Analyzing vision at the complexity level

Combining feature- and correspondence-based methods for visual object recognition

We present an object recognition system built on a combination of feature- and correspondence-based pattern recognizers. The feature-based part, called preselection network, is a single-layer feedforward network weighted with the amount of information contributed by each feature to the decision at hand. For processing arbitrary objects, we employ small, regular graphs whose nodes are attributed with Gabor amplitudes, termed parquet graphs. The preselection network can quickly rule out most irrelevant matches and leaves only the ambiguous cases, so-called model candidates, to be verified by a rudimentary version of elastic graph matching, a standard correspondence-based technique for face and object recognition. According to the model, graphs are constructed that describe the object in the input image well. We report the results of experiments on standard databases for object recognition. The method achieved high recognition rates on identity and pose. Unlike many other models, it can also cope with varying background, multiple objects, and partial occlusion.

Connectivity and signal intensity in the parieto-occipital cortex predicts top-down attentional effect in visual masking: an fMRI study based on individual differences

Top-down attention affects even the early stages of visual processing. For example, several studies have reported that instructions prior to the presentation of visual stimuli can both enhance and reduce visual masking. The finding that top-down processing influences perceptual processing is called the attentional effect. However, the magnitude of the attentional effect differs between individuals, and how these differences relate to brain activation remains to be explained. One possibility would be that activation intensity predicts the magnitude of the attentional effect. Another possible explanation would be that effective connectivity among activated areas determines the attentional effect. In the present study, we used structural equation modeling to analyze individual differences in the attentional effect on visual masking, in relation to the signal and connectivity strength of activated brain regions prior to presentation of the visual stimuli. The results showed that signal intensity was positively correlated with attentional effect in the occipital areas, but not in fronto-parietal areas, and the effect was also positively correlated with connective efficiency from the right intraparietal sulcus (IPS) to the bilateral fusiform gyrus (GF). Furthermore, a higher degree of effective connections from the right IPS to the GF led to greater neural activity in the GF. We therefore propose that the effective modulator in the parietal areas and strong activation in the visual areas together and in cooperation predict higher attentional effects in visual processing.

An Attentional Mechanism for Selecting Appropriate Actions Afforded by Graspable Objects

An object may afford a number of different actions. In this article, we show that an attentional mechanism inhibits competing motor programs that could elicit erroneous actions. Participants made a speeded key press to categorize the second of two successively presented door handles that were rotated at varying orientations relative to one another. Their responding hand was compatible or incompatible with the graspable part of the door handles (rightward or leftward facing). Compatible responses were faster than incompatible responses if the two handles shared an identical orientation, but they were slower if the two handles were aligned at slightly dissimilar orientations. Such suppressive surround effects are hallmarks of attentional processing in the visual domain, but they have never been observed behaviorally in the motor domain. This finding delineates a common mechanism involved in two of the most important functions of the brain: processing sensory data and preparing actions based on that information.

Outline of a novel architecture for cortical computation

In this paper a novel architecture for cortical computation has been proposed. This architecture is composed of computing paths consisting of neurons and synapses. These paths have been decomposed into lateral, longitudinal and vertical components. Cortical computation has then been decomposed into lateral computation (LaC), longitudinal computation (LoC) and vertical computation (VeC). It has been shown that various loop structures in the cortical circuit play important roles in cortical computation as well as in memory storage and retrieval, keeping in conformity with the molecular basis of short and long term memory. A new learning scheme for the brain has also been proposed and how it is implemented within the proposed architecture has been explained. A few mathematical results about the architecture have been proposed, some of which are without proof.

The center-surround profile of the focus of attention arises from recurrent processing in visual cortex

We recently demonstrated with magnetoencephalographic recordings in human observers that the focus of attention in visual search has a spatial profile consisting of a center enhancement surrounded by a narrow zone of sensory attenuation. Here, we report new data from 2 experiments providing insights into the cortical processes that cause the surround attenuation. We show that surround suppression appears in search tasks that require spatial scrutiny, that is the precise binding of search-relevant features at the target's location but not in tasks that permit target discrimination without precise localization. Furthermore, we demonstrate that surround attenuation is linked with a stronger recurrent activity modulation in early visual cortex. Finally, we show that surround suppression appears with a delay (more than 175 ms) that is beyond the time course of the initial feedforward sweep of processing in the visual system. These observations together indicate that the suppressive surround is associated with recurrent processing and binding in the visual cortex.

Computational complexity analysis can help, but first we need a theory

Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.

Modeling the top-down influences on the lateral interactions in the visual cortex

Attention modulates the amount of excitatory and inhibitory lateral interactions in the visual cortex. A recurrent neural network is proposed to account for modulatory influence of top-down signals. In the model, two types of inhibitions are distinguished: dendritic and lateral inhibitions. Dendritic inhibition regulates the amount of impact that surrounding cells may exert on a target cell via the dendrites of excitatory neurons and the dendrites of subpopulation of inhibitory neurons mediating lateral inhibition. Attention increases the amount of dendritic inhibition and prevents contextual interactions, while it has no effect on the target cell when there is no surround input. Computer simulations showed that the proposed model is able to exhibit properties of attentional gating. In the condition of focused attention, neural activity in the presence of surrounding stimuli is restored to the level as when the target stimulus is presented alone. Moreover, the model is able to show contrast gain and response gain on the contrast sensitivity function depending on the strength of the dendritic inhibition.

Brain injury and the eye

Visual loss associated with brain damage is the single greatest cause of visual impairment in children in developed countries. Damage may occur in any of five separate visual systems: primary visual cortex, visual associative cortex area, optic radiations, optic nerves, and visual attention pathways. Improving our understanding of the pathophysiology of these causes for visual loss may lead to better rehabilitation and educational strategies for these children.

Attentional costs in multiple-object tracking

Attentional demands of multiple-object tracking were demonstrated using a dual-task paradigm. Participants were asked to make speeded responses based on the pitch of a tone, while at the same time tracking four of eight identical dots. Tracking difficulty was manipulated either concurrent with or after the tone task. If increasing tracking difficulty increases attentional demands, its effect should be larger when it occurs concurrent with the tone. In Experiment 1, tracking difficulty was manipulated by having all dots briefly attract one another on some trials, causing a transient increase in dot proximity and speed. Results showed that increasing proximity and speed had a significantly larger effect when it occurred at the same time as the tone task. Experiments 2 and 3 showed that manipulating either proximity or speed independently was sufficient to produce this pattern of results. Experiment 4 manipulated object contrast, which affected tracking performance equally whether it occurred concurrent with or after the tone task. Overall, results support the view that the moment-to-moment tracking of multiple objects demands attention. Understanding what factors increase the attentional demands of tracking may help to explain why tracking is sometimes successful and at other times fails.

The blinking spotlight of attention

Increasing evidence suggests that attention can concurrently select multiple locations; yet it is not clear whether this ability relies on continuous allocation of attention to the different targets (a "parallel" strategy) or whether attention switches rapidly between the targets (a periodic "sampling" strategy). Here, we propose a method to distinguish between these two alternatives. The human psychometric function for detection of a single target as a function of its duration can be used to predict the corresponding function for two or more attended targets. Importantly, the predicted curves differ, depending on whether a parallel or sampling strategy is assumed. For a challenging detection task, we found that human performance was best reflected by a sampling model, indicating that multiple items of interest were processed in series at a rate of approximately seven items per second. Surprisingly, the data suggested that attention operated in this periodic regime, even when it was focused on a single target. That is, attention might rely on an intrinsically periodic process.

Predicting visual fixations on video based on low-level visual features

To what extent can a computational model of the bottom-up visual attention predict what an observer is looking at? What is the contribution of the low-level visual features in the attention deployment? To answer these questions, a new spatio-temporal computational model is proposed. This model incorporates several visual features; therefore, a fusion algorithm is required to combine the different saliency maps (achromatic, chromatic and temporal). To quantitatively assess the model performances, eye movements were recorded while naive observers viewed natural dynamic scenes. Four completing metrics have been used. In addition, predictions from the proposed model are compared to the predictions from a state of the art model [Itti's model (Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254-1259)] and from three non-biologically plausible models (uniform, flicker and centered models). Regardless of the metric used, the proposed model shows significant improvement over the selected benchmarking models (except the centered model). Conclusions are drawn regarding both the influence of low-level visual features over time and the central bias in an eye tracking experiment.

