Search Results for author:
Found 16 papers, 8 papers with code
Divergences between Language Models and Human Brains
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.
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity
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.
Same Cause; Different Effects in the Brain
It is then natural to ask: "Is the activity in these different brain zones caused by the stimulus properties in the same way?"
Behavior measures are predicted by how information is encoded in an individual's brain
We hypothesize that individual differences in how information is encoded in the brain are task-specific and predict different behavior measures.
Can fMRI reveal the representation of syntactic structure in the brain?
One is to identify areas that are correlated with semantic processing load.
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
Real-world generalization, e. g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences.
Syntactic representations in the human brain: beyond effort-based metrics
We see that regions well-predicted by syntactic features are distributed in the language system and are not distinguishable from those processing semantics.
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
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.
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity
Encoding models based on task features predict activity in different regions across the whole brain.
Inducing brain-relevant bias in natural language processing models
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.
Self-Discriminative Learning for Unsupervised Document Embedding
Unsupervised document representation learning is an important task providing pre-trained features for NLP applications.
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Our results reveal differences in the context-related representations across these models.
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
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.
Nonparametric Independence Testing for Small Sample Sizes
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.
