Dear PyMVPA community, I'm struggling to get my head around this. I'm running a searchlight with 20 participants, trying to classify two categories. I run a one sample t test (50% chance) and I find that region X classifies accurately the two categories, but no other region does. Then I created a dissimilarity map for each participant comparing the two same categories. Then I run a one sample t test on the dissimilarity maps (chance calculated by permutating the labels) I find that region Y has the biggest difference between the two categories.
The way I always understood the classification was that if the two categories were consistently different, then the classifier would be able to predict which is which. So when I test for dissimilarity, two patterns sufficiently different should be accurately distinguished, right? Can anyone point me towards a paper or someplace where I can understand why I find these different results? Best regards, Alyson
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