Figures 9 and 10 show the results of inferring on the three different continuous weights mixture models we have described on the visual paradigm and audio paradigm SPMs respectively. The spatial maps in the figures are unthresholded marginal posterior means of , i.e. for all three classes of deactivation, non-activation and activation. Interestingly, the audio dataset shows a large amount of deactivation. If we qualitatively consider the data shown in the top left of figure 10 then the spatial pattern of deactivation would seem to be strongly supported. Model 2, with the spatial smoothness parameter set to imposes too much spatial smoothness. Indeed, if we look at the adaptively determined spatial smoothness in the box plot of figure 8, then the MRF smoothness parameter, , for the visual and audio datasets is far less than the fixed value of used for model 2.

Figures 11 and 12
show maps of the weights for the activation class,
, thresholded to leave only
those voxels with
for the visual paradigm
and audio paradigm SPMs respectively. The choice of threshold on
is, as with any thresholding, a decision that needs
to be made by the experimenter. However, this is the *only*
time that any value in the inference of the model has to be chosen.
Indeed, even when we choose the threshold
for
, there is a natural choice to make. That is we
can choose the threshold of which gives us an equal
loss function where the chance of a false positive is equal to
the chance of a false negative.