This is a point that often comes up in “refutations” of the existence of g. People argue, essentially, that even though tests are correlated, they might be produced by many independent causes. I’d go further—we know there are many causes. While intelligence is strongly heritable, it’s highly polygenic. Dozens of genes are already known to be linked to it, and more are likely to be discovered. It’s harder to quantify environmental influences, but there are surely many that matter there, too.
So, no, there’s no magical number g hidden in our brains, just like there’s no single number in our bodies that says how good we are at running, balancing, or throwing stuff. But that doesn’t change the fact that a single number provides a good description of how good we are at various mental tasks.
I disagree here. g can totally exist while being a wildly heterogenous mixture of different causes. As a point of comparison, consider temperature; there are many different things that can influence the temperature of an object, such as absorbing energy from or emitting energy via light/other EM waves, exothermic or endothermic chemical reactions, contact friction with a moving object, and similar. The key point is that all of these different causes of temperature variation follow the same causal rules with regards to the resulting temperature.
When it comes to the polygenic influence on g, the same pattern arises as it does for temperature; while there are many different genetic factors that influence performance in cognitive tasks, many of them do so in a uniform way, improving performance across all tasks. We could think of g as resulting from the sum of such cross-cutting influences, similar to how we might think of temperature variation as resulting from the sum of various heating and cooling influences. (Well, mathematically it’s more complicated than that, but the basic point holds.)
Importantly, this notion of g is distinct from the average performance across tests (which we might call IQ). For instance, you can increase your performance on a test (IQ score) by getting practice or instruction for the test, but this doesn’t “transfer” to other tasks. The lack of cross-task transfer distinguishes g from these other things, and also it is what makes g so useful (since something that makes you better at everything will… well, make you better at everything).
Can I check if I understand your point correctly? I suggested we know that g has many causes since so many genes are relevant and thus f you opened up a brain, you wouldn’t be able to “find” g in any particular place. It’s the product of a whole bunch of different genes, each of which is just coding for some protein, and they all interact in complex ways. If I understand you correctly, you’re pointing out that there could be a sort of “causal bottleneck” of sorts. For example, maybe all the different genes have complex effects, but all that really matters is how they affect neuronal calcium channel efficiency or something. Thus, if you opened up a brain, you could just check how efficient the calcium channels are and you’re done. Is that right?
If this is right, I do agree that I seem to be over-claiming a bit here. There’s nothing that precludes the possibility of a “bottleneck” as far as I know, (though it seems sorta implausible in my not-at-all-informed opinion)
Well, there’s sort of a spectrum of different positions one could take with regards to the realism of g:
One could argue that g is pure artifact of the method, and not relevant at all. For instance, some people argue that IQ tests just measure “how good you are at tests”, argue that things like test-taking anxiety or whatever are major influences on the test scores, etc..
One could argue that g is not a common underlying cause of performance on tests, but instead a convenient summary statistic; e.g. maybe one believes that different abilities are connected in a “network”, such that learned skill at one ability transfers to other “nearby” abilities. In that case, the g loadings would be a measure of how central the tests are in the network.
One could argue that there are indeed common causes that have widespread influence on cognitive ability, and that summing these common causes together gives you g, without necessarily committing to the notion that there is some clean biological bottleneck for those common causes.
One could argue that there is a simple biological parameter which acts as a causal bottleneck representing g.
Of these, the closest position that your post came to was option 2, though unlike e.g. mutualists, you didn’t commit to any one explanation for the positive manifold. That is, in your post, you wrote “It does not mean that number causes test performance to be correlated.”, which I’d take to be distancing oneself from positions 3+. Meanwhile, out of these, my comment defended something inbetween options 3 and 4.
You seem to be asking me about option 4. I agree that strong versions of option 4 seem implausible, for probably similar reasons to you; it seems like there is a functional coordination of distinct factors that produce intelligence, and so you wouldn’t expect strong versions of option 4 to hold.
