random half baked thoughts from a sleep deprived jet lagged mind: my guess is that the few largest principal components of variance of human intelligence are something like:
a general factor that affects all cognitive abilities uniformly (this is a sum of a bazillion things. they could be something physiological like better cardiovascular function, or more efficient mitochondria or something; or maybe there’s some pretty general learning/architecture hyperparameter akin to lr or aspect ratio that simply has better or worse configurations. each small change helps/hurts a little bit). having a better general factor makes you better at pattern recognition and prediction, which is the foundation of all intelligence. whether this is learning a policy or a world model, you need to be able to spot regularities in the world to exploit to have any hope of making good predictions.may
a systematization factor (how much to be inclined towards using the machinery of pattern recognition towards finding and operating using explicit rules about the world, vs using that machinery implicitly and relying on intuition). this is the autist vs normie axis. importantly, it’s not like normies are born with hard coded social skills modules, for the same reason that humans aren’t born with language modules (sorry chomsky). we learn these things by being general reasoners placed in an environment where we are exposed to language and social interactions, etc. it just turns out that systematizing is generally really good for certain kinds of domains (math, CS) and pretty bad for other domains (social interaction). I think this explains why some people of decent general intelligence just cannot grasp basic CS concepts, or vice versa for social interaction. this is because as the number of layers of abstraction increases, it becomes increasingly difficult to model the system from the bottom up, until at some point there’s a phase transition where it becomes better to model the system top down and give up on any hope of ever understanding it mechanistically. this is exacerbated through life because people generally accumulate knowledge (both explicit and tacit) faster in the domains in which they already excel, and because of declining neuroplasticity with age. modern society is increasingly constructed to be amenable to systematization (via laws, contracts, standards, etc), because systematization is necessary to govern and scale a civilization. (I think this axis also explains a correlation with embodiment/integratedness, though it’s unclear under this theory how exactly the causality should work there. maybe only being able to systematize makes it harder to model the self? intuitively, it feels like intervening on integratedness causes one to become better at non-systematizing reasoning as a whole, though that could just be because we’re actually intervening on the common cause and using self-modelling as a tight feedback loop)
there are other big components (exploration vs exploitation, risk tolerance, creativity, memory) that aren’t explained here.
I might clean up my thinking and write something more comprehensible later. none of these ideas are novel, but I think a lot can be gained through pinning them down exactly. unfortunately, on priors, this kind of theoretical speculation is rarely useful. though it might be possible to test parts of theories like this experimentally.
why is ADHD also strongly correlated with systematization? it could just be worse self modelling—ADHD happens when your brain’s model of its own priorities and motivations falls out of sync from your brain’s actual priorities and motivations. if you’re bad at understanding yourself, you will misunderstand your priorities, and also you will not be able to control your priorities, because you won’t know what kinds of evidence will really persuade your brain to adopt a specific priority, and your brain will learn that it can’t really trust you to assign it priorities to satisfy its motives (burnout).
why do stimulants help ADHD? well, they short circuit the part where your brain figures out what priorities to trust based on whether they achieve your true motives. if your brain has already learned that your self model is bad at picking actions that eventually pay off towards its true motives, it won’t put its full effort behind those actions. if you can trick it by making every action feel like it’s paying off, you can get it to go along.
honestly unclear whether this is good or bad. on the one hand, if your self model has fallen out of sync, this is pretty necessary to get things done, and could get you out of a bad feedback loop (ADHD is really bad for noticing that your self model has fallen horribly out of sync and acting effectively on it!). some would argue on naturalistic grounds that ideally the true long term solution is to use your brain’s machinery the way it was always intended, by deeply understanding and accepting (and possibly modifying) your actual motives/priorities and having them steer your actions. the other option is to permanently circumvent your motivation system, to turn it into a rubber stamp for whatever decrees are handed down from the self model, which, forever unmoored from needing to model the self, is no longer an understanding of the self but rather an aspirational endpoint towards which the self is molded. I genuinely don’t know which is better as an end goal.
why do stimulants help ADHD? well, they short circuit the part where your brain figures out what priorities to trust based on whether they achieve your true motives
I view taking stimulants more as a move to get the more reflective parts of my brain more power (“getting my taxes done is good, because we need to do it eventually, now is actually a good time, doing my taxes now will be as boring as doing them in the future, rather than playing magic the gathering now”) in steering compared to my more primitive “true motives” that tend to be hyperbolicly discounted (“dosing in bed is nice”, “washing dishes is boring”, “doing taxes is boring”). Maybe I am horrible at self-modelling, but the part where the self model is out of sync as an explanation why the self-reflective parts have less steering power seems unnecessary.
it is kind of funny that caring a lot about reflective stability of alignment proposals and paradoxes arising from self modelling (e.g in action counterfactuals) is most common in the people who are the worst at modelling themselves
I think you’re framing the intuition vs. systematization relationship in a limiting way. From a predictive coding perspective, these aren’t opposing traits on an “autist vs normie axis”, they’re complementary processes working within the same neural architecture.
