The (traditional) univariate analysis of MEG (magnetoencephalography) activity recorded in human subjects when they perform a simple task such as « did you hear BA or DA? », shows that the decision is encoded very early and very focally in the human brain. On the contrary, new decoding techniques of neuronal activity by machine-learning suggest that decisional information is present all over the brain. Building on the temporal precision of the MEG technique, we show that machine-decoding algorithms capture patterns of neuronal activity that follow the decision and, hence, do not reveal how our brain uses information contained in these patterns.
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