<div dir="ltr">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.</div>
<div class="gmail_extra"><br><br><div class="gmail_quote">On Mon, May 5, 2014 at 10:57 PM, J.A. Etzel <span dir="ltr"><<a href="mailto:jetzel@artsci.wustl.edu" target="_blank">jetzel@artsci.wustl.edu</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div class="">On 5/5/2014 2:51 PM, Vadim Axel wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
I personally use always normalized and I do not think that this<br>
should matter too much. I think given that normalization introduces<br>
some smoothing, it may probably even increase predictions - as Hans<br>
Op De Beeck showed that smoothing might be helpful for prediction<br>
rate.<br>
</blockquote>
<br></div>
Unfortunately, *should* matter doesn't always mean *does* matter, and<br>
I'm very hesitant to draw too many conclusions from experiences with smoothing: some spatial normalization algorithms are far, far different than Gaussian smoothing.<br>
<br>
That doesn't mean to never spatially normalize, but I would certainly never assume that it's a neutral procedure.<br>
<br>
Jo<div class="HOEnZb"><div class="h5"><br>
<br>
<br>
-- <br>
Joset A. Etzel, Ph.D.<br>
Research Analyst<br>
Cognitive Control & Psychopathology Lab<br>
Washington University in St. Louis<br>
<a href="http://mvpa.blogspot.com/" target="_blank">http://mvpa.blogspot.com/</a><br>
<br>
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