I have seen people adding a tiny random number to pmod if pmod remains constant, thereby "bypassing" the restriction. But I am not sure how accuracy this would be.

Also, if there are several runs for that task, if pmod is constant in one run, but not in the other runs. I am wondering if researchers can combine/concat the runs so that overall you can have varied pmod.

A third alternative might be: treat different levels of pmod (like ratings 1,2,3) as different regressor. For example Emotion1, Emotion2, Emotion3, Neutral1, Neutral2, Neutral3.

On Wed, Nov 23, 2016 at 10:28 AM, Mike <[log in to unmask]> wrote:

Dear Mclaren,

Thank you for your response.

By the way, I wonder why SPM uses an orthogonalization strategy to deal with parametric modulator (pm), I mean, the first pm explains the rest variance unexplained by the main regressor, and then the second pm explains the rest variance unexplained by both the main regressor and the first regressor (i.e., main regressor, first pm, and second pm are all orthogonal to each other, right?). Why not just use a multiple linear regression model, like the one in the multiple regression in the second-level model in SPM?

Mike