Causal and anti-causal learning in pattern recognition for neuroimaging

Sebastian Weichwald; Bernhard Schölkopf; Tonio Ball; Moritz Grosse-Wentrup

doi:10.1109/prni.2014.6858551

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arXiv · 1512.04808

Causal and anti-causal learning in pattern recognition for neuroimaging

Sebastian Weichwald·Bernhard Schölkopf·Tonio Ball·Moritz Grosse-Wentrup

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Abstract

Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we argue that this distinction is not sufficient: Relevant features in encoding- and decoding models carry a different meaning depending on whether they represent causal- or anti-causal relations. We provide a theoretical justification for this argument and conclude that causal inference is essential for interpretation in neuroimaging.

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Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup. 2015-12-15. Causal and anti-causal learning in pattern recognition for neuroimaging. https://doi.org/10.1109/prni.2014.6858551

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