The Mystery of Why So Many American Men Aren't Working

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CHARLOTTE, North Carolina—John LaRue is having a tough time of it these days. He used to move things for people, advertising his services on Craigslist. But work slowed up, and he became homeless and started sleeping in his truck, until, that is, someone stole it.

Now, he told me, he’s fighting alcoholism and his health is deteriorating from living on the streets. I met LaRue at a Social Security office outside of Charlotte, where he was hiding his belongings in the bushes because he didn’t have anywhere to keep them and wasn’t allowed to bring them inside. “I feel like there’s a cloud over my head,” he told me. “It’s just been one thing after another.”

LaRue is one among many. In 1957, 97 percent of men in America ages 25 to 54 were either working or looking for work. Today, only 89 percent are. Italy is the only OECD country with a lower labor-force participation rate for men in their prime years. Just why there are so many men who aren’t working is a matter of debate. In a 2016 report, President Obama’s Council of Economic Advisers examined the declining labor-force participation rate and suggested that a drop-off in good jobs for low-skilled men was part of the explanation. Wages, the report theorized, are so low for many jobs that don’t require a college education that men don’t find it worth it to seek out bad jobs. A lack of job training and job-search assistance—when compared to other OECD countries—makes it more difficult for men to move into more lucrative fields. And a surge in incarceration has made it more difficult for men to find work when they leave prison, according to the report.

Conservative scholars have a different view. In his 2016 book, Men Without Work, Nicholas Eberstadt of the American Enterprise Institute put forward two arguments: First, that as social welfare programs have gotten more generous, they’ve lured men away from trying to find a job, and, second, that a large share of the men who are not working are ones with criminal records who have not been able to find a job, and have thus given up. To some degree, Eberstadt puts the blame on American men themselves. “It is impossible to imagine any earlier generation in which such a huge swath of prime-age men would voluntarily absent themselves from the workforce, living instead on the largesse of women they knew and taxpayers they did not,” he writes.

In all likelihood, men’s labor-force participation is declining because of a combination of the reasons mentioned above. But there’s another theory that deserves mentioning, especially because it fits with recent research about the declining health outcomes among American men. That theory suggests that American men are dropping out of the workforce because they are suffering from serious health conditions that make it difficult for them to work. As their health deteriorates, they’re getting on pain medications, which then make it even more difficult to re-enter the workforce.

Princeton economist Alan Krueger argued this theory late last year at a conference at the Federal Reserve Bank of Boston, and in an October 2016 paper circulated by the National Bureau of Economic Research. In his research, he found that almost half of working-age men who were not in the labor force were taking pain medication on a daily basis, and that two-thirds of those men were taking prescription medication. These men also reported more functional disabilities: Krueger found that 43 percent of prime-aged men who are out of the labor force report their health as fair or poor, compared with 12 percent of employed men and 16 percent of unemployed men. Health-related problems “are a substantial barrier to work that would have to be addressed to significantly reverse their downward trend in participation,” Krueger writes.

Krueger’s work looks specifically at pain medication, but the health problems keeping Americans out of the workforce may be broader than that. Millions of Americans are increasingly struggling with obesity and with diabetes, as well as with alcoholism. A 2015 paper by husband-and-wife team Anne Case and Angus Deaton in the Proceedings of the National Academy of Sciences found that while the mortality rates for people aged 45 to 52 in most rich countries declined by 2 percent per year, mortality rates for U.S. whites rose by half a percent each year starting in 1998. “Deaths of despair” such as suicide, alcohol and drug poisoning, and alcohol-related liver disease killed many of these men, the paper found.

As my colleague Olga Khazan has written, a subsequent analysis by the Commonwealth Fund found that things like heart disease, diabetes, and respiratory disease were contributing to some of the increased mortality among middle-aged Americans. There are now 30 million Americans living with diabetes, more than three times the number living with the disease in the early 1990s. And a recent study has suggested that diabetes might be more of a factor in American mortality than was previously thought—perhaps the third leading cause of death in America, after cancer and heart disease. (Diabetes is prone to under-counting because the official cause of death is often something else.) Obesity and diabetes have been shown to disproportionately affect people with a high-school education or less—the same group who are disappearing from the labor force.  

“Obesity and diabetes are disabling, and they are one explanation for reduced labor-force participation,” Andrew Stokes, a Boston University professor and one of the authors of the diabetes study, told me.

Indeed, of the half a dozen men (and one woman) in North Carolina I talked to who had dropped out of the labor force, many told me of physical challenges that have made it difficult to work a regular job. Charles Lucas, 52, said that he had worked in fast food for a decade until his body got to a place where he could no longer stand. He’s had a few heart attacks, he told me, wheezing as he stood in line to apply for disability benefits. He’s been rejected for disability before. “I don’t know what I could do anymore” for work, he told me. He lives with his father, who gets Social Security.

