this article. This paper has also benefited greatly from constructive feedback from Gordon Logan, Mike Stadler, and our other reviewers. We thank Joanie Sanchez for her assistance in running Experiment 1. This research was supported by a Social Science Faculty Research Award from Yale University. Portions of this research were presented at the Annual Meeting of the Association for Research in Ophthalmology and Vision, Fort Lauderdale, FL, in May, 1997, and at the Annual Meeting of the Psychonomic Society, Philadelphia, PA, in November, 1997

Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scenecentered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.

www.elsevier.com/locate/cognit This paper argues that a theory of situated vision, suited for the dual purposes of object recognition and the control of action, will have to provide something more than a system that constructs a conceptual representation from visual stimuli: it will also need to provide a special kind of direct (preconceptual, unmediated) connection between elements of a visual representation and certain elements in the world. Like natural language demonstratives (such as `this ' or `that') this direct connection allows entities to be referred to without being categorized or conceptualized. Several reasons are given for why we need such a preconceptual mechanism which individuates and keeps track of several individual objects in the world. One is that early vision must pick out and compute the relation among several individual objects while ignoring their properties. Another is that incrementally computing and updating representations of a dynamic scene requires keeping track of token individuals despite changes in their properties or locations. It is then noted that a mechanism meeting these requirements has already been proposed in order to account for a number of disparate empirical phenomena, including subitizing, search-subset selection and multiple object tracking

I propose that pre-attentive computational mechanisms in primary visual cortex create a saliency map. This map awards higher responses to more salient image locations; these responses are those of conventional V1 cells tuned to input features, such as ...

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

When an observer detects a target in a rapid stream of visual stimuli, there is a brief period of time during which the detection of subsequent targets is impaired. In this study, event-related potentials (ERPs) were recorded from normal adult observers to determine whether this "attentional blink " reflects a suppression of perceptual processes or an impairment in postperceptual processes. No suppression was observed during the attentional blink interval for ERP components corresponding to sensory processing (the P1 and N1 components) or semantic analysis (the N400 component). However, complete suppression was observed for an ERP component that has been hypothesized to reflect the updating of working memory (the P3 component). Results indicate that the attentional blink reflects an impairment in a postperceptual stage of processing. Over the past several decades, the vast majority of studies of visual attention have examined the operation of attention across space. In the visual search task, for example, a target item must be detected within an array of distractor items that are presented at different locations from the target. In recent

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 &

It has generally been assumed that rapid visual search is based on simple features and that spatial relations between features are irrelevant for this task. Seven experiments involving search for line drawings contradict this assumption; a major determinant of search is the presence of line junctions. Arrow- and Y-junctions were detected rapidly in isolation and when they were embedded in drawings of rectangular polyhedra. Search for T-junctions was considerably slower. Drawings containing T-junctions often gave rise to very slow search even when distinguishing arrow- or Y-junctions were present. This sensitivity to line relations suggests that preattentive processes can extract 3-dimensional orientation from line drawings. A computational model is outlined for how this may be accomplished in early human vision. Although we are still a long way from a complete understanding of visual perception, considerable progress has been made in our understanding of its earliest stages (see Zucker, 1987). These stages are concerned with the extrac-tion of information from the retinal image, and as such are generally assumed to be carried out by processes operating in parallel across the visual field. They are also generally assumed to be

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?

in dynamic scenes