However, it seems reasonable to me to define g as being the sum of whichever factors have an positive effect on all cognitive abilities. That is, if you have some genetic variant which makes people better at recognizing patterns, discriminating senses, more knowledgeable, etc., then one could just consider this variant to be part of g. This would lead to g being composed of a larger number of heterogeneous factors, some of which might possibly not be directly observable anymore (e.g. if they are environmental factors that can no longer be tracked); but I don’t see anything wrong with that? It would still satisfy the relevant causal properties.
(Of course then there’s the question of whether all of the different causes have identical proportions of effect on the abilities, or if some influence one ability more and others influence another ability more. The study I linked tested for this and only included genetic variants that had effects in proportion to the g factor loadings. But I’m not sure their tests were well-powered enough to test it exactly. If there is too much heterogeneity between the different causes, then it might make sense to split them into more homogeneous clusters of causes. But that’s for future research to figure out.)
Thanks, very clear! I guess the position I want to take is just that the data in the post gives reasonable evidence for g being at least the convenient summary statistic in 2 (and doesn’t preclude 3 or 4).
What I was really trying to get at in the original quote is that some people seem to consider this to be the canonical position on g:
Factor analysis provides rigorous statistical proof that there is some single underlying event that produces all the correlations between mental tests.
There are lots of articles that (while not explicitly stating the above position) refute it at length, and get passed around as proof that g is a myth. It’s certainly true that position 5 is false (in multiple ways), but I just wanted to say that this doesn’t mean anything for the evidence we have for 2.
I agree that a simple factor analysis does not provide anything even close to proof of 3 or 4, but I think it’s worth noting that the evidence on g goes beyond the factor-analytic, e.g. with the studies I linked.
Thanks for pointing out those papers, which I agree can get at issues that simple correlations can’t. Still, to avoid scope-creep, I’ve taken the less courageous approach of (1) mentioning that the “breadth” of the effects of genes is an active research topic and (2) editing the original paragraph you linked to to be more modest, talking about “does the above data imply” rather than “is it true that”. (I’d rather avoid directly addressing 3 and 4 since I think that doing those claims justice would require more work than I can put in here.) Anyway, thanks again for your comments, it’s useful for me to think of this spectrum of different “notions of g”.
I disagree here. g can totally exist while being a wildly heterogenous mixture of different causes. As a point of comparison, consider temperature; there are many different things that can influence the temperature of an object, such as absorbing energy from or emitting energy via light/other EM waves, exothermic or endothermic chemical reactions, contact friction with a moving object, and similar. The key point is that all of these different causes of temperature variation follow the same causal rules with regards to the resulting temperature.
When it comes to the polygenic influence on g, the same pattern arises as it does for temperature; while there are many different genetic factors that influence performance in cognitive tasks, many of them do so in a uniform way, improving performance across all tasks. We could think of g as resulting from the sum of such cross-cutting influences, similar to how we might think of temperature variation as resulting from the sum of various heating and cooling influences. (Well, mathematically it’s more complicated than that, but the basic point holds.)
Importantly, this notion of g is distinct from the average performance across tests (which we might call IQ). For instance, you can increase your performance on a test (IQ score) by getting practice or instruction for the test, but this doesn’t “transfer” to other tasks. The lack of cross-task transfer distinguishes g from these other things, and also it is what makes g so useful (since something that makes you better at everything will… well, make you better at everything).
Can I check if I understand your point correctly? I suggested we know that g has many causes since so many genes are relevant and thus f you opened up a brain, you wouldn’t be able to “find” g in any particular place. It’s the product of a whole bunch of different genes, each of which is just coding for some protein, and they all interact in complex ways. If I understand you correctly, you’re pointing out that there could be a sort of “causal bottleneck” of sorts. For example, maybe all the different genes have complex effects, but all that really matters is how they affect neuronal calcium channel efficiency or something. Thus, if you opened up a brain, you could just check how efficient the calcium channels are and you’re done. Is that right?
