> i have an analysis approach question:
> event -related design with behavior measure (e.g. reaction time)
> what do you think about analyzing the data on the single subject
> level as an
> ancova, where 1 EV has 1's for all events, zeros elsewhere, and
> another EV
> has the demeaned behavior measure at each event, zeros elsewhere
> binning events into fast events or slow events, separate EVs, and then
> performing a contrast at the first level to pass up.
> so option A is the discrete approach, option B is a binary approach.
I would suggest a third approach. If you look at http://www.fmrib.ox.ac.uk/fsl/feat5/detail.html#stats
you'll find a description of the "3 column format" for specifying
event-related designs. The third column can be used to put in a
parametric modulatory effect. You would then make one EV that model
the average effect of these events and another one that models the
Let us say you have 3 events that ocurred a 5 30 and 50 seconds. Let
us further say reaction times were 2 1 and 3 seconds respectively. For
your modulatory EV you would then specify
5 0.1 0
30 0.1 -1
50 0.1 1
where the second column gives you "eventy events" and the third column
is the mean-corrected reaction times.
You can then use the contrast [0 1] (assuming that the first column is
the vanilla events) to take the dependence on reaction time to the 2nd
Note that this assumes that the reaction times have a effect on the
amplitude of the response, and not on the duration. In principle one
could equally well assume the reverse, and unfortunately it is very
hard to test since a changes in amplitude and duration will result in
very similar models for event related designs.
Good luck Jesper