Hierarchical and modular attention

Abstract

The flow of visual input reaching the eye consists of huge amounts of time-varying information. It is crucial for both biological vision and automated systems to perceive and comprehend such a constantly changing environment within a relatively short processing time. To cope with such a computational challenge, one should locate and analyze only the information relevant to the current task by quickly focusing on selected areas of the scene as needed. Attention makes perception computationally tractable and helps with tasks such as object recognition. Attention permeates the whole stream of visual computation, it is both hierarchical and modular, and it involves representations, processing and strategies. Attentional mechanisms are intimately related to adaptation processes, and high-level attention corresponds to competitive, functional and learned behavioral programs. Attention consists of both data- and model-driven processes and their relationships, and it covers several levels such as sensory, reactive and behavioral processes. An example of how attention can be implemented considers time-varying imagery and it shows how functional linked pyramids and zoom lens operations lead to the generation of visual saccades. Both the time-varying imagery and the corresponding recognition memory are organized as pyramids and uniform indexing and classification interfaces using an attention pyramid are established. This paper concludes with a discussion on promising venues for future research that are most likely to enhance our understanding of attentional mechanisms.