Attention is thought to be a part of a larger cluster of mecha-nisms that serve to orient a cognitive system, to filter contentswith respect to their task relevance, and to devote more com-putation to certain options than to others. All these activitiesproceed under the plausible assumption that not all informationcan be or ought to be processed for a system to satisfice in anever changing world. In this paper, we describe an attention-centric cognitive system called ARCADIA that demonstratesthe orienting, filtering, and resource-skewing functions men-tioned above. The demonstration involves maintaining focuson cognitive tasks in a dynamic environment. While ARCA-DIA carries out a task, limits on its attentional capacity resultin “inattentional blindness” under circumstances analogous tothose where people fail to perceive otherwise salient stimuli.

With few exceptions, architectural approaches to modeling cognition have historically emphasized what happens in the mind following the transduction of environmental signals into percepts. To our knowledge, none of these architectures implements a sophisticated, general theory of human attention. In this paper we summarize progress to date on a new cognitive architecture called ARCADIA that gives a central role to attention in both perception and cognition. First, we give an overview of the architecture, comparing it to other approaches when appropriate. Second, we present a model of incremental object construction and property binding in ARCADIA using the well known change blindness phenomena to illustrate the time course of object perception and its dependence on attention. Finally, we discuss near-term challenges and future plans

This paper presents a theoretical framework for modeling human visual attention. The framework's core claim is that three mechanisms drive attention: selection, which picks out an item for further processing; engagement, which tags a selected item as relevant or irrelevant to the current task; and enhancement, which increases sensitivity to task-relevant items and decreases sensitivity to task-irrelevant items. Building on these mechanisms, the framework is able to explain human performance on attentionally demanding tasks like visual search and multiple object tracking, and it supports a broad range of predictions about the interactions between such tasks.

We present a computational model of attentional capture inhumans. The model distinguishes between automatic mecha-nisms that directly determine the focus of visual attention, anddeliberate mental actions an individual can perform to influ-ence these mechanisms. The automatic mechanisms select anobject as the focus of attention and enhance its location andfeatures, so that nearby or similar objects are likely to be se-lected in the future. The deliberate actions include engagingwith a selected object to further enhance its features, and re-trieving a previously selected object from memory. By per-forming these actions, the model is able to exert limited top-down control over capture, increasing the probability that task-relevant objects will be attended and irrelevant objects will beignored. To evaluate the model, we conduct a simulation of arecent visual search study, demonstrating that the model canaccount for three established factors that are known to influ-ence capture.

We present a computational model exploring goal-directeddeployment of attention during object tracking. Once selected,objects are tracked in parallel, but serial attention can bedirected to an object that is visually crowded and in danger ofbeing lost. An attended object’s future position can beextrapolated from its past motion trajectory, allowing theobject to be tracked even when it is briefly occluded. Usingthe model, we demonstrate that the difficulty of trackingthrough occlusions increases with the number of objectsbecause they compete for serial attention.

We provide a new computational model of working memory inthe complex span task implemented in the ARCADIA cogni-tive framework. While there exist implementations of workingmemory successful enough to account for many of the bench-mark findings in the working memory literature, we demon-strate that further progress requires the integration of thesemodels with a rich conception of attention. ARCADIA pro-vides this intersection, allowing for precise control of the focusof attention on a time scale fine enough to begin to disentan-gle the overlapping effects of interference, temporal decay, andattentional refreshing.

The rich literature on multiple object tracking (MOT)conclusively demonstrates that humans are able to visuallytrack a small number of objects. There is considerably lessagreement on what perceptual and cognitive processes areinvolved. While it is clear that MOT is attentionallydemanding, various accounts of MOT performance centrallyinvolve pre-attentional mechanisms as well. In this paper wepresent an account of object tracking in the ARCADIAcognitive system that treats MOT as dependent upon both pre-attentive and attention-bound processes. We show that withminimal addition this model replicates a variety of corephenomena in the MOT literature and provides an algorithmicexplanation of human performance limitations.