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That is a very idealistic perspective. There are certainly fast learners due to the fact they are faster at cross domain synthesis.
In my experience when I am able to pick something up quickly it's because I can exploit cross domain knowledge. I have ready-made analogies to things I understand, or I understand the domain which informs the fundamentals of the new domain.
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in contrast: the sport i know best, hoops, a common pattern for generational players is for them to be late bloomers because they grow up short, developing skills and competitive toughness, then get lucky and grow a half-foot late in puberty
who is that? Not Lebron, definitely not Jokic, SGA was 4 star recruit?
https://press.princeton.edu/books/paperback/9780691133805/ol...
That book used artwork valuation as a performance measure and analyzed it over top artist's lifetimes finding two patterns. The "Young Genius" where an artist has a vision and realizes some innovation and their most valuable works center around that with value tapering off over their life. Picasso. (Who had two peaks but still fit the pattern.) Contrast to the "Old Master." This is someone who keeps refining their craft and their most valuable works and innovations are their late life works. Cézanne.
Wasn't this study immediately debunked due to bad statistical methods? See https://zenodo.org/records/18002186 > Using simple simulations,we show that this pattern arises naturally from collider bias when selection into elitesamples depends on both early and adult performance. Consequently, associationsestimated within elite samples are descriptively accurate for the selected population,but causally misleading, and s…
If the paper is true, then, yeesh! That's a pretty big miss on the part of Güllich et al.
Reading through the very short paper there, it seems to not have gone through review yet (typos, mispellings, etc). Also, it's not clear that the data in the tables or the figure are from Güllich's work or are simulations meant to illustrate their idea (" True and estimated covariate effects in the presence of simulated collider bias in the full and selected samples"). Being more clear where the data is coming from may help the argument, but I likely just missed some sentence or something.
I'll be interested to see where this goes. That Güllich managed to get the paper into Science in the first place lends credence to them having gone through something as simple as Berkson's Paradox and have accounted for that. It's not everyday you get something as 'soft' as that paper into Science, after all. If not, then wow! standards for review really have slipped!
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Berkson's Paradox seems to rely on the selection criteria being a combination of the two traits in question -- in the example I keep reading about, only "famous" actors are selected, and actors can be famous if they are either highly talented or highly attractive. But in TFA, surely the "high performance" selection filter applies only to the adult performance level? To put it another way: If selection was restricted…
>Berkson's Paradox seems to rely on the selection criteria being a combination of the two traits in question 100% correct. For traits x and y, selecting for datapoints in the region x + y > z will always yield a spurious negative correlation for sufficiently uncorrelated data, since the boundary of the inequality x + y > z is a negatively sloping line. >But in TFA, surely the "high performance" selection filter appli…
> The different pattern of predictor effects observed among adult world-class athletes
This seems to be selecting on just one trait (high adult performance). Where is the second trait (high childhood performance) mentioned?
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The problem being: access to a prestiges career or opportunity is generally predicated on climbing the academics achievement ladder at an increasingly early age. This leaves the more esoteric people out in the cold. If your not a true prodigy whose achievements outshine the highly credentialed you will struggle to get on.
In the tech industry, some of the people with the most prestigious careers are literally college dropouts. There are many paths to success, not all are linear.
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> It's like how software engineers who are at Google are worse if they're better competitive programmers. That's not true. It didn't replicate and Norvig has said as much somewhere on HN, IIRC. (I also agree with the other criticisms that this 'old vs young' setup in OP is obviously at least partially, and perhaps entirely, regression to the mean and Berkson.)
Thanks for sharing. I didn't know that.