John Crain, 43, used to work in construction, until alcoholism, divorce, and a death in his family led him to drop out of the workforce. Crain, who is currently homeless, is trying to get his life back together, but spends most of his days holding a sign by the side of the road asking for money. (He makes about $50 a day, he said.)

What is making men sicker than they used to be? I had thought it might be that the difficult jobs worked by Americans over their lifetimes might have worn them down physically, especially after I talked to Sandra White, 49. She could barely walk, and has had multiple surgeries on her back. She spent most of her life waiting tables and doing cleaning jobs on construction sites. The work has impacted her body, she told me. “It’s strenuous work, and it took a toll on my back,” she said.

But Krueger says that jobs are less physically demanding than they used to be, and so it doesn’t make sense that jobs would now be exacting a worse toll. What’s more, he said, workplaces have gotten safer over time, so Americans should be experiencing fewer work-related ailments.

What’s changed may be how people have reacted to pain, he said. Before, they worked through it. Now, they go to their doctors and get on pain medications. Doctors may be prescribing these pain medications too frequently: Recent studies have shown that doctors who prescribe opioids are more likely to have patients that use the drugs chronically.

“One of the things I conclude from my research is that if we are going to turn this around, we need to address the epidemic of widespread use of pain medication,” Krueger told me.

What has also changed is Americans’ eating habits. Obesity is on the rise in part because Americans now eat more ultra-processed foods that are high in sugar, and drink more sugar-sweetened beverages. One study has suggested that more than half of Americans’ calories now come from these “ultra-processed foods.” Part of this is because unhealthy foods are, by and large, cheaper than healthy ones; they also require little to no preparation, and many people enjoy how they taste. Some scientists argue that government policies have played a role in increasing how much sugar Americans consume—by providing farm subsidies, they say, the U.S. government encourages the production of cheap corn that ends up in high-fructose corn syrup, which is used in many processed foods.

Of course, this may be a mutually reinforcing cycle: Changes in the labor market over the last half-century may also be contributing to the declining health outcomes of Americans. More Americans now work in the service industry and in jobs with unpredictable schedules. Such workers may find it more difficult to exercise and eat a healthy diet. “Your occupation does really make an imprint on your health status,” said Stokes, the Boston University professor. Long-term night-shift work, for example, has been linked to an increased risk of heart disease and obesity. And some jobs are not conducive to healthy living: Around 86 percent of U.S. truck drivers are overweight or obese. Then again, people who aren’t in great shape may go into trucking, because it doesn’t require all that much physical activity. So it’s difficult to tease out what is a cause and what is an effect.

Likewise, the state of the economy may also be pushing people to turn to alcohol and drugs, which makes them less healthy. As jobs disappear, working-age men may drink or do drugs, as entertainment, to self-medicate their unhappiness, or both. They then find it hard to find and hold jobs. They are unhealthier because they don’t have something like a job motivating them to stay engaged and substance-free.

Most policymakers thinking up prescriptions for fixing declining labor-force participation focus on jobs. They suggest investing in public infrastructure to increase demand for workers and subsidizing programs that pay people to work temporarily. But this research suggests that gains could also come from investing in public-health programs that seek to encourage better eating, less smoking, and frequent exercise. In other words, policies may need to respond to the idea that the American man is in a state of despair not just because his labor prospects have dimmed but because his chance at good health has dimmed, too.



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Think that flying a commercial airliner is an easy gig? Cockpit footage of a pilot battling turbulence and intense winds shows some serious skill that very few have [Scary]

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TonnageVT: Sin_City_Superhero: How can I tell, from that video, that the airplane isn't sitting on the tarmac, and he's just playing around with the yoke?

Well, the GPWS callouts are a pretty clear indictation.

The flap position indicator lights look good but you could be on the ground.



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New Twist on Sofa Problem That Stumped Mathematicians and Furniture Movers

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Hobby 3-D Printing Leads to New Insights into Moving Sofa Problem

By Becky Oskin

Most of us have struggled with the mathematical puzzle known as the “moving sofa problem.” It poses a deceptively simple question: What is the largest sofa that can pivot around an L-shaped hallway corner?

A mover will tell you to just stand the sofa on end. But imagine the sofa is impossible to lift, squish or tilt. Although it still seems easy to solve, the moving sofa problem has stymied math sleuths for more than 50 years. That’s because the challenge for mathematicians is both finding the largest sofa and proving it to be the largest. Without a proof, it’s always possible someone will come along with a better solution.

“It’s a surprisingly tough problem,” said math professor Dan Romik, chair of the Department of Mathematics at UC Davis. “It’s so simple you can explain it to a child in five minutes, but no one has found a proof yet.

