Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis

P. Stiers*, L. Falbo, A. Goulas, T. van Gog, Anique de Bruin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content.
Original languageEnglish
Pages (from-to)11–23
Number of pages13
Early online date13 Feb 2016
Publication statusPublished - 15 May 2016

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