Visual attention is a basic cognitive process; it reflects the ability to select and process one object or small region of space in the visual scene. Albeit basic, the concept of visual attention has garnered much interest and many different computational theories have been proposed to explain its neural origins. In this workshop, we will celebrate the strengths and examine the weaknesses of three different theories of attention. Following a brief tutorial lecture, we will debate the relative merits the Biased Competition Model of Attention, the Salience Map of Attention, and the Selective Tuning Model of Attention. Through this debating process, we will consider the strengths and weaknesses of alternative models as well. Finally, we will debate the computational principles that underlie these models, such as the relevance of information theory, signal detection theory, and optimality principles in capturing the influence of visual attention in neural activity and in behavior. Indeed, how might dynamical systems be designed to model attentive behavior? The unorthodox organization of this workshop will encourage candid discussions -- the invited speakers will discuss the merits of one of these models and/or the weaknesses of another. We hope you join us in this venture.