The largest area that will fit around a corner is called the “sofa constant” (yes, really). It is measured in units where one unit corresponds to the width of the hallway.

Sofa moving round turn

The Gerver sofa is the largest found that will fit round a single turn. It has a “sofa constant” of 2.22 units, where one unit represents the width of the hallway. Dan Romik/UC Davis

Inspired by his passion for 3-D printing, Romik recently tackled a twist on the sofa problem called the ambidextrous moving sofa. In this scenario, the sofa must maneuver around both left and right 90-degree turns. His findings are published online and appear in the journal Experimental Mathematics.

Eureka Moment

Romik, who specializes in combinatorics, enjoys pondering tough questions about shapes and structures. But it was a hobby that sparked Romik’s interest in the moving sofa problem—he wanted to 3-D print a sofa and hallway. “I’m excited by how 3-D technology can be used in math,” said Romik, who has a 3-D printer at home. “Having something you can move around with your hands can really help your intuition.”

The Gerver sofa—which resembles an old telephone handset—is the biggest sofa found to date for a one-turn hallway. As Romik tinkered with translating Gerver’s equations into something a 3-D printer can understand, he became engrossed in the mathematics underlying Gerver’s solution. Romik ended up devoting several months to developing new equations and writing computer code that refined and extended Gerver’s ideas. “All this time I did not think I was doing research. I was just playing around,” he said. “Then, in January 2016, I had to put this aside for a few months. When I went back to the program in April, I had a lightbulb flash. Maybe the methods I used for the Gerver sofa could be used for something else.”

Romik decided to tackle the problem of a hallway with two turns. When tasked with fitting a sofa through the hallway corners, Romik’s software spit out a shape resembling a dumbbell, with symmetrical curves joined by a narrow center. “I remember sitting in a café when I saw this new shape for the first time,” Romik said. “It was such a beautiful moment.”

Romik's

Romik’s Ambidextrous Sofa is the largest sofa to fit round two turns. It has a sofa constant of 1.64. Dan Romik/UC Davis

Finding Symmetry

Like the Gerver sofa, Romik’s ambidextrous sofa is still only a best guess. But Romik’s findings show the question can still lead to new mathematical insights. “Although the moving sofa problem may appear abstract, the solution involves new mathematical techniques that can pave the way to more complex ideas,” Romik said. “There’s still lots to discover in math.”

More information

Dan Romik’s page on the moving sofa problem (with animations and 3-D printer files)

Read the full paper (ArXiv)

Becky Oskin writes for the Division of Mathematical and Physical Sciences, College of Letters and Science. Follow her on Twitter @beckyoskin.



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RedMonk Programming Language Rankings: January 2017

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After clearing a series of obstacles – some mundane and irrelevant, others much less so – it’s time to publish our the bi-annual RedMonk Programming Language Rankings. As many are aware, these rankings are a continuation of the original work that Drew Conway and John Myles White first looked at the question late in 2010. From a macro perspective, the process remains the same: we extract language rankings from GitHub and Stack Overflow, and combine them for a ranking that attempts to reflect both code (GitHub) and discussion (Stack Overflow) traction. The idea is not to offer a statistically valid representation of current usage, but rather to correlate language discussion (Stack Overflow) and usage (GitHub) in an effort to extract insights into potential future adoption trends.

In January 2014, we were forced to make a change to the way that GitHub’s rankings were collected because GitHub stopped providing them. This quarter’s run features the first major change in how these rankings are conducted since then. To help understand how this change was made and why it was necessary, here’s a brief explanation of our GitHub ranking process.

The Process to Date

In our early language ranking runs we pulled the data directly from GitHub’s Explore page. GitHub ceased publishing the rankings there, however, and in 2014 we found a new data source using the GitHub Archive public dataset on Google BigQuery.

Our query counted repository languages (excluding forked repos) by aggregating total created events. Though now defunct, our previous query was similar to the one in this Stack Overflow answer.

This query worked from 2014 through our last run in June 2016. However, we again needed to adjust our query due to changes in the GitHub Archive table structure as well as changes in GitHub’s API that impacted GitHub Archive’s language data. These changes provided the opportunity to evaluate our data source.

Our Updated Process

In June 2016, GitHub and Google announced a second public data set for publicly licensed repos. We initially explored the languages table on this dataset as our new potential source. This data had that benefit of providing multiple languages for a repository based on the number of bytes used per language, which in theory could give a more accurate representation of languages rather than using a repo’s primary language.
However, we found that the results from this data were suboptimal because:

  • This data only includes licensed repositories, a much smaller subset than the public repositories of GitHub Archive.
  • Furthermore, the process of recognizing licenses is occasionally brittle, even further limiting the available data.
  • The definition of languages expanded beyond what we have historically represented and included things like config files and typesetting systems.
  • In what was our ultimate deciding factor, the results of this query were significantly less correlated with previous GitHub language data as well as Stack Overflow data.

