I was discussing this in private with another member of the list but
we cannot fully understand it.
When we bypass orthogonalization the variance of the model is
explained by all regressors in a kind of *competition*. I don't
understand how this competition works statistically but actually I
need the regressors to compete as much as possible with each other.
This way I can compare them in a paired t-test at the 2nd level in
order to find areas where one explains more variance than the other
(independently of the order I put them in SPM). Does this make sense
Also discussing with my friend, I thought having one model with 5
non-orthogonalized (i.e. independent) parametric modulations is like
having 5 GLMs. Apparently this is not true because in the first case
we have X variance exlpained by 5 modulations, while in the second
case we have X variance explained by 1 modulation each time. But
wouldn't the comparison in a paired t-test produce the same *winner* ?
It make sense logically: if two collinear regressors A and B explain
50% variance, with regressor A 30%, and regressor B 40%, their
variance overlaps but regressor B will result with higher T values, no
matter of measured in the same GLM or in two separate GLMs. So is it
better to keep them in the same GLM or split them up? Would the result
be the same?
Somebody advised to orthogonalize modulations (not with each-other
but) with the main condition . Does this produce any benefit?
Help is highly appreciated from experts or non-experts. :)
Ruhr University Bochum