Food for thought: I remember at one point competing against my best friend in a middle school spelling bee. We had a few rounds going back and forth and finally there was a word that I couldn't...
Food for thought: I remember at one point competing against my best friend in a middle school spelling bee. We had a few rounds going back and forth and finally there was a word that I couldn't quite figure out - there were two ways to spell it by my reckoning and I ended up gambling on the wrong way. My friend immediately spelled it the other way and won the competition. I remember being a little frustrated in the moment, but I was also pretty excited that we both made it to the end. I wonder how knowing the people you're competing against might factor into your emotions.
Question to any readers: has this ever happened to you - where you were hoping to do better or you narrowly missed coming out on top?
We studied photos of 413 Olympic athletes taken during medal ceremonies between 2000 and 2016. The photos came from the Olympic World Library and Getty Images and included athletes from 67 countries. We also incorporated Sports Illustrated’s Olympic finish predictions, because we wanted to see whether athletes’ facial expressions would be affected if they had exceeded expectations or underperformed.
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Even though second-place finishers had just performed objectively better than third-place finishers, the AI found that bronze medalists, on average, appeared happier than silver medalists.
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Silver medalists form an upward comparison, imagining a different outcome – “I almost won gold.” Bronze medalists, on the other hand, form a downward comparison: “At least I won a medal” or “It could have been worse.”
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We found evidence consistent with both category-based and expectation-based counterfactual accounts of Olympic medalists’ expressions. Unsurprisingly, our analysis also found that gold medalists are far more likely to smile than the other two medalists, and people who finished better than expected were also more likely to smile, regardless of their medal.
Prior studies haven’t been able to thoroughly test this phenomenon. But by using artificial intelligence, we were able to test these two theories on a large and diverse set of image data for the first time.
I wish they had expanded upon this statement (maybe they do in their actual paper, but the previous studies linked in the article seem pretty thorough to me). This phenomenon has been known for a...
Prior studies haven’t been able to thoroughly test this phenomenon. But by using artificial intelligence, we were able to test these two theories on a large and diverse set of image data for the first time.
I wish they had expanded upon this statement (maybe they do in their actual paper, but the previous studies linked in the article seem pretty thorough to me). This phenomenon has been known for a while (since the first study in 1995). Replicating studies is always welcome, but they're presenting this as though they did something novel.
And is 413 athletes over 5 Olympic Games (maybe 9 if you include Winter Olympics) really a large set of image data? Granted, it's an order of magnitude larger than the previous studies, but there's more medal winners at a single Olympic Games (339x3 this year in Paris, 340x3 in Tokyo) than in their dataset. They could just look at Paris this year and have 678 examples to go through. Maybe I'm just put off by the use of AI while still having such a small dataset. One of big advantages of AI is that it can go through millions of data records. Its use here is to remove bias (a good thing), but it just feels like they didn't use it to its full potential. They also didn't explain how the manually coded facial recognition algorithm is worse than the AI algorithm (which will also have bias).
To get a bronze medal typically you "win" a game against an opponent. To get silver, you lose. Exception being score based sports like figure skating, etc. Losing, even to get a silver, still...
To get a bronze medal typically you "win" a game against an opponent. To get silver, you lose. Exception being score based sports like figure skating, etc. Losing, even to get a silver, still stings as a loss. But winning to get a bronze still feels like a win.
Food for thought: I remember at one point competing against my best friend in a middle school spelling bee. We had a few rounds going back and forth and finally there was a word that I couldn't quite figure out - there were two ways to spell it by my reckoning and I ended up gambling on the wrong way. My friend immediately spelled it the other way and won the competition. I remember being a little frustrated in the moment, but I was also pretty excited that we both made it to the end. I wonder how knowing the people you're competing against might factor into your emotions.
Question to any readers: has this ever happened to you - where you were hoping to do better or you narrowly missed coming out on top?
Second place means you're the first loser. An idiom popularized by Dale Earnhardt Sr.
Also in the wise words of Reese Bobby, "If you ain't first, you're last."
Key points:
I wish they had expanded upon this statement (maybe they do in their actual paper, but the previous studies linked in the article seem pretty thorough to me). This phenomenon has been known for a while (since the first study in 1995). Replicating studies is always welcome, but they're presenting this as though they did something novel.
And is 413 athletes over 5 Olympic Games (maybe 9 if you include Winter Olympics) really a large set of image data? Granted, it's an order of magnitude larger than the previous studies, but there's more medal winners at a single Olympic Games (339x3 this year in Paris, 340x3 in Tokyo) than in their dataset. They could just look at Paris this year and have 678 examples to go through. Maybe I'm just put off by the use of AI while still having such a small dataset. One of big advantages of AI is that it can go through millions of data records. Its use here is to remove bias (a good thing), but it just feels like they didn't use it to its full potential. They also didn't explain how the manually coded facial recognition algorithm is worse than the AI algorithm (which will also have bias).
To get a bronze medal typically you "win" a game against an opponent. To get silver, you lose. Exception being score based sports like figure skating, etc. Losing, even to get a silver, still stings as a loss. But winning to get a bronze still feels like a win.