We also briefly explored the GH Torrent project. While this was an interesting data set that could be a great resource for curious individuals, its licensing prohibited our use in this instance.

This ultimately led us back to GitHub Archive. Though we could not access the same language data that we had previously, we were able to query language by pull request. Our query resembles the one GitHub used to assemble the 2016 State of the Octoverse.

We endeavored to make the new query as comparable as possible to the previous process.

  • Language is based on the base repository language. While this continues to have the caveats outlined below, it does have the benefit of cohesion with our previous methodology.
  • We exclude forked repos.
  • We use the aggregated history to determine ranking (though based on the table structure changes this can no longer be accomplished via a single query.)

The primary change is that the GitHub portion of the language ranking is now based on pull requests rather than repos. While this means we couldn’t replicate the rankings as they were before, the results were generally correlated with our past runs and were the best method available. On the positive side, it also eliminates the most common complaint regarding the rankings historically: that measurements by repo might overestimate a given language’s importance – JavaScript, most frequently.

The Net

The obvious question in the wake of this procedural change concerns impact. How do this quarter’s rankings compare with our last run? There are two answers to that: first, the change within the GitHub portion of our rankings; second, the change in our rankings overall with the unaffected Stack Overflow results weighted in. In both cases, it depends on where in the Top 20 a language is ranked. Within our Top 10 languages, for example, the average ranking change for the GitHub only results was a significant but not enormous 1.2 spots. In the back half of the Top 20, however, the average change in a language’s position was 5.7.

When we weight in the Stack Overflow results, predictably, these differentials are somewhat more modest. Within the Top 10, languages moved on average only half a spot. And even in the much more volatile back half, the end change in the overall rankings was a mere three spots.

This is, to be sure, the most significant change since we started performing this analysis. But as mentioned, after testing various approaches, this is the one most tightly correlated and thus offering the greatest continuity between our previous rankings.

With that major update out of the way, please keep in mind the other usual caveats.

  • To be included in this analysis, a language must be observable within both GitHub and Stack Overflow.
  • No claims are made here that these rankings are representative of general usage more broadly. They are nothing more or less than an examination of the correlation between two populations we believe to be predictive of future use, hence their value.
  • There are many potential communities that could be surveyed for this analysis. GitHub and Stack Overflow are used here first because of their size and second because of their public exposure of the data necessary for the analysis. We encourage, however, interested parties to perform their own analyses using other sources.
  • All numerical rankings should be taken with a grain of salt. We rank by numbers here strictly for the sake of interest. In general, the numerical ranking is substantially less relevant than the language’s tier or grouping. In many cases, one spot on the list is not distinguishable from the next. The separation between language tiers on the plot, however, is generally representative of substantial differences in relative popularity.
  • In addition, the further down the rankings one goes, the less data available to rank languages by. Beyond the top tiers of languages, depending on the snapshot, the amount of data to assess is minute, and the actual placement of languages becomes less reliable the further down the list one proceeds.

With that, here is the first quarter plot for 2017.

(Click to embiggen)

Besides the above plot, which can be difficult to parse even at full size, we offer the following numerical rankings. As will be observed, this run produced several ties which are reflected below (they are listed out here alphabetically rather than consolidated as ties because the latter approach led to misunderstandings). Note that this is actually a list of the Top 23 languages, not Top 20, because of said ties.

1 JavaScript
2 Java
3 Python
4 PHP
5 C#
5 C++
7 CSS
7 Ruby
9 C
10 Objective-C
11 Scala
11 Shell
11 Swift
14 R
15 Go
15 Perl
17 TypeScript
18 PowerShell
19 Haskell
20 Clojure
20 CoffeeScript
20 Lua
20 Matlab

Updated process or no, JavaScript and Java retain their respective positions atop our rankings. The lack of movement in JavaScript is particularly notable given that some argued that measuring by repo overweighted JavaScript’s actual significance versus a metric like pull requests, the basis for the new query. PHP has dropped a spot for the first time in the history of our rankings, but remains enormously popular even at the number four spot. Out of all of the languages in the top ten, on the other hand, Python benefitted the most from the change in our GitHub ranking process: where the average movement was one spot, Python jumped three spots, hence its leapfrogging of PHP. Outside of that, the only really notable movement in the top ten was Ruby dropping from five to seven.

Lower down in the order, however, things get more interesting. A few comments on languages with notable movement, in no particular order.

