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Binocular disparity provides critical information about three-dimensional (3D) structures to support perception and action. In the past decade significant progress has been made in uncovering human brain areas engaged in the processing of binocular disparity signals. Yet, the fine-scale brain processing underlying 3D perception remains unknown. Here, we use ultra-high-field (7T) functional imaging at submillimeter resolution to examine fine-scale BOLD fMRI signals involved in 3D perception. In particular, we sought to interrogate the local circuitry involved in disparity processing by sampling fMRI responses at different positions relative to the cortical surface (i.e., across cortical depths corresponding to layers). We tested for representations related to 3D perception by presenting participants (male and female, N = 8) with stimuli that enable stable stereoscopic perception [i.e., correlated random dot stereograms (RDS)] versus those that do not (i.e., anticorrelated RDS). Using multivoxel pattern analysis (MVPA), we demonstrate cortical depth-specific representations in areas V3A and V7 as indicated by stronger pattern responses for correlated than for anticorrelated stimuli in upper rather than deeper layers. Examining informational connectivity, we find higher feedforward layer-to-layer connectivity for correlated than anticorrelated stimuli between V3A and V7. Further, we observe disparity-specific feedback from V3A to V1 and from V7 to V3A. Our findings provide evidence for the role of V3A as a key nexus for disparity processing, which is implicated in feedforward and feedback signals related to the perceptual estimation of 3D structures. SIGNIFICANCE STATEMENT Binocular vision plays a significant role in supporting our interactions with the surrounding environment. The fine-scale neural mechanisms that underlie the brain9s skill in extracting 3D structures from binocular signals are poorly understood. Here, we capitalize on recent advances in ultra-high-field functional imaging to interrogate human brain circuits involved in 3D perception at submillimeter resolution. We provide evidence for the role of area V3A as a key nexus for disparity processing, which is implicated in feedforward and feedback signals related to the perceptual estimation of 3D structures from binocular signals. These fine-scale measurements help bridge the gap between animal neurophysiology and human fMRI studies investigating cross-scale circuits, from micro circuits to global brain networks for 3D perception.

Seeing movement promotes survival. It results from an uncertain interplay between evolution and experience, making it hard to isolate the drivers of computational architectures found in brains. Here we seek insight into motion perception using a neural network (MotionNet) trained on moving images to classify velocity. The network recapitulates key properties of motion direction and speed processing in biological brains, and we use it to derive, and test, understanding of motion (mis)perception at the computational, neural, and perceptual levels. We show that diverse motion characteristics are largely explained by the statistical structure of natural images, rather than motion per se. First, we show how neural and perceptual biases for particular motion directions can result from the orientation structure of natural images. Second, we demonstrate an interrelation between speed and direction preferences in (macaque) MT neurons that can be explained by image autocorrelation. Third, we show that natural image statistics mean that speed and image contrast are related quantities. Finally, using behavioral tests (humans, both sexes), we show that it is knowledge of the speed-contrast association that accounts for motion illusions, rather than the distribution of movements in the environment (the “slow world” prior) as premised by Bayesian accounts. Together, this provides an exposition of motion speed and direction estimation, and produces concrete predictions for future neurophysiological experiments. More broadly, we demonstrate the conceptual value of marrying artificial systems with biological characterization, moving beyond “black box” reproduction of an architecture to advance understanding of complex systems, such as the brain.SIGNIFICANCE STATEMENTUsing an artificial systems approach, we show that physiological properties of motion can result from natural image structure. In particular, we show that the anisotropic distribution of orientations in natural statistics is sufficient to explain the cardinal bias for motion direction. We show that inherent autocorrelation in natural images means that speed and direction are related quantities, which could shape the relationship between speed and direction tuning of MT neurons. Finally, we show that movement speed and image contrast are related in moving natural images, and that motion misperception can be explained by this speed-contrast association not a “slow world” prior.

