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

Abstract

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.

Figure 1

A rendering of base rates of activation across 3,489 studies in the literature; increasingly bright yellow/red colors reflect more frequent activation across all studies, with the reddest regions active in more than 20% of all studies. Regions of most frequent activation included the anterior cingulate cortex, anterior insula, and dorsolateral prefrontal cortex. Reprinted with permission from Yarkoni et al., 2011.