  • R: The preferred language for a growing number of statisticians, data scientists and other analytical types had been enjoying a incremental rise, moving from 15 to a steady 13 and finally jumping to 12 in our last run. This time around, however, the language falls back two spots to number 14. This is principally attributable to a softening in its GitHub ranking in the new process. Unlike its competitor in the analytical space, Python, which rose three spots along that axis, R fell five spots in our GitHub rankings even as its Stack Overflow ranking rose one place. This minor movement, however, says little about R’s current or future performance; like PHP, the language remains popular in spite of a step back.
  • Swift: On the opposite end of the R, Swift was a major beneficiary of the new GitHub process, jumping eight spots from 24 to 16 on our GitHub rankings. While the language appears to be entering something of a trough of disillusionment from a market perception standpoint, with major hype giving way to skepticism in many quarters, its statistical performance according to the observable metrics we track remains strong. Swift has reached a Top 15 ranking faster than any other language we have tracked since we’ve been performing these rankings. Its strong performance from a GitHub perspective suggests that the wider, multi-platform approach taken by the language is paying benefits. As we’ve said since it first entered our rankings, Swift remains a language to watch.

  • Go: While Go also benefitted from the new ranking model, jumping four spots in the GitHub portion of our ranking system, that wasn’t enough to keep up with Swift which leapfrogged it. To some extent, this isn’t a surprise, as Go had neither the built in draw of iOS mobile app development nor is it generally positioned as a front and back end language as Swift increasingly is. More to the point, while it might have held static, a ranking of 15 is impressive for an infrastructure runtime.

  • TypeScript: Last quarter, this was what we believed was the question facing TypeScript: “The question facing the language isn’t whether it can grow, but whether it has the momentum to crack the Top 20 in the next two to three quarters, leapfrogging the likes of CoffeeScript and Lua in the process.” Well, consider that question answered. Of all of the top tier languages, none jumped more than TypeScript on our GitHub rankings, as the JavaScript superset moved up 17 points. While it also saw improvement in its Stack Overflow numbers, it was the GitHub improvement that vaulted it nine spots up and into the Top 20. We didn’t have time to explore the basis for this movement, but it seems reasonable to suspect that Angular is playing a role.

  • PowerShell: As mentioned above, no top tier language outperformed TypeScript on the GitHub portion of our rankings, but one language equaled it. PowerShell moved from 36 within the GitHub rankings to 19 to match TypeScript’s 17 point jump, and that was enough to nudge it into the Top 20 overall from its prior ranking of 25. While we can’t prove causation, it is interesting to note that this dramatic improvement from PowerShell comes one quarter after it was released as open source software. Between PowerShell and TypeScript, not to mention C#’s sustained performance, Microsoft has reason to be pleased about is programming language investments.

  • Rust: One of the biggest overall gainers of any of the measured languages, Rust leaped from 47 on our board to 26 – one spot behind Visual Basic. This comes two quarters after the language not only stalled, but actually gave up ground in our last rankings. What a difference a few months can make. By our metrics, Rust went from the 46th most popular language on GitHub to the 18th. Some of that is potentially a result of the new process, of course, but no other language grew faster. Granted, it’s easier for Rust to achieve that kind of growth than for a language already in the top tier, but nevertheless Rust’s performance is impressive. It’s possible that Rust is finally turning the corner and becoming the mainstream language that many expected it could be. We’ll be watching its movement over the next few quarters to assess Rust’s potential for moving into the Top 20.

Credit: My colleague Rachel Stephens evaluated the available options for extracting rankings from GitHub data, and wrote and executed the queries that are responsible for the GitHub axis in these rankings.



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An ancient memorization strategy might cause lasting changes to the brain

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Weird as it might sound, there are competitive rememberers out there who can memorize a deck of cards in seconds or dozens of words in minutes. So, naturally, someone decided to study them. It turns out that practicing their techniques doesn't just improve your memory — it can also change how your brain works.

The right kind of memory training may cause lasting changes to the brain

There’s been a long-standing debate about whether memory athletes are born with superior memories, or whether their abilities are due to their training regimens. These tend to include an ancient memorization strategy called the method of loci, which involves visualizing important pieces of information placed at key stops along a mental journey. This journey can be an imaginary walk through your house or a local park, or your drive to work. The important thing is that you can mentally move back through it to retrieve the pieces of information you stored. (The ancient Greeks are said to have used it to remember important texts.)

Boris Nikolai Konrad, a memory coach and athlete who’s in the Guinness Book of World Records for memorizing 201 names and faces in just 15 minutes, chalks his superior memory abilities up to training with this and other mnemonic techniques. “It's a sport like any other,” Konrad told The Verge. Only, he adds, “you're not moving that much.” But practicing is key.

Simon Reinhard at South German Memory Championship 2010.


To find out what’s going on in top-level rememberers’ brains, Konrad teamed up with neuroscientist Martin Dresler at Radboud University in the Netherlands. They recruited 23 of the top 50 memory competitors in the world. All were between the ages of 20 and 36. Then, the scientists scanned the memory athletes’ brains while they were just relaxing, and also while they memorized a list of 72 words.

