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
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, 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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Communication dans un congrès
ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015), Jul 2015, Lille, France. 2015
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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), Jul 2015, Lille, France. 2015. 〈hal-01187297〉

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