Artificial intelligence could help us better understand the effects of psychedelic drugs, by analysing narrative reports written by people who are using them.

Scientists barely understand how existing psychedelic drugs work to alter perception and intensify emotions, let alone keep pace with new ones flooding the market often sold as “bath salts” or “herbal incense”.

Enter artificial intelligence. Matthew Baggott of the University of Chicago and colleagues used machine-learning algorithms a type of artificial intelligence that can learn about a given subject by analysing massive amounts of data to examine 1000 reports uploaded to the website Erowid by people who had taken mind-altering drugs.

They found that the frequency with which certain words appeared could identify the drug taken with 51 per cent accuracy on average compared with 10 per cent by chance. MDMA (ecstasy) usage was identified with an accuracy of 87 per cent.

The drug DMT (N,N-dimethyltryptamine) acts on the brain in different ways from the drug Salvia (Salvia divinorum), but the algorithms inferred that both elicit a similar response. This might be because both are typically smoked and so enter the bloodstream quickly, says Baggott. “Smoked psychedelic drugs may ‘hit’ people hard and fast in a similar way.”

Baggott hopes the work will aid research into the effects of new and existing drugs. “You need to start with some theories about the effects of a drug,” he says. “Machine learning can help us form those theories.”

Journal reference: arxiv.org/abs/1206.0312

If you would like to reuse any content from New Scientist, either in print or online, please contact the syndication department first for permission. New Scientist does not own rights to photos, but there are a variety of licensing options available for use of articles and graphics we own the copyright to.

Only subscribers may leave comments on this article. Please log in.

Only personal subscribers may leave comments on this article

Read the rest here: