The team discovered and further described several attentional mechanisms using psychophysical experiments and computational models. Their findings emphasized that vision is not a passive process but requires active decisions about how to move the eyes and where to look next. The team could show that object-based attention, which has been primarily investigated in reduced and specifically tailored visual environments, strongly influences human sampling strategies in the real world. Object-based attention requires a representation of the scene in terms of objects on which attentional mechanisms and selection can act. They propose a mechanism for forming these object representations through an Active InterCONect (AICON) between the object segmentation and the decision-making process of where and when to move the eyes. Further findings of this project include how fixational eye movements enable edge detection, how eye movement characteristics reflect object-based attention, how objects influence the decisions to make eye movements, and how our knowledge about a scene being dynamic or static influences our exploration behavior.