Causal interpretation rules for encoding and decoding models in neuroimaging

TL;DR

This paper clarifies the causal interpretability of encoding and decoding models in neuroimaging, emphasizing the conditions under which causal statements are justified, especially in stimulus-based paradigms, supported by EEG data analysis.

Contribution

It provides a theoretical framework distinguishing when encoding and decoding models support causal interpretations in neuroimaging studies.

Findings

Encoding models in stimulus-based settings support causal interpretations.

Combining encoding and decoding models yields deeper causal insights.

Empirical validation using EEG data during a visuo-motor task.

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.