The really interesting thing as of late is that if I "show" the agent, as part of its input, its global error metric alongside forcing (moving the reticle directly) it out of boredom toward higher information gain, I can eventually stop the forcing because it learns to force itself out of boredom. It seems to learn the association between a rapidly declining error and a shift to a more interesting input. I just have to facilitate the bootstrapping.

It eventually exhibits more and more sophisticated behavioral sequences (higher cycle before repeating) and the same at higher levels with the attentional changes.

All layers perform the same function. They only differ because of the very different "world" to which they are exposed.