title: The different stages of visual recognition need different attentional binding strategies.
abstract: Many think that visual attention needs an executive to allocate resources. Although the cortex exhibits substantial plasticity, dynamic allocation of neurons seems outside its capability. Suppose instead that the visual processing architecture is fixed, but can be 'tuned' dynamically to task requirements: the only remaining resource that can be allocated is time. How can this fixed, yet tunable, structure be used over periods of time longer than one feed-forward pass? With the goal of developing a computational theory and model of vision and attention that has both biological predictive power as well as utility for computer vision, this paper proposes that by using multiple passes of the visual processing hierarchy, both bottom-up and top-down, and using task information to tune the processing prior to each pass, we can explain the different recognition behaviors that human vision exhibits. By examining in detail the basic computational infrastructure provided by the Selective Tuning model and using its functionality, four different binding processes - Convergence Binding and Partial, Full and Iterative Recurrence Binding - are introduced and tied to specific recognition tasks and their time course. The key is a provable method to trace neural activations through multiple representations from higher order levels of the visual processing network down to the early levels.
authors.affiliation: Department of Computer Science and Engineering, York University, 4700 Keele Street, Toronto, Ont., Canada M3J 1P3. tsotsos@cse.yorku.ca
authors.forename: John K
authors.initials: JK
authors.lastname: Tsotsos
authors.forename: Antonio J
authors.initials: AJ
authors.lastname: Rodríguez-Sánchez
authors.forename: Albert L
authors.initials: AL
authors.lastname: Rothenstein
authors.forename: Eugene
authors.initials: E
authors.lastname: Simine
mesh_terms: D000465:Algorithms; D001288:Attention / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology