Andrew J. Anderson

TL;DR: These findings demonstrate that, when successfully comprehending natural speech, the human brain responds to the contextual semantic content of each word in a relatively time-locked fashion.

TL;DR: A novel approach is addressed using a recently introduced method for quantifying the semantic context of speech and relating it to a commonly used method for indexing low-level auditory encoding of speech to suggest a mechanism that links top-down prior information with bottom-up sensory processing in the context of natural, narrative speech listening.

TL;DR: The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.

TL;DR: This work applies state-of-the-art computational models to decode functional Magnetic Resonance Imaging activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns, and confirms that current computational models are sufficiently advanced to assist in investigating the representational structure of abstract concepts in the brain.

TL;DR: By capturing latent visual-semantic structure their models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity, and verify the benefit of combining multimodal data to model human-like semantic representations.