"This approach was also able to identify subgroups of children with different levels of cognitive control and performance monitoring, or the ability to modify one’s strategy after making an error."
This should surprise no one. You took a large population and found subpopulations within it. If you want to look at a population average, then use the population data. If you want to look at kids with specific attention needs (guessing ADHD since medical related) then design a study to select for children fitting that criteria, including subtypes.
This seems like the type of thing that should have had a study about study design done long ago that they could have followed to help them structure their own population selection.
The Maximegalon Institute of Slowly and Painfully Working Out the Surprisingly Obvious (MISPWOSO) is a fictional research institution from Douglas Adams' The Hitchhiker's Guide to the Galaxy series.
The point isn’t that they found subgroups, the point is the method they used to find them — namely, analyzing individual brain scans rather than averaging them out first.
Medical professionals have a history of not necessarily having complete understanding of the maths they use in their work. Classic example of a nutritionist 'inventing' the trapezoid rule for calculating area under a curve, and then naming it after herself. And then many many other medical people unironically using said method and citing her.
A) these aren’t “medical people”, they’re neuroscientists and psychologists. Comparing them to a nutritionist seems especially cruel!
B) “some people have been wrong before” is not a reason to think you know better than the authors of an upcoming Nature article based on a few layperson-targeted paragraphs summarizing the paper from a very high level.
> “some people have been wrong before” is not a reason to think you know better than the authors of an upcoming Nature article based on a few layperson-targeted paragraphs summarizing the paper from a very high level.
Nor is "this paper is going to appear in Nature" a reason not to wonder whether there might be something that the authors don't know. The whole point of science is that anyone can make an informed critique and self-evaluation of it, with no necessity of depending on a priesthood to interpret it. You can point out the flaws in giantg2's argument https://news.ycombinator.com/item?id=47995899, but neither the venue of the paper, nor the fact that the argument is directed at laypeople in a forum frequented by laypeople, seems to me inherently to indicate such flaws.
That seems awfully like an appeal to authority. Your parent comment doesn't just vaguely snipe, but points out reasons this should have been expected. Those reasons could potentially not be valid, or not present the whole picture, but "the researchers are from Stanford" doesn't rebut them.
While interesting, I get a few questions from this:
- As another commenter said, this is a known disadvantage of averages. I'm curious if it's possible to get a median result from per-individual averages. I'm not familiar enough with how this research is done to get a result.
- Was any effort made to re-test individuals in a second/third/etc session, showing consistent patterns to the brain activity? I know it was consistent within a session, but I'm curious if it might change week over week.
The specific counterintuitive result is mentioned toward the end of the article, and I'm having some trouble understanding it:
> when analyzing average trends in groups of children, slower reaction times to the “Go” signal were linked to increased activity in many brain regions, including the default mode network
> However, when an individual had a slower reaction time to the “Go” signal, activity decreased in the default mode network — the opposite of the group-level pattern.
Now, I don't know anything about neuroscience or brain development, but hopefully I can explain the statistics in a way useful to you.
Imagine there are two groups A and B.
One group, A, has slower reactions on average and high average activity
The other, Group B, has higher reactions and lower than the Group A's activity.
Yet inside both groups the general trend is that if someone is slower than the average reaction of their group then they're also below the average activity for their group.
If we look at the overall means without distinguishing groups, slower reaction is correlated positively with higher activity (kids from group A have higher activity and slower reaction in general, which pushes the correlation upwards. As long as the relationship in Group B isn't too strong the upward trend from Group A can easily dominate overall correlation) but inside each group the trend is actually the opposite.
This applies pretty much every time you're comparing samples. If I understood your quote correctly, they're studying a child's reaction time vs activity level by comparing the same kid in different times. The same logic applies, a person can exhibit the opposite trend to the populational average due to the same mechanism above. This can be even more dramatic, because once you start looking at averages you start losing time dependency information.
More broadly (and more formally), multivariate covariance splits in within-group and between-group terms, so if the signs of the terms are different the magnitude of one can dominate the overall sum and flip the sign.
One way to think of it — I didn't read the article in depth so this is just an example — is in terms of overall individual differences in speed and activity level. Then, you could have slower persons having increased activity relative to faster persons, but it still be true that when a slower person had an even slower signal reaction, their activity went down, and when a faster person had a slower signal reaction, their activity went down as well.
It's a classic psychological phenomenon, where individual differences are obscuring time course patterns and vice versa.
Of course, this sidesteps the question of why (in the hypothetical example) the overall individual differences exist. Assuming those general individual differences are reliable and "real", you still have to explain why they are there, and if they predict significant outcomes, why they do, and so forth.
The message of the paper is good, although I think the press release (not surprisingly) overstates the significance of the paper. I think these kinds of issues have received a lot more attention in the literature in the last decade or so in neuroscience. It also sort of sidesteps a lot of the more thorny questions about truly person-specific patterns and how to determine when they're meaningful.
Hmm I think all these replies are overcomplicating things.
At a group level, some kids are slower at this Stop/Go task than others. The group difference appears to be this increased broad-scale brain activity: the slow group is overall more prone to distraction and daydreaming.
However, at an individual level, slowing down on the task means increasing your focus (and decreasing brain activity in irrelevant regions), regardless of whether you were in the slow group or the fast group. So the group-level difference is not necessarily as profound as it might appear, and applying "slow group" with too broad a brush means you're going to sweep up some kids who are naturally cautious and focused.
