmetry in cortical profile across tasks is particularly noteworthy given that the visual input was identical and that the tasks were matched for difficulty in performance. We confirmed the asymmetry in a subsequent psychophysical experiment in which participants detected changes in either object location or shape, while ignoring the other, task-irrelevant dimension. Detection of a location change was slowed by an irrelevant shape change matched for difficulty, but the reverse did not hold. We conclude that both ventral and dorsal visual streams contribute to shape perception, but that location processing appears to be essentially a function of the dorsal visual pathway.

Linking brain to behavior for the visual perception of figures and objects

The dissociation of a figure from its background is an essential feat of visual perception, as it allows us to detect, recognize, and interact with shapes and objects in our environment. In order to understand how the human brain gives rise to the perception of figures, we here review experiments that explore the links between activity in visual cortex and performance of perceptual tasks related to figure perception. We organize our review according to a proposed model that attempts to contextualize figure processing within the more general framework of object processing in the brain. Overall, the current literature provides us with individual linking hypotheses as to cortical regions that are necessary for particular tasks related to figure perception. Attempts to reach a more complete understanding of how the brain instantiates figure and object perception, however, will have to consider the temporal interaction between the many regions involved, the details of which may vary widely across different tasks.

Has brain imaging discovered anything new about how the brain works?

There have now been roughly 130,000 papers on fMRI. While these have clearly contributed to our understanding of the functional anatomy of the human brain, it is less clear that they have changed the way in which we think about the brain. The issue, in other words, is whether they have established new principles about how the brain works. In this paper we offer as an example one new principle, partly to lay down the criteria that are required for establishing a new principle, and partly to encourage others to offer other principles. Our example concerns the flexible flow of information through the cortex that must occur according to the demands of the task or current context. We suggest that this flexibility is achieved by feedback connections from the prefrontal and parietal cortex, and that these include connections to sensory and motor areas. However, the nature of the selective effect differs. The parietal cortex can select both within and across processing streams. By across streams we mean that it can have the same influence on different streams, for example the dorsal and ventral visual systems. However, only the prefrontal cortex can also select between processing streams. The difference between the prefrontal and parietal effects is due to their different positions within the processing hierarchy.

Feature attention evokes task-specific pattern selectivity in V4 neurons

A hallmark of visual cortical neurons is their selectivity for stimulus pattern features, such as color, orientation, or shape. In most cases this feature selectivity is hard-wired, with selectivity manifest from the beginning of the response. Here we show that when a task requires that a monkey distinguish between patterns, V4 develops a selectivity for the sought-after pattern, which it does not manifest in a task that does not require the monkey to distinguish between patterns. When a monkey looks for a target object among an array of distractors, V4 neurons become selective for the target ∼50 ms after the visual latency independent of the impending saccade direction. However, when the monkey has to only make a saccade to the spatial location of the same objects without discriminating their pattern, V4 neurons do not distinguish the search target from the distractors. This selectivity for stimulus pattern develops roughly 40 ms after the same neurons' selectivity for basic pattern features like orientation or color. We suggest that this late-developing selectivity is related to the phenomenon of feature attention and may contribute to the mechanisms by which the brain finds the target in visual search.