Throughout the brain, information from individual sources converges onto higher order neurons. For example, information from the two eyes first converges in binocular neurons in area V1. Some neurons appear tuned to similarities between sources of information, which makes intuitive sense in a system striving to match multiple sensory signals to a single external cause, i.e., establish causal inference. However, there are also neurons that are tuned to dissimilar information. In particular, some binocular neurons respond maximally to a dark feature in one eye and a light feature in the other. Despite compelling neurophysiological and behavioural evidence supporting the existence of these neurons (Cumming & Parker, 1997; Janssen, Vogels, Liu, & Orban, 2003; Katyal, Vergeer, He, He, & Engel, 2018; Kingdom, Jennings, & Georgeson, 2018; Tsao, Conway, & Livingstone, 2003), their function has remained opaque. To determine how neural mechanisms tuned to dissimilarities support perception, here we use electroencephalography to measure human observers’ steady-state visually evoked potentials (SSVEPs) in response to change in depth after prolonged viewing of anticorrelated and correlated random-dot stereograms (RDS). We find that adaptation to anticorrelated RDS results in larger SSVEPs, while adaptation to correlated RDS has no effect. These results are consistent with recent theoretical work suggesting ‘what not’ neurons play a suppressive role in supporting stereopsis (Goncalves & Welchman, 2017); that is, selective adaptation of neurons tuned to binocular mismatches reduces suppression resulting in increased neural excitability., This work was supported by the Leverhulme Trust (ECF-2017-573 to R. R.), the Isaac Newton Trust (17.08(o) to R. R.), and the Wellcome Trust (095183/Z/10/Z to A. E. W. and 206495/Z/17/Z to E. M.).

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.

Extracting the structure of complex environments is at the core of our ability to interpret the present and predict the future. This skill is important for a range of behaviours from navigating a new city to learning music and language. Classical approaches that investigate our ability to extract the principles of organisation that govern complex environments focus on reward-based learning. Yet, the human brain is shown to be expert at learning generative structure based on mere exposure and without explicit reward. Individuals are shown to adapt to-unbeknownst to them-changes in the environment's temporal statistics and predict future events. Further, we present evidence for a common brain architecture for unsupervised structure learning and reward-based learning, suggesting that the brain is built on the premise that 'learning is its own reward' to support adaptive behaviour.

Visual perception of three-dimensional (3D) structure is important for object recognition, grasping, and manipulation. The 3D structure of a surface can be defined in terms of its slant and tilt. Previous work has shown that slant and tilt are represented in the posterior and ventral intraparietal sulcus of the human brain; however, it is unclear whether the representation of these features is functionally organized within this region. Here we use phase-encoded presentation of 3D planar surfaces with linear gradients defined by horizontal binocular disparity while measuring fMRI activity to test whether the representation of 3D surface tilt is functionally organized within visual cortex. We find functionally defined structures within V3A and V7. Most notably, in one participant we find that the tilt preference is unilaterally organized in a pinwheel-like structure, similar to those observed for orientation preference in V1, which encompasses most of area V3A. These findings indicate that 3D orientation is functionally organized within the human visual cortex, and the evidence suggesting the presence of a large pinwheel-like structure indicates that this type of organization may be applied canonically within the brain at multiple scales.

The International Brain Initiative (IBI) has been established to coordinate efforts across existing and emerging national and regional brain initiatives. This NeuroView describes how to be involved and the new opportunities for global collaboration that are emerging between scientists, scientific societies, funders, industry, government, and society.

Successful human behavior depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor cortico-striatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive cortico-striatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Electrophysiological evidence suggested primarily the involvement of the middle temporal (MT) area in depth cue integration in macaques, as opposed to human imaging data pinpointing area V3B/kinetic occipital area (V3B/KO). To clarify this conundrum, we decoded monkey functional MRI (fMRI) responses evoked by stimuli signaling near or far depths defined by binocular disparity, relative motion, and their combination, and we compared results with those from an identical experiment previously performed in humans. Responses in macaque area MT are more discriminable when two cues concurrently signal depth, and information provided by one cue is diagnostic of depth indicated by the other. This suggests that monkey area MT computes fusion of disparity and motion depth signals, exactly as shown for human area V3B/KO. Hence, these data reconcile previously reported discrepancies between depth processing in human and monkey by showing the involvement of the dorsal stream in depth cue integration using the same technique, despite the engagement of different regions. ispartof: PLOS Biology vol:17 issue:3 ispartof: location:United States status: published

