Predicting your cravings with Machine Learning

Jennifer Marcus’ article UCLA neuroscientists demonstrate crucial advances in ‘brain reading’ details a study done  by theLaboratory of Integrative Neuroimaging Technology.

UCLA neuroscientist researchers just released the promising results of a study, where they measured the prediction of machine learning algorithms on ‘brain reading’ (also called ‘brain decoding’). The research was funded by the National Institute on Drug Abuse, and lead by Dr. Ariana Anderson. They used cigarette smokers, showing to some of them videos that will make them feel the crave for nicotine, in which case they where instructed to fight their addiction. They scanned their brains during the process, in order to capture their mental active zones, and used that information as input for the machine learning programs. “We detected whether people were watching and resisting cravings, indulging in them, or watching videos that were unrelated to smoking or cravings,” said Anderson, the used ML methods could infer up to 90% of accuracy, if the person has been put in a craving situation or not, and even if it has been fighting it. They could anticipate and predict their future mental state, in a similar way as the text-entry tools on cell phones do, predicting your next word based on the first letters. The functional RMIs also shown the regions of the brain that were used to fight the nicotine addiction, what they expect to be of great help to control any other drug cravings.

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