Deriving behavioural receptive fields for visually completed contours

The visual system is constantly faced with the problem of identifying partially occluded objects from incomplete images cast on the retinae. Phenomenologically, the visual system seems to fill in missing information by interpolating illusory and occluded contours at points of occlusion, so that we perceive complete objects. Previous behavioural [1] [2] [3] [4] [5] [6] [7] and physiological [8] [9] [10] [11] [12] studies suggest that the visual system treats illusory and occluded contours like luminance-defined contours in many respects. None of these studies has, however, directly shown that illusory and occluded contours are actually used to perform perceptual tasks. Here, we use a response-classification technique [13] [14] [15] [16] [17] [18] [19] [20] to answer this question directly. This technique provides pictorial representations - 'classification images' - that show which parts of a stimulus observers use to make perceptual decisions, effectively deriving behavioural receptive fields. Here we show that illusory and occluded contours appear in observers' classification images, providing the first direct evidence that observers use perceptually interpolated contours to recognize objects. These results offer a compelling demonstration of how visual processing acts on completed representations, and illustrate a powerful new technique for constraining models of visual completion.     

Distributed neural plasticity for shape learning in the human visual cortex

Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes.     

Spaced initial stimulus familiarization enhances novelty preference in Long-Evans rats

Berlyne [Berlyne, D.E., 1950. Novelty and curiosity as determinants of exploratory behaviour. Brit. J. Psychol. 41, 68–80] first illustrated that rats prefer to explore novel objects over ones with which they have had previous experience. Recently, variants on this novel object recognition (NOR) task have become widely popular and have been employed in numerous neuroscience and behavioral pharmacological studies investigating memory processes. Given this popularity, a thorough understanding of the various behavioral processes involved in novelty reaction and preference is essential. The current study compared the effects of spaced and massed initial stimulus exposures upon later object exploration and novel stimulus preference in Long-Evans rats. Results illustrated that a distributed initial stimulus familiarization procedure promoted greater novel object preference than did a massed procedure, and suggest that the novel object recognition task is sensitive to spacing effects in a similar fashion to more traditional learning paradigms. The mechanisms underlying such spacing effects are briefly discussed.     

Brain activity accompanying perception of implied motion in abstract paintings

Early 20th century artists including Duchamp and Balla tried to portray moving objects on a static canvas by superimposing objects in successive portrayals of an action. We investigated whether implied motion in those paintings is associated with activation of motion-sensitive area MT+. In Experiment 1, we found that observers rated these kinds of paintings higher in portraying motion than they did other abstract paintings in which motion is not intended. We also found that observers who had previously experienced abstract paintings with implied motion tended to give higher motion ratings to that class of paintings. In Experiment 2, we used functional magnetic resonance imaging (fMRI) to measure brain activity of observers while viewing abstract paintings receiving the highest and the lowest motion rating scores in Experiment 1. We found MT+, but not primary visual cortex (V1), showed greater BOLD responses to abstract paintings with implied motion than to abstract paintings with little motion impression, but only in observers with prior experience viewing those kinds of paintings. These results imply that the neural machinery ordinarily engaged during perception of real visual motion is activated when people view paintings explicitly designed to convey a sense of visual motion. Experience, however, is necessary to achieve this sense of motion.     

Fragment-based learning of visual object categories

When we perceive a visual object, we implicitly or explicitly associate it with a category we know. It is known that the visual system can use local, informative image fragments of a given object, rather than the whole object, to classify it into a familiar category. How we acquire informative fragments has remained unclear. Here, we show that human observers acquire informative fragments during the initial learning of categories. We created new, but naturalistic, classes of visual objects by using a novel "virtual phylogenesis" (VP) algorithm that simulates key aspects of how biological categories evolve. Subjects were trained to distinguish two of these classes by using whole exemplar objects, not fragments. We hypothesized that if the visual system learns informative object fragments during category learning, then subjects must be able to perform the newly learned categorization by using only the fragments as opposed to whole objects. We found that subjects were able to successfully perform the classification task by using each of the informative fragments by itself, but not by using any of the comparable, but uninformative, fragments. Our results not only reveal that novel categories can be learned by discovering informative fragments but also introduce and illustrate the use of VP as a versatile tool for category-learning research.     