Depth perception is better when observers view stimuli containing a mixture of bright and dark visual features. It is currently unclear where in the visual system sensory processing benefits from the availability of different contrast polarity. To address this question, we applied transcranial magnetic stimulation to the visual cortex to modulate normal neural activity during processing of single- or mixed-polarity random-dot stereograms. In line with previous work, participants gave significantly better depth judgments for mixed-polarity stimuli. Stimulation of early visual cortex (V1/V2) significantly increased this benefit for mixed-polarity stimuli, and it did not affect performance for single-polarity stimuli. Stimulation of disparity responsive areas V3a and LO had no effect on perception. Our findings show that disparity processing in early visual cortex gives rise to the mixed-polarity benefit. This is consistent with computational models of stereopsis at the level of V1 that produce a mixed polarity benefit.

Modulations in light intensity across a visual image could be caused by a flat object with varying pigmentation, such as wallpaper, or differential light reflection from a three-dimensional shape made of uniform material, such as curtains. A new study identifies key image cues that help the brain work out which interpretation to select.

Global context can dramatically influence local visual perception. This phenomenon is well-documented for monocular features, e.g., the Kanizsa triangle. It has been demonstrated for binocular matching: the disambiguation of the Wallpaper Illusion (Brewster, 1844) via the luminance of the background (Anderson & Nakayama, 1994). For monocular features, there is evidence that global context can influence neuronal responses as early as V1 (Muckli et al., 2015). However, for binocular matching, the activity in this area of the visual cortex is thought to represent local processing, suggesting that the influence of global context may occur at later stages of cortical processing. Here we sought to test if binocular matching is influenced by contextual effects in V1, using fMRI to measure brain activity while participants viewed perceptually ambiguous “wallpaper” stereograms whose depth was disambiguated by the luminance of the surrounding region. We localized voxels in V1 corresponding to the ambiguous region of the pattern, i.e., where the signal received from the eyes was not predictive of depth, and despite the ambiguity of the input signal, using multi-voxel pattern analysis we were able to reliably decode perceived (near/far) depth from the activity of these voxels. These findings indicate that stereoscopic related neural activity is influenced by global context as early as V1.

Visual motion perception underpins behaviours ranging from navigation to depth perception and grasping. Our limited access to biological systems constrain our understanding of how motion is processed within the brain. Here we explore properties of motion perception in biological systems by training a neural network (‘MotionNetxy’) to estimate the velocity image sequences. The network recapitulates key characteristics of motion processing in biological brains, and we use our complete access to its structure explore and understand motion (mis)perception at the computational-, neural-, and perceptual-levels. First, we find that the network recapitulates the biological response to reverse-phi motion in terms of direction. We further find that it overestimates the speed of slow reverse-phi motion while underestimating the speed of fast reverse-phi motion because of the correlation between reverse-phi motion and the spatiotemporal receptive fields tuned to motion in opposite directions. Second, we find that the distribution of spatiotemporal tuning properties in the V1 and MT layers of the network are similar to those observed in biological systems. We then show that compared to MT units tuned to fast speeds, those tuned to slow speeds primarily receive input from V1 units tuned to high spatial frequency and low temporal frequency. Third, we find that there is a positive correlation between the pattern-motion and speed selectivity of MT units. Finally, we show that the network captures human underestimation of low coherence motion stimuli, and that this is due to pooling of noise and signal motion. These findings provide biologically plausible explanations for well-known phenomena, and produce concrete predictions for future psychophysical and neurophysiological experiments.

Human perception is remarkably flexible: We experience vivid three-dimensional (3D) structure under diverse conditions, from the seemingly random magic-eye stereograms to the aesthetically beautiful, but obviously flat, canvases of the Old Masters. How does the brain achieve this apparently effortless robustness? Using brain imaging we are beginning to discover how different parts of the visual cortex support 3D perception by tracing different computations in the dorsal and ventral pathways. This review concentrates on studies of binocular disparity and its combination with other depth cues. This work suggests that the dorsal visual cortex is strongly engaged by 3D information and is involved in integrating signals to represent the structure of viewed surfaces. The ventral cortex may store representations of object configurations and the features required for task performance. These differences can be broadly understood in terms of the different computational demands of reducing estimator variance versus increasing the separation between exemplars.

