Testing cognitive theories with multivariate pattern analysis of neuroimaging data

Marius V. Peelen () and Paul E. Downing ()
Additional contact information
Marius V. Peelen: Radboud University
Paul E. Downing: Bangor University

Nature Human Behaviour, 2023, vol. 7, issue 9, 1430-1441

Abstract: Abstract Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the ‘how’ of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41562-023-01680-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nathum:v:7:y:2023:i:9:d:10.1038_s41562-023-01680-z

Ordering information: This journal article can be ordered from
https://www.nature.com/nathumbehav/

DOI: 10.1038/s41562-023-01680-z

Access Statistics for this article

Nature Human Behaviour is currently edited by Stavroula Kousta

More articles in Nature Human Behaviour from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().