Maybe it will help if I make the model more formal, since there are multiple variables and it can be a little hard to see what’s going on if you don’t already have the intuitions to track them from normal words.
Let m be a measure of self esteem as a result of seeing some evidence e about the world (an observation). The prediction error is the extent to which E:E→M (the expectation relation that goes from evidence to a measure of self esteem) diverges from A:E→M (the relation that calculates the actual update in measure of self esteem). So prediction error looks like when E(e)=m≠A(e), and the larger the difference between E(e) and A(e) the larger the error.
A self-esteem set point, s∈M, is a measure of self esteem you are targeting such that if A(e)≠s then you want (set an expectation that E(e′)=s for some as yet unobserved evidence e′) to increase or lower your observed self-esteem A(e) such that it matches s.
Warning: This is an off-the-cuff model of the theory I just made up right now, so it’s probably non-standard and I’d have to think/read about the formalism more to fully endorse it (it’s also a little slopping in a couple places because I’m out of practice). I mean to use it only as a pedagogical tool here.
When I suggest we can fix self-esteem, I mean we can work to adjust A(e) and E(e) so that they better match, and work to alter A such that the actual esteem you observe yourself to have also matches the set point s. What that looks like in the case of wanting more esteem than you currently have and repeatedly expecting to have more esteem than you observe yourself having (the case where E(e)<A(e)) and it being true that A(e)<s. The fix in this case is to take actions that cause E(e) to rise to match A(e) and take actions that cause A(e) to rise to match s, and even better if this can be done in concert by making A conditional on |E(e)−A(e)| such that A(e) increases as A(e)−E(e) decreases towards 0.
Maybe it will help if I make the model more formal, since there are multiple variables and it can be a little hard to see what’s going on if you don’t already have the intuitions to track them from normal words.
Let m be a measure of self esteem as a result of seeing some evidence e about the world (an observation). The prediction error is the extent to which E:E→M (the expectation relation that goes from evidence to a measure of self esteem) diverges from A:E→M (the relation that calculates the actual update in measure of self esteem). So prediction error looks like when E(e)=m≠A(e), and the larger the difference between E(e) and A(e) the larger the error.
A self-esteem set point, s∈M, is a measure of self esteem you are targeting such that if A(e)≠s then you want (set an expectation that E(e′)=s for some as yet unobserved evidence e′) to increase or lower your observed self-esteem A(e) such that it matches s.
Warning: This is an off-the-cuff model of the theory I just made up right now, so it’s probably non-standard and I’d have to think/read about the formalism more to fully endorse it (it’s also a little slopping in a couple places because I’m out of practice). I mean to use it only as a pedagogical tool here.
When I suggest we can fix self-esteem, I mean we can work to adjust A(e) and E(e) so that they better match, and work to alter A such that the actual esteem you observe yourself to have also matches the set point s. What that looks like in the case of wanting more esteem than you currently have and repeatedly expecting to have more esteem than you observe yourself having (the case where E(e)<A(e)) and it being true that A(e)<s. The fix in this case is to take actions that cause E(e) to rise to match A(e) and take actions that cause A(e) to rise to match s, and even better if this can be done in concert by making A conditional on |E(e)−A(e)| such that A(e) increases as A(e)−E(e) decreases towards 0.