Causal and anti-causal learning in pattern recognition for neuroimaging

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....

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Bibliographic Details
Published in2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
Main Authors Weichwald, Sebastian, Scholkopf, Bernhard, Ball, Tonio, Grosse-Wentrup, Moritz
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Brain models
Data models
Decoding
Encoding
Neuroimaging
Pattern recognition
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DOI10.1109/PRNI.2014.6858551

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Summary: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.
DOI:10.1109/PRNI.2014.6858551