Dear Rik and SPMers,
These discussions are very helpful to me. Thank you!
However, I'm still trying to figure out how to use parametric modulations to
model more than 2 event types. As in the original post:
>You probably want to covary out common RT effects.
>The way to do this is to collapse all your trials into one trial-type,
>then enter two parametric modulations. One modulation would be RTs,
>as above. The other would be a "categorical" modulation that indicates
>whether each trial is of type1 or type2. A contrast of [1] on the
>column for this "categorical" modulation will identify regions that
>show a difference between your trial-types, having covaried out
>common RT effects. (You can generalise to more than two trial-types
>by adding yet further parametric modulations for main effects,
>interactions, etc.)
I think somebody has asked simliar questions before but I couldn't find any
answers to this.
I understand that if I only have two event types, I can do what's described
above. (But I also have a question about how to easily check the direction
of the differences, ie which event type is greater than the other).
But let's say if I have 4 event types (conditions): type 1,2,3,4.
How should I model them then?
One modulation for RT, one for event types (1,2,3,4), & one for interaction?
Actually I don't really understand what the interaction means here, so as in
the example in:
http://www.fil.ion.ucl.ac.uk/~wpenny/datasets/face-rep/SPM2/README-SPM2.txt
which models the interaction between lag and fame.
And after modeling this 4 even types, how do I make the contrasts to see
"which two" are different?
Can someone kindly shed me some lights or point me to relevant references or
examples?
Thank you very much!!
Chun-Yu
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