Meet the VR of psychedelic drugs.
Imagine you’re looking around at a bustling city square, complete with shopkeepers and heavy foot traffic. But swirling jewel tones cover the ground, a muted haze flows through the air, and flowing, bulbous images of dogs and birds are attached to the people passing by. You know you’re neither dreaming nor drunk. It’s entirely possible, thanks to new research, that you’re hooked up to the “Hallucination Machine.”
The Hallucination Machine was built by a team researchers from the Sussex University’s Sackler Center for Conscious Science, including the center’s co-founder, neuroscientist Anil Seth, Ph.D. In a paper published Wednesday in Scientific Reports, Seth and his colleagues explain they created the Hallucination Machine as a means to study the mechanisms underlying altered states of consciousness without needing to use psychedelic drugs. This tool, they claim, is like a drug in its ability to make people feel like they are hallucinating.
Creating this altered state in human subjects, they explain, is tricky. Typically, people reach altered states because of psychopathological conditions or psychoactive substances, like LSD and psilocybin. Scientists have induced altered states in study participants with these drugs before to study the neural underpinnings at play, but the process is far from perfect. The Sussex University team explains that, because psychedelics have many physiological effects, it’s difficult to pinpoint exactly what’s changing in terms of consciousness.
With the Hallucination Machine, the researchers write, they are able to “simulate visual hallucinatory experiences in a biologically plausible and ecologically valid way.” The tool, which, unlike a drug, does not directly alter the person’s neurophysiology, combines virtual reality and machine learning. When a person wears it, they are immersed in “hallucinations” by watching 360-degree panoramic videos of video scenes with a VR head-mounted display. These videos are modified with an algorithm called Deep Dream, a computer program created by Google engineer Alexander Mordvintsev that modifies natural images to reflect images categorized by a neural network.
Deep Dream happens to insert a lot of images of dogs into the video, but researchers aren’t quite sure why. “One thing people always ask us is why there are so many dogs,” co-author David Schwartzman, Ph.D., told The Times on Monday.
“The short answer is we don’t know.”
In their study, the researchers used two experiments to demonstrate that the Hallucination Machine creates “visual phenomenology” — hallucinations — similar to those induced classical psychedelics. In the first, 12 participants used the machine, experienced the trippy VR, and then were asked how the experience altered from watching normal videos and being on a psychedelic drug. The participants overwhelmingly reported the experience was much different than watching a control video but qualitatively similar to being on drugs, especially psilocybin.
In the second experiment, 22 participants used the Hallucination Machine and then watched a control video. As they watched each video, they completed a task to test their perception of the passing of time. Neither using the Hallucination Machine nor watching the video caused temporal distortion. This was an important discovery, the researchers point out, because in previous studies on altered states of consciousness in which people did take drugs, they reported being confused about the passing of time. The new observations suggest to the researchers that it’s not being in an altered state that causes temporal distortion, it’s the drugs.
Examining the brain in an altered state of consciousness is important to scientists who are still are seeking to understand the biological basis of consciousness as a whole. A major hurdle to this area of study has been the use of and accessibility to psychedelic drugs. The Hallucination Machine may change this, and in turn, allow us to learn more about the unknown ways our minds can perceive ourselves and the world. (Click to Source)
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