The team, and their co-investigators at Stanford University, found that the memory athletes’ brains don’t appear to be built any differently from yours or mine, according to results they published in the journal Neuron. “That was quite surprising, since these are really the best memorizers in the world,” Dresler says. “And still, they didn’t show a single memory structure, any single region or collection of regions that was anatomically strikingly different from normal control subjects.”

Even so, their brains don’t work the way yours or mine does. The athletes were able to recall at least 70 of the 72 words they studied — compared to an average of only 39 words for the non-athletes they were compared to. What’s more, while the professional rememberers’ brains were structurally similar to the control group, the memory athletes’ brain scans showed unique patterns of activity, where brain regions that are involved in memory and cognition were statistically more likely to fire together.

How to build your memory palace

Uncredited image from Pixabay (CC0)
Uncredited image from Pixabay (CC0)
Image: Pixabay

The method of loci is also known as the memory palace, and Konrad says the first step is to make up a set of locations. The place doesn’t matter as much as your familiarity with it. Then you create a map in your mind with a series of stops. The first stop might be your front door. The second could be the table next to it where you put down your sunglasses. Then, when you’re given a list of words to memorize, you visualize scenes that link the words with each stop.

Say the first word is “keys,” Konrad says. You probably wouldn’t want to imagine sticking your keys in the lock — that’s too mundane. It’s better to picture something more vivid: “Maybe someone throwing keys, like arrows in a dart board, into your front door — and you're a bit scared of that,” Konrad says. That would stick in your memory. So then, when you want to remember the words, you picture yourself walking through the memory journey you plotted out. And when you see the keys sticking out like throwing knives from your front door, you’ll remember the word: keys.

To figure out if the method of loci is behind these memory superpowers, Konrad and Dresler divided 51 men in their 20s who had never trained for or participated in memory competitions into three different groups: one group was trained in the method of loci, and they practiced using an online course for six weeks, 30 minutes per day. One control group got a training regimen of the same length, but played a simple short-term memory game that didn’t involve any strategy. And the other control group didn’t have to do anything but show up for brain scans and memory tests.

Sure enough, after the six weeks were up, the group that trained with the method of loci demolished their previous scores on the second set of memory tests — recalling an average of 62 of the 72 words, an increase of about 36 words. By contrast, the group practicing with the memory game and the group that didn’t train at all barely improved.

Additionally, the same patterns of brain activity that showed up in the memory athletes also started emerging in the group trained with the method of loci. That was true even at rest, when they weren’t trying to memorize anything. That implies the right kind of memory training causes lasting changes to the brain, says Jee Kim, a neuroscientist investigating memory at the University of Melbourne who was not involved in this study.

So, why does the method of loci work so well? After all, brain-training games like the ones marketed by Lumosity don’t actually make you smarter (which is why Lumosity had to pay $2 million to settle charges of deceptive advertising). And this latest study bears that out — the group playing the short-term memory brain-training game didn’t improve significantly.

But the group using the method of loci did — and right now, Konrad and Dresler can only speculate as to why. They think it works by taking abstract, unrelated pieces of information and making them signposts on a route — which incorporates navigational and spatial memory skills humans evolved over time. Ancient people probably didn’t need to remember the order of playing cards of hundreds of names. But, Konrad says, “It was always important for humans to remember where is shelter, where is food, where is danger — and therefore our brains can do that easily.”

The same patterns of brain activity started emerging in the group that trained like the memory athletes

The paper did have some limitations: for one thing, the participants in the training portion of the study were all male, and were all relatively young. So it’s tough to generalize the findings to the broader population — after all, anyone who’s tried to learn a new language as an adult knows that younger brains are much more primed for learning. The University of Melbourne’s Kim told The Verge in an email that the methods and analyses are sound, and the study adds a new piece to the puzzle. But she also wants to know what biological changes are driving the changes in brain activity, and how that changes with age.

Dresler says those are questions he and his colleagues hope to answer in the future, with more people and more advanced methods. And in the meantime, the take-home point is that memory skills can be learned. “It shows that superior memory on that level is not something that is just inborn talent, but is something that essentially can be learned by everyone,” Dresler says — before adding the caveat, “at least, to a certain extent — probably not everyone will win the world championships.”



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Zelda: The Best Armor to Survive Every Environment

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Unlike previous installments of The Legend of Zelda, Breath of the Wild lets Link don a great variety of Attire and Armor to defend himself with, keep him warm, or just look plain stylish.

Note that many types of Armor can be upgraded by seeking out the Great Fairy Fountains hiding in Hyrule. After giving the Great Fairies some rupees, you can then upgrade armor with certain materials - and upgrade them further with rarer materials. Some armor will even provide bonuses if the whole set is worn after upgrading the armor pieces to rank 2.

