Neural mechanisms of object recognition

Single-unit recordings from behaving monkeys and human functional magnetic resonance imaging studies have continued to provide a host of experimental data on the properties and mechanisms of object recognition in cortex. Recent advances in object recognition, spanning issues regarding invariance, selectivity, representation and levels of recognition have allowed us to propose a putative model of object recognition in cortex.     

Neuropsychological studies of object recognition

It is well established that disorders of visual perception are associated with lesions in the right hemisphere. Performances on tasks as disparate as the identification of objects from unusual views of objects drawn so as to overlap, of fragmented letters, of familiar faces, and of anomalous features in drawings, have been shown to be impaired in patients with focal right posterior lesions. A series of investigations are reviewed, directed towards analysing the basis of these deficits. Explanations in terms of primary visual impairment can be rejected, as can an account in terms of faulty figure-ground organization. It is argued that a wide variety of such perceptual deficits--all of which are concerned with meaningful visual stimuli--can be encompassed by the notion of faulty perceptual categorization at an early post-sensory stage of object recognition. Moreover, there is evidence suggesting that some of these various perceptual deficits can be mutually dissociated. The concept of perceptual categorization is discussed in the wider context of tentative model of object recognition.     

Models of object recognition.

Progress in the understanding of visual recognition in the past year has been signified by the demonstration of computational feasibility of and psychophysical support for two-dimensional view-interpolation methods.     

Interference with bottom-up feature detection by higher-level object recognition

Drawing portraits upside down is a trick that allows novice artists to reproduce lower-level image features, e.g., contours, while reducing interference from higher-level face cognition. Limiting the available processing time to suffice for lower- but not higher-level operations is a more general way of reducing interference. We elucidate this interference in a novel visual-search task to find a target among distractors. The target had a unique lower-level orientation feature but was identical to distractors in its higher-level object shape. Through bottom-up processes, the unique feature attracted gaze to the target. Subsequently, recognizing the attended object as identically shaped as the distractors, viewpoint invariant object recognition interfered. Consequently, gaze often abandoned the target to search elsewhere. If the search stimulus was extinguished at time T after the gaze arrived at the target, reports of target location were more accurate for shorter (T<500 ms) presentations. This object-to-feature interference, though perhaps unexpected, could underlie common phenomena such as the visual-search asymmetry that finding a familiar letter N among its mirror images is more difficult than the converse. Our results should enable additional examination of known phenomena and interactions between different levels of visual processes.     

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.     

Disruptive camouflage impairs object recognition

Whether hiding from predators, or avoiding battlefield casualties, camouflage is widely employed to prevent detection. Disruptive coloration is a seemingly well-known camouflage mechanism proposed to function by breaking up an object''s salient features (for example their characteristic outline), rendering objects more difficult to recognize. However, while a wide range of animals are thought to evade detection using disruptive patterns, there is no direct experimental evidence that disruptive coloration impairs recognition. Using humans searching for computer-generated moth targets, we demonstrate that the number of edge-intersecting patches on a target reduces the likelihood of it being detected, even at the expense of reduced background matching. Crucially, eye-tracking data show that targets with more edge-intersecting patches were looked at for longer periods prior to attack, and passed-over more frequently during search tasks. We therefore show directly that edge patches enhance survivorship by impairing recognition, confirming that disruptive coloration is a distinct camouflage strategy, not simply an artefact of background matching.     

The crowded window of object recognition revealed by cortical population codes

Science China Life Sciences -     

Field of attention for instantaneous object recognition

Background

Instantaneous object discrimination and categorization are fundamental cognitive capacities performed with the guidance of visual attention. Visual attention enables selection of a salient object within a limited area of the visual field; we referred to as “field of attention” (FA). Though there is some evidence concerning the spatial extent of object recognition, the following questions still remain unknown: (a) how large is the FA for rapid object categorization, (b) how accuracy of attention is distributed over the FA, and (c) how fast complex objects can be categorized when presented against backgrounds formed by natural scenes.

Methodology/Principal Findings

To answer these questions, we used a visual perceptual task in which subjects were asked to focus their attention on a point while being required to categorize briefly flashed (20 ms) photographs of natural scenes by indicating whether or not these contained an animal. By measuring the accuracy of categorization at different eccentricities from the fixation point, we were able to determine the spatial extent and the distribution of accuracy over the FA, as well as the speed of categorizing objects using stimulus onset asynchrony (SOA). Our results revealed that subjects are able to rapidly categorize complex natural images within about 0.1 s without eye movement, and showed that the FA for instantaneous image categorization covers a visual field extending 20°×24°, and accuracy was highest (>90%) at the center of FA and declined with increasing eccentricity.

Conclusions/Significance

In conclusion, human beings are able to categorize complex natural images at a glance over a large extent of the visual field without eye movement.     

Low-level correlations between object properties and viewpoint can cause viewpoint-dependent object recognition

Viewpoint-dependent recognition performance of 3-D objects has often been taken as an indication of a viewpoint-dependent object representation. This viewpoint dependence is most often found using metrically manipulated objects. We aim to investigate whether instead these results can be explained by viewpoint and object property (e.g. curvature) information not being processed independently at a lower level, prior to object recognition itself. Multidimensional signal detection theory offers a useful framework, allowing us to model this as a low-level correlation between the internal noise distributions of viewpoint and object property dimensions. In Experiment 1, we measured these correlations using both Yes/No and adjustment tasks. We found a good correspondence across tasks, but large individual differences. In Experiment 2, we compared these results to the viewpoint dependence of object recognition through a Yes/No categorization task. We found that viewpoint-independent object recognition could not be fully reached using our stimuli, and that the pattern of viewpoint dependence was strongly correlated with the low-level correlations we measured earlier. In part, however, the viewpoint was abstracted despite these correlations. We conclude that low-level correlations do exist prior to object recognition, and can offer an explanation for some viewpoint effects on the discrimination of metrically manipulated 3-D objects.     

Classification of insects by echolocating greater horseshoe bats

Summary Echolocating greater horseshoe bats (Rhinolophus ferrumequinum) detect insects by concentrating on the characteristic amplitude- and frequency modulation pattern fluttering insects impose on the returning echoes. This study shows that horseshoe bats can also further analyse insect echoes and thus recognize and categorize the kind of insect they are echolocating.Four greater horseshoe bats were trained in a twoalternative forced-choice procedure to choose the echo of one particular insect species turning its side towards the bat (Fig. 1). The bats were able to discriminate with over 90% correct choices between the reward-positive echo and the echoes of other insect species all fluttering with exactly the same wingbeat rate (Fig. 4).When the angular orientation of the reward-positive insect was changed (Fig. 2), the bats still preferred these unknown echoes over echoes from other insect species (Fig. 5) without any further training. Because the untrained bats did not show any prey preference, this indicates that the bats were able to perform an aspect-anglein-dependent classification of insects.Finally we tested what parameters in the echo were responsible for species recognition. It turned out that the bats especially used the small echo-modulations in between glints as a source of information (Fig. 7). Neither the amplitudenor the frequencymodulation of the echoes alone was sufficient for recognition of the insect species (Fig. 8). Bats performed a pattern recognition task based on complex computations of several acoustic parameters, an ability which might be termed cognitive.Abbreviations AM amplitude modulation - CF constant frequency - FM frequency modulation - S+ positive stimulus - S- negative stimulus     

Mechanisms of visual object recognition studied in monkeys

Cells in area TE of the inferotemporal cortex of the monkey brain selectively respond to various moderately complex object-features, and those responding to similar features cluster in a columnar region elongated vertical to the cortical surface. Although cells within a column respond to similar features, their selectivity is not identical. The data of optical imaging in TE have suggested that the borders between neighboring columns are not discrete but columns representing related features overlap one another. We have also found, by training adult monkeys for discrimination of a specific set of shapes, that such a long-term training increases the proportion of TE cells responding to the shapes used in the training even in the adult. The data suggested that TE plays important roles in discrimination of complex shapes and in visual expert learning of discriminating a certain class of objects in the adult.     

Cortical mechanisms of sensory learning and object recognition

Learning about the world through our senses constrains our ability to recognise our surroundings. Experience shapes perception. What is the neural basis for object recognition and how are learning-induced changes in recognition manifested in neural populations? We consider first the location of neurons that appear to be critical for object recognition, before describing what is known about their function. Two complementary processes of object recognition are considered: discrimination among diagnostic object features and generalization across non-diagnostic features. Neural plasticity appears to underlie the development of discrimination and generalization for a given set of features, though tracking these changes directly over the course of learning has remained an elusive task.     

Symmetry perception by poultry chicks and its implications for three-dimensional object recognition

Bilateral symmetry is visually salient to diverse animals including birds, but whereas experimental studies typically use bilaterally symmetrical two-dimensional patterns that are viewed approximately fronto-parallel; in nature, animals observe three-dimensional objects from all angles. Many animals and plant structures have a plane of bilateral symmetry. Here, we first (experiment I) give evidence that young poultry chicks readily generalize bilateral symmetry as a feature of two-dimensional patterns in fronto-parallel view. We then test the ability of chicks to recognize symmetry in images that would be produced by the transformed view produced by a 40° horizontal combined with a 20° vertical rotation of a pattern on a spherical surface. Experiment II gives evidence that chicks trained to distinguish symmetrical from asymmetrical patterns treat rotated views of symmetrical 'objects' as symmetrical. Experiment III gives evidence that chicks trained to discriminate rotated views of symmetrical 'objects' from asymmetrical patterns generalize to novel symmetrical objects either in fronto-parallel or rotated view. These findings emphasize the importance of bilateral symmetry for three-dimensional object recognition and raise questions about the underlying mechanisms of symmetry perception.