In a sense, but it is a bit more devious. It basically invalidates all past fMRI studies. Not that anyone should have taken those seriously, but it looks like another nail in the coffin. fMRI analysis is (was?) basically: squeeze each brain scan into a standard box, then average the BOLD responses (that's roughly oxygen usage between 3s and 9s after activity). This abstract says that --at least in some cases-- those averages are wrong. Not just hiding information through aggregation, but flat-out lying.
Just from reading the link, I do see an objection: they studied repetitions, which are known to be different from the initial response, so this may not be the fMRI's eulogy.
Yup, to no one’s surprise (least of all the investigators), doing neuroscience by correlating cortex regions with cognitive activities is extremely clunky at best. Very robust finding confirming this tho, thanks for sharing!
Now that we’re finally moving to the next stage of neuroscience due inscrutable latent systems (aka LLMs), I can’t help but feel some nostalgia. It’s all fun and games until someone makes a lie detection helmet that actually works…
This should surprise no one. You took a large population and found subpopulations within it. If you want to look at a population average, then use the population data. If you want to look at kids with specific attention needs (guessing ADHD since medical related) then design a study to select for children fitting that criteria, including subtypes.
This seems like the type of thing that should have had a study about study design done long ago that they could have followed to help them structure their own population selection.
The Maximegalon Institute of Slowly and Painfully Working Out the Surprisingly Obvious (MISPWOSO) is a fictional research institution from Douglas Adams' The Hitchhiker's Guide to the Galaxy series.
https://en.wikipedia.org/wiki/Tai%27s_model
B) “some people have been wrong before” is not a reason to think you know better than the authors of an upcoming Nature article based on a few layperson-targeted paragraphs summarizing the paper from a very high level.
Nor is "this paper is going to appear in Nature" a reason not to wonder whether there might be something that the authors don't know. The whole point of science is that anyone can make an informed critique and self-evaluation of it, with no necessity of depending on a priesthood to interpret it. You can point out the flaws in giantg2's argument https://news.ycombinator.com/item?id=47995899, but neither the venue of the paper, nor the fact that the argument is directed at laypeople in a forum frequented by laypeople, seems to me inherently to indicate such flaws.
- As another commenter said, this is a known disadvantage of averages. I'm curious if it's possible to get a median result from per-individual averages. I'm not familiar enough with how this research is done to get a result.
- Was any effort made to re-test individuals in a second/third/etc session, showing consistent patterns to the brain activity? I know it was consistent within a session, but I'm curious if it might change week over week.
https://tvtropes.org/pmwiki/pmwiki.php/Main/Flanderization
> when analyzing average trends in groups of children, slower reaction times to the “Go” signal were linked to increased activity in many brain regions, including the default mode network
> However, when an individual had a slower reaction time to the “Go” signal, activity decreased in the default mode network — the opposite of the group-level pattern.
Imagine there are two groups A and B. One group, A, has slower reactions on average and high average activity The other, Group B, has higher reactions and lower than the Group A's activity. Yet inside both groups the general trend is that if someone is slower than the average reaction of their group then they're also below the average activity for their group.
If we look at the overall means without distinguishing groups, slower reaction is correlated positively with higher activity (kids from group A have higher activity and slower reaction in general, which pushes the correlation upwards. As long as the relationship in Group B isn't too strong the upward trend from Group A can easily dominate overall correlation) but inside each group the trend is actually the opposite.
This applies pretty much every time you're comparing samples. If I understood your quote correctly, they're studying a child's reaction time vs activity level by comparing the same kid in different times. The same logic applies, a person can exhibit the opposite trend to the populational average due to the same mechanism above. This can be even more dramatic, because once you start looking at averages you start losing time dependency information.
More broadly (and more formally), multivariate covariance splits in within-group and between-group terms, so if the signs of the terms are different the magnitude of one can dominate the overall sum and flip the sign.
It's a classic psychological phenomenon, where individual differences are obscuring time course patterns and vice versa.
Of course, this sidesteps the question of why (in the hypothetical example) the overall individual differences exist. Assuming those general individual differences are reliable and "real", you still have to explain why they are there, and if they predict significant outcomes, why they do, and so forth.
The message of the paper is good, although I think the press release (not surprisingly) overstates the significance of the paper. I think these kinds of issues have received a lot more attention in the literature in the last decade or so in neuroscience. It also sort of sidesteps a lot of the more thorny questions about truly person-specific patterns and how to determine when they're meaningful.
Denying that first impulse, thinking about it, and then acting is slow.
https://en.wikipedia.org/wiki/Simpson%27s_paradox
At a group level, some kids are slower at this Stop/Go task than others. The group difference appears to be this increased broad-scale brain activity: the slow group is overall more prone to distraction and daydreaming.
However, at an individual level, slowing down on the task means increasing your focus (and decreasing brain activity in irrelevant regions), regardless of whether you were in the slow group or the fast group. So the group-level difference is not necessarily as profound as it might appear, and applying "slow group" with too broad a brush means you're going to sweep up some kids who are naturally cautious and focused.
Just from reading the link, I do see an objection: they studied repetitions, which are known to be different from the initial response, so this may not be the fMRI's eulogy.
Now that we’re finally moving to the next stage of neuroscience due inscrutable latent systems (aka LLMs), I can’t help but feel some nostalgia. It’s all fun and games until someone makes a lie detection helmet that actually works…