In another Minority Report-like research finding, a UCLA research team has made crucial advances in brain reading, using fMRI and machine learning methods to predict reactions of smokers experiencing nicotine cravings. For the study, smokers sometimes watched videos meant to induce cravings, sometimes watched neutral videos and at sometimes watched no video at all. By measuring the brain networks active over time during the scans, the resulting machine learning algorithms were able to anticipate changes in subjects underlying neurocognitive structure, predicting with a high degree of accuracy (90 percent for some of the models tested) what they were watching and, as far as cravings were concerned, how they were reacting to what they viewed. We detected whether people were watching and resisting cravings, indulging in them, or watching videos that were unrelated to smoking or cravings, said Anderson, who completed her Ph.D.
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