What does your brain look like on Christmas? That's what Danish researchers set out to find out, using fMRI brain scans to determine who has "bah humbug" syndrome and who truly believes.
When the researchers showed pictures to two groups of people, the fMRI showed different areas of the brain associated with spirituality, self transcendence and recognition of facial emotion lighting up in response in the people who had positive associations with Christmas traditions, compared to a calmer response in a group that didn't celebrate Christmas.
The study, which was published in the BMJ Christmas issue, known for its spoofs and quirky research, spotlights how fMRI research often overreaches, producing questionable results.
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So did the research actually pinpoint the Christmas spirit?
"If you dig deep enough, you'll find all the explanations you're looking for," study co-author Bryan Haddock of Rigshospitalet, a hospital affiliated with Copenhagen University in Denmark, said.
"They didn't control for Hanukkah," Michael Atherton, a former educational neuroscience researcher at the University of Minnesota, quipped.
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"If they'd looked at Hanukkah memories I'd be surprised if they didn't get the same areas [to show activation]," he said. "It could be for birthdays, too - anything involving eating and opening presents."
You can't know, in other words, that 20 or 30 other types of experiences wouldn't trigger the same thing.
The Christmas spirit researchers, who met at nights and on weekends to work on the project, would agree. It's too difficult, they admit, to do a proper study on exactly what goes on in the brain when people think about a holiday.
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"It's a bit of a debate we're trying to make lighter," Haddock said. "You can argue that you can understand the Christmas spirit better from this, or a Grinch can say, this is bollocks - localizing it doesn't make anyone any wiser."
The BMJ Christmas issue often highlights big issues in medical research with seemingly trivial subject matter. In this case, the researchers took a break from their regular research on migraines to do the Christmas brain scans.
"Although merry and intriguing, these findings should be interpreted with caution," they explain in the press release. "Something as magical and complex as the Christmas spirit cannot be fully explained by, or limited to, the mapped brain activity alone."
Cerebral areas where the 'Christmas group' had a significantly higher increase in cerebral activity than the 'non-Christmas group' while images viewed had a Christmas theme, according to new research.
It's easy to mistake this photo for some kind of surreal landscape painting, but this image in fact shows off the imagination of Google's advanced image detection software. Similar to an artist with a blank canvas, Google's software constructed this image out of nothing, or essentially nothing, anyway. This photo began as random noise before software engineers coaxed this pattern out of their machines. How is it possible for software to demonstrate what appears to be an artistic sensibility? It all begins with what is basically an artificial brain.
Artificial neural networks are systems consisting of between 10 and 30 stacked layers of synthetic neurons. In order to train the network, "each image is fed into the input layer, which then talks to the next layer, until eventually the 'output' layer is reached,"
the engineers wrote in a blog post detailing their findings
. The layers work together to identify an image. The first layer detects the most basic information, such as the outline of the image. The next layers hone in on details about the shapes. The final output layer provides the "answer," or identification of the subject of an image. Shown is Google's image software before and after processing an image of two ibis grazing to detect their outlines.
Searching for shapes in clouds isn't just a human pastime anymore. Google engineers trained the software to identify patterns by feeding millions of images to the artificial neural network. Give the software constraints, and it will scout out patterns to recognize objects even in photos where the search targets are not present. In this photo, for example, Google's software, like a daydreamer staring at the clouds, finds all kinds of different animals in the sky. This pattern emerged because the neural network was trained primarily on images of animals.
How the machine is trained will determine its bias in terms of recognizing certain objects within an otherwise unfamiliar image. In this photo, a horizon becomes a pagoda; a tree is morphed into building; and a leaf is identified as a bird after image processing. The objects may have similar outlines to their counterparts, but all of the entries in the "before" images aren't a part of the software's image vocabulary, so the system improvises.
When the software acknowledges an object, it modifies a photo to exaggerate the presence of that known pattern. Even if the software is able to correctly recognize the animals it has been trained to spot, image detection may be a little overzealous in identifying familiar shapes, particularly after the engineers send the photo back, telling the software to find more of the same, and thereby creating a feedback loop. In this photo of a knight, the software appears to recognize the horse, but also renders the faces of other animals on the knight's helmet, globe and saddle, among other places.
Taken a step further, using the same image over several cycles in which the output is fed through over and over again, the artificial neural network will restructure an image into the shapes and patterns it has been trained to recognize. Again borrowing from an image library heavy on animals, this landscape scene is transformed into a psychedelic dream scene where clouds are apparently made of dogs.
At its most extreme, the neural network can transform an image that started as random noise into a recognizable but still somewhat abstract kaleidoscopic expression of objects with which the software is most familiar. Here, the software has detected a seemingly limitless number of arches in what was a random collection of pixels with no coherence whatsoever.
This landscape was created with a series of buildings. Google is developing this technology in order to boost its image recognition software. Future photo services might recognize an object, a location or a face in a photo. The engineers also suggest that the software could one day be a tool for artists that unlocks a new form of creative expression and may even shed light on the creative process more broadly.