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Why We Dream: Biological Theory Roundup

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We've all had strange dreams: as a kid I was not in the least frightened by the Count Chocula character while awake, but I suffered through many a nightmare about him, his fangs dripping chocolate blood as he stalked me, Bela Lugosi-style, through the eerily empty halls of my school. God knows why. Other dreams make even less sense: I'm packing for a trip when there's a knock at the door. It's Fedex. For some reason, supreme court nominee Sandra Sotomayor has overnighted me a kitten.

So why does the brain produce this narrative junk? We still don't know why for certain, but the last few decades have produced a number of interesting evolutionary theories that move beyond old-hat Jungian archetypes or Freudian "wish fulfillment," and Scientific American's Jesse Bering recently laid out the Darwinian contenders.

Brain conditioning

If your brain went completely dark all night, the theory goes, it would begin to lose function just as rarely-used muscles will atrophy.

Several researchers, including the psychophysiologist Fred Snyder, argued that the adaptive purpose of dreaming may therefore be primarily to stimulate the brain or to keep it "in shape" during prolonged periods of inactivity. Later research offered support for this general idea. For example, specific categories of neurotransmitters were shown to be highly active during this period, while others seemingly "rested."

In other words, as psychologist Steven Pinker puts it, "Dreaming might be a kind of screen saver in which it doesn't really matter what the content is as long as certain parts of the brain are active."

External vigilance

Most dreams are notably lacking in olfactory and auditory content, and one theory holds that that's because if they were, the dreamer would be particularly susceptible to real-world threats like fire or noisy predators.

Being a "light sleeper" in relation to these other sensory domains had adaptive benefits, and since we're in the dark anyway and our eyes are closed, there's less of a risk in hallucinating in our secret visual worlds while our brains are being recharged.

Threat Simulation

This theory holds that dreams function as practice run-throughs for dangerous situations that may occur in the real world; they're drills. (Of course, this theory doesn't explain my kitten-in-the-mail dream; what was that preparing me for?)

"By giving rise to a full-scale hallucinatory world of subjective experience during sleep, the dream production mechanism provides an ideal and safe environment for such sustained practice by selecting threatening waking events and simulating them repeatedly in various combinations." What we should see in contemporary dreams, argues Revonsuo, are "threat scripts" depicting primitive themes of danger that would likely have been relevant in the ancestral environment, such as being chased, falling and so on.

Dreaming as problem solving

According to Harvard University psychologist Deirdre Barrett, "sleeping on it" really works in terms of real-world problem solving, and may actually be the evolutionary purpose of dreaming (even if those dreams don't always make sense to us.) In other words --

-- dreamscapes provided our ancestors (and therefore us) with a sort of creative canvas for solving real-world problems. In support of this, Barrett describes the work of Stanford University psychologist William Dement, who in the early 1970s instructed hundreds of undergraduate students to work on a set of challenging brainteasers before bedtime, so that they'd fall asleep with the problems still on their mind.

What do you think?

Painting by Jamal Vrno.

You can follow my weird dreamlife via Twitter.

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iStock // Ekaterina Minaeva
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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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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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