Modeling attention to salient proto-objects

Selective visual attention is believed to be responsible for serializing visual information for recognizing one object at a time in a complex scene. But how can we attend to objects before they are recognized? In coherence theory of visual cognition, so-called proto-objects form volatile units of visual information that can be accessed by selective attention and subsequently validated as actual objects. We propose a biologically plausible model of forming and attending to proto-objects in natural scenes. We demonstrate that the suggested model can enable a model of object recognition in cortex to expand from recognizing individual objects in isolation to sequentially recognizing all objects in a more complex scene.

Psychophysical tests of the hypothesis of a bottom-up saliency map in primary visual cortex

A unique vertical bar among horizontal bars is salient and pops out perceptually. Physiological data have suggested that mechanisms in the primary visual cortex (V1) contribute to the high saliency of such a unique basic feature, but indicated little regarding whether V1 plays an essential or peripheral role in input-driven or bottom-up saliency. Meanwhile, a biologically based V1 model has suggested that V1 mechanisms can also explain bottom-up saliencies beyond the pop-out of basic features, such as the low saliency of a unique conjunction feature such as a red vertical bar among red horizontal and green vertical bars, under the hypothesis that the bottom-up saliency at any location is signaled by the activity of the most active cell responding to it regardless of the cell's preferred features such as color and orientation. The model can account for phenomena such as the difficulties in conjunction feature search, asymmetries in visual search, and how background irregularities affect ease of search. In this paper, we report nontrivial predictions from the V1 saliency hypothesis, and their psychophysical tests and confirmations. The prediction that most clearly distinguishes the V1 saliency hypothesis from other models is that task-irrelevant features could interfere in visual search or segmentation tasks which rely significantly on bottom-up saliency. For instance, irrelevant colors can interfere in an orientation-based task, and the presence of horizontal and vertical bars can impair performance in a task based on oblique bars. Furthermore, properties of the intracortical interactions and neural selectivities in V1 predict specific emergent phenomena associated with visual grouping. Our findings support the idea that a bottom-up saliency map can be at a lower visual area than traditionally expected, with implications for top-down selection mechanisms.

Only a fraction of visual input can be selected for attentional scrutiny, often by focusing on a limited extent of the visual space. The selected location is often determined by the bottom-up visual inputs rather than the top-down intentions. For example, a red dot among green ones automatically attracts attention and is said to be salient. Physiological data have suggested that the primary visual cortex (V1) in the brain contributes to creating such bottom-up saliencies from visual inputs, but indicated little on whether V1 plays an essential or peripheral role in creating a saliency map of the input space to guide attention. Traditional psychological frameworks, based mainly on behavioral data, have implicated higher-level brain areas for the saliency map. Recently, it has been hypothesized that V1 creates this saliency map, such that the image location whose visual input evokes the highest response among all V1 output neurons is most likely selected from a visual scene for attentional processing. This paper derives nontrivial predictions from this hypothesis and presents their psychophysical tests and confirmations. Our findings suggest that bottom-up saliency is computed at a lower brain area than previously expected, and have implications on top-down attentional mechanisms.

Direct neurophysiological evidence for spatial suppression surrounding the focus of attention in vision

The spatial focus of attention has traditionally been envisioned as a simple spatial gradient of enhanced activity that falls off monotonically with increasing distance. Here, we show with high-density magnetoencephalographic recordings in human observers that the focus of attention is not a simple monotonic gradient but instead contains an excitatory peak surrounded by a narrow inhibitory region. To demonstrate this center-surround profile, we asked subjects to focus attention onto a color pop-out target and then presented probe stimuli at various distances from the target. We observed that the electromagnetic response to the probe was enhanced when the probe was presented at the location of the target, but the probe response was suppressed in a narrow zone surrounding the target and then recovered at more distant locations. Withdrawing attention from the pop-out target by engaging observers in a demanding foveal task eliminated this pattern, confirming a truly attention-driven effect. These results indicate that neural enhancement and suppression coexist in a spatially structured manner that is optimal to attenuate the most deleterious noise during visual object identification.

Perceptual encoding efficiency in visual search

The authors present 10 experiments that challenge some central assumptions of the dominant theories of visual search. Their results reveal that the complexity (or redundancy) of nontarget items is a crucial but overlooked determinant of search efficiency. The authors offer a new theoretical outline that emphasizes the importance of nontarget encoding efficiency, and they test this proposal using dot pattern stimuli adapted from W. R. Garner and D. E. Clement (1963). The results provide converging support for the importance of nontarget encoding efficiency in accounting for visual search performance.

The role of attention in binding shape to color

Pictures of easily-identifiable objects with novel colors (e.g. a blue frog) or of forms with arbitrary colors (e.g. a green triangle) were presented briefly at 10.6 degrees eccentricity. Stimuli had strong outlines and vivid fill colors (red, green, yellow, blue, or purple). The same pictures were repeated once in each block of 30 trials for 6, 9, or 12 blocks, and recognition was probed after each block. Shapes were acquired quickly, within 3-4 blocks, whether attention was focused on the pictures or split to a demanding foveal task. Color-shape acquisition was also fast with focused attention, but stabilized at a low level with split attention. Delaying the foveal task restored color-shape acquisition. We suggest that attention facilitates the creation and maintenance of novel color-shape bindings in the visual periphery; without attention, binding is less effective.

Tuning Curve Shift by Attention Modulation in Cortical Neurons: a Computational Study of its Mechanisms

Physiological studies of visual attention have demonstrated that focusing attention near a visual cortical neuron's receptive field (RF) results in enhanced evoked activity and RF shift. In this work, we explored the mechanisms of attention induced RF shifts in cortical network models that receive an attentional 'spotlight'. Our main results are threefold. First, whereas a 'spotlight' input always produces toward-attention shift of the population activity profile, we found that toward-attention shifts in RFs of single cells requires multiplicative gain modulation. Secondly, in a feedforward two-layer model, focal attentional gain modulation in first-layer neurons induces RF shift in second-layer neurons downstream. In contrast to experimental observations, the feedforward model typically fails to produce RF shifts in second-layer neurons when attention is directed beyond RF boundaries. We then show that an additive spotlight input combined with a recurrent network mechanism can produce the observed RF shift. Inhibitory effects in a surround of the attentional focus accentuate this RF shift and induce RF shrinking. Thirdly, we considered interrelationship between visual selective attention and adaptation. Our analysis predicts that the RF size is enlarged (respectively reduced) by attentional signal directed near a cell's RF center in a recurrent network (resp. in a feedforward network); the opposite is true for visual adaptation. Therefore, a refined estimation of the RF size during attention and after adaptation would provide a probe to differentiate recurrent versus feedforward mechanisms for RF shifts.

Attention, short-term memory, and action selection: A unifying theory

Cognitive behaviour requires complex context-dependent processing of information that emerges from the links between attentional perceptual processes, working memory and reward-based evaluation of the performed actions. We describe a computational neuroscience theoretical framework which shows how an attentional state held in a short term memory in the prefrontal cortex can by top-down processing influence ventral and dorsal stream cortical areas using biased competition to account for many aspects of visual attention. We also show how within the prefrontal cortex an attentional bias can influence the mapping of sensory inputs to motor outputs, and thus play an important role in decision making. We also show how the absence of expected rewards can switch an attentional bias signal, and thus rapidly and flexibly alter cognitive performance. This theoretical framework incorporates spiking and synaptic dynamics which enable single neuron responses, fMRI activations, psychophysical results, the effects of pharmacological agents, and the effects of damage to parts of the system to be explicitly simulated and predicted. This computational neuroscience framework provides an approach for integrating different levels of investigation of brain function, and for understanding the relations between them. The models also directly address how bottom-up and top-down processes interact in visual cognition, and show how some apparently serial processes reflect the operation of interacting parallel distributed systems.

