no code implementations • 7 Oct 2024 • Andrew F. Luo, Jacob Yeung, Rushikesh Zawar, Shaurya Dewan, Margaret M. Henderson, Leila Wehbe, Michael J. Tarr
To overcome the challenge presented by the co-occurrence of multiple categories in natural images, we introduce BrainSAIL (Semantic Attribution and Image Localization), a method for isolating specific neurally-activating visual concepts in images.
1 code implementation • 19 Jun 2024 • Rushikesh Zawar, Shaurya Dewan, Andrew F. Luo, Margaret M. Henderson, Michael J. Tarr, Leila Wehbe
To the best of our knowledge, we are the first to release a diffusion dataset with semantic attributions.
1 code implementation • 15 Nov 2023 • Yuchen Zhou, Emmy Liu, Graham Neubig, Michael J. Tarr, Leila Wehbe
Do machines and humans process language in similar ways?
no code implementations • 6 Oct 2023 • Andrew F. Luo, Margaret M. Henderson, Michael J. Tarr, Leila Wehbe
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.
1 code implementation • 21 Feb 2022 • Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe
It is then natural to ask: "Is the activity in these different brain zones caused by the stimulus properties in the same way?"
1 code implementation • 11 Dec 2021 • Jennifer Williams, Leila Wehbe
We hypothesize that individual differences in how information is encoded in the brain are task-specific and predict different behavior measures.
1 code implementation • NeurIPS 2021 • Aniketh Janardhan Reddy, Leila Wehbe
One is to identify areas that are correlated with semantic processing load.
no code implementations • 23 Aug 2021 • Peer Herholz, Eddy Fortier, Mariya Toneva, Nicolas Farrugia, Leila Wehbe, Valentina Borghesani
Real-world generalization, e. g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences.
no code implementations • 1 Jan 2021 • Aniketh Janardhan Reddy, Leila Wehbe
We see that regions well-predicted by syntactic features are distributed in the language system and are not distinguishable from those processing semantics.
1 code implementation • NeurIPS 2020 • Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell
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.
1 code implementation • NeurIPS 2019 • Yu-An Wang, Michael Tarr, Leila Wehbe
Encoding models based on task features predict activity in different regions across the whole brain.
1 code implementation • NeurIPS 2019 • 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 in the brain.
no code implementations • NAACL 2019 • Hong-You Chen, Chin-Hua Hu, Leila Wehbe, Shou-De Lin
Unsupervised document representation learning is an important task providing pre-trained features for NLP applications.
1 code implementation • NeurIPS 2019 • Mariya Toneva, Leila Wehbe
Our results reveal differences in the context-related representations across these models.
no code implementations • 6 Jan 2017 • Leila Wehbe, Anwar Nunez-Elizalde, Marcel van Gerven, Irina Rish, Brian Murphy, Moritz Grosse-Wentrup, Georg Langs, Guillermo Cecchi
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.
no code implementations • 7 Jun 2014 • 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 variables $X, Y$ are independent or not, a question that comes up in many fields like causality and neuroscience.