Predictive coding research shows our brains use both bottom-up signals (intuition) and top-down predictions (systematization) in a dynamic interplay . These are integrated parts of how our brains process information. One person can excel at both.
What appears as preference for systematization reflects differences in how prediction errors are weighted and processed—not a fundamental limitation. You can develop both capacities because they use the same underlying predictive machinery.
I would, however, agree with your take that most people don’t do this but that is because they generally don’t search for prediction error after a certain point since it is easier to just live in your secure bubble. So you’re right in that this is probably how it looks like in practice since people will just use the strat (top-down systemization or bottom-up intuition) that has lead to the most amount of reward in the past.
Predictive coding research shows our brains use both bottom-up signals (intuition) and top-down predictions (systematization) in a dynamic interplay . These are integrated parts of how our brains process information. One person can excel at both.
I wonder to what degree the genome has “solved” intelligence. You could imagine perhaps that we are all sort of noisy instantiations of the ideal intelligence, and that reduction in noise (possibly mainly literal cortex-to-cortex SNR) is mostly what results in intelligence variations. Even considering this, the genome probably does not encode a truly complete solution in the sense that there are plenty of cases where there are mental skills that have the potential for positive feedback and a positive correlation, but basically don’t. The genome probably has no understanding of the geometric langlands conjecture. That is to say, there are deep and useful truths, especially ones that are pointing out symmetries between extremely deep natural categories, and we have not adapted to them at a deep level yet. Therefore the positive manifold of all mental skills is very much still under construction. One could then wonder to what degree variance comes from genetic denoising and what fraction comes from aligning to novel-to-genome deep truths. All that said, may be ill-posed, defining noise and novelty here seems like it could be hard.
random half baked thoughts from a sleep deprived jet lagged mind: my guess is that the few largest principal components of variance of human intelligence are something like:
a general factor that affects all cognitive abilities uniformly (this is a sum of a bazillion things. they could be something physiological like better cardiovascular function, or more efficient mitochondria or something; or maybe there’s some pretty general learning/architecture hyperparameter akin to lr or aspect ratio that simply has better or worse configurations. each small change helps/hurts a little bit). having a better general factor makes you better at pattern recognition and prediction, which is the foundation of all intelligence. whether this is learning a policy or a world model, you need to be able to spot regularities in the world to exploit to have any hope of making good predictions.may
a systematization factor (how much to be inclined towards using the machinery of pattern recognition towards finding and operating using explicit rules about the world, vs using that machinery implicitly and relying on intuition). this is the autist vs normie axis. importantly, it’s not like normies are born with hard coded social skills modules, for the same reason that humans aren’t born with language modules (sorry chomsky). we learn these things by being general reasoners placed in an environment where we are exposed to language and social interactions, etc. it just turns out that systematizing is generally really good for certain kinds of domains (math, CS) and pretty bad for other domains (social interaction). I think this explains why some people of decent general intelligence just cannot grasp basic CS concepts, or vice versa for social interaction. this is because as the number of layers of abstraction increases, it becomes increasingly difficult to model the system from the bottom up, until at some point there’s a phase transition where it becomes better to model the system top down and give up on any hope of ever understanding it mechanistically. this is exacerbated through life because people generally accumulate knowledge (both explicit and tacit) faster in the domains in which they already excel, and because of declining neuroplasticity with age. modern society is increasingly constructed to be amenable to systematization (via laws, contracts, standards, etc), because systematization is necessary to govern and scale a civilization. (I think this axis also explains a correlation with embodiment/integratedness, though it’s unclear under this theory how exactly the causality should work there. maybe only being able to systematize makes it harder to model the self? intuitively, it feels like intervening on integratedness causes one to become better at non-systematizing reasoning as a whole, though that could just be because we’re actually intervening on the common cause and using self-modelling as a tight feedback loop)
there are other big components (exploration vs exploitation, risk tolerance, creativity, memory) that aren’t explained here.