See the list of Attire below to learn more.

EditArmor Sets

EditUnique Armor Pieces

EditAmiibo Exclusive Easter Egg Armor

EditThe Best Armor to Survive Harsh Environments

Looking to survive in Hyrule's most lethal environments? The armor below will help you travel the coldest tundras and hottest volcanos!

EditCold Resistant Armor

EditHeat Resistant Armor

EditFlame Guard

EditShock (Lightning) Resistance



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Why Jordan Peele's 'Get Out' just made history

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But the significance of "Get Out" speaks to another fundamental truth: Hollywood has no idea what audiences want to pay for.

I saw "Get Out" just before it opened, and it was easy to see this was a film that would blow up the box office. I imagine it was the same feeling people experienced when they first screamed through the original "Nightmare on Elm Street" in 1984 or 2004's "Saw," both films that set box office records for horror films and created standards for the genre that would be copied for many years.

Last year, I sat through many films -- and I'll bet you did, too -- wondering, "Who in Hollywood thought this would be a good film?" Some of the epic flops of 2016? "Gods of Egypt," "Snowden" and "Alice Through the Looking Glass." What did these have in common? A-list talent, gargantuan budgets -- and little diversity in front of and behind the cameras.

This isn't the fault of the actors or directors as much as the executives whose word controls the green light. "Get Out" and several other films in the past year prove audiences want the America they know or would like to know, which is nuanced, complicated and unpredictable. Jordan Peele proves this is even true for fans who go to see horror movies, arguably the genre most riddled with stereotyping.

Peele is unknown as a writer-director, drawing his relatively modest fame from Comedy Central's "Key & Peele." The cast is far from A-list, clearly stars on the rise. The budget was a reported $4.5 million. But what "Get Out" had was Peele, an example of what can happen when the rules are broken and you make way for new voices and fresh cinematic experiences. Honestly, the way filmmaking is transitioning, with today's technology, and groundbreaking ways to consume media, if Hollywood doesn't catch up to the rest of the country, major movie studios stand to lose millions.

Lastly, here is a reason why "Get Out" has hit all the right notes. Similar to the release of Wes Craven's "Scream," or Tobe Hooper's "Poltergeist," the film perfectly captures the fears of the culture at the moment of its release.

In 1982, it was the "me generation," capitalism at all costs, setting the stage for the plot line of suburban greed in "Poltergeist," which resulted in evil spirits torturing a 1980s nuclear family who were the perfect embodiment of the lives Americans wanted for themselves. In 1996, America was afraid of the dirty, grunge-loving teen. Hence, the angry generation X kids of "Scream" were brutally murdered, paying for their sex, drugs and bad attitudes.

Now, our biggest fear is ideology. We are in a cultural civil war, with race at center stage, and "Get Out" perfectly captures the fears mainstream, white, middle America holds of liberalism and the browning of America. It also captures the fears that liberals and brown America hold of mainstream, white, middle America.

Mark my words, social horror is the new trend. However, I doubt anyone will be able to capture Peele's magic. Thankfully, Peele has four more social films in the works. He has single-handedly created a new genre -- and as a black filmmaker made history in the process.



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Zelda: Trade Monster Parts for Awesome Gear

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In The Legend of Zelda: Breath of the Wild, there exists a secret merchant by the name of Kilton - who sells exclusive monster-themed merchandise in exchange for Monster Parts. However, finding him can be tricky.

EditFinding Kilton

Kilton can be reached at any time after leaving the Great Plateau, but you must journey to the far reaches of Deep Akkala in the top Northeastern section of the map. After unlocking the Akkala Tower, look for a large skull-shaped lake. Kilton can be found on the left eye island - opposite a shrine on a tall pillar.

Reaching the lake, you'll find that Kilton only appears at night. Once speaking with Kilton, he'll tell you he's ready to open his shop: Fang and Bone, and will disappear shortly - but not before telling you where he plans to open up shop.

After this, Kilton can be found at night on the outskirts of the following towns and villages:

He usually appears just outside of town, and can be spotted by the tall balloon-like pack he keeps behind him.

EditExchanging and Spending Mon

Kilton comes with his own kind of currency-based system: Mon. Mon is collected by trading monster parts in with Kilton. The more rare and valuable the monster part, the more it will be worth in currency. It can then be used to buy unique armor and equipment not found anywhere else.