Push-Pull Mechanism of Selective Attention in Human Extrastriate Cortex

Selective attention operates in visual cortex by facilitating processing of selected stimuli and by filtering out unwanted information from nearby distracters over circumscribed regions of visual space. The neural representation of unattended stimuli outside this focus of attention is less well understood. We studied the neural fate of unattended stimuli using functional magnetic resonance imaging by dissociating the activity evoked by attended (target) stimuli presented to the periphery of a visual hemifield and unattended (distracter) stimuli presented simultaneously to a corresponding location of the contralateral hemifield. Subjects covertly directed attention to a series of target stimuli and performed either a low or a high attentional-load search task on a stream of otherwise identical stimuli. With this task, target-search-related activity increased with increasing attentional load, whereas distracter-related activity decreased with increasing load in areas V4 and TEO but not in early areas V1 and V2. This finding presents evidence for a load-dependent push-pull mechanism of selective attention that operates over large portions of the visual field at intermediate processing stages. This mechanism appeared to be controlled by a distributed frontoparietal network of brain areas that reflected processes related to target selection during spatially directed attention.

The role of salience in localized attentional interference

Observers were cued to attend to two discs from an array and made a discrimination of a target presented within one of the discs. In Experiments 1 and 2, the relative attentional salience of the two attended items was manipulated via the cues (size changes in Experiment 1; size and color changes in Experiment 2). In Experiment 3, the relative salience was manipulated via the luminance contrast of the items themselves. In Experiment 4, relative attentional salience was controlled through a probability manipulation. In all experiments, target performance improved with the relative salience of the target, as well as with increased spatial separation between the two items. This localized interference between cued items varied with visual field. Results are discussed in the context of competition-based models of attentional selection.

The functional logic of cortico-pulvinar connections

The pulvinar is an 'associative' thalamic nucleus, meaning that most of its input and output relationships are formed with the cerebral cortex. The function of this circuitry is little understood and its anatomy, though much investigated, is notably recondite. This is because pulvinar connection patterns disrespect the architectural subunits (anterior, medial, lateral and inferior pulvinar nuclei) that have been the traditional reference system. This article presents a simplified, global model of the organization of cortico-pulvinar connections so as to pursue their structure-function relationships. Connections between the cortex and pulvinar are topographically organized, and as a result the pulvinar contains a 'map' of the cortical sheet. However, the topography is very blurred. Hence the pulvinar connection zones of nearby cortical areas overlap, allowing indirect transcortical communication via the pulvinar. A general observation is that indirect cortico-pulvino-cortical circuits tend to mimic direct cortico-cortical pathways: this is termed 'the replication principle'. It is equally apt for certain pairs (or groups) of nearby cortical areas that happen not to connect with each other. The 'replication' of this non-connection is achieved by discontinuities and dislocations of the cortical topography within the pulvinar, such that the associated pair of connection zones do not overlap. Certain of these deformations can be used to divide the global cortical topography into specific sub-domains, which form the natural units of a connectional subdivision of the pulvinar. A substantial part of the pulvinar also expresses visual topography, reflecting visual maps in occipital cortex. There are just two well-ordered visual maps in the pulvinar, that both receive projections from area V1, and several other occipital areas; the resulting duplication of cortical topography means that each visual map also acts as a separate connection domain. In summary, the model identifies four topographically ordered connection domains, and reconciles the coexistence of visual and cortical maps in two of them. The replication principle operates at and below the level of domain structure. It is argued that cortico-pulvinar circuitry replicates the pattern of cortical circuitry but not its function, playing a more regulatory role instead. Thalamic neurons differ from cortical neurons in their inherent rhythmicity, and the pattern of cortico-thalamic connections must govern the formation of specific resonant circuits. The broad implication is that the pulvinar acts to coordinate cortical information processing by facilitating and sustaining the formation of synchronized trans-areal assemblies; a more pointed suggestion is that, owing to the considerable blurring of cortical topography in the pulvinar, rival cortical assemblies may be in competition to recruit thalamic elements in order to outlast each other in activity.

Feature-based attention in human visual cortex: simulation of fMRI data

We use a computational neuroscience approach to study the role of feature-based attention in visual perception. This model is used to numerically simulate a visual attention experiment. The neurodynamical system consists of many interconnected modules that can be related to the dorsal and ventral paths of the visual cortex. The biased competition hypothesis is taken into account within the model. From the experimental point of view, measurements exist, which confirm that feature-based attention influences visual cortical responses to stimuli outside the attended location. These measurements show that attention to a given stimulus attribute (in this case "color red") increases the response of cortical visual areas to a spatially distant, ignored stimulus that shares the same attribute. Our neurodynamical model is used to numerically compute the neural activity of area V4 corresponding to such ignored stimulus, giving a good description of the experimental data.

