Just tried some basic visual discrimination in the FFA (10 subjects), for normalized and non-normalized data. The rates were slightly better for the normalized. Obviously, I do not pretend to draw a general conclusion.
On Mon, May 5, 2014 at 10:57 PM, J.A. Etzel <[email protected]> wrote: > 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/ > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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