Visual attention is an important cognitive concept for the daily life of  humans, but it is still not fully understood. Due to this, it is also  rarely utilized in computer vision systems despite its strong benefits  for human visual perception, due to missing understanding and  uncertainty how to implement attention correctly. However, understanding  visual attention is challenging as it has many and seemingly-different  aspects, both at neuronal and behavioral level. Thus, it is very hard to  give a uniform explanation of visual attention that accounts for all  aspects. To tackle this problem, this doctoral thesis has the goal to  identify a common set of neuronal mechanisms, which underlie both  neuronal and behavioral aspects, and thus allows to explain a wide range  of phenomena. In the thesis, the chosen aspects are multiple  neurophysiological effects, real-world object localization, and a visual  masking paradigm (OSM). In each of the considered fields, the work also advances the current state of-the-art to better understand this aspect of attention itself. The three chosen aspects highlight that the approach can account for crucial neurophysiological, functional, and  behavioral properties of visual attention. As the mechanisms are able to  account for attention under such a variation of view points, they might  constitute the general neuronal substrate of visual attention in the  cortex. Therefore, the work provides a deeper understanding of attention  for the computer vision community, and in form of the neuronal  mechanisms a concrete prototype implementation to incorporate this  crucial aspect of human perception into future computer vision systems.