[pymvpa] null classification performance in the presence of strong univariate signal??

Nick Oosterhof nikolaas.oosterhof at unitn.it
Mon Sep 8 10:50:51 UTC 2014

On Sep 8, 2014, at 12:09 PM, David Soto <d.soto.b at gmail.com> wrote:

> however I should say that I still do not get why using the very same input data, the univariate GLM picks the difference between the*cued and uncued* conditions but the MVPA seems not 

From what I understood, you were trying to use the beta estimates /from/ the GLM as input for MVPA. 

The difference is that the GLM uses N samples (with N the number of acquired volumes; i.e. as a timeseries) as input, whereas your MVPA approach only uses 2 (which is much smaller than N). Two samples is just not enough to do MVPA, just as two samples is insufficient for any meaningful univariate statistics.

You can use all N volumes as input for MVPA, for example see: http://www.pymvpa.org/examples/searchlight.html

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