Learning and neural plasticity in visual object recognition

The capability of the adult primate visual system for rapid and accurate recognition of targets in cluttered, natural scenes far surpasses the abilities of state-of-the-art artificial vision systems. Understanding this capability remains a fundamental challenge in visual neuroscience. Recent experimental evidence suggests that adaptive coding strategies facilitated by underlying neural plasticity enable the adult brain to learn from visual experience and shape its ability to integrate and recognize coherent visual objects.     

Innate and learned components of human visual preference.

BACKGROUND: Recent claims in neuroscience and evolutionary biology suggest that the aesthetic sense reflects preferences for image signals whose characteristics best fit innate brain mechanisms of visual recognition. RESULTS: This hypothesis was tested by behaviourally measuring, for a set of initially unfamiliar images, the effects of category learning on preference judgements by humans, and by relating the observed data to computationally reconstructed internal representations of categorical concepts. Category learning induced complex shifts in preference behaviour. Two distinct factors - complexity and bilateral symmetry - could be identified from the data as determinants of preference judgements. The effect of the complexity factor varied with object knowledge acquired through category learning. In contrast, the impact of the symmetry factor proved to be unaffected by learning experience. Computer simulations suggested that the preference for pattern complexity relies on active (top-down) mechanisms of visual recognition, whereas the preference for pattern symmetry depends on automatic (bottom-up) mechanisms. CONCLUSIONS: Human visual preferences are not fully determined by (objective) structural regularities of image stimuli but also depend on their learned (subjective) interpretation. These two aspects are reflected in distinct complementary factors underlying preference judgements, and may be related to complementary modes of visual processing in the brain.     

The human EEG in recognition of noisy visual images accompanied by music

The recognition of noisy visual images (Arabic numerals) carried out under the conditions of recognition accompanied by classical or rock music of a specified power improves the recognition. When music-accompanied recognition of visual images is carried out, the structure of coherent relations (their number and significance level) observed during recognition without musical accompaniment is activated and the left-sided asymmetry of the coherence values is also retained (instead of the right-sided asymmetry, which appears in response to music alone). In other words, when the recognition dominant is formed at the behavioral level, changes in the spatial organization of the neocortical electrical processes also seem to follow the dominant principle.     

Category learning induces position invariance of pattern recognition across the visual field

Human object recognition is considered to be largely invariant to translation across the visual field. However, the origin of this invariance to positional changes has remained elusive, since numerous studies found that the ability to discriminate between visual patterns develops in a largely location-specific manner, with only a limited transfer to novel visual field positions. In order to reconcile these contradicting observations, we traced the acquisition of categories of unfamiliar grey-level patterns within an interleaved learning and testing paradigm that involved either the same or different retinal locations. Our results show that position invariance is an emergent property of category learning. Pattern categories acquired over several hours at a fixed location in either the peripheral or central visual field gradually become accessible at new locations without any position-specific feedback. Furthermore, categories of novel patterns presented in the left hemifield are distinctly faster learnt and better generalized to other locations than those learnt in the right hemifield. Our results suggest that during learning initially position-specific representations of categories based on spatial pattern structure become encoded in a relational, position-invariant format. Such representational shifts may provide a generic mechanism to achieve perceptual invariance in object recognition.     

Cortical mechanisms specific to explicit visual object recognition

The cortical mechanisms associated with conscious object recognition were studied using functional magnetic resonance imaging (fMRI). Participants were required to recognize pictures of masked objects that were presented very briefly, randomly and repeatedly. This design yielded a gradual accomplishment of successful recognition. Cortical activity in a ventrotemporal visual region was linearly correlated with perception of object identity. Therefore, although object recognition is rapid, awareness of an object's identity is not a discrete phenomenon but rather associated with gradually increasing cortical activity. Furthermore, the focus of the activity in the temporal cortex shifted anteriorly as subjects reported an increased knowledge regarding identity. The results presented here provide new insights into the processes underlying explicit object recognition, as well as the analysis that takes place immediately before and after recognition is possible.     

Sensitivity variations in the visual system,contrast resolution and eye movements

Attention is drawn to the fact that under normal visual conditions the sensitivity of the receptor units of the visual system are subject to spatial and temporal variations, and that consequently in performing pattern recognition the visual cortex has to discriminate between external luminance structure and internal sensitivity structure. It is suggested that eye movements are the method by which this discrimination is performed. In a simplified model analysis it is shown that eye movements are a suitable mechanism for this discrimination. Implications of this model for detection threshold and stabilized retinal images are discussed. A new interpretation of the adaptation to sine wave grids is given.     

A computational theory of visual attention.

A computational theory of visual attention is presented. The basic theory (TVA) combines the biased-choice model for single-stimulus recognition with the fixed-capacity independent race model (FIRM) for selection from multi-element displays. TVA organizes a large body of experimental findings on performance in visual recognition and attention tasks. A recent development (CTVA) combines TVA with a theory of perceptual grouping by proximity. CTVA explains effects of perceptual grouping and spatial distance between items in multi-element displays. A new account of spatial focusing is proposed in this paper. The account provides a framework for understanding visual search as an interplay between serial and parallel processes.     

Compensation for disruptions of visual function in the dog after partial exclusion of the neocortex

The recovery of visual disturbances provoked by partial neocortical deafferentation was studied in dogs with two sets of visual patterns: figures and differently oriented lines. The disturbances were more prominent, when the dogs had to solve the most complex visual tasks (6 stimuli showed to a dog simultaneously). There was a distinct recovery of figure recognition in the process of compensation, i. e. in 4--6 months after the neocortical deafferentation. On the contrary, recognition of oriented lines did not improve. Probably the differences between the two sorts of recognition mentioned above may be explained by various changes of their mechanisms' properties in the process of compensation.     

Overt visual attention as a causal factor of perceptual awareness

Our everyday conscious experience of the visual world is fundamentally shaped by the interaction of overt visual attention and object awareness. Although the principal impact of both components is undisputed, it is still unclear how they interact. Here we recorded eye-movements preceding and following conscious object recognition, collected during the free inspection of ambiguous and corresponding unambiguous stimuli. Using this paradigm, we demonstrate that fixations recorded prior to object awareness predict the later recognized object identity, and that subjects accumulate more evidence that is consistent with their later percept than for the alternative. The timing of reached awareness was verified by a reaction-time based correction method and also based on changes in pupil dilation. Control experiments, in which we manipulated the initial locus of visual attention, confirm a causal influence of overt attention on the subsequent result of object perception. The current study thus demonstrates that distinct patterns of overt attentional selection precede object awareness and thereby directly builds on recent electrophysiological findings suggesting two distinct neuronal mechanisms underlying the two phenomena. Our results emphasize the crucial importance of overt visual attention in the formation of our conscious experience of the visual world.     

A cognitive set and top-down processes in the functional organization of visual recognition

A top-down processing model and the role of frontal and prefrontal cortex in the neural mechanisms of recognition of verbal and non-verbal visual stimuli, are discussed. Stimulus recognition is considered to be a result of integration of the bottom-up and top-down streams. The cognitive set phenomenon can be used for studying the downward control in the neurocognitive visual system and in functional organisation of stimuli recognition.     

