[pymvpa] spatial normalization for MVPA - is it good or bad?

J.A. Etzel jetzel at artsci.wustl.edu
Mon May 5 19:57:54 UTC 2014


On 5/5/2014 2:51 PM, Vadim Axel wrote:
> I personally use always normalized and I do not think that this
> should matter too much. I think given that normalization introduces
> some smoothing, it may probably even increase predictions - as Hans
> Op De Beeck showed that smoothing might be helpful for prediction
> rate.

Unfortunately, *should* matter doesn't always mean *does* matter, and
I'm very hesitant to draw too many conclusions from experiences with 
smoothing: some spatial normalization algorithms are far, far different 
than Gaussian smoothing.

That doesn't mean to never spatially normalize, but I would certainly 
never assume that it's a neutral procedure.

Jo


-- 
Joset A. Etzel, Ph.D.
Research Analyst
Cognitive Control & Psychopathology Lab
Washington University in St. Louis
http://mvpa.blogspot.com/



More information about the Pkg-ExpPsy-PyMVPA mailing list