Hi Lauren,
does this overall model specification look appropriate given my main questions?
What is the nature of the factor "Design"? For centering, it could be centered around the mean or a conceptually interpretable value (e.g., 0) depending on the specific questions you want to address.
In general, make sure that the inclusion of all covariates can be justified as discussed in this blogpost.
1. -gltLabel 3 'S-O' -gltCode 3 'Face : 1*Self -1*Other'
Is it correct that this contrast tests the difference in brain activity between self vs. other faces, while controlling for Scanner and Design and holding Age and SelfAdv at their centered values (i.e., the sample mean)?
Yes, that's correct.
2. -gltLabel 7 'S-O*SelfAdv' -gltCode 7 'Face : 1*Self -1*Other SelfAdv :'
Based on examples in the 3dMVM documentation, it seems like this contrast tests whether the Self vs. Other brain activity difference varies as a function of SelfAdv. Is that the correct interpretation? If so, could you help unpack exactly what the coefficient represents?
- Interpretation of negative values
When I look at the results for gltLabel 7, all significant clusters have negative values.
Is the correct interpretation that as the behavioral Self Advantage decreases, the neural difference between Self vs. Other increases in those regions?
Or is there a better way to conceptualize that effect? For example, how would I determine whether the change is being driven primarily by the self condition, the other condition, or both?
In this case, each face is hypothesized to be associated with Self Advantage in familiarity ratings. The estimate from -gltCode 7 shows the difference in the amount of association between the two face types. To parse the difference further, I suggest that you add one more estimate:
... -gltCode ? 1*Other SelfAdv :' \
Then you would be able to check what the sign of the association difference means by combining three estimates: the newly added one, -gltCode 4, and -gltCode 7.
Gang Chen