Hemifield effects in multiple identity tracking

In everyday life, we often need to attentively track moving objects. A previous study has claimed that this tracking occurs independently in the left and right visual hemifields (Alvarez & Cavanagh, 2005, Psychological Science,16, 637–647). Specifically, it was shown that observers were much more accurate at tracking objects that were spread over both visual hemifields as opposed to when all were confined to a single visual hemifield. In that study, observers were not required to remember the identities of the objects. Conversely, in real life, there is seldom any benefit to tracking an object unless you can also recall its identity. It has been predicted that when observers are required to remember the identities of the tracked objects a bilateral advantage should no longer be observed (Oksama & Hyönä, 2008, Cognitive Psychology, 56, 237–283). We tested this prediction and found that a bilateral advantage still occurred, though it was not as strong as when observers were not required to remember the identities of the targets. Even in the later case we found that tracking was not completely independent in the two visual hemifields. We present a combined model of multiple object tracking and multiple identity tracking that can explain our data.     

Learning to identify near-acuity letters, either with or without flankers, results in improved letter size and spacing limits in adults with amblyopia

Amblyopia is a developmental abnormality that results in deficits for a wide range of visual tasks, most notably, the reduced ability to see fine details, the loss in contrast sensitivity especially for small objects and the difficulty in seeing objects in clutter (crowding). The primary goal of this study was to evaluate whether crowding can be ameliorated in adults with amblyopia through perceptual learning using a flanked letter identification task that was designed to reduce crowding, and if so, whether the improvements transfer to untrained visual functions: visual acuity, contrast sensitivity and the size of visual span (the amount of information obtained in one fixation). To evaluate whether the improvements following this training task were specific to training with flankers, we also trained another group of adult observers with amblyopia using a single letter identification task that was designed to improve letter contrast sensitivity, not crowding. Following 10,000 trials of training, both groups of observers showed improvements in the respective training task. The improvements generalized to improved visual acuity, letter contrast sensitivity, size of the visual span, and reduced crowding. The magnitude of the improvement for each of these measurements was similar in the two training groups. Perceptual learning regimens aimed at reducing crowding or improving letter contrast sensitivity are both effective in improving visual acuity, contrast sensitivity for near-acuity objects and reducing the crowding effect, and could be useful as a clinical treatment for amblyopia.     

How Lateral Connections and Spiking Dynamics May Separate Multiple Objects Moving Together

Over successive stages, the ventral visual system of the primate brain develops neurons that respond selectively to particular objects or faces with translation, size and view invariance. The powerful neural representations found in Inferotemporal cortex form a remarkably rapid and robust basis for object recognition which belies the difficulties faced by the system when learning in natural visual environments. A central issue in understanding the process of biological object recognition is how these neurons learn to form separate representations of objects from complex visual scenes composed of multiple objects. We show how a one-layer competitive network comprised of ‘spiking’ neurons is able to learn separate transformation-invariant representations (exemplified by one-dimensional translations) of visual objects that are always seen together moving in lock-step, but separated in space. This is achieved by combining ‘Mexican hat’ functional lateral connectivity with cell firing-rate adaptation to temporally segment input representations of competing stimuli through anti-phase oscillations (perceptual cycles). These spiking dynamics are quickly and reliably generated, enabling selective modification of the feed-forward connections to neurons in the next layer through Spike-Time-Dependent Plasticity (STDP), resulting in separate translation-invariant representations of each stimulus. Variations in key properties of the model are investigated with respect to the network’s ability to develop appropriate input representations and subsequently output representations through STDP. Contrary to earlier rate-coded models of this learning process, this work shows how spiking neural networks may learn about more than one stimulus together without suffering from the ‘superposition catastrophe’. We take these results to suggest that spiking dynamics are key to understanding biological visual object recognition.     

Invariant visual object recognition: a model, with lighting invariance.

How are invariant representations of objects formed in the visual cortex? We describe a neurophysiological and computational approach which focusses on a feature hierarchy model in which invariant representations can be built by self-organizing learning based on the statistics of the visual input. The model can use temporal continuity in an associative synaptic learning rule with a short term memory trace, and/or it can use spatial continuity in Continuous Transformation learning. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and in this paper we show also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in for example spatial and object search tasks. The model has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene.     

Unconscious letter discrimination is enhanced by association with conscious color perception in visual form agnosia

Adaptive behavior guided by unconscious visual cues occurs in patients with various kinds of brain damage as well as in normal observers, all of whom can process visual information of which they are fully unaware [1] [2] [3] [4] [5] [6] [7] [8]. Little is known on the possibility that unconscious vision is influenced by visual cues that have access to consciousness [9]. Here we report a 'blind' letter discrimination induced through a semantic interaction with conscious color processing in a patient who is agnosic for visual shapes, but has normal color vision and visual imagery. In seeing the initial letters of color names printed in different colors, it is normally easier to name the print color when it is congruent with the initial letter of the color name than when it is not [10]. The patient could discriminate the initial letters of the words 'red' and 'green' printed in the corresponding colors significantly above chance but without any conscious accompaniment, whereas he performed at chance with the reverse color-letter mapping as well as in standard tests of letter reading. We suggest that the consciously perceived colors activated a representation of the corresponding word names and their component letters, which in turn brought out a partially successful, unconscious processing of visual inputs corresponding to the activated letter representations.     

