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 &

The seemingly effortless ability to perceive meaningful objects in an integrated scene actually depends on complex visual processes. The `binding problem' concerns the way in which we select and integrate the separate features of objects in the correct combinations. Experiments suggest that attention plays a central role in solving this problem. Some neurological patients show a dramatic breakdown in the ability to see several objects; their deficits suggest a role for the parietal cortex inthe binding process. However, indirect measures of priming and interference suggest that more information may be implicitly available than we can consciously access.

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?

We describe an architecture for invariant visual detection and recognition. Learning is performed in a single central module. The architecture makes use of copies of retinotopic layers of local features, with a particular design of inputs and outputs, that allows them to be primed either to attend to a particular location, or to attend to a particular object representation. In the former

In a typical visual search experiment, observers look through a set of items for a designated target that may or may not be present. Reaction time (RT) is measured as a function of the number of items in the display (set size), and inferences about the underlying search processes are based on the slopes of the resulting RT × Set Size functions. Most search experiments involve 5 to 15 subjects performing a few hundred trials each. In this retrospective study, I examine results from 2,500 experimental sessions of a few hundred trials each (approximately 1 million total trials). These data represent a wide variety of search tasks. The resulting picture of human search behavior requires changes in our theories of visual search.

As computer use becomes more visual in nature, researchers and designers of computer systems would like to gain some insight into the visual search strategies of computer users and the characteristics of displays that encourage the most efficient of these strategies. Icons, which are becoming increasingly prevalent, serve as the focus for a set of studies on the interaction of human vision with computer displays. Previous work (Fleetwood & Byrne, 2002) presented a study of "icon search" and a set of computational models of the task in the ACT-R/PM architecture. Presented here are an eye tracking study, conducted to examine the search strategies of users, and a revised model based on the results of the eye tracking study. The revised model incorporates EMMA (Salvucci, 2001) and changes in search strategy. Findings indicate key environmental influences of icon search (particularly set size and icon quality), evaluate the vision module in the underlying cognitive architecture, and provide some illumination on the strategies of users.

Experiments are presented showing that visual search for Mueller-Lyer (ML) stimuli is based on complete configurations, rather than component segments. Segments easily detected in isolation were difficult to detect when embedded in a configuration, indicating preemption by low-level groups. This preemption—which caused stimulus components to become inaccessible to rapid search—was an all-ornothing effect, and so could serve as a powerful test of grouping. It is shown that these effects are unlikely to be due to blurring by simple spatial filters at early visual levels. It is proposed instead that they are due to more sophisticated processes that rapidly bind contour fragments into spatially-extended assemblies. These results support the view that rapid visual search cannot access the primitives formed at the earliest stages of visual processing; rather, it can access only higher-level, more ecologically-relevant structures. The processes that underlie human vision are often divided into two fundamentally different classes: operations that are carried out in parallel over space, and operations that are not (e.g., Neisser, 1967; von Helmholtz, 1867/1962). For the most part, parallel processes are rapid (i.e., they occur within a few hundred milliseconds), effortless, and automatic (i.e., they cannot be affected by immediate changes in higher-level goals), whereas nonparallel processes are slower, more effortful, and nonautomatic. In its current embodiment, this dichotomy divides vision into an early preattentive and a subsequent attentive stage (e.g.,

Research on the perception of texture gradients has relied heavily on the subjective reports of observers engaged in free-viewing. We asked whether these findings generalized to speeded performance. Experiment 1 showed that an important aspect of subjective perception—sizeconstancy scaling with perceived distance—also predicted the speed of pop-out visual search for cylinders viewed against a texture gradient. Experiment 2 showed that this finding could not be attributed to the local contrast between search items and the background texture. Experiment 3 assessed the relative contributions of 2 separable dimensions of texture gradients—perspective (radial spreading) and compression (foreshortening)—finding them to be independent in the more rapid search conditions (long target among shorter distractors) but combined in their influence in the slower conditions (short target among longer distractors). When observers view the texture gradient shown in Figure 1A they usually report seeing a flat surface recede into the distance, despite the fact that a two-dimensional (2-D) image alone cannot specify the three-dimensional (3-D) surface that gave rise to the projection. This study asked whether the factors influencing the perceived slant of such texture gradients also influences rapid visual search for objects placed on their surface. Although a large number of previous studies have examined the perception of slant in texture gradients (e.g., Flock,

This paper describes a parallel neural net architecture for efficient and robust attentive visual selection in generic gray level images. Objects are represented through flexible star type planar arrangements of binary local features, which are in turn star type planar arrangements of oriented edges. Candidate locations are detected over a range of scales and other deformations. The flexibility of the arrangements provides the required invariance. Training involves selecting a small number of stable local features, from a predefined pool, which are well localized on registered examples of the object. Training therefore requires only small data sets and can be implemented through Hebbian learning in a central `memory module'. No changes need to be made to the network for detecting new objects except for learning their representation in the memory module. Analogies with the visual system are discussed. 1 Introduction The issue at hand is attentive visual selection in gray ...