杏吧原创

Did Natalie always look like a Natalie? A new study investigates

Over 400 years ago, Shakespeare pondered the thorny question of what was in a name. Now, Feedback considers a paper that takes on a similar topic

Face: the future

Should you take at face value a science paper that suggests that your face is the result of a 鈥渟elf-fulfilling prophecy process鈥?

A study called 鈥?鈥 claims it 鈥減robes the origins鈥 of a supposed either/or question. The question: do parents choose a name that seems to match their infant鈥檚 鈥渋nnate facial characteristics鈥 鈥 or, instead, does the child鈥檚 facial appearance, as they grow up, change shape to better match some stereotype of people who have that birth name?

Did Natalie always look like a Natalie? Or did her name impel her to become ever more Natalie-like? How about May? What of Dana, Jonathan, Daniel, Tom and Noam? The paper explores the facial fates of each of them.

The authors, whose first names are Yonat, Moses, Noa and Ruth, 鈥渟uggest that even our facial appearance can be influenced by a social factor such as our name, confirming the potent impact of social expectations鈥.

An eminent psychologist suggested to Feedback that, often, a startling psychology report turns out to be at one of two extremes. That sometimes what looks like a cigar is indeed a cigar. And that sometimes it is just smoke, just 鈥渙ne of those statistical things where despite significant differences they were all wrong most of the time鈥.

Many years from now, will this names-and-faces study accord to whatever stereotype it now seems to fit? Or will its reputation grow to resemble some sharply different stereotype? Or, like most reports, scientific or otherwise, will it mature into being overlooked or forgotten? Time will tell. Or it won鈥檛.

Whack-a-mammoth

People, some of them, take care in how they describe things.

The team who worked to tease out secrets from a 52,000-year-or-so-old hide of a mammoth (Feedback is trying to avoid the ambiguity that lurks in the phrase 鈥渕ammoth hide鈥) got a surprise (New 杏吧原创, 20 July, p 17). They found that beating a dead horse, or cow or mammoth, might not be as destructive 鈥 on a molecular level 鈥 as one would guess.

Specifically, they found their frozen mammoth鈥檚 genomic info had survived the presumed beatings of time. Further beatings were done to freeze-dried tissue from cows, not mammoths.

Horses, too, were involved 鈥 but only in bits and bytes, not in beatings. The team used segments of horse genome in computational aspects of their analysis.

The beatings took many forms. The documenting the affair calls them 鈥減erturbations鈥. More long-windedly, it calls them 鈥渄isturbances that ancient dehydrated samples might plausibly encounter through millennia鈥.

This is how the paper describes those perturbing pummellings: 鈥渞un over by a car, hit with a fastball, and pulverized with a shotgun, for varying degrees of mechanical impact; baking in a toaster oven for 1 h, microwaving for 6 min, dropping into liquid nitrogen, for thermal disturbance; and soaking, in either plain water or with added lemon juice鈥.

In choosing words, there is potential not just to improve, but to go even further. PETA (People for the Ethical Treatment of Animals) published a 鈥淐omplete List of Animal-Friendly Idioms鈥. It as a matter of taste to not say 鈥渂eat a dead horse鈥, and instead say 鈥渇eed a fed horse鈥.

A little bit random

Though you may not have heard anyone say 鈥渟tochastic accuracy鈥, chances are you can recognise it.

When you examine a bunch of reported 鈥渇acts鈥 and notice that many of them really are not facts, you are seeing stochastic accuracy. You are detecting the presence of randomness.

Stochastic accuracy can be subtle, tricky to see. Sometimes, it pops into clarity only with painstaking technical measurements. Sharana Kumar Shivanand at the Alan Turing Institute in London did that recently in a about covariance estimation using h-statistics. 鈥淭he objective of this paper鈥, he writes with technical scrupulosity, 鈥渋s to analyse and ensure that only the stochastic accuracy is less than epsilon squared, divided by two鈥.

Sometimes, though, it leaps out at you. Feedback invites you to find a shiny example of an official pronouncement that displays stochastic accuracy. Send a link, or other non-random evidence, to feedback@newscientist.com.

A ghostly confession

A one-foot-in-the-graves-of-academe note arrived here, in response to the discussion about 鈥渉oly ghostwriters鈥.

These are senior department members who automatically get co-authorship credit for research done by people of lower status (Feedback, 22 June).

The note says:鈥滻 have been asked on two occasions by research students to add my name to the authorship of their papers because they felt that it would give them more credibility. I agreed. I did at least read the manuscripts and suggest changes.

鈥淚 wrote another paper with help on the methods section from the first author. A third person involved in the work asked to replace me as last author because it would help his/her career. My career was as good as over, so I agreed to be last-but-one.鈥

The writer eschews sharing credit for that note, and goes a step further, saying: 鈥淚f you use this, please keep me anonymous.鈥

Marc Abrahams created the Ig Nobel Prize ceremony and聽co-founded聽the magazine Annals of Improbable Research. Earlier, he worked on unusual ways to use computers. His website is聽

Got a story for Feedback?

You can send stories to Feedback by email at feedback@newscientist.com. Please include your home address. This week鈥檚 and past Feedbacks can be seen on our website.

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