Causal and anti-causal learning in pattern recognition for neuroimaging
Machine Learning
2015-12-16 v1 Machine Learning
Neurons and Cognition
Methodology
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.
Cite
@article{arxiv.1512.04808,
title = {Causal and anti-causal learning in pattern recognition for neuroimaging},
author = {Sebastian Weichwald and Bernhard Schölkopf and Tonio Ball and Moritz Grosse-Wentrup},
journal= {arXiv preprint arXiv:1512.04808},
year = {2015}
}
Comments
accepted manuscript