Neural mechanisms of human texture processing: texture boundary detection and visual search

Texture of various appearances, geometric distortions, spatial frequency content and densities is utilized by the human visual system to segregate items from background and to enable recognition of complex geometric forms. For automatic, or pre-attentive, segmentation of a visual scene, sophisticated analysis and comparison of surface properties over wide areas of the visual field are required. We investigated the neural substrate underlying human texture processing, particularly the computational mechanisms of texture boundary detection. We present a neural network model which uses as building blocks model cortical areas that are bi-directionally linked to implement cycles of feedforward and feedback interaction for signal detection, hypothesis generation and testing within the infero-temporal pathway of form processing. In the spirit of Jake Beck's early investigations our model particularly builds upon two key hypotheses, namely that (i) texture segregation is based on boundary detection, rather than clustering homogeneous items, and (ii) texture boundaries are detected mainly on the basis of larger scenic contexts mediated by higher cortical areas, such as area V4. The latter constraint provides a basis for element grouping in accordance to the Gestalt laws of similarity and good continuation. It is shown through simulations that the model integrates a variety of psychophysical findings on texture processing and provides a link to the underlying physiology. The functional role of feedback processing is demonstrated by context dependent modulation of V1 cell activation, leading to sharply localized detection of texture boundaries. It furthermore explains why pre-attentive processing in visual search tasks can be directly linked to texture boundary processing as revealed by recent EEG studies on visual search.     

Temporal codes and sparse representations: A key to understanding rapid processing in the visual system

Where neural information processing is concerned, there is no debate about the fact that spikes are the basic currency for transmitting information between neurons. How the brain actually uses them to encode information remains more controversial. It is commonly assumed that neuronal firing rate is the key variable, but the speed with which images can be analysed by the visual system poses a major challenge for rate-based approaches. We will thus expose here the possibility that the brain makes use of the spatio-temporal structure of spike patterns to encode information. We then consider how such rapid selective neural responses can be generated rapidly through spike-timing-dependent plasticity (STDP) and how these selectivities can be used for visual representation and recognition. Finally, we show how temporal codes and sparse representations may very well arise one from another and explain some of the remarkable features of processing in the visual system.     

Reconstructing mental object representations: a machine vision approach to human visual recognition

This paper introduces a new approach to assess visual representations underlying the recognition of objects. Human performance is modeled by CLARET, a machine learning and matching system, based on inductive logic programming and graph matching principles. The model is applied to data of a learning experiment addressing the role of prior experience in the ontogenesis of mental object representations. Prior experience was varied in terms of sensory modality, i.e. visual versus haptic versus visuohaptic. The analysis revealed distinct differences between the representational formats used by subjects with haptic versus those with no prior object experience. These differences suggest that prior haptic exploration stimulates the evolution of object representations which are characterized by an increased differentiation between attribute values and a pronounced structural encoding.     

Visual saliency and spike timing in the ventral visual pathway.

Visual saliency is a fundamental yet hard to define property of objects or locations in the visual world. In a context where objects and their representations compete to dominate our perception, saliency can be thought of as the "juice" that makes objects win the race. It is often assumed that saliency is extracted and represented in an explicit saliency map, which serves to determine the location of spatial attention at any given time. It is then by drawing attention to a salient object that it can be recognized or categorized. I argue against this classical view that visual "bottom-up" saliency automatically recruits the attentional system prior to object recognition. A number of visual processing tasks are clearly performed too fast for such a costly strategy to be employed. Rather, visual attention could simply act by biasing a saliency-based object recognition system. Under natural conditions of stimulation, saliency can be represented implicitly throughout the ventral visual pathway, independent of any explicit saliency map. At any given level, the most activated cells of the neural population simply represent the most salient locations. The notion of saliency itself grows increasingly complex throughout the system, mostly based on luminance contrast until information reaches visual cortex, gradually incorporating information about features such as orientation or color in primary visual cortex and early extrastriate areas, and finally the identity and behavioral relevance of objects in temporal cortex and beyond. Under these conditions the object that dominates perception, i.e. the object yielding the strongest (or the first) selective neural response, is by definition the one whose features are most "salient"--without the need for any external saliency map. In addition, I suggest that such an implicit representation of saliency can be best encoded in the relative times of the first spikes fired in a given neuronal population. In accordance with our subjective experience that saliency and attention do not modify the appearance of objects, the feed-forward propagation of this first spike wave could serve to trigger saliency-based object recognition outside the realm of awareness, while conscious perceptions could be mediated by the remaining discharges of longer neuronal spike trains.     

