Neural representations of the concepts in simple sentences: Concept activation prediction and context effects

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.