This was an amazing article, thank you for posting it!
The above isn't quite true in all senses in all RL algorithms. For example, in policy gradient algorithms (http://www.scholarpedia.org/article/Policy_gradient_methods for a good but fairly technical introduction) it is quite important in practice to subtract a baseline value from the reward that is fed into the policy gradient update. (Note that the baseline can be and most profitably is chosen to be dynamic - it's a function of the state the agent is in. I think it's usually just chosen to be V(s) = max Q(s,a).) The algorithm will in theory converge to the right value without the baseline, but subtracting the baseline speeds convergence up significantly. If one guesses that the brain is using a policy-gradients-like algorithm, a similar principle would presumably apply. This actually dovetails quite nicely with observed human psychology - good/bad/neutral is a thing, but it seems to be defined largely with respect to our expectation of what was going to happen in the situation we were in. For example, many people get shitty when it turns out they aren't going to end up having sex that they thought they were going to have - so here the theory would be that the baseline value was actually quite high (they were anticipating a peak experience) and the policy gradients update will essentially treat this as an aversive stimulus, which makes no sense without the existence of the baseline.
It's closer to being true of Q-learning algorithms, but here too there is a catch - whatever value you assign to never-before-seen states can have a pretty dramatic effect on exploration dynamics, at least in tabular environments (i.e. environments with negligible generalization). So here too one would expect that there is a evolutionarily appropriate level of optimism to apply to genuinely novel situations about which it is difficult to form an a priori judgment, and the difference between this and the value you assign to known situations is at least probably known-to-evolution.