Modeling feature-based attention as an active top-down inference process

Vision is a crucial sensor. It provides a very rich collection of information about our environment. The difficulty in vision arises, since this information is not obvious in the image, it has to be constructed. Wheres earlier approaches have favored a bottom-up approach, which maps the image onto an internal representation of the world, more recent approaches search for alternatives and develop frameworks which make use of top-down connections. In these approaches vision is inherently a constructive process which makes use of a priory information. Following this line of research, a model of primate object perception is presented and used to simulate an object detection task in natural scenes. The model predicts that early responses in extrastriate visual areas are modulated by the visual goal.