Summary: A new study reveals current machine learning algorithms may not be reliable in identifying brain regions associated with processing specific syllables. Researchers report machine learning may be effective at decoding mental activity from neuroimaging data, but is not quite so effective at decoding specific information processing mechanisms in the brain.
For about the last ten years, researchers have been using artificial intelligence techniques called machine learning to decode human brain activity. Applied to neuroimaging data, these algorithms can reconstitute what we see, hear, and even what we think.
For example, they show that words with similar meanings are grouped together in zones in different parts of our brain. However, by recording brain activity during a simple task—whether one hears BA or DA—neuroscientists from the University of Geneva (UNIGE), Switzerland, and the Ecole normale supérieure (ENS) in Paris now show that the brain does not necessarily use the regions of the brain identified by machine learning to perform a task.
Above all, these regions reflect the mental associations related to this task. While machine learning is thus effective for decoding mental activity, it is not necessarily effective for understanding the specific information processing mechanisms in the brain.
The results are available in the PNAS journal. Modern neuroscientific data techniques have recently highlighted how the brain spatially organises the portrayal of word sounds, which researchers were able to precisely map by region of activity. Read more from neurosciencenews.com…
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