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

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