Visual exploration and object recognition by lattice deformation

Mechanisms of explicit object recognition are often difficult to investigate and require stimuli with controlled features whose expression can be manipulated in a precise quantitative fashion. Here, we developed a novel method (called "Dots"), for generating visual stimuli, which is based on the progressive deformation of a regular lattice of dots, driven by local contour information from images of objects. By applying progressively larger deformation to the lattice, the latter conveys progressively more information about the target object. Stimuli generated with the presented method enable a precise control of object-related information content while preserving low-level image statistics, globally, and affecting them only little, locally. We show that such stimuli are useful for investigating object recognition under a naturalistic setting--free visual exploration--enabling a clear dissociation between object detection and explicit recognition. Using the introduced stimuli, we show that top-down modulation induced by previous exposure to target objects can greatly influence perceptual decisions, lowering perceptual thresholds not only for object recognition but also for object detection (visual hysteresis). Visual hysteresis is target-specific, its expression and magnitude depending on the identity of individual objects. Relying on the particular features of dot stimuli and on eye-tracking measurements, we further demonstrate that top-down processes guide visual exploration, controlling how visual information is integrated by successive fixations. Prior knowledge about objects can guide saccades/fixations to sample locations that are supposed to be highly informative, even when the actual information is missing from those locations in the stimulus. The duration of individual fixations is modulated by the novelty and difficulty of the stimulus, likely reflecting cognitive demand.     

Sound facilitates visual learning

Numerous studies show that practice can result in performance improvements on low-level visual perceptual tasks [1-5]. However, such learning is characteristically difficult and slow, requiring many days of training [6-8]. Here, we show that a multisensory audiovisual training procedure facilitates visual learning and results in significantly faster learning than unisensory visual training. We trained one group of subjects with an audiovisual motion-detection task and a second group with a visual motion-detection task, and compared performance on trials containing only visual signals across ten days of training. Whereas observers in both groups showed improvements of visual sensitivity with training, subjects trained with multisensory stimuli showed significantly more learning both within and across training sessions. These benefits of multisensory training are particularly surprising given that the learning of visual motion stimuli is generally thought to be mediated by low-level visual brain areas [6, 9, 10]. Although crossmodal interactions are ubiquitous in human perceptual processing [11-13], the contribution of crossmodal information to perceptual learning has not been studied previously. Our results show that multisensory interactions can be exploited to yield more efficient learning of sensory information and suggest that multisensory training programs would be most effective for the acquisition of new skills.     

Visual categorization: when categories fall to pieces

We cannot help but categorize the visual world into objects like cats and faces. An intriguing new study shows that observers automatically discover informative fragments of visual objects during category learning.     

Spatial attention changes excitability of human visual cortex to direct stimulation

Conscious perception depends not only on sensory input, but also on attention [1, 2]. Recent studies in monkeys [3-6] and humans [7-12] suggest that influences of spatial attention on visual awareness may reflect top-down influences on excitability of visual cortex. Here we tested this specifically, by providing direct input into human visual cortex via cortical transcranial magnetic stimulation (TMS) to produce illusory visual percepts, called phosphenes. We found that a lower TMS intensity was needed to elicit a conscious phosphene when its apparent spatial location was attended, rather than unattended. Our results indicate that spatial attention can enhance visual-cortex excitability, and visual awareness, even when sensory signals from the eye via the thalamic pathway are bypassed.     

Visual, haptic and cross-modal recognition of objects and scenes.

In this article we review current literature on cross-modal recognition and present new findings from our studies on object and scene recognition. Specifically, we address the questions of what is the nature of the representation underlying each sensory system that facilitates convergence across the senses and how perception is modified by the interaction of the senses. In the first set of our experiments, the recognition of unfamiliar objects within and across the visual and haptic modalities was investigated under conditions of changes in orientation (0 degrees or 180 degrees ). An orientation change increased recognition errors within each modality but this effect was reduced across modalities. Our results suggest that cross-modal object representations of objects are mediated by surface-dependent representations. In a second series of experiments, we investigated how spatial information is integrated across modalities and viewpoint using scenes of familiar, 3D objects as stimuli. We found that scene recognition performance was less efficient when there was either a change in modality, or in orientation, between learning and test. Furthermore, haptic learning was selectively disrupted by a verbal interpolation task. Our findings are discussed with reference to separate spatial encoding of visual and haptic scenes. We conclude by discussing a number of constraints under which cross-modal integration is optimal for object recognition. These constraints include the nature of the task, and the amount of spatial and temporal congruency of information across the modalities.     

