For a list of all posts go here.
Introduction
In the previous posts, we explored how core aspects of subjective experience — motivation, addiction-like behavior, and fear — can emerge from simple computational principles. By modeling dopaminergic and noradrenergic dynamics within an active inference framework, we saw how agents can be driven to pursue goals, become sensitized to cues, or withdraw from perceived threats.
These models were deliberately minimal.
They operated in abstract environments, with simple state spaces and clearly defined targets. Their purpose was not realism, but clarity: to isolate the mechanisms by which internal drives shape behavior.
But real agents do not live in grid worlds.
They perceive complex, high-dimensional environments. They process language, images, and sound. They act continuously, not in discrete steps. And their internal states are not directly observable, but must be inferred from rich sensory streams.
This raises a natural question:
👉What happens when these computational principles are brought into contact with the real world?
