Causal interpretation rules for encoding and decoding models in neuroimaging

Neuroimage. 2015 Apr 15;110:48-59. doi: 10.1016/j.neuroimage.2015.01.036. Epub 2015 Jan 24.

Abstract

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.

Keywords: Causal inference; Decoding models; Encoding models; Interpretation; Pattern recognition.

MeSH terms

  • Adult
  • Algorithms
  • Brain Mapping / methods
  • Causality
  • Electroencephalography
  • Feedback, Sensory
  • Humans
  • Image Processing, Computer-Assisted*
  • Learning / physiology
  • Male
  • Models, Neurological*
  • Neural Networks, Computer
  • Neuroimaging / methods*
  • Neuroimaging / statistics & numerical data*
  • Pattern Recognition, Automated
  • Psychomotor Performance / physiology
  • Young Adult