Neural Representation of Lexical Concepts

PROJECT SUMMARY/ABSTRACT Approximately two million people in the U.S. suffer from acquired language disorders, known as aphasias. These patients can present with strikingly different patterns of semantic language impairments. For example, they can be more impaired at processing concrete than abstract concepts or vice-versa, and particular categories of concepts such as animals or artifacts can be differentially affected. One major obstacle to understanding the mechanisms underlying semantic language disorders is a larger theoretical gap in our understanding of semantic word processing in the healthy human brain, particularly in terms of how it relates to perceptual, motor, and memory processes. The main goal of this project is to develop a generative model of how semantic representations are encoded in the brain based on interpretable, neurologically plausible dimensions. Our approach is based in part on human ratings of the relevance of several neurally-grounded features of phenomenal experience to the meaning of each word, including perceptual, motor, and affective components. Starting with a set of 48 features, we will identify the subset of features (and their respective weights) that best predicts human performance in semantic tasks, as well as the similarity structure of neural activity patterns associated with individual word meanings. Aim 1 will generate empirical norms of pairwise semantic similarity for a set of 640 English nouns and for a set of 300 English verbs, including both implicit (priming) and explicit (ratings) measures, and will identify which model features are essential for predicting those similarity measures for each word set. It will also determine whether taxonomic and distributional information contribute to semantic representation independently of experiential information. Aim 2 will characterize the similarity structure and information content of the neural activation patterns encoding the meanings of individual nouns and verbs. Finally, Aim 3 will use resting-state connectivity, at the individual participant level, to identify the cortical subnetworks that make up the semantic representation system and characterize their information content. The project will result in a quantitative, feature-based model of word meaning that will provide a theoretically principled approach to decoding semantic representations directly from brain activity, enabling the development of accurate and computationally efficient speech prosthetic devices via brain-computer interface.