The following parts can be exchanged for the indicated amount:

Monster Parts
Mon
Octo Balloon
1
Moblin Horn, Keese Wing, Ancient Screw, Monster Extract
2
Bokoblin Horn, Chuchu Jelly
3
Bokoblin Fang, Lizalfos Horn, White Chuchu Jelly, Red Chuchu Jelly, Yellow Chuchu Jelly, Octorok Tentacle, Ancient Spring
5
Moblin Fang, Fire Keese Wing, Electric Keese Wing
6
Lizalfos Talon
8
Bokoblin Guts, Keese Eyeball, Ancient Gear
10
Moblin Guts, Lizalfos Tail
12
Octorok Eyeball
13
Molduga Fin, Hinox Toenail
15
Icy Lizalfos Tail, Red Lizalfos Tail, Yellow Lizalfos Tail
16
Hinox Tooth
18
Lynel Horn, Ancient Shaft
20
Lynel Hoof
30
Hinox Guts, Ancient Core
40
Dragon's Scale
60
Lynel Guts, Giant Ancient Core
100
Shard of Dragon's Fang
110
Shard of Dragon's Horn
150

Once you have enough Mon, you can talk to Kilton at night to spend your currency on several items. These include special masks that let you fool specific monster types into thinking you're one of them - until other monsters see and attack you that is.

Depending on how far you have progressed in the game, Kilton will offer new and exciting items, culminating in the awesome Dark Armor Set that makes you look like Dark Link, and increases your speed at night.

Item Name
Cost
Requirement
Monster Extract
9
N/A
Wooden Mop
9
N/A
Bokoblin Mask
99
N/A
Spring-Loaded Hammer
199
Free 1 Divine Beast
Moblin Mask
199
Free 1 Divine Beast
Lizalfos Mask
299
Clear 2 Divine Beasts
Lynel Mask
999
Clear 3 Divine Beasts
Monster Bridle
399
Clear 2 Divine Beasts
Monster Saddle
299
Clear 2 Divine Beasts
Dark Hood
1,999
Clear All Divine Beasts
Dark Tunic
999
Clear All Divine Beasts
Dark Trousers
999
Clear All Divine Beasts


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Staal Wild About the Wild

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Eric Staal is wild about his new team, the Minnesota Wild. The former captain of the Carolina Hurricanes was back on the ice he called home for a dozen years, in Thursday night’s game between his current and former teams.

Staal Having Fun

In an interview with Hurricanes television play by play announcer John Forslund, Staal said he is having a lot of fun with his new team. He said there are veteran players on the Wild that really want to win. This is a key difference compared with what Staal faced when he was trying unsuccessfully to lead the Hurricanes back to the playoffs for nearly seven full seasons. There seemed to be a constant state of flux surrounding the team. Now he can just play hockey without the baggage.

Wild head coach Bruce Boudreau said in an article at usatoday.com on Jan. 3o, “I think he’s put into position to succeed. He’s not in a rebuild.” Everything about this season points in a direction for Staal that is like a new start, almost a re-birth on the ice. One of the greatest players in Hurricanes’ history, who began to be greatly criticized by fans wanting the playoffs, is in a place to let his skill speak for itself.

Brother Jordan Staal said before the game Thursday night that being on a winning team was definitely good for Eric, who is noticeably playing much better this season than in his last few seasons with the Hurricanes. He said that having to be the leader on a struggling team is a lot of pressure on any player, pressure that is non-existent with the Wild.

Similar Numbers But Different Situation

“Noticeably playing much better” can be a deceiving thing to say, as a look at Staal’s numbers this season shows that they are not much different than what he was putting up with the Hurricanes in the past few years. He is currently second in points on the Wild at 53, 23 goals and 30 assists. Staal’s last three full seasons with the ‘Canes he put up 54, 61, and 53 points respectively.

The difference this season for Staal is that he is surrounded by teammates that can put up their share of points. For example, the Wild has Mikael Granlund with 65 points, Mikko Koivu with 52, and a host of others in the 40’s and 30’s. By contrast, the points leader on the Hurricanes is Jeff Skinner at 46 points, followed by Victor Rask at 41. There are six Hurricanes in the 30’s for point totals so far this season.

What this boils down to is that Staal can be on the Wild and just play and have fun for a change. He has guys around him who are putting up enough points to make the team playoff-worthy, and he doesn’t have to live with the constant expectations and disappointment that the ‘Canes fans sent is way.

Staal and goalie Cam Ward were the remaining heroes of the team’s 2006 Stanley Cup miracle. The fans in Raleigh expected Staal and Ward to lead the team back to the playoffs and a chance for another Cup, but for various reasons, it was not to be. Staal returned to his former ice to play hockey for the Wild and enjoy the great memories made in Raleigh’s PNC Arena. To talk smack with his brother and listen to Ward chirp at him.

Some Hurricanes fans got in a few jabs of their own after the game which the ‘Canes won 3-1.

Staal will likely have the last laugh this season though, as those same fans will be watching him in the playoffs.



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Saturday assorted links

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The post Saturday assorted links appeared first on Marginal REVOLUTION.



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