[pymvpa] Help

[Jorge H]

Hi Chris,


I´m working with raw data. Do you think that it would matter?


Thanks. Jorge


Dr. Jorge Carlos Hevia Orozco
Unidad de Analisis de Imagenes, Instituto de Neurobiologia, UNAM campus Juriquilla


________________________________
De: Pkg-ExpPsy-PyMVPA <pkg-exppsy-pymvpa-bounces+jhevia=hotmail.com at lists.alioth.debian.org> en nombre de Christopher Markiewicz <effigies at bu.edu>
Enviado: lunes, 6 de noviembre de 2017 12:55 p. m.
Para: Development and support of PyMVPA
Asunto: Re: [pymvpa] Help

Hi Jorge,

Yarik and Nick are definitely going to be better resources than I will on this issue, but it occurs to me to ask whether your input datasets consist of a single beta per condition (per run), or a beta per event? I've found that classification accuracies are much higher than chance across the whole brain when using a single beta per condition, while a beta estimate per event tends to give a distribution (across voxels) with a mean very close to chance. I'm not familiar with the hyperalignment algorithm, but, if it doesn't account for the spatial distribution of classification accuracies, you might need to add a correction step to adjust the distribution to fit the expected distribution.

Chris

On Mon, Nov 6, 2017 at 12:03 PM, Jorge H <jhevia at hotmail.com<mailto:jhevia at hotmail.com>> wrote:

Thank you for the fast answer back!


When I say very high accuracies I mean .95 for brain areas related with the social task we applied (for example, rTPJ) and .95 for brain areas not related with my task (for example, primary motor areas). On the other hand, in the run that we are using for the cross-validation, we have the same number of targets for each of the conditions. Finally, I will randomise the targets in different positions in the run. I will let you know as soon as I get the results on next days.


I just want to make sure that I'm not getting potentially "fake news"!


Thanks a lot.


Jorge



Dr. Jorge Carlos Hevia Orozco
Unidad de Analisis de Imagenes, Instituto de Neurobiologia, UNAM campus Juriquilla


________________________________
De: Pkg-ExpPsy-PyMVPA <pkg-exppsy-pymvpa-bounces+jhevia=hotmail.com at lists.alioth.debian.org<mailto:hotmail.com at lists.alioth.debian.org>> en nombre de Nick Oosterhof <n.n.oosterhof at googlemail.com<mailto:n.n.oosterhof at googlemail.com>>
Enviado: lunes, 6 de noviembre de 2017 10:21 a. m.
Para: Development and support of PyMVPA
Asunto: Re: [pymvpa] Help

Greetings,

On 6 November 2017 at 16:52, Jorge H <jhevia at hotmail.com<mailto:jhevia at hotmail.com>> wrote:

Hi my name is Jorge and I'm running a searchlight hyperanalignment however when I run the analysis using anatomical masks of brain areas completely not related with the task, I got very high accuracies! I would appreciate if somebody give a quick look to my script and let me know if detect something wrong.

At first sight I didn't see anything wrong. Things to consider:

- what do you mean by 'very high accuracies' ?
- is your design balanced, i.e. an equal number of samples for each combination of target and chunks?
- if you randomise the targets manually (so that there should be no information about sample conditions), do you still observe 'very high accuracies'?






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