In my experiment I presented an ambiguous auditory stimulus (a syllable intermediate between da and ga) to one ear and a disambiguating acoustic cue (high or low third formant frequency) to the other ear. In ~70% of the trials, participants gave a response that is consistent with the presented cue. They integrate the cue.
Now, I would like to compare neural representations that are driven by the stimulus acoustics (the presented cue) from representations that are driven by the participants response (ga or da).
I built two cross-validation models, in one I train a classifier to decode the presented acoustic cue, in the other the responses given by the participants. As a dependent variable, I used area under the curve minus chance (as suggested for 2nd level stats in spm). The most simple approach I can imagine would be to simply normalize and smooth the AUC minus chance maps and to do a second level paired sample ttest.
I think there are some problems related to this as described in your article. Moreover, I would ignore whether the derived patterns are significant at the firstlevel.
Hebart, Martin N., und Chris I. Baker. „Deconstructing Multivariate Decoding for the Study of Brain Function“. NeuroImage, New advances in encoding and decoding of brain signals, 180 (15. Oktober 2018): 4–18. https://doi.org/10.1016/j.neuroimage.2017.08.005.
For a binary classification problem with accuracy as dependent variable you would probably recommend firstlevel permutations and second level prevalence statistics as implemented in TDT.
I was wondering what analysis procedure you would recommend in the described case.
Many thanks for your advice.