LAUSR

&NA; Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory‐driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above‐chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent‐verb‐object proto‐sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. HighlightsThe neural signature of the concepts in a sentence can be accurately predicted.A new concept's semantic properties can predict its activation pattern.Word activations in sentence contexts better predict sentence activation than words in isolation.Context neurally highlights the context‐relevant semantic component of a concept.