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Perception relies on integrating information within and between the senses, but how does the brain decide which pieces of information should be integrated and which kept separate? Here we demonstrate how proscription can be used to solve this problem: certain neurons respond best to unrealistic combinations of features to provide ‘what not’ information that drives suppression of unlikely perceptual interpretations. First, we present a model that captures both improved perception when signals are consistent (and thus should be integrated) and robust estimation when signals are conflicting. Second, we test for signatures of proscription in the human brain. We show that concentrations of inhibitory neurotransmitter GABA in a brain region intricately involved in integrating cues (V3B/KO) correlate with robust integration. Finally, we show that perturbing excitation/inhibition impairs integration. These results highlight the role of proscription in robust perception and demonstrate the functional purpose of ‘what not’ sensors in supporting sensory estimation., Perception relies on information integration but it is unclear how the brain decides which information to integrate and which to keep separate. Here, the authors develop and test a biologically inspired model of cue-integration, implicating a key role for GABAergic proscription in robust perception.

Summary Binocular stereopsis is one of the primary cues for three-dimensional (3D) vision in species ranging from insects to primates. Understanding how the brain extracts depth from two different retinal images represents a tractable challenge in sensory neuroscience that has so far evaded full explanation. Central to current thinking is the idea that the brain needs to identify matching features in the two retinal images (i.e., solving the “stereoscopic correspondence problem”) so that the depth of objects in the world can be triangulated. Although intuitive, this approach fails to account for key physiological and perceptual observations. We show that formulating the problem to identify “correct matches” is suboptimal and propose an alternative, based on optimal information encoding, that mixes disparity detection with “proscription”: exploiting dissimilar features to provide evidence against unlikely interpretations. We demonstrate the role of these “what not” responses in a neural network optimized to extract depth in natural images. The network combines information for and against the likely depth structure of the viewed scene, naturally reproducing key characteristics of both neural responses and perceptual interpretations. We capture the encoding and readout computations of the network in simple analytical form and derive a binocular likelihood model that provides a unified account of long-standing puzzles in 3D vision at the physiological and perceptual levels. We suggest that marrying detection with proscription provides an effective coding strategy for sensory estimation that may be useful for diverse feature domains (e.g., motion) and multisensory integration., Highlights • The brain uses “what not” detectors to facilitate 3D vision • Binocular mismatches are used to drive suppression of incompatible depths • Proscription accounts for depth perception without binocular correspondence • A simple analytical model captures perceptual and neural responses, Goncalves and Welchman show that long-standing puzzles for the physiology and perception of 3D vision are explained by the brain’s use of “what not” detectors. These facilitate stereopsis by providing evidence against interpretations that are incompatible with the true structure of the scene.

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory–motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to changes in the environment's statistics. We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits (including hippocampal cortex) that support learning of the exact temporal statistics.

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique whose effects on neural activity can be uncertain. Within the visual cortex, phosphenes are a useful marker of TMS: They indicate the induction of neural activation that propagates and creates a conscious percept. However, we currently do not know how susceptible different areas of the visual cortex are to TMS-induced phosphenes. In this study, we systematically map out locations in the visual cortex where stimulation triggered phosphenes. We relate this to the retinotopic organization and the location of object- and motion-selective areas, identified by functional magnetic resonance imaging (fMRI) measurements. Our results show that TMS can reliably induce phosphenes in early (V1, V2d, and V2v) and dorsal (V3d and V3a) visual areas close to the interhemispheric cleft. However, phosphenes are less likely in more lateral locations (hMT+/V5 and LOC). This suggests that early and dorsal visual areas are particularly amenable to TMS and that TMS can be used to probe the functional role of these areas.

The visual system's flexibility in estimating depth is remarkable: We readily perceive 3-D structure under diverse conditions from the seemingly random dots of a “magic eye” stereogram to the aesthetically beautiful, but obviously flat, canvasses of the Old Masters. Yet, 3-D perception is often enhanced when different cues specify the same depth. This perceptual process is understood as Bayesian inference that improves sensory estimates. Despite considerable behavioral support for this theory, insights into the cortical circuits involved are limited. Moreover, extant work tested quantitatively similar cues, reducing some of the challenges associated with integrating computationally and qualitatively different signals. Here we address this challenge by measuring fMRI responses to depth structures defined by shading, binocular disparity, and their combination. We quantified information about depth configurations (convex “bumps” vs. concave “dimples”) in different visual cortical areas using pattern classification analysis. We found that fMRI responses in dorsal visual area V3B/KO were more discriminable when disparity and shading concurrently signaled depth, in line with the predictions of cue integration. Importantly, by relating fMRI and psychophysical tests of integration, we observed a close association between depth judgments and activity in this area. Finally, using a cross-cue transfer test, we found that fMRI responses evoked by one cue afford classification of responses evoked by the other. This reveals a generalized depth representation in dorsal visual cortex that combines qualitatively different information in line with 3-D perception.

Visual judgments critically depend on (1) the detection of meaningful items from cluttered backgrounds and (2) the discrimination of an item from highly similar alternatives. Learning and experience are known to facilitate these processes, but the specificity with which these processes operate is poorly understood. Here we use psychophysical measures of human participants to test learning in two types of commonly used tasks that target segmentation (signal-in-noise, or “coarse” tasks) versus the discrimination of highly similar items (feature difference, or “fine” tasks). First, we consider the processing of binocular disparity signals, examining performance on signal-in-noise and feature difference tasks after a period of training on one of these tasks. Second, we consider the generality of learning between different visual features, testing performance on both task types for displays defined by disparity, motion, or orientation. We show that training on a feature difference task also improves performance on signal-in-noise tasks, but only for the same visual feature. By contrast, training on a signal-in-noise task has limited benefits for fine judgments of the same feature but supports learning that generalizes to signal-in-noise tasks for other features. These findings indicate that commonly used signal-in-noise tasks require at least three distinct components: feature representations, signal-specific selection, and a generalized process that enhances segmentation. As such, there is clear potential to harness areas of commonality (both within and between cues) to improve impaired perceptual functions.

Highlights ► Second-order cues normally support layer decomposition at long but not short presentation times. ► Perceptual learning is used to train observers to perform the task at short presentation times. ► The transfer of learning is consistent with low level changes in perceptual processing., Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition—the process of distinguishing between reflectance and illumination changes—is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (Schofield et al., 2010). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250 ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5 days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow.

Synchronising movements with events in the surrounding environment is an ubiquitous aspect of everyday behaviour. Often, information about a stream of events is available across sensory modalities. While it is clear that we synchronise more accurately to auditory cues than other modalities, little is known about how the brain combines multisensory signals to produce accurately timed actions. Here, we investigate multisensory integration for sensorimotor synchronisation. We extend the prevailing linear phase correction model for movement synchronisation, describing asynchrony variance in terms of sensory, motor and timekeeper components. Then we assess multisensory cue integration, deriving predictions based on the optimal combination of event time, defined across different sensory modalities. Participants tapped in time with metronomes presented via auditory, visual and tactile modalities, under either unimodal or bimodal presentation conditions. Temporal regularity was manipulated between modalities by applying jitter to one of the metronomes. Results matched the model predictions closely for all except high jitter level conditions in audio–visual and audio–tactile combinations, where a bias for auditory signals was observed. We suggest that, in the production of repetitive timed actions, cues are optimally integrated in terms of both sensory and temporal reliability of events. However, when temporal discrepancy between cues is high they are treated independently, with movements timed to the cue with the highest sensory reliability.

Everyday behaviour involves a trade-off between planned actions and reaction to environmental events. Evidence from neurophysiology, neurology and functional brain imaging suggests different neural bases for the control of different movement types. Here we develop a behavioural paradigm to test movement dynamics for intentional versus reaction movements and provide evidence for a ‘reactive advantage’ in movement execution, whereby the same action is executed faster in reaction to an opponent. We placed pairs of participants in competition with each other to make a series of button presses. Within-subject analysis of movement times revealed a 10 per cent benefit for reactive actions. This was maintained when opponents performed dissimilar actions, and when participants competed against a computer, suggesting that the effect is not related to facilitation produced by action observation. Rather, faster ballistic movements may be a general property of reactive motor control, potentially providing a useful means of promoting survival.

