Svetlana V. Shinkareva

TL;DR: A computational model is presented that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available, trained with a combination of data from a trillion-word text corpus and observed f MRI data associated with viewing several dozen concrete nouns.

TL;DR: A quantitative, coordinate‐based meta‐analysis combined data from 303 participants across 19 functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies to identify the differences in neural representation of abstract and concrete concepts, suggesting greater engagement of the verbal system for processing of abstract concepts and greater engagement among healthy adults via mental imagery.

TL;DR: The ability to reliably identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, is demonstrated, indicating the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts.

TL;DR: It is demonstrated that it is possible to classify the task that a person is engaged in from their eye movements using multivariate pattern classification, which has important theoretical implications for computational and neural models of eye movement control.

TL;DR: Findings indicate consistent triggering of semantic representations using different stimulus formats and suggest the presence of stable, distributed, and identifiable neural states that are common to pictorial and verbal input referring to object categories.