Set size effects in the macaque striate cortex

Attentive processing is often described as a competition for resources among stimuli by mutual suppression. This is supported by findings that activity in extrastriate cortex is suppressed when several stimuli are presented simultaneously, compared to a single stimulus. In this study, we randomly varied the number of simultaneously presented figures (set size) in an attention-demanding change detection task, while we recorded multiunit activity in striate cortex (V1) in monkeys. After figure-background segregation, activity was suppressed as set size increased. This effect was stronger and started earlier among cells stimulated by the background than those stimulated by the figures themselves. As a consequence, contextual modulation, a correlate of figure-background segregation, increased with set size, approximately 100 msec after its initial generation. The results indicate that suppression of responses under increasing attentional demands differentially affects figure and background responses in area V1.

Representation and perception of scenic layout

This paper presents a cognitive approach to on-line spatial perception within scenes. A theoretical framework is developed, based on the idea that experience with a scene can activate a complex representation of layout that facilitates subsequent processing of spatial relations within the scene. The representations integrate significant, relevant scenic information and are substantial in amount or extent. The representations are active across short periods of time and across changes in the retinal position of the image. These claims were supported in a series of experiments in which pictures of scenes (primes) facilitated subsequent spatial relations processing within the scenes. The prime-induced representations integrated object identity and layout, were broad in scope, involved both foreground and background information, and were effective across changes in image position.

Recognition by top-down and bottom-up processing in cortex: the control of selective attention

Visual recognition is achieved by a hierarchy of bidirectionally connected cortical areas. The entry of signals into higher areas involves the serial sampling of information within a movable window of attention. Here we explore how the cortex can move this window and integrate the sampled information. To make this concrete, we modeled the process of visual word recognition by hierarchical cortical areas representing features, letters, and words. At the start of the recognition process, nodes representing all contextually possible words are active. Simple connectivity rules allow a parallel top-down (T-D) computation of the relative probability of each feature at each location given the set of active words. This information is then used to guide the window of attention to information-rich features (e.g., a feature that is present in the visual image but has lowest probability). Bottom-up processing of this feature excludes words that do not contain it and leads to T-D recomputation of feature probabilities. Recognition occurs after several such cycles when all but one word has been excluded. We show that when 950 words are stored in long-term memory, recognition occurs after an average of 4.9 cycles. Because covert attention can be moved every 20-30 ms, word recognition could be as fast as determined experimentally (<200 ms of cortical processing). This model accounts for the findings that recognition time depends logarithmically on set size, recognition time is reduced when context reduces the number of possible targets, the time to classify a nonword decreases when its approximation to English decreases, and in high level cortex, the firing of neurons tuned to an object increases progressively as its recognition occurs. More generally the model provides a physiologically plausible view of how bi-directional signal flow in cortex guides attention to produce efficient recognition.

The selective tuning model of attention: psychophysical evidence for a suppressive annulus around an attended item

The selective tuning model [Artif. Intell. 78 (1995) 507] is a neurobiologically plausible neural network model of visual attention. One of its key predictions is that to simultaneously solve the problems of convergence of neural input and selection of attended items, the portions of the visual neural network that process an attended stimulus must be surrounded by inhibition. To test this hypothesis, we mapped the attentional field around an attended location in a matching task where the subject's attention was directed to a cued target while the distance of a probe item to the target was varied systematically. The main result was that accuracy increased with inter-target separation. The observed pattern of variation of accuracy with distance provided strong evidence in favor of the critical prediction of the model that attention is actively inhibited in the immediate vicinity of an attended location.

The neural basis of biased competition in human visual cortex

A typical scene contains many different objects that compete for neural representation due to the limited processing capacity of the visual system. At the neural level, competition among multiple stimuli is evidenced by the mutual suppression of their visually evoked responses and occurs most strongly at the level of the receptive field. The competition among multiple objects can be biased by both bottom-up sensory-driven mechanisms and top-down influences, such as selective attention. Functional brain imaging studies reveal that biasing signals due to selective attention can modulate neural activity in visual cortex not only in the presence, but also in the absence of visual stimulation. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals likely derives from a distributed network of areas in frontal and parietal cortex. Attention-related activity in frontal and parietal areas does not reflect attentional modulation of visually evoked responses, but rather the attentional operations themselves.

