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

[Vadim Axel]

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 <jetzel at artsci.wustl.edu> 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/
>
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