[Mne_analysis] temporal decoding: group analysis for mixed design
JR KING
jeanremi.king at gmail.com
Tue Dec 19 06:32:42 EST 2017
On 19 December 2017 at 12:24, Phillip Alday <phillip.alday at mpi.nl> wrote:
> Answering both proposals with the same word: Maybe.
>
> 'Non-parametric' doesn't mean 'without assumption' (not even 'without
> distributional assumption') -- the assumptions just tend to be quite
> different. Even the bootstrap has assumptions. You have to check whether
> those assumptions conflict with the inherent boundedness of performance
> scores. It's the boundedness of the scores that's the core of the problem.
>
You're right, I should have been more precise. In my understanding, the
non-parametric tests that are commonly encountered in second-level decoding
analyses such as permutation, wilcoxon, mann whitney do not make
assumptions on the distribution across subjects that are impacted by the
issue raised by the authors (i.e. that information is never negative, and
thus that the distribution is bounded).
JR
>
> There are several parametric tests that could model scores bounded on
> [0,1] -- binomial GLMs, and perhaps even the usual t-test if the
> variable is transformed appropriately (e.g. (x - 0.5) * 2 to move the
> score to [-1, 1], followed by the Fisher transformation), converting AUC
> to d', etc.
>
> Phillip
>
> On 19/12/17 12:16, Andrew R. Dykstra wrote:
> > Hi JR,
> >
> > Is there a reference for that? i.e., that non-parametric stats aren't
> > subject to the same inferential issue as t-tests?
> >
> > Thanks, Andy
> >
> > On Tue, Dec 19, 2017 at 12:12 PM JR KING <jeanremi.king at gmail.com
> > <mailto:jeanremi.king at gmail.com>> wrote:
> >
> > While this paper raises an important subtlety (a significant t-test
> > over subjects' decoding accuracies indicates that there "are some
> > people in the population whose fMRI data carry information about the
> > experimental condition — but" [...] not that there is "an effect
> > that is typical in the population"), my understanding is that this
> > particular issue is not a problem for non-parametric statistics.
> >
> > On 19 December 2017 at 11:15, Alday, Phillip <Phillip.Alday at mpi.nl
> > <mailto:Phillip.Alday at mpi.nl>> wrote:
> >
> > Do be careful when doing group-level statistics via inferential
> > statistics on accuracy scores -- Allefeld at al 2016 show some
> > of the problems with the naive approach using things like
> > t-tests or ANOVA. You could use a Binomial/Bernoulli regression
> > model to get around some of points they raise without needing to
> > use their minimum information statistic.
> >
> > Best,
> >
> > Phillip
> >
> >
> > On 18/12/17 11:24, JR KING wrote:
> >> Dear Yi-hui
> >>
> >> Decoding is generally not really adapted for mix-designed, as
> >> the models are traditionally fit at the single subject level -
> >> i.e. your model cannot be easily optimized to look for an
> >> across-subject effect.
> >>
> >> You can however compare the decoding scores across
> >> subjects/conditions as a first approximation, and specify
> >> individual subjects' score as your random variable.
> >>
> >> For multifactorial within-subject effects, a simple approach
> >> can be to implement RSA; we recently added this example in MNE:
> >> https://mne-tools.github.io/stable/auto_examples/decoding/
> decoding_rsa.html
> >>
> >> I will refer you to Kriegoskorte's RSA papers to see how you
> >> adapt this analysis to your specific needs,
> >>
> >> Kindest regards,
> >>
> >> Jean-Rémi
> >>
> >> On 18 December 2017 at 02:05, Yi-hui Hung
> >> <vedahung1116 at gmail.com <mailto:vedahung1116 at gmail.com>> wrote:
> >>
> >> Hello MNE experts,
> >>
> >> I have MEG data with two within-subject factors (each
> >> having 2 and 3 levels) and one between-subject factor (2
> >> levels). I performed decoding analysis on my MEG data by
> >> using the function " time_decod.fit". The question is how
> >> to perform group analysis in subject's decoding data in
> >> MNE (or outside MNE) for my mixed design (2 x 3 x 2
> >> factorial design). Besides, I have another dependent
> >> variable by using "predict_proba" function to get the
> >> predicting probability. I want to test whether the
> >> distribution of predicting probability differ according to
> >> my design. Whether the difference of the distribution
> >> continues in time (e.g., 200-300ms after stimulus onset)
> >> does not matter. Suggestions will be appreciated.
> >>
> >>
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> > --
> > Andrew R. Dykstra, PhD
> > Department of Neurology
> > Ruprecht-Karls-Universität Heidelberg
> > andrew.dykstra at med.uni-heidelberg.de
> > <mailto:andrew.dykstra at med.uni-heidelberg.de>
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