I might clean up my thinking and write something more comprehensible later. none of these ideas are novel, but I think a lot can be gained through pinning them down exactly. unfortunately, on priors, this kind of theoretical speculation is rarely useful. though it might be possible to test parts of theories like this experimentally.
why is ADHD also strongly correlated with systematization? it could just be worse self modelling—ADHD happens when your brain’s model of its own priorities and motivations falls out of sync from your brain’s actual priorities and motivations. if you’re bad at understanding yourself, you will misunderstand your priorities, and also you will not be able to control your priorities, because you won’t know what kinds of evidence will really persuade your brain to adopt a specific priority, and your brain will learn that it can’t really trust you to assign it priorities to satisfy its motives (burnout).
why do stimulants help ADHD? well, they short circuit the part where your brain figures out what priorities to trust based on whether they achieve your true motives. if your brain has already learned that your self model is bad at picking actions that eventually pay off towards its true motives, it won’t put its full effort behind those actions. if you can trick it by making every action feel like it’s paying off, you can get it to go along.
honestly unclear whether this is good or bad. on the one hand, if your self model has fallen out of sync, this is pretty necessary to get things done, and could get you out of a bad feedback loop (ADHD is really bad for noticing that your self model has fallen horribly out of sync and acting effectively on it!). some would argue on naturalistic grounds that ideally the true long term solution is to use your brain’s machinery the way it was always intended, by deeply understanding and accepting (and possibly modifying) your actual motives/priorities and having them steer your actions. the other option is to permanently circumvent your motivation system, to turn it into a rubber stamp for whatever decrees are handed down from the self model, which, forever unmoored from needing to model the self, is no longer an understanding of the self but rather an aspirational endpoint towards which the self is molded. I genuinely don’t know which is better as an end goal.
I view taking stimulants more as a move to get the more reflective parts of my brain more power (“getting my taxes done is good, because we need to do it eventually, now is actually a good time, doing my taxes now will be as boring as doing them in the future, rather than playing magic the gathering now”) in steering compared to my more primitive “true motives” that tend to be hyperbolicly discounted (“dosing in bed is nice”, “washing dishes is boring”, “doing taxes is boring”). Maybe I am horrible at self-modelling, but the part where the self model is out of sync as an explanation why the self-reflective parts have less steering power seems unnecessary.
it is kind of funny that caring a lot about reflective stability of alignment proposals and paradoxes arising from self modelling (e.g in action counterfactuals) is most common in the people who are the worst at modelling themselves
do you think that Stanovich’s reflective mind and need for cognition are downstream from these two?
I think you’re framing the intuition vs. systematization relationship in a limiting way. From a predictive coding perspective, these aren’t opposing traits on an “autist vs normie axis”, they’re complementary processes working within the same neural architecture.
Predictive coding research shows our brains use both bottom-up signals (intuition) and top-down predictions (systematization) in a dynamic interplay . These are integrated parts of how our brains process information. One person can excel at both.
What appears as preference for systematization reflects differences in how prediction errors are weighted and processed—not a fundamental limitation. You can develop both capacities because they use the same underlying predictive machinery.
I would, however, agree with your take that most people don’t do this but that is because they generally don’t search for prediction error after a certain point since it is easier to just live in your secure bubble. So you’re right in that this is probably how it looks like in practice since people will just use the strat (top-down systemization or bottom-up intuition) that has lead to the most amount of reward in the past.
Shorter blog on how emotions interact with this hierarchical processing system—https://blog.dropbox.com/topics/work-culture/the-mind-at-work—lisa-feldman-barrett-on-the-metabolism-of-emot
Relating this to Kahnemahns’s system 1 and 2 work—https://pmc.ncbi.nlm.nih.gov/articles/PMC8979207/
edit: first link was formatted wrong.
Link is broken, can you reshare?
Fixed the comment, thanks!
(Here it is otherwise:) https://pmc.ncbi.nlm.nih.gov/articles/PMC5390700/
I wonder to what degree the genome has “solved” intelligence. You could imagine perhaps that we are all sort of noisy instantiations of the ideal intelligence, and that reduction in noise (possibly mainly literal cortex-to-cortex SNR) is mostly what results in intelligence variations. Even considering this, the genome probably does not encode a truly complete solution in the sense that there are plenty of cases where there are mental skills that have the potential for positive feedback and a positive correlation, but basically don’t. The genome probably has no understanding of the geometric langlands conjecture. That is to say, there are deep and useful truths, especially ones that are pointing out symmetries between extremely deep natural categories, and we have not adapted to them at a deep level yet. Therefore the positive manifold of all mental skills is very much still under construction. One could then wonder to what degree variance comes from genetic denoising and what fraction comes from aligning to novel-to-genome deep truths. All that said, may be ill-posed, defining noise and novelty here seems like it could be hard.