Hi fellow PyMVPA users, I have a non-software-related question for you all. Imagine a scenario where there are N runs in a scanning session and a searchlight is used to compute transfer error from an N-fold cross validation over all voxels. If there are only two categories you are trying to classify, how would interpret large spatially contiguous clusters of voxels that are performing significantly below chance (20% correct or so) appearing in the results? Could it be that there is something changing in the data in relation to the number of times the subject has seen examples of a category? If mis-classification could be caused by an across run repetition-suppression type effect, would re-running the searchlights with an odd-even split and seeing if these voxels are at chance be a legitimate way to show there is meaning in the mis-classification of my svm's?
I haven't been able to find any neuroimaging papers that address or report below-chance performance, does anyone know of one? Maybe it would be better to search the machine-learning literature? I hope to hear your thoughts on this Thanks, Matt
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