140 citations · 209 across the 19 of their papers we have counts for
19 papers
Flexibility and Invariance of Object Representations in the Human Brain
Julien Dirani, Shankar Chawla, Leila Wehbe +1
The human brain represents objects in a way that is both invariant across instances and flexible enough to support different contexts and tasks. Yet how the brain reconciles these…
Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model
Beige Jerry Jin, Leila Wehbe
Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and…
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
Shreya Saha, Shurui Li, Greta Tuckute +5
The human language system represents both linguistic forms and meanings, but the abstractness of the meaning representations remains debated. Here, we searched for abstract represe…
Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
Muquan Yu, Mu Nan, Hossein Adeli +6
Understanding functional representations within higher visual cortex is a fundamental question in computational neuroscience. While artificial neural networks pretrained on large-s…
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
Andrew F. Luo, Jacob Yeung, Rushikesh Zawar +4
We introduce BrainSAIL, a method for linking neural selectivity with spatially distributed semantic visual concepts in natural scenes. BrainSAIL leverages recent advances in large-…
StableSemantics: A Synthetic Language-Vision Dataset of Semantic Representations in Naturalistic Images
Rushikesh Zawar, Shaurya Dewan, Andrew F. Luo +3
Understanding the semantics of visual scenes is a fundamental challenge in Computer Vision. A key aspect of this challenge is that objects sharing similar semantic meanings or func…
Divergences between Language Models and Human Brains
Yuchen Zhou, Emmy Liu, Graham Neubig +2
Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the in…
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity
Andrew F. Luo, Margaret M. Henderson, Michael J. Tarr +1
Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neur…
Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models
Andrew F. Luo, Margaret M. Henderson, Leila Wehbe +1
A long standing goal in neuroscience has been to elucidate the functional organization of the brain. Within higher visual cortex, functional accounts have remained relatively coars…
Same Cause; Different Effects in the Brain
Mariya Toneva, Jennifer Williams, Anand Bollu +2
To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find…
Behavior measures are predicted by how information is encoded in an individual's brain
Jennifer Williams, Leila Wehbe
Similar to how differences in the proficiency of the cardiovascular and musculoskeletal system predict an individual's athletic ability, differences in how the same brain region en…
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
Peer Herholz, Eddy Fortier, Mariya Toneva +3
Real-world generalization, e.g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences. Such a seemingly easy behavioral…
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
Mariya Toneva, Otilia Stretcu, Barnabas Poczos +2
How meaning is represented in the brain is still one of the big open questions in neuroscience. Does a word (e.g., bird) always have the same representation, or does the task under…
Inducing brain-relevant bias in natural language processing models
Dan Schwartz, Mariya Toneva, Leila Wehbe
Progress in natural language processing (NLP) models that estimate representations of word sequences has recently been leveraged to improve the understanding of language processing…
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Mariya Toneva, Leila Wehbe
Neural networks models for NLP are typically implemented without the explicit encoding of language rules and yet they are able to break one performance record after another. This h…
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
Leila Wehbe, Anwar Nunez-Elizalde, Marcel van Gerven +5
This workshop explores the interface between cognitive neuroscience and recent advances in AI fields that aim to reproduce human performance such as natural language processing and…
Proceedings of the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at NIPS 2015
I. Rish, L. Wehbe, G. Langs +3
This volume is a collection of contributions from the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information Processing Systems (NIPS…
Nonparametric Independence Testing for Small Sample Sizes
Aaditya Ramdas, Leila Wehbe
This paper deals with the problem of nonparametric independence testing, a fundamental decision-theoretic problem that asks if two arbitrary (possibly multivariate) random variable…
Regularized brain reading with shrinkage and smoothing
Leila Wehbe, Aaditya Ramdas, Rebecca C. Steorts +1
Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct…