Multivariate pattern analysis (MVPA) methods such as decoding and representational
similarity analysis (RSA) are growing rapidly in popularity for the analysis of …

Every human cognitive function, such as visual object recognition, is realized in a complex
spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation …

In natural environments, visual and auditory stimulation elicit responses across a large set of
brain regions in a fraction of a second, yielding representations of the multimodal scene and …

Multivariate pattern analysis (MVPA) is a recently-developed approach for functional
magnetic resonance imaging (fMRI) data analyses. Compared with the traditional univariate …

To interact with objects in complex environments, we must know what they are and where
they are in spite of challenging viewing conditions. Here, we investigated where, how and …

Is vision necessary for the development of the categorical organization of the Ventral
Occipito-Temporal Cortex (VOTC)? We used fMRI to characterize VOTC responses to eight …

Using multivoxel pattern analysis (MVPA), we studied how distributed visual representations
in human occipitotemporal cortex are modulated by attention and link their modulation to …

Multivariate decoding methods applied to neuroimaging data have become the standard in
cognitive neuroscience for unravelling statistical dependencies between brain activation …

A visual object is characterized by multiple visual features, including its identity, position and
size. Despite the usefulness of identity and nonidentity features in vision and their joint …

Humans recognize faces and objects with high speed and accuracy regardless of their
orientation. Recent studies have proposed that orientation invariance in face recognition …