Bad Habits Hard-Wired in the Brain
Our vices leaves an imprint on a part of the brain responsible for compulsive behavior and addiction.
Three weeks into 2016 and many resolutions are as stale as a bottle of champagne left open after a New Year's party. Everyone has some kind of self-destructive behavior we'd be better off without, be it smoking or an unhealthy diet. Bad habits are hard to break, but why is that?
According to new research by Duke University researchers, our vices are hard-wired in a region of brain responsible for compulsive behavior and addiction, leaving an enduring mark that pushes us to succumb to our cravings. The greater the change in the brain circuitry, the more difficult it is to kick a habit.
For their study, published today in the journal Neuron, the Duke scientists analyzed the brains of mice who developed a sugar dependency and compared those with otherwise healthy mice. The rodents developed the habit by pressing a small lever to receive a sugary treat. After the sweets were taken away, the sugar-addicted mice continued pressing the lever even without the reward.
Within the brain is an area known as the basal ganglia, which is responsible for motor action and compulsive behaviors. Two pathways in the basal ganglia carry separate, opposing messages, "stop" and "go."
For the mice who formed a sugar dependency, the researchers were surprised to find both the "stop" and "go" signals ramped up in the brain compared with ordinary mice.
The timing of the activation of these pathways differed in the two groups as well. For the mice that formed the habit, the go pathway lit up before stop. The opposite pattern emerged in the other test group. The head start given to the go pathway could explain difficulties in self control for those working to correct compulsive behaviors.
Encouraged by the researchers, some mice were able to break the habit when the scientists rewarded only the rodents who stopped pressing the lever, though the mice most able to do this were the ones with the weaker go pathways.
Knowing the influence of addictive, self-destructive behaviors on brain circuitry could lead to treatments to break them, such as transcranial magnetic stimulation, or using harmless magnetic pulses to stimulate the brain, an avenue some researchers have already begun exploring.
Last year, one Boston start-up, called Pavlok, unveiled their own solution to breaking bad habits: a wristband that shocks the user any time he or she tries to engage in a designated self-destructive action.
Its creators told Reuters last year that the shock is meant to disrupt the neural patterns that form the bad habits, discouraging their repetition in the future. The Pavlok developers also encourage replacing bad habits with good ones to bolster success.
Anyone hoping to avoid magnets or shock treatments, instead preferring to pop a pill, may be disappointed. Any drug targeting the basal ganglia with the purpose of altering habits might prove challenging to create given the complexity of this region of the brain, the Duke researchers admit.
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 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.