We describe a compelling motion illusion elicited by a huge billboard placed along a street, depicting a building that contains strong perspective cues. When observers move fast along the opposite sidewalk, they perceive the depicted building as rotating in their direction of travel. This is a special case of the ‘following’, or ‘pointing out of the picture’, illusion that elicits a strong illusory motion percept. Here we discuss the cause of the illusory motion and suggest that the brain relies on the depicted perspective cues to infer a 3-D shape and a concomitant motion that is incompatible with the physical pictorial surface.

In natural settings, our eyes tend to track approaching objects. To estimate motion, the brain should thus take account of eye movements, perhaps using retinal cues (retinal slip of static objects) or extra-retinal signals (motor commands). Previous work suggests that extra-retinal ocular vergence signals do not support the perceptual judgments. Here, we re-evaluate this conclusion, studying motion judgments based on retinal slip and extra-retinal signals. We find that (1) each cue can be sufficient, and, (2) retinal and extra-retinal signals are combined, when estimating motion-in-depth. This challenges the accepted view that observers are essentially blind to eye vergence changes. © 2009 Elsevier Ltd. All rights reserved.

Perceiving the three-dimensional (3D) properties of the environment relies on the brain bringing together ambiguous cues (e.g., binocular disparity, shading, texture) with information gained from short- and long-term experience. Perceptual aftereffects, in which the perception of an ambiguous 3D stimulus is biased away from the shape of a previously viewed stimulus, provide a sensitive means of probing this process, yet little is known about their neural basis. Here, we investigate 3D aftereffects using psychophysical and functional MRI (fMRI) adaptation paradigms to gain insight into the cortical circuits that mediate the perceptual interpretation of ambiguous depth signals. Using two classic bistable stimuli (Mach card, kinetic depth effect), we test aftereffects produced by 3D shapes defined by binocular (disparity) or monocular (texture, shading) depth cues. We show that the processing of ambiguous 3D stimuli in dorsal visual cortical areas (V3B/KO, V7) and posterior parietal regions is modulated by adaptation in line with perceptual aftereffects. Similar behavioral and fMRI adaptation effects for the two types of bistable stimuli suggest common neural substrates for depth aftereffects independent of the inducing depth cues (disparity, texture, shading). In line with current thinking about the role of adaptation in sensory optimization, our findings provide evidence that estimation of 3D shape in dorsal cortical areas takes account of the adaptive context to resolve depth ambiguity and interpret 3D structure.

Synchronizing our actions with external events is a task we perform without apparent effort. Its foundation relies on accurate temporal control that is widely accepted to take one of two different modes of implementation: explicit timing for discrete actions and implicit timing for smooth continuous movements. Here we assess synchronisation performance for different types of action and test the degree to which each action supports corrective updating following changes in the environment. Participants performed three different finger actions in time with an auditory pacing stimulus allowing us to assess synchronisation performance. Presenting a single perturbation to the otherwise regular metronome allowed us to examine corrections supported by movements varying in their mode of timing implementation. We find that discrete actions are less variable and support faster error correction. As such, discrete actions may be preferred when engaging in time-critical adaptive behaviour with people and objects in a dynamic environment.

