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Correlations of correlations are not reliable statistics: implications for multivariate pattern analysis.

Bertrand Thirion 1, 2 Fabian Pedregosa 3, 2 Michael Eickenberg 2, 1 Gaël Varoquaux 2, 1
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Representational Similarity Analysis is a popular framework to flexibly represent the statistical dependencies between multi-voxel patterns on the one hand, and sensory or cognitive stimuli on the other hand. It has been used in an inferen-tial framework, whereby significance is given by a permutation test on the samples. In this paper , we outline an issue with this statistical procedure: namely that the so-called pattern similarity used can be influenced by various effects, such as noise variance, which can lead to inflated type I error rates. What we propose is to rely instead on proper linear models.
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Submitted on : Wednesday, August 26, 2015 - 3:11:22 PM
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  • HAL Id : hal-01187297, version 1


Bertrand Thirion, Fabian Pedregosa, Michael Eickenberg, Gaël Varoquaux. Correlations of correlations are not reliable statistics: implications for multivariate pattern analysis.. ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015), Bertrand Thirion, Lars Kai Hansen, Sanmi Koyejo, Jul 2015, Lille, France. ⟨hal-01187297⟩



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