Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora

Understanding how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts, has proven difficult. Theoretical models have consistently failed to provide accurate predictions of human judgments, as has the application of machine learning algorithms to large-scale, text-based corpora (embedding spaces). Based on the hypothesis that context plays a critical role in human cognition, we show that generating embedding spaces using contextually-constrained text corpora greatly improves their ability to predict human judgments. Additionally, we introduce a novel context-based method for extracting interpretable feature information (e.g., size) from embedding spaces. Our findings suggest that contextually-constraining large-scale text corpora, coupled with applying state-of-the-art machine learning algorithms, may improve the correspondence between representations derived using such methods and those underlying human semantic structure. This promises to provide novel insight into human similarity judgments and designing algorithms that can interact effectively with human semantic knowledge.

Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions

Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts whose cortical position is highly variable. As such, most alignment methods fail to accurately map functional regions of interest (ROIs) across participants. To address this problem, we propose a locally optimized registration method that directly predicts the location of a seed ROI on a separate target cortical sheet by maximizing the functional correlation between their time courses, while simultaneously allowing for non-smooth local deformations in region topology. Our method outperforms the two most commonly used alternatives (anatomical landmark-based AFNI alignment and cortical convexity-based FreeSurfer alignment) in overlap between predicted region and functionally-defined LOC. Furthermore, the maps obtained using our method are more consistent across subjects than both baseline measures. Critically, our method represents an important step forward towards predicting brain regions without explicit localizer scans and deciphering the poorly understood relationship between the location of functional regions, their anatomical extent, and the consistency of computations those regions perform across people.