Hello,
I don't know how relevant this is to the present discussion, but I saw a
poster at OHBM by Jack Grinband, where he advocated MODELING RT's rather
than covarying for them (ie rather than modeling events as having a zero
duration, model them as having a duration the length of each RT). While I'm
not quite convinced that this is a better method, I do think that it may
show interesting things (ie brain regions whose time course is more closely
linked to the RT than to the event onset). I'd be happy to hear what others
think about this, as well. Hope this helps!
Dani
Daniel Simmonds
Developmental Cognitive Neurology
Kennedy Krieger Institute
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On Tue, 14 Nov 2006 21:12:36 +0000, Chun-Yu Lin <[log in to unmask]> wrote:
>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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