Abstract: Even though much has recently been learned about the neural representation of individual
concepts and categories, neuroimaging research is only beginning to reveal how more complex
thoughts, such as event and state descriptions, are neurally represented. We present a predictive com-
putational theory of the neural representations of individual events and states as they are described in
240 sentences. Regression models were trained to determine the mapping between 42 neurally plausi-
ble semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activa-
tion patterns of various cortical regions that process different types of information. Given a semantic
characterization of the content of a sentence that is new to the model, the model can reliably predict
the resulting neural signature, or, given an observed neural signature of a new sentence, the model
can predict its semantic content. The models were also reliably generalizable across participants. This
computational model provides an account of the brain representation of a complex yet fundamental
unit of thought, namely, the conceptual content of a proposition. In addition to characterizing a sen-
tence representation at the level of the semantic and thematic features of its component concepts, factor
analysis was used to develop a higher level characterization of a sentence, specifying the general type
of event representation that the sentence evokes (e.g., a social interaction versus a change of physical
state) and the voxel locations most strongly associated with each of the factors. Hum Brain Mapp
00:000–000, 2017.

http://www.ccbi.cmu.edu/reprints/Wang_Just_HBM-2017_Journal-preprint.pdf