What Happens in Our Brains When We Hallucinate?
Normally our brains can tell which signals are from the outside world and which come from our minds. But occasionally something can go awry.
Voices in your head? Visions of things that aren't there? You don't have to have schizophrenia or take LSD to have a hallucination, and they don't always have to be scary either.
Hallucinations are actually fairly common.
"It turns out that many everyday high functioning people occasionally do have what technically is a hallucination," said Professor John McGrath from the Queensland Brain Institute.
McGrath recently found that nearly 1 in 20 of the general population report hearing or seeing things - when fully awake - that others don't.
So what is a hallucination? It's a "false perception" of reality and it can occur with a whole range of senses, but the most common ones are visual and auditory hallucinations, said McGrath.
Normally our brain is good at distinguishing between a sound or image that is occurring in the outside world, and one that is just a product of our mind. But occasionally something can go awry.
One major theory is that hallucinations are caused when something goes wrong in the relationship between the brain's frontal lobe and the sensory cortex, said neuropsychologist Flavie Waters from the University of Western Australia.
For example, research suggests auditory hallucinations experienced by people with schizophrenia involve an overactive auditory cortex, the part of the brain that processes sound, said Waters.
This results in random sounds and speech fragments being generated.
Similarly, people with Parkinson's disease appear to have an overactive visual cortex, which results in images being generated in their brain of things that aren't actually there.
Psychoactive drugs could also upset the relationship between the sense processing parts of the brain and the frontal lobe in a similar way, said Waters.
"It allows the processing of images and sound that would normally be inhibited," she said.
The big question is whether the same kind of processes are responsible for less extreme hallucinations.
Hallucinations aren't always intrusive, negative and scary, even in conditions like schizophrenia.
About 70 percent of healthy people experience benign hallucinations when they are falling asleep, said Waters. This includes hearing their name being called, the phone ringing or seeing someone sitting at the end of their bed.
Research into this kind of hallucination is in its very early days, said Waters.
"In the past 100 years it's always been about schizophrenia in the past couple of years we've suddenly ramped up investigations outside of schizophrenia," she said.
"We're still trying to understand whether there are different forms of hallucinations or whether there is only one type that takes different shapes. And what makes a hallucination distressing in some situations and not in others?"
Waters' best guess is that "everyday" hallucinations may share common mechanisms with more serious hallucinations.
She said factors including lack of sleep, stress, grief, and trauma could make the brain more vulnerable to hallucinations by upsetting the relationship between the sensory cortex and the frontal lobe.
"When your brain works well, your frontal lobe is the driver of the car; it decides what's going to happen and is in control of the rest of the brain," she said.
"But when we have lack of sleep and stress and grief, then our frontal lobe just goes on holiday a little bit and doesn't have that supervisory capacity anymore, and it lets the sensory cortex just do what it wants."
Interestingly, certain hallucinations are seen as part of normal life, and indeed encouraged in some cultures, said Waters.
"In some cultures it's acceptable, for example, to hear the voices of your dead relatives," she said.
"Some cultures are more likely to hear the voice of God or the voice of the Devil," he said.
He said young people were more prone to hallucinations and this could be because their brain circuitry was less robust.
"They may say ‘I hear voices' at 14 and then you ask them at 21 and they don't have them anymore."
This originally appeared on ABC Science Online.
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.