>Hi Alberto,
>I would recommend two regressors in the design matrix
>a) onset, RT_t,1
>b) onset, RT_t, RT_m
>where RT_t is the trial RT and RT_m is the mean RT for the experimental condition of the trial. I would not orthogonalize.
hi jack
ok. Since i have three sessions (that i "merge" through a 2nd level analysis) and my reaction time changes across sessions (they reduce, subjects learn).
Should I use as RT_m the single session mean?, rather than the 3 sessions mean. it should be so.
>> (cA-iA)- (cB-iB) = 1 -1 -1 1
>Your contrast of interest for this design matrix is [0 1]. That is, the (cA-iA)-(cB-iB) contrast is implicit in the second regressor, so you >won't need to do a 1 -1 -1 1 or 1 -1 contrast and also no need to add RT to the second level analysis.
>cheers,
Here I lost you. I have 6 different task conditions each represented by two EV (the target and the answer), I add a third EV, the b), with a dynamic 3rd column. my contrast (cA-iA)-(cB-iB) account for differences between the two different congruent/incongruent comparison. how could [0 1] do this?.
I assume that to investigate each condition i previously did [1 0 ....] and now I do [0 0 1 .....]
but my (cA-iA)-(cB-iB) contrast is still a [...1 ... 1... -1 ... -1] but done with each 3rd EV of each condition.
is it correct ?
P.S.
I setup such an analysis, the feat_model warned me over the similarity among EV. of course EV1 and EV3 of each condition have same onset and duration. can i simply ignore this warning?
thanks in advance (and for what you already did)
cheers
Alberto
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