Hi Rick,
Thanks very much for your reply!
I am doing some analysis on the classical false-belief localizer task. In the task, false belief question (10s) and the answer phase (4s), false photo question (10s) and the answer phase (4s) was separated by 12 seconds fixation. The order of false belief and false photo condition was randomized, of course.
In the initial analysis, I used a GAM to get the contrast of belief-photo. Some researchers used a BOXCAR function instead of a GAM. However, I found that the GAM give me best fit in my sample.
And then I run a PPI analysis by adding the PPI terms to the orignial GLM.
Although I get some interesting findings, I am anxious that what I found might be a residual of task-coactivation.
So I come here and ask the question to make sure if it is preferable to fit all the task-relevant variance before doing PPI analysis.
I think using TENT can best capture the task-relevant activations.

If I understand it correctly, you agree with my point that it should be better to use TENT to explain all task-relevant variance as we can, at least in the case of a classical event-related design.

I am not sure about the block design and the rapid event-related design. Theoretically, we can also use TENT, right?

thanks very much!
-lz