Visual learning in multiple-object tracking

Background

Tracking moving objects in space is important for the maintenance of spatiotemporal continuity in everyday visual tasks. In the laboratory, this ability is tested using the Multiple Object Tracking (MOT) task, where participants track a subset of moving objects with attention over an extended period of time. The ability to track multiple objects with attention is severely limited. Recent research has shown that this ability may improve with extensive practice (e.g., from action videogame playing). However, whether tracking also improves in a short training session with repeated trajectories has rarely been investigated. In this study we examine the role of visual learning in multiple-object tracking and characterize how varieties of attention interact with visual learning.

Methodology/Principal Findings

Participants first conducted attentive tracking on trials with repeated motion trajectories for a short session. In a transfer phase we used the same motion trajectories but changed the role of tracking targets and nontargets. We found that compared with novel trials, tracking was enhanced only when the target subset was the same as that used during training. Learning did not transfer when the previously trained targets and nontargets switched roles or mixed up. However, learning was not specific to the trained temporal order as it transferred to trials where the motion was played backwards.

Conclusions/Significance

These findings suggest that a demanding task of tracking multiple objects can benefit from learning of repeated motion trajectories. Such learning potentially facilitates tracking in natural vision, although learning is largely confined to the trajectories of attended objects. Furthermore, we showed that learning in attentive tracking relies on relational coding of all target trajectories. Surprisingly, learning was not specific to the trained temporal context, probably because observers have learned motion paths of each trajectory independently of the exact temporal order.     

Visual modelling

The first purpose of this paper is to present a neural net model of the visual cortex of higher vertebrates based on the electrophysiological properties of the ganglion cells. This model takes Hebb's law [1] as the physiological learning rule for synaptic modification. The model consists of 85 × 85 neurons forming a layer similar to the cortex. The neurones are massively connected via weights that are typically adapted. We simulate several input patterns and show that the model reproduces the pattern recognition, contours pictures and moving perception.     

A preference for contralateral stimuli in human object- and face-selective cortex

Visual input from the left and right visual fields is processed predominantly in the contralateral hemisphere. Here we investigated whether this preference for contralateral over ipsilateral stimuli is also found in high-level visual areas that are important for the recognition of objects and faces. Human subjects were scanned with functional magnetic resonance imaging (fMRI) while they viewed and attended faces, objects, scenes, and scrambled images in the left or right visual field. With our stimulation protocol, primary visual cortex responded only to contralateral stimuli. The contralateral preference was smaller in object- and face-selective regions, and it was smallest in the fusiform gyrus. Nevertheless, each region showed a significant preference for contralateral stimuli. These results indicate that sensitivity to stimulus position is present even in high-level ventral visual cortex.     

Filling-in and suppression of visual perception from context: a Bayesian account of perceptual biases by contextual influences

Visual object recognition and sensitivity to image features are largely influenced by contextual inputs. We study influences by contextual bars on the bias to perceive or infer the presence of a target bar, rather than on the sensitivity to image features. Human observers judged from a briefly presented stimulus whether a target bar of a known orientation and shape is present at the center of a display, given a weak or missing input contrast at the target location with or without a context of other bars. Observers are more likely to perceive a target when the context has a weaker rather than stronger contrast. When the context can perceptually group well with the would-be target, weak contrast contextual bars bias the observers to perceive a target relative to the condition without contexts, as if to fill in the target. Meanwhile, high-contrast contextual bars, regardless of whether they group well with the target, bias the observers to perceive no target. A Bayesian model of visual inference is shown to account for the data well, illustrating that the context influences the perception in two ways: (1) biasing observers' prior belief that a target should be present according to visual grouping principles, and (2) biasing observers' internal model of the likely input contrasts caused by a target bar. According to this model, our data suggest that the context does not influence the perceived target contrast despite its influence on the bias to perceive the target's presence, thereby suggesting that cortical areas beyond the primary visual cortex are responsible for the visual inferences.     

Learning invariant object recognition in the visual system with continuous transformations

The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm “continuous transformation learning” which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.