To address this question, we examine the differences between LM representations and the human brain's responses to language, specifically by examining a dataset of Magnetoencephalography (MEG) responses to a written narrative.
Our results show that BrainSCUBA is a promising means for understanding functional preferences in the brain, and provides motivation for further hypothesis-driven investigation of visual cortex.
We hypothesize that individual differences in how information is encoded in the brain are task-specific and predict different behavior measures.
Real-world generalization, e. g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences.
We see that regions well-predicted by syntactic features are distributed in the language system and are not distinguishable from those processing semantics.
These results suggest that only the end of semantic processing of a word is task-dependent, and pose a challenge for future research to formulate new hypotheses for earlier task effects as a function of the task and stimuli.
Encoding models based on task features predict activity in different regions across the whole brain.
Progress in natural language processing (NLP) models that estimate representations of word sequences has recently been leveraged to improve the understanding of language processing in the brain.
Unsupervised document representation learning is an important task providing pre-trained features for NLP applications.
The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus.
This paper deals with the problem of nonparametric independence testing, a fundamental decision-theoretic problem that asks if two arbitrary (possibly multivariate) random variables $X, Y$ are independent or not, a question that comes up in many fields like causality and neuroscience.