Why don't people reinforcement-learn to delude themselves? It would be very rewarding for me to believe that alignment is solved, everyone loves me, I've won at life as hard as possible. I think I do reinforcement learning over my own thought processes. So why don't I delude myself?

On my model of people, rewards provide ~"policy gradients" which update everything, but most importantly shards. I think eg the world model will have a ton more data from self-supervised learning, and so on net most of its bits won't come from reward gradients.

For example, if I reinforcement-learned to perceive a huge stack of cash in the corner of the room, by eg imagining that being there, which increases my concept-activations on that being there, which in fact causes a positive reward event in my brain, so naturally credit assignment should say I should believe that even harder... That would incur a ton of low-level self-supervised-learning predictive error on what my rods and cones are in fact indicating, and perhaps I self-supervised meta-learn not to develop delusions like that at all.