Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling
Catriona L. Scrivener; Tijl Grootswagers; Alexandra Woolgar · 2026 · European Journal of Neuroscience
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Multivariate pattern analysis (MVPA) is a popular technique that can distinguish between condition-specific patterns of activation. Applied to neuroimaging data, MVPA decoding for inference uses above chance decoding to identify statistically robust condition-specific information in neuroimaging data, which may be missed by univariate methods. However, several analysis choices influence decoding results, and the combined effects of these choices have not been fully evaluated. In particular, an increasingly popular approach is to average data from several trials together before training an MVPA
Abstract by Catriona L. Scrivener; Tijl Grootswagers; Alexandra Woolgar, European Journal of Neuroscience (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1111/ejn.70601