Mechanisms of visual attention in the human cortex

A typical scene contains many different objects that, because of the limited processing capacity of the visual system, compete for neural representation. The competition among multiple objects in visual cortex can be biased by both bottom-up sensory-driven mechanisms and top-down influences, such as selective attention. Functional brain imaging studies reveal that, both in the absence and in the presence of visual stimulation, biasing signals due to selective attention can modulate neural activity in visual cortex in several ways. Although the competition among stimuli for representation is ultimately resolved within visual cortex, the source of top-down biasing signals derives from a network of areas in frontal and parietal cortex.

Reliability and dimensionality of judgments of visually textured materials

We extended perceptual studies of the Brodatz set of textured materials. In the experiments, texture perception for different texture sets, viewing distances, or lighting intensities was examined. Subjects compared one pair of textures at a time. The main task was to rapidly rate all of the texture pairs on a number scale for their overall dissimilarities first and then for their dissimilarities according to six specified attributes (e.g., texture contrast). The implied dimensionality of perceptual texture space was usually at least four, rather than three. All six attributes proved to be useful predictors of overall dissimilarity, especially coarseness and regularity. The novel attribute texture lightness, an assessment of mean surface reflectance, was important when viewing conditions were wide-ranging. We were impressed by the general validity of texture judgments across subject, texture set, and comfortable viewing distances or lighting intensities. The attributes are nonorthogonal directions in four-dimensional perceptual space and are probably not narrow linear axes. In a supplementary experiment, we studied a completely different task: identifying textures from a distance. The dimensionality for this more refined task is similar to that for rating judgments, so our findings may have general application.

The control of low-level information flow in the visual system

Visual information processing needs to be error free and efficient. Our visual system tries to achieve the first goal by accommodating a wide variety of visual algorithms for the extraction of the relevant features in the scene, while at the same time the second goal is addressed by controlling the amount of visual information flow in the network employing selective attention. Attentional or pre-attentional mechanisms are found throughout many visual areas and these processes may start as early as in the visual thalamus (lateral geniculate nucleus, LGN). In this review we pay particular attention to experimental and theoretical findings which indicate that even low-level structures, such as LGN and V1, can play a major role in the flow-control of visual information.

Dynamics of perceptual oscillations in form vision

Certain periodic dot patterns (Marroquin patterns) generate a percept of dynamically oscillating circles, and analogous effects were explored by op artists in the 1960s. Here we show psychophysically that circles are perceived in these patterns only around specific points that are quantitatively predicted by a neural model of configural units hypothesized to reside in cortical area V4. Circles superimposed on the pattern mask perception of illusory circles. A neural model of lateral inhibitory interactions among V4 configural units showing spike-frequency adaptation quantitatively accounts for the human data. The model is consistent with ideas on the neural basis of attention in V4, and it suggests that attention may be biased via neuromodulation of slow hyperpolarizing potentials in cortical neurons.

From parallel to serial processing: a computational study of visual search

A novel computational model of a preattentive system performing visual search is presented. The model processes displays of lines, reproduced from Wolfe, Friedman-Hill, Stewart, and O'Connell's (1992) and Treisman and Sato's (1990) visual-search experiments. The response times measured in these experiments suggest that some of the displays are searched serially, whereas others are scanned in parallel. Our neural network model operates in two phases. First, the visual displays are compressed via standard methods (principal component analysis), to overcome assumed biological capacity limitations. Second, the compressed representations are further processed to identify a target in the display. The model succeeds in fast detection of targets in experimentally labeled parallel displays, but fails with serial ones. Analysis of the compressed internal representations reveals that compressed parallel displays contain global information that enables instantaneous target detection. However, in representations of serial displays, this global information is obscure, and hence, a target detection system should resort to a serial, attentional scan of local features across the display. Our analysis provides a numerical criterion that is strongly correlated with the experimental response time slopes and enables us to reformulate Duncan and Humphreys's (1989) search surface, using precise quantitative measures. Our findings provide further insight into the important debate concerning the dichotomous versus continuous views of parallel/serial visual search.