Human Performance in Complex Dynamic Tasks - A Unified Predictive Processing Approach

Humans are very efficient in many complex dynamic tasks. Apparently easy and simple activities like picking a cup, walking in a crowd or driving are in fact underpinned by sophisticated information processing, of which we are not usually aware. This it is starkly revealed in artificial intelligence and robotics, and humans still vastly outperform computers on such sensorimotor tasks. This suggests techniques for organizing perception and action discovered by the human brain during development and evolution that could be highly valuable to the development of future AI.
We develop a unified computational model of visual target interception and avoidance - a core human sensorimotor capacity. More life-like robotics, autonomous vehicles, aerospace pilot and driver training, and sports psychology are applications of such knowledge. The fundamental interest lies in the revealing the ways our brain allows us to interact with complex dynamic situations so efficiently and effortlessly.