Visual Feature Binding within the Selective Tuning Attention Framework

We present a biologically plausible computational model for solving the visual feature binding problem, based on recent results regarding the time course and processing sequence in the primate visual system. The feature binding problem appears due to the distributed nature of visual processing in the primate brain, and the gradual loss of spatial information along the processing hierarchy. This paper puts forward the proposal 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 primate vision exhibits. To accomplish this, four different kinds of binding processes are introduced and are tied directly to specific recognition tasks and their time course. The model relies on the reentrant connections so ubiquitous in the primate brain to recover spatial information, and thus allow features represented in different parts of the brain to be integrated in a unitary conscious percept. We show how different tasks and stimuli have different binding requirements, and present a unified framework within the Selective Tuning model of visual attention.