Multivariate pattern analysis (MVPA) is an increasingly popular approach for characterizing
the information present in neural activity as measured by fMRI. For neuroimaging …

Are some people generally more successful using cognitive regulation or does it depend on
the choice domain? Why? We combined behavioral computational modeling and …

Many powerful human emotional thoughts are generated in the absence of a precipitating
event in the environment. Here, we tested whether we can decode the valence of internally …

In recent years, neuroimaging research in cognitive neuroscience has increasingly used
multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we …

We regularly infer other people's thoughts and feelings from observing their actions, but how
this ability contributes to successful social behavior and interactions remains unknown. We …

People selectively help others based on perceptions of their merit or need. Here, we
develop a neurocomputational account of how these social perceptions translate into social …

Abstract Modern Machine Learning (ML) has significantly advanced various research fields,
but the opaque nature of ML models hinders their adoption in several domains. Explainable …

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic
resonance imaging (f MRI) data to decode brain states from spatially distributed activation …

The use of various techniques for measuring brain activation has led to a dramatic increase
in knowledge about how the brain is involved in language. One of these techniques is …

Data-driven models drawn from statistical correlations between brain activity and behavior
are used to inform theory-driven models, such as those described by computational models …