Causal interpretation rules for encoding and decoding models in neuroimaging

Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 110; S. 48 - 59
Hauptverfasser: Weichwald, Sebastian, Meyer, Timm, Özdenizci, Ozan, Schölkopf, Bernhard, Ball, Tonio, Grosse-Wentrup, Moritz
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 15.04.2015
Elsevier Limited
Schlagworte:
Adult
Algorithms
Brain Mapping - methods
Causal inference
Causality
Cognition & reasoning
Decoding models
Electroencephalography
Encoding models
Feedback, Sensory
Humans
Image Processing, Computer-Assisted
Interpretation
Learning - physiology
Male
Medical imaging
Models, Neurological
Neural Networks (Computer)
Neuroimaging - methods
Neuroimaging - statistics & numerical data
Pattern recognition
Pattern Recognition, Automated
Psychomotor Performance - physiology
Random variables
Studies
Young Adult
Causal inference
Pattern recognition
Encoding models
Decoding models
Interpretation
ISSN:1053-8119, 1095-9572, 1095-9572
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Zusammenfassung:Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and decoding models is not sufficient for this purpose: relevant features in encoding and decoding models carry a different meaning in stimulus- and in response-based experimental paradigms.We show that only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models trained on the same data, however, we obtain insights into causal relations beyond those that are implied by each individual model type. We illustrate the empirical relevance of our theoretical findings on EEG data recorded during a visuo-motor learning task. •We interpret encoding and decoding models in a causal framework.•Stimulus- and response-based experiments support different causal statements.•Encoding models in stimulus-based paradigms afford unambiguous causal statements.•Decoding models do not support unambiguous causal statements.•Combining encoding and decoding models yields further causal insights.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2015.01.036