So we can't replicate results across laboratories without establishing precise

a priori hypotheses.

But which hypotheses should we choose?

Well one of the first studies I did was a study where we were looking

in the anterior cingulate cortex.

We went to the literature.

it was a very small literature at the time.

And we said, let's come up with an anterior cingulate region of interest.

But even then,

there are many possible coordinates to choose from to make a sphere around.

So what we're seeing here is, I can choose to make a sphere, a region of interest

sphere, around any number of different coordinates in the cingulate,

which allows me huge flexibility.

I have no idea which I should choose because the coordinates are all

over the place.

So this is a real problem.

Which do I choose?

And meta-analysis can help us find a consensus solution,

which is the average across them.

So prior findings reflect a mix of true and false positives.

Not all these coordinates are true positives.

And not all of them are in the locations where they really should be, right,

there's noise in the spatial process as well.

So testing all of them causes a multiple comparisons problem,

causes me to lose power, and/or find more false positives.

It also inflates the effect sizes due to voxel selection bias.

But if I chose a priori regions or

patterns from meta-analysis that can solve both the problems.