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5 Things Magicians Knew Before Scientists Did

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Magicians are in the business of testing the limits and nature of human perception. It's no surprise, then, that today's cognitive scientists are uncovering features of the mind that magicians have understood (and exploited) for hundreds of years. A close look at some of the many books on conjuring published since the 16th century reveals insights that are only now making their way into the scientific literature.

1. Don’t Look Now, But...

Sleight-of-hand artists have long used subtle eye movements to manipulate the attention of their audiences. In their 1909 book The Art of Magic, T. Nelson Downs and John Northern Hilliard wrote that “it is scarcely necessary to say,” that while performing a secret maneuver, “[t]he eyes of the performer ... must never for an instant glance at the right hand” as it executes the sleight. “Should the performer forget himself in this respect,” they caution, “the audience will instantly suspect” a move has occurred.

In recent years, the effect of "gaze perception" on everything from attention to social cognition has become a rich area of psychological research. Not surprisingly, magic has proven a useful experimental tool. In their 2009 article in the journal Visual Cognition [PDF], for instance, researchers at the University of Durham measured how a magician’s gaze influenced the attention of 32 spectators during a trick. Sure enough, the authors found that “participants spent less time looking at the critical hand when the magician’s gaze was used to misdirect their attention away from the hand.” Downs and Hilliard had scooped them by a century.

2. What’s the Difference?

Yet another topic to catch fire among cognitive scientists in the last two decades is so-called “change blindness,” or, as researchers Daniel Simons and Ronald Rensink have described it, “the striking failure to see large changes that normally would be noticed easily.” In one representative experiment, a researcher stops pedestrians on a college campus to ask for directions. This exchange is briefly interrupted by two individuals carrying a large door, during which time the original researcher is replaced by a different person entirely. In more than half the cases, the pedestrians giving directions didn’t notice when their interlocutor completely transformed into a new person.

Of course, magicians got there first. In the domain of card magic, for example, many methods rely on minor visual discrepancies that, even to a close observer, are all but invisible. Some effects require two similar-looking cards—the eight of spades and eight of clubs, say—to be swapped, often quite brazenly. Perhaps the earliest published mention of this specific principle appeared in August Roterberg’s 1897 book New Era Card Tricks.

3. Pick a Side Dish, Any Side Dish

Methods for simulating free choice are among the oldest tools available to magicians. Just consider the countless techniques for “forcing” a card while maintaining the appearance of free selection from the deck. The idea existed at least as far back as 1584, when Reginald Scot published The discoverie of witchcraft, the earliest known English-language book to provide detailed descriptions of conjuring tricks.

And yet, the insight that irrelevant, invisible factors can influence our decisions in predictable and unnoticed ways is just now getting its due in the academic world, most notably among practitioners of behavioral economics. The field has produced a steady stream of bestselling books, and earned one of its forefathers, Daniel Kahneman, the 2002 Nobel Memorial Prize in Economic Sciences. It’s also become a favorite of policy experts like Cass Sunstein, who has argued vigorously for using insights from behavioral economics to secretly “nudge” citizens towards certain decisions, whether saving for retirement or choosing healthier foods.

4. Where Were You the Night the Elephant Disappeared?

Imperfect memories can be a conjurer’s best friend. For audiences, magic performances often seem more impressive—and impossible—in retrospect. As one writer notes in a 1918 issue of the British magic publication the Magic Circular, it is to an audience’s “lapse of memory that we owe half of the wondrous accounts of things that never happen but which enhance our reputation nevertheless.” Indeed, some performers are skilled at encouraging exaggerated memories in ways I’m not at liberty to discuss here.

Our tendency to create less-than-accurate memories after the fact—what psychologists sometimes call “reconstructive memory”—has been gaining much notice lately, particularly with regards to its effects on eyewitness testimony in the American legal system. Psychologist Elizabeth Loftus has found, for instance, that the questions “asked immediately after an event can introduce new—and not necessarily correct—information which is then added” to a witness’ memory [PDF].

5. The Audience is Always Right—Unfortunately

Cognitive shortcomings don’t always work to a magician’s advantage. As working performers know all too well, it’s not uncommon for an audience member to interrupt a trick by shouting out an incorrect explanation for the effect being performed (“it’s up your sleeve!” and “magnets!” are perennial favorites). Even when such ill-considered assertions explain nothing at all (how could a magnet be involved in a coin vanish?), it’s sometimes enough to leave audiences unimpressed.

Such episodes serve as textbook examples of what psychologists Frank Keil and Leonid Rozenblit have named the “illusion of explanatory depth,” or the feeling that we “understand complex phenomena with far greater precision, coherence, and depth than [we] really do.” As they write in a 2002 paper in the journal Cognitive Science [PDF], “laypeople ... usually remain unaware of the incompleteness of their theories,” in part because they “rarely have to offer full explanations for most of the phenomena that they think they understand.” I still say it was magnets.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Scientists Think They Know How Whales Got So Big
May 24, 2017
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It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]