If this is right, I do agree that I seem to be over-claiming a bit here. There’s nothing that precludes the possibility of a “bottleneck” as far as I know, (though it seems sorta implausible in my not-at-all-informed opinion)
Well, there’s sort of a spectrum of different positions one could take with regards to the realism of g:
One could argue that g is pure artifact of the method, and not relevant at all. For instance, some people argue that IQ tests just measure “how good you are at tests”, argue that things like test-taking anxiety or whatever are major influences on the test scores, etc..
One could argue that g is not a common underlying cause of performance on tests, but instead a convenient summary statistic; e.g. maybe one believes that different abilities are connected in a “network”, such that learned skill at one ability transfers to other “nearby” abilities. In that case, the g loadings would be a measure of how central the tests are in the network.
One could argue that there are indeed common causes that have widespread influence on cognitive ability, and that summing these common causes together gives you g, without necessarily committing to the notion that there is some clean biological bottleneck for those common causes.
One could argue that there is a simple biological parameter which acts as a causal bottleneck representing g.
Of these, the closest position that your post came to was option 2, though unlike e.g. mutualists, you didn’t commit to any one explanation for the positive manifold. That is, in your post, you wrote “It does not mean that number causes test performance to be correlated.”, which I’d take to be distancing oneself from positions 3+. Meanwhile, out of these, my comment defended something inbetween options 3 and 4.
You seem to be asking me about option 4. I agree that strong versions of option 4 seem implausible, for probably similar reasons to you; it seems like there is a functional coordination of distinct factors that produce intelligence, and so you wouldn’t expect strong versions of option 4 to hold.
However, it seems reasonable to me to define g as being the sum of whichever factors have an positive effect on all cognitive abilities. That is, if you have some genetic variant which makes people better at recognizing patterns, discriminating senses, more knowledgeable, etc., then one could just consider this variant to be part of g. This would lead to g being composed of a larger number of heterogeneous factors, some of which might possibly not be directly observable anymore (e.g. if they are environmental factors that can no longer be tracked); but I don’t see anything wrong with that? It would still satisfy the relevant causal properties.
(Of course then there’s the question of whether all of the different causes have identical proportions of effect on the abilities, or if some influence one ability more and others influence another ability more. The study I linked tested for this and only included genetic variants that had effects in proportion to the g factor loadings. But I’m not sure their tests were well-powered enough to test it exactly. If there is too much heterogeneity between the different causes, then it might make sense to split them into more homogeneous clusters of causes. But that’s for future research to figure out.)
Thanks, very clear! I guess the position I want to take is just that the data in the post gives reasonable evidence for g being at least the convenient summary statistic in 2 (and doesn’t preclude 3 or 4).
What I was really trying to get at in the original quote is that some people seem to consider this to be the canonical position on g:
Factor analysis provides rigorous statistical proof that there is some single underlying event that produces all the correlations between mental tests.
There are lots of articles that (while not explicitly stating the above position) refute it at length, and get passed around as proof that g is a myth. It’s certainly true that position 5 is false (in multiple ways), but I just wanted to say that this doesn’t mean anything for the evidence we have for 2.
I agree that a simple factor analysis does not provide anything even close to proof of 3 or 4, but I think it’s worth noting that the evidence on g goes beyond the factor-analytic, e.g. with the studies I linked.
Thanks for pointing out those papers, which I agree can get at issues that simple correlations can’t. Still, to avoid scope-creep, I’ve taken the less courageous approach of (1) mentioning that the “breadth” of the effects of genes is an active research topic and (2) editing the original paragraph you linked to to be more modest, talking about “does the above data imply” rather than “is it true that”. (I’d rather avoid directly addressing 3 and 4 since I think that doing those claims justice would require more work than I can put in here.) Anyway, thanks again for your comments, it’s useful for me to think of this spectrum of different “notions of g”.