  In this paper we present STAR-RT - the first working prototype of Selective
Tuning Attention Reference (STAR) model and Cognitive Programs (CPs). The
Selective Tuning (ST) model received substantial support through psychological
and neurophysiological experiments. The STAR framework expands ST and applies
it to practical visual tasks. In order to do so, similarly to many cognitive
architectures, STAR combines the visual hierarchy (based on ST) with the
executive controller, working and short-term memory components and fixation
controller. CPs in turn enable the communication among all these elements for
visual task execution. To test the relevance of the system in a realistic
context, we implemented the necessary components of STAR and designed CPs for
playing two closed-source video games - Canabaltand Robot Unicorn Attack. Since
both games run in a browser window, our algorithm has the same amount of
information and the same amount of time to react to the events on the screen as
a human player would. STAR-RT plays both games in real time using only visual
input and achieves scores comparable to human expert players. It thus provides
an existence proof for the utility of the particular CP structure and
primitives used and the potential for continued experimentation and
verification of their utility in broader scenarios.
