Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states.

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 computational 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 plausible semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activation 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 resu

SUBMITTER: Wang J 

PROVIDER: S-EPMC6867144 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature