A few comments.
Concerning contrasts: If there are no trials of a particular type then don't use this session for any related contrast. There's simply nothing to look at. Some dummy regressor might reflect anything but the assumed brain activation for this condition. When averaging across sessions you will also contaminate the estimates for these sessions. Leaving this aside, I would look at differences between error types only if you have at least a few trials for each of the categories. Otherwise it's probably comparing noise with noise. It might also be problematic because some subjects are prone to mistakes (many trials, better to estimate) and others aren't (few trials, noisy, possibly bad estimate).
Concerning the proposed dummy regressor, does this work? I just tried with a data set consisting of a single run with 234 volumes, TR = 2 s. For the single-subject model I used the default microtime resolution of 16 and a microtime onset of 8.
When the onset of the dummy regressor is identical to the end of the experiment = 468 s with a duration of 0 s, then the regressor can't be properly estimated (beta not uniquely specified) and one cannot define corresponding contrasts. However, the voxels of the beta image are not 0 or NaN but have very small values +/- 10^-13.
The earliest onset that results in a unique beta for parameter estimability is 466.8124, but 466.8125 does not work - why? With 466.8124 the values in the design matrix range between ~ +/- 10^-9 after high-pass filtering (default 128 Hz, seems like it introduces some riples), the last volume has a value of 10^-7 (BOLD response starts). The beta image is full of extreme values with a range of approx. +/- 10^6. So definitely nothing meaningful. In comparison the beta zero has values of 100 - 250. The beta estimates of the other regressors are somewhat different compared to the model without the dummy regressor, but it's the same pattern.
Now this is a single observation of course. In this particular case it seems the dummy regressor is a nonsense regressor. Probably it's much better to have one df less/more instead of anything like that. But I would be interested in Karl's message quoted above.
Best,
Helmut
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