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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?

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A fundamental aspect of multiple task management is attending to new stimuli and integrating associated task requirements into an ongoing task set- this is ``interruption management'' (IM). Anecdotal evidence and field studies indicate the frequency and consequences of interruptions, however experimental investigations of mechanisms influencing IM are scarce. Interruptions on commercial flightdecks are numerous, of various forms, and have been cited as contributing factors in many aviation incident and accident reports. This research grounds an experimental investigation of flightdeck interruptions in a proposed IM stage model. This model organizes basic research, identifies influencing mechanisms, and suggests appropriate dependent measures for IM. Fourteen airline pilots participated in a flightdeck simulation experiment to investigate the general effects of performing an interrupting task and interrupted procedure, and the effects of specific task factors: (1) modality; (2) embeddedness, or goal­level, of an interruption; (3) strength of association, or coupling­strength, between interrupted tasks; (4) semantic similarity; and (5) environmental stress. General effects of interruptions were extremely robust. All individual task factors significantly affected interruption management, except ``similarity.'' Results extend the Interruption Management model, and are interpreted for their implications for interrupted flightdeck performance and intervention strategies for mitigating their effects on the flightdeck.

When individuals act alone, they can internally coordinate the actions at hand. Such coordination is not feasible when individuals act together in a group. The present research examines to what extent groups encounter specific challenges when acting jointly and whether these challenges impede extending planning into the future. Individuals and groups carried out a tracking task that required learning a new anticipatory control strategy. The results show that groups face additional demands that are harder to overcome when planning needs to be extended into the future. Information about others ’ actions is a necessary condition for groups to effectively learn to extend their plans. Possible mechanisms for exerting and learning anticipatory control are discussed. Researchers in the area of action planning and action control use a host of diverse tasks to investigate the cognitive functions that enable one to coordinate action alternatives. Examples include selecting and programming arbitrary actions in response to arbitrary stimuli (Hommel & Prinz, 1997), switching between arbitrary tasks (Allport, 1993; Mayr & Keele, 2000; Rogers & Monsell, 1995), and carrying out two tasks at the same time (Meyer &

The effective control of attentional focus is an essential requirement in mental reasoning based on mental models and mental images, as well as in the interaction with external diagrams. In this paper, we argue for spatial organization principles common to various mental subsystems that entail a noncentralistic control of focus. We give a brief overview of mental spatial reasoning and present a review of psychological findings related to cognitive control. We review existing modeling approaches that realize control of focus in imagery, scene recognition, and mental animation. Based on these foundations, we identify basic spatial organizing principles that are shared by the diverse subsystems collaborating in mental spatial reasoning. We discuss the implications of these principles in the framework of a computational modeling approach and give an outline of the conception of control of focus in our computational architecture Casimir.

My first poster presentation at a scientific meeting was no success. I offered a new theoretical framework on stimulus and response representation (the later theory of event coding; Hommel, Müsseler, Aschersleben, and Prinz 2001a) together with supportive data and hoped to attract the interest of all the big shots working on stimulus–response compatibility. But no one came. One year later I presented a much less inspired study but made one crucial move: I put the A-word in the title, with the effect that my poster was one of the most crowded, and long after the session was over I was still heavily engaged in discussions. This is just one of many examples demonstrating that cognitive scientists love attention as a topic. In contrast to sensory and motor processes, say, which rather smell like hardware and mechanics, the concept of attention seems to directly connect to what makes us human, as it somehow expresses our individual needs and wishes, preferences, and interests. The drawback of this attractiveness is that the concept is more often than not used as a wastebasket, a container that serves as a pseudo-explanation for the phenomena we still fail to understand—so that “attention” is explained by the workings of an “attentional system.” One of

this paper. 2002 Psychology Press Ltd http://www.tandf.co.uk/journals/pp/13506285.html DOI:10.1080/13506280143000511 VISUAL COGNITION, 2002, 9 (4/5), 392--420 action. The first one, commonly called "attention", has been thoroughly investigated for decades in experimental psychology and, more recently, also in the neurosciences (for recent overviews, see Allport, 1983; Desimone & Duncan, 1995; Posner & Petersen, 1990). Most research in this area, the present work included, has been concentrated on visual attention, hence, the selection of visual stimuli or objects. Although many questions concerning the details of attentional selection are still unsettled (Allport, 1993), most researchers agree in that visual attention is in some sense capacity-limited, dealing with only one object (or few objects) at a time. In the process of selecting a given visual object, spatial stimulus information seems to play a major role, presumably because object features are cross-linked and integrated by referencing their common location in space (e.g., LaBerge & Brown, 1989; Schneider, 1999; Treisman, 1988; Van der Heijden, 1992; Wolfe, 1994)

We propose a model to interpret Neurovascular XRA images interactively. This attentionallybased interactive model, AIM, exploits human interaction as part of the solution. AIM posits two channels of interaction: context ("what to look for"), and focus-of-attention ("where to look") as the locus of spatial information exchange between the user and the machine. In an AIM system,the user specifies a context (e.g. a carotid vessel) and directs the attentional spotlight to focus machine processing.AIM involves the user with the computer as integral partner and facilitates varying degrees of human intervention in the process. A hierarchy of context abstractions permits the system to function more autonomously (doing high-level tasks like extracting an arterial vessel) in routine interpretation, and to require more user intervention (e.g. locating arterial wall boundaries) as the image complexity increases. This is especially important in medical imaging where the medical professional must h...

Large changes in a scene often become difficult to notice if made during an eye movement, image flicker, movie cut, or other such disturbance. It is argued here that this change blindness can serve as a useful tool to explore various aspects of vision. This argument centers around the proposal that focused attention is needed for the explicit perception of change. Given this, the study of change perception can provide a useful way to determine the nature of visual attention, and to cast new light on the way that it is — and is not — involved in visual perception. To illustrate the power of this approach, this paper surveys its use in exploring three different aspects of vision. The first concerns the general nature of seeing. To explain why change blindness can be easily induced in experiments but apparently not in everyday life, it is proposed that perception involves a �irtual representation, where object representations do not accumulate, but are formed as needed. An architecture containing both attentional and nonattentional streams is proposed as a way to implement this scheme. The second aspect concerns the ability of observers to detect change even when they have no visual experience of it. This sensing is found to take on at least two forms: detection without visual experience (but still with conscious awareness), and detection without any awareness at all. It is proposed that these are both due to the operation of a nonattentional visual stream. The final aspect considered is the nature of visual attention itself — the mechanisms involved when scrutinizing items. Experiments using controlled stimuli show the existence of various limits on visual search for change. It is shown that these limits provide a powerful means to map out the attentional mechanisms involved. © 2000