Testing cognitive theories with multivariate pattern analysis of neuroimaging data

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. Peelen and Downing review the use of multivariate pattern analysis in cognitive neuroscience to study cognition at the functional level.

Automated summary

Multivariate pattern analysis (MVPA) has become a powerful method for analyzing functional imaging data, allowing for the testing of cognitive theories in various domains, such as perception, attention, memory, navigation, emotion, social cognition, and motor control. This review highlights the strengths of MVPA in understanding the mechanisms behind human cognition and its ability to test predictions at the item or event level, while also discussing limitations and future directions.