Improving the performance of the amblyopic visual system

Experience-dependent plasticity is closely linked with the development of sensory function; however, there is also growing evidence for plasticity in the adult visual system. This review re-examines the notion of a sensitive period for the treatment of amblyopia in the light of recent experimental and clinical evidence for neural plasticity. One recently proposed method for improving the effectiveness and efficiency of treatment that has received considerable attention is ‘perceptual learning’. Specifically, both children and adults with amblyopia can improve their perceptual performance through extensive practice on a challenging visual task. The results suggest that perceptual learning may be effective in improving a range of visual performance and, importantly, the improvements may transfer to visual acuity. Recent studies have sought to explore the limits and time course of perceptual learning as an adjunct to occlusion and to investigate the neural mechanisms underlying the visual improvement. These findings, along with the results of new clinical trials, suggest that it might be time to reconsider our notions about neural plasticity in amblyopia.     

Research progress on Drosophila visual cognition in China

Visual cognition,as one of the fundamental aspects of cognitive neuroscience,is generally associated with high-order brain functions in animals and human.Drosophila,as a model organism,shares certain features of visual cognition in common with mammals at the genetic,molecular,cellular,and even higher behavioral levels.From learning and memory to decision making,Drosophila covers a broad spectrum of higher cognitive behaviors beyond what we had expected.Armed with powerful tools of genetic manipulation in Drosophila,an increasing number of studies have been conducted in order to elucidate the neural circuit mechanisms underlying these cognitive behaviors from a genes-brain-behavior perspective.The goal of this review is to integrate the most important studies on visual cognition in Drosophila carried out in mainland China during the last decade into a body of knowledge encompassing both the basic neural operations and circuitry of higher brain function in Drosophila.Here,we consider a series of the higher cognitive behaviors beyond learning and memory,such as visual pattern recognition,feature and context generalization,different feature memory traces,salience-based decision,attention-like behavior,and cross-modal leaning and memory.We discuss the possible general gain-gating mechanism implementing by dopamine-mushroom body circuit in fly's visual cognition.We hope that our brief review on this aspect will inspire further study on visual cognition in flies,or even beyond.     

One-trial visual recognition in cats: the role of the rhinal cortex

Visual recognition memory in primates is mediated at least in part by the perirhinal and entorhinal (i.e., rhinal) cortices. To examine the role of these structures in cats' visual recognition memory, we performed combined electrolytic rhinal (perirhinal and entorhinal) lesions in a group of cats trained in visual delayed matching-to-sample with trial-unique objects in the modified Wisconsin General Testing Apparatus. Sham-operated and intact cats were used as control groups. Cats with rhinal lesions did not differ from the control sham-operated and unoperated groups in initial learning of the rules of the task; difference between experimental and control groups under conditions of minimum 5-sec delay was nonsignificant as well. However, significant difference between experimental and control groups was revealed under conditions of testing with 10-sec delay. This finding suggests a disorder in the visual recognition memory.     

Adult visual cortical plasticity

The visual cortex has the capacity for experience-dependent change, or cortical plasticity, that is retained throughout life. Plasticity is invoked for encoding information during perceptual learning, by internally representing the regularities of the visual environment, which is useful for facilitating intermediate-level vision-contour integration and surface segmentation. The same mechanisms have adaptive value for functional recovery after CNS damage, such as that associated with stroke or neurodegenerative disease. A common feature to plasticity in primary visual cortex (V1) is an association field that links contour elements across the visual field. The circuitry underlying the association field includes a plexus of long-range horizontal connections formed by cortical pyramidal cells. These connections undergo rapid and exuberant sprouting and pruning in response to removal of sensory input, which can account for the topographic reorganization following retinal lesions. Similar alterations in cortical circuitry may be involved in perceptual learning, and the changes observed in V1 may be representative of how learned information is encoded throughout the cerebral cortex.