Processing of binocular disparity is thought to be widespread throughout cortex, highlighting its importance for perception and action. Yet the computations and functional roles underlying this activity across areas remain largely unknown. Here, we trace the neural representations mediating depth perception across human brain areas using multivariate analysis methods and high-resolution imaging. Presenting disparity-defined planes, we determine functional magnetic resonance imaging (fMRI) selectivity to near versus far depth positions. First, we test the perceptual relevance of this selectivity, comparing the pattern-based decoding of fMRI responses evoked by random dot stereograms that support depth perception (correlated RDS) with the decoding of stimuli containing disparities to which the perceptual system is blind (anticorrelated RDS). Preferential disparity selectivity for correlated stimuli in dorsal (visual and parietal) areas and higher ventral area LO (lateral occipital area) suggests encoding of perceptually relevant information, in contrast to early (V1, V2) and intermediate ventral (V3v, V4) visual cortical areas that show similar selectivity for both correlated and anticorrelated stimuli. Second, manipulating disparity parametrically, we show that dorsal areas encode the metric disparity structure of the viewed stimuli (i.e., disparity magnitude), whereas ventral area LO appears to represent depth position in a categorical manner (i.e., disparity sign). Our findings suggest that activity in both visual streams is commensurate with the use of disparity for depth perception but the neural computations may differ. Intriguingly, perceptually relevant responses in the dorsal stream are tuned to disparity content and emerge at a comparatively earlier stage than categorical representations for depth position in the ventral stream. ispartof: Journal of Neuroscience vol:28 issue:44 pages:11315-27 ispartof: location:United States status: published

Binocular disparity, the slight differences between the images registered by our two eyes, provides an important cue when estimating the three-dimensional (3D) structure of the complex environment we inhabit. Sensitivity to binocular disparity is evident at multiple levels of the visual hierarchy in the primate brain, from early visual cortex to parietal and temporal areas. However, the relationship between activity in these areas and key perceptual functions that exploit disparity information for 3D shape perception remains an important open question. Here we investigate the link between human cortical activity and the perception of disparity-defined shape, measuring fMRI responses concurrently with psychophysical shape judgments. We parametrically degraded the coherence of shapes by shuffling the spatial position of dots whose disparity defined the 3D structure and investigated the effect of this stimulus manipulation on both cortical activity and shape discrimination. We report significant relationships between shape coherence and fMRI response in both dorsal (V3, hMT+/V5) and ventral (LOC) visual areas that correspond to the observers' discrimination performance. In contrast to previous suggestions of a dichotomy of disparity-related processes in the ventral and dorsal streams, these findings are consistent with proposed interactions between these pathways that may mediate a continuum of processes important in perceiving 3D shape from coarse contour segmentation to fine curvature estimation.

When planning interactions with nearby objects, our brain uses visual information to estimate shape, material composition, and surface structure before we come into contact with them. Here we analyse brain activations elicited by different types of visual appearance, measuring fMRI responses to objects that are glossy, matte, rough, or textured. In addition to activation in visual areas, we found that fMRI responses are evoked in the secondary somatosensory area (S2) when looking at glossy and rough surfaces. This activity could be reliably discriminated on the basis of tactile-related visual properties (gloss, rough, and matte), but importantly, other visual properties (i.e., coloured texture) did not substantially change fMRI activity. The activity could not be solely due to tactile imagination, as asking explicitly to imagine such surface properties did not lead to the same results. These findings suggest that visual cues to an object's surface properties evoke activity in neural circuits associated with tactile stimulation. This activation may reflect the a-priori probability of the physics of the interaction (i.e., the expectation of upcoming friction) that can be used to plan finger placement and grasp force., Highlights • Secondary somatosensory area responds to tactile-related visual properties. • Visual inputs are necessary to elicit this somatosensory activation. • This visual-somatosensory crossmodal network may facilitate action planning.

The brain's skill in estimating the 3-D orientation of viewed surfaces supports a range of behaviors, from placing an object on a nearby table, to planning the best route when hill walking. This ability relies on integrating depth signals across extensive regions of space that exceed the receptive fields of early sensory neurons. Although hierarchical selection and pooling is central to understanding of the ventral visual pathway, the successive operations in the dorsal stream are poorly understood. Here we use computational modeling of human fMRI signals to probe the computations that extract 3-D surface orientation from binocular disparity. To understand how representations evolve across the hierarchy, we developed an inference approach using a series of generative models to explain the empirical fMRI data in different cortical areas. Specifically, we simulated the responses of candidate visual processing algorithms and tested how well they explained fMRI responses. Thereby we demonstrate a hierarchical refinement of visual representations moving from the representation of edges and figure–ground segmentation (V1, V2) to spatially extensive disparity gradients in V3A. We show that responses in V3A are little affected by low-level image covariates, and have a partial tolerance to the overall depth position. Finally, we show that responses in V3A parallel perceptual judgments of slant. This reveals a relatively short computational hierarchy that captures key information about the 3-D structure of nearby surfaces, and more generally demonstrates an analysis approach that may be of merit in a diverse range of brain imaging domains.

