Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding

A common goal of neuroimaging research is to use imaging data to identify the mental processes that are engaged when a subject performs a mental task. The use of reasoning from activation to mental functions, known as "reverse inference," has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question. In this Perspective, I outline the critique of informal reverse inference and describe a number of new developments that provide the ability to more formally test the predictive power of neuroimaging data.