It would be good for Gang to add details here, but let me offer my interpretation…
Volume 0 shows the SetA-SetB mean. This volume should be the same with and without a covariate (assuming the covariate is de-meaned by 3dttest++).
Volume 1 shows the corresponding t-stat for the model against that without the mean removed. In the case of a covariate, the values will generally be much lower, as it would only show the incremental variance explained above the model with a covariate.
Similarly, volume 2 shows the slope (or the incremental fit/effect) of the covariate. I don’t think it is a difference of slopes, but a single slope for the covariate “regressor”. Note that for a paired t-test (and by default at least), the mean covariate of each set would be subtracted out (to leave a mean of zero for each set covariate) before the slope (scalar fit) is computed.
Notably, this is not showing differences in SetA and SetB with the covariate held constant, that is more what volume 0 is (where that constant is zero).
And then volume 3 would show the significance of the covariate in the t-test, indicating where the variance explained by the inclusion of the de-meaned IQs is high.
To put this in the context of a more standard linear regression, form this paired t-test with covariates by first computing those paired differences and subtracting the mean from each group’s covariate (IQ).
If the subject differences are put together, this could be pictured as the “time series” input to 3dDeconvolve that you are fitting a model to. The base model is just -polort 0 (modeling the mean of those differences), while the (de-meaned) covariates would be an additional -stim_file component. In the -bucket output, the polort 0 beta would be the mean “effect”, the mean difference, while the IQ beta would be the slope of the IQ regressor, the best scalar fit of the covariate “time series” to the data. And then the t-stats would show the significance of those regressors.
Does this seem reasonable?