Visually guided action and interaction depends on the brain’s ability to (a) extract and (b) discriminate meaningful targets from complex retinal inputs. Binocular disparity is known to facilitate this process, and it is an open question how activity in different parts of the visual cortex relates to these fundamental visual abilities. Here we examined fMRI responses related to performance on two different tasks (signal-in-noise “coarse” and feature difference “fine” tasks) that have been widely used in previous work, and are believed to differentially target the visual processes of signal extraction and feature discrimination. We used multi-voxel pattern analysis to decode depth positions (near vs. far) from the fMRI activity evoked while participants were engaged in these tasks. To look for similarities between perceptual judgments and brain activity, we constructed ‘fMR-metric’ functions that described decoding performance as a function of signal magnitude. Thereafter we compared fMR-metric and psychometric functions, and report an association between judged depth and fMRI responses in the posterior parietal cortex during performance on both tasks. This highlights common stages of processing during perceptual performance on these tasks.

The binocular disparity between the views of the world registered by the left and right eyes provides a powerful signal about the depth structure of the environment. Despite increasing knowledge of the cortical areas that process disparity from animal models, comparatively little is known about the local architecture of stereoscopic processing in the human brain. Here, we take advantage of the high spatial specificity and image contrast offered by 7 tesla fMRI to test for systematic organization of disparity representations in the human brain. Participants viewed random dot stereogram stimuli depicting different depth positions while we recorded fMRI responses from dorsomedial visual cortex. We repeated measurements across three separate imaging sessions. Using a series of computational modeling approaches, we report three main advances in understanding disparity organization in the human brain. First, we show that disparity preferences are clustered and that this organization persists across imaging sessions, particularly in area V3A. Second, we observe differences between the local distribution of voxel responses in early and dorsomedial visual areas, suggesting different cortical organization. Third, using modeling of voxel responses, we show that higher dorsal areas (V3A, V3B/KO) have properties that are characteristic of human depth judgments: a simple model that uses tuning parameters estimated from fMRI data captures known variations in human psychophysical performance. Together, these findings indicate that human dorsal visual cortex contains selective cortical structures for disparity that may support the neural computations that underlie depth perception.

Visually guided action and interaction depends on the brain's ability to (a) extract and (b) discriminate meaningful targets from complex retinal inputs. Binocular disparity is known to facilitate this process, and it is an open question how activity in different parts of the visual cortex relates to these fundamental visual abilities. Here we examined fMRI responses related to performance on two different tasks (signal-in-noise "coarse" and feature difference "fine" tasks) that have been widely used in previous work, and are believed to differentially target the visual processes of signal extraction and feature discrimination. We used multi-voxel pattern analysis to decode depth positions (near vs. far) from the fMRI activity evoked while participants were engaged in these tasks. To look for similarities between perceptual judgments and brain activity, we constructed 'fMR-metric' functions that described decoding performance as a function of signal magnitude. Thereafter we compared fMR-metric and psychometric functions, and report an association between judged depth and fMRI responses in the posterior parietal cortex during performance on both tasks. This highlights common stages of processing during perceptual performance on these tasks.

How do we decide whether an object approaching us will hit us? Information in the optic array should provide information sufficient for us to determine the approaching trajectory of a projectile. However, observers’ reports of angular trajectories near the mid-sagittal plane have suggested that, when using binocular information, observers perceive trajectory angles as larger than they actually are (Harris Dean, 2003; J. Exp. Psych, in press). We examine the generality of this previous report by examining the perception of trajectory direction; first for computer rendered, stereoscopically presented, rich-cue objects, and then trajectory perception for real objects moving in the world. We find that, even under rich cue conditions and with real moving objects, observers show positive bias, overestimating the angle of approach when movement is near the mid-sagittal plane. The findings question whether the visual system can make explicit estimates of the 3-D location and movement of objects in depth.

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