Dear TDT experts,
In the following paper, by computing classifier performance at each volume, the authors confirmed that classification accuracy generally conformed to the pattern of a hemodynamic response function, with peak accuracy achieved 4–8 s poststimulus onset (Fig. S2C).
How could I do this type of analysis? Are there some specific way to do this in TDT? They used SPM. So, I have the following idea to achieve that: In a single participant, I could make various first level analyses. Each one (first level) will have the same data regarding the task (files, regressors, duration, etc), but different time onsets. For example, in the first one a trial with a duration of 4s was suppossed to start at 0, so the onset will be at 0s. In the second (first level) the onset for that same trial will be at 2s. Then in the third (first level) the onset have to be at 4s.
After that, the next step should be doing the estimates for each first level and then get the mean accuracies for each one. Is this idea make sense? I attach the figure from the paper’s supporting information.
(Kuhl et al. 2010)