... This article models how these interactions help visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex shapes its circuits to match environmental constraints. New computational ideas about feedback systems suggest how neocortex develops and learns in a stable way, and why top-down attention requires converging bottom-up inputs to fully activate cortical cells, whereas perceptual groupings do not.

Spatial attention: Visual selection and deployment over space The attentional spotlight and spatial cueing Attentional shifts, splits, and resolution Object-based Selection The visual search paradigm Top-down and bottom-up control of attention Inhibitory mechanisms of attention Invalid cueing Negative priming Inhibition of return Temporal attention: Visual selection and deployment over time Single target search Attentional blink and attentional dwell time Repetition blindness NEURAL MECHANISMS OF SELECTION Single-cell physiological method Event-related potentials Functional imaging: PET and fMRI

This article presents a theory that inte~ates space-based and object-based approaches to visual attention. The theory puts together M. P. van Oeffelen and P. G. Vos's ( 1982, 1983) COntour DEtector (CODE) theory of perceptual grouping by proximity with C. Bundesen's (1990) theory of visual attention (TVA). CODE provides input to TVA, accounting for spatially based between-object selection, and TVA converts the input to output, accounting for feature- and category-based withinobject selection. CODE clusters nearby items into perceptual groups that are both perceptual objects and regions of space, thereby integrating object-based and space-based approaches to attention. The combined theory provides a quantitative account of the effects of grouping by proximity and dis~nce between items on reaction time and accuracy data in 7 empirical situations that shaped the current literature on visual spatial attention. For the last decade the attention literature has been embroiled in a debate over the nature of visual spatial attention that focuses on the "thing " that attention selects (e.g., Baylis &

This article develops the FACADE theory of 3-dimensional (3-D) vision and figure-ground separation to explain data concerning how 2-dimensional pictures give rise to 3-D percepts of occluding and occluded objects. The model describes how geometrical and contrastive properties of a picture can either cooperate or compete when fonning the boundaries and surface representations that subserve conscious percepts. Spatially long-range cooperation and spatially short-range competition work together to separate the boundaries of occluding figures from their occluded neighbors. This boundary ownership process is sensitive to image T junctions at which occluded figures contact occluding figures. These boundaries control the filling-in of color within multiple depth-sensitive surface representations. Feedback between surface and boundary representations strengthens consistent boundaries while inhibiting inconsistent ones. Both the boundary and the surface representations of occluded objects may be amodally completed, while the surface representations of unoccluded objects become visible through modal completion. Functional roles for conscious modal and amodal representations in object recognition, spatial attention, and reaching behaviors are discussed. Model interactions are interpreted in tenns of visual, temporal, and parietal cortices. The human urge to represent the three-dimensional (3-D)

This book chapter examines the role of spatial attention from a computational perspective. It is intended as an overview for cognitive scientists interested in computational modeling of attentional phenomena. Because the function of attention can be understood only in its relation to visual information processing, we model not only the attentional system itself, but also the process of object recognition. We begin by presenting a basic model of object recognition, showing that interference prevents the system from reliably processing multiple, complex stimuli, and then we show how a simple mechanism of attentional selection can reduce this interference. Our first goal is to present a model that is computationally adequate, that is, a model that has the computational power to perform the sort of visual information processing tasks that people do. We then turn to simulations showing that the model can account for diverse experimental data, including: the benefit of attentional precuing, the time course of attention shifts, the effect of spatial uncertainty, the effect of irrelevant stimuli, the relation of object-based and location-based selection, and visual search. We conclude with a discussion of basic questions about computation modeling, including: Why build computational models? What makes a model compelling? When is a model right or wrong? Should one opt for depth or breadth in model coverage?

Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented examples. In many cases, MAGIC discovers grouping heuristics similar to those previously proposed, but it also has the capability of finding nonintuitive structural regularities in images. Grouping is performed by a relaxation network that attempts to dynamically bind related features. Features transmit a complex-valued signal (amplitude and phase) to one another; binding can thus be represented by phase locking related features. MAGIC's training procedure is a generalizatio...

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One way of perceptually organizing a complex visual scene is to attend selectively to information in a particular physical location. Another way of reducing the complexity in the input is to attend selectively to an individual object in the scene and to process its elements preferentially. This latter, object-based attention process was examined, and the predicted superiority for reporting features from 1 relative to 2 objects was replicated in a series of experiments. This object-based process was robust even under conditions of occlusion, although there were some boundary conditions on its operation. Finally, an account of the data is provided via simulations of the findings in a computational model. The claim is that object-based attention arises from a mechanism that groups together those features based on internal representations developed over perceptual experience and then preferentially gates these features for later, selective processing. Humans are exceptionally good at recognizing objects in natural visual scenes despite the fact that such scenes usually contain multiple, overlapping objects. One way in which individuals organize this complex input to minimize the

Two tasks were used to evaluate the grain of visual attention, the minimum spacing at which attention can select individual items. First, observers performed a tracking task at many viewing distances. Performance dropped to chance levels at small display sizes even though, in all conditions, observers could easily resolve the items and their motions. The limiting size for selection was roughly the same whether tracking one or three targets, suggesting that the resolution limit acts independently of the capacity limit of attention. Second, the closest spacing that still allowed individuation of single items in dense, static displays was examined. This critical spacing was about 50% coarser in the radial direction compared to the tangential direction, and was coarser in the upper as opposed to the lower visual field. The results suggest that no more than about 72 items can be arrayed in the central 30 degrees of the visual field while still allowing attentional access to each individuall...

Observers inspected normal, high quality colour displays of everyday visual scenes while their eye movements were recorded. A large display change occurred each time an eye blink occurred. Display changes could either involve “Central Interest” or “Marginal Interest” locations, as determined from descriptions obtained from independent judges in a prior pilot experiment. Visual salience, as determined by luminance, colour, and position of the Central and Marginal Interest changes were equalized. The results obtained were very similar to those obtained in prior experiments showing failure to detect changes occurring simultaneously with saccades, flicker, or “mudsplashes” in the visual scene: Many changes were very hard to detect, and Marginal Interest changes were harder to detect than Central Interest changes. Analysis of eye movements showed, as expected, that the probability of detecting a change depended on the eye’s distance from the change location. However a surprising finding was that both for Central and Marginal Interest changes, even when observers were directly fixating the change locations (within 1 degree), more than 40 % of the time they still failed to see the changes. It seems that looking at something does not guarantee you “see” it.