Search Results for author: Leila Wehbe

Found 15 papers, 6 papers with code

Same Cause; Different Effects in the Brain

1 code implementation21 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?"

Behavior measures are predicted by how information is encoded in an individual's brain

no code implementations11 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.

A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models

no code implementations23 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.

Syntactic representations in the human brain: beyond effort-based metrics

no code implementations1 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.

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

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.

Inducing brain-relevant bias in natural language processing models

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.

Language Modelling Natural Language Processing

Nonparametric Independence Testing for Small Sample Sizes

no code implementations7 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.

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