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Hello, I have a question about how to model a parametric design with some specific characteristics. In a rapid event related design, we are presenting sentences that have been independently associated with semantic ratings. The stimuli can be grouped into low, medium and high stimuli (where the low condition is really the absence of the semantic feature and the other two are of medium and high intensity). Hopefully, only areas that are associated with the specific semantic feature investigated here should be sensitive to the manipulation. These are the areas that we are trying to identify. We have modeled our pilot data from a few subjects in three different ways. I thought that these ways of looking at the data should turn out to be fairly similar, but I am puzzled now by some differences that we got. In the first analysis, we created three custom files with three EVs (low, medium high), putting the semantic ratings as weights (the ratings are in a scale from 1 to 7). Then we computed the contrast [-1 0 +1], to capture the linear trend, as well as other contrasts such as [0 -1 1], [-1 1 0]. In the second analysis, we created similar custom files but this time we assign a weight of 1 to each of the EVs (low medium high). Then again we calculated the contrasts as above. In the third analysis, we demeaned the ratings (subtracting the overall mean rating from each individual rating) and put them on three EVs as above. This time, the “low” condition has negative values, the medium has values around 0 and the high has values around 1. The first two analyses look fairly similar, say, for the first contrast [-1 0 +1]. There are some difference in the size of the activated areas but a lot of overlap (as I expected). However, demeaning the ratings gives us something completely different (and not entirely meaningful, like lost of activity in the cerebellum) and I am not quite sure why this is. Are we applying the wrong logic? Thanks Silvia