Ian Charest

Membre académique associé

Professeur adjoint, Université de Montréal, Département de psychologie

Sujets de recherche

Apprentissage profond

Neurosciences computationnelles

Traitement du langage naturel

Vision par ordinateur

Site web

Biographie

Le professeur Ian Charest est un chercheur en neuroscience cognitive et computationnelle qui s'intéresse surtout à la vision et à l'audition de haut niveau. Il dirige le laboratoire Charest à l'Université de Montréal, où son équipe et lui étudient la reconnaissance visuelle dans le cerveau à l'aide de techniques de neuro-imagerie telles que la magnétoélectroencéphalographie (M-EEG) et l'imagerie par résonance magnétique fonctionnelle (IRMf). Son travail utilise des techniques avancées de modélisation et d'analyse, notamment l'apprentissage automatique, l'analyse de similarité représentationnelle (RSA) et les réseaux de neurones artificiels (ANN) pour mieux comprendre le fonctionnement du cerveau humain. Les thèmes de recherche actuels du laboratoire comprennent le traitement de l'information dans le cerveau pendant la perception, ainsi que la mémoire et la conscience visuelles lors de la reconnaissance et de l'interprétation de scènes naturelles et d'objets visuels. Le laboratoire est actuellement financé par une subvention à la découverte du Conseil de recherches en sciences naturelles et en génie (CRSNG) pour étudier l'interaction entre la vision et la sémantique. De plus, le professeur Charest dirige une chaire Courtois en neuroscience cognitive et computationnelle qui vise à développer une plateforme en ligne pour la recherche interdisciplinaire de données comportementales, computationnelles et de neuro-imagerie.

Étudiants actuels

Peter Brotherwood Brotherwood

Doctorat - UdeM

Co-superviseur⋅e :

Shahab Bakhtiari

p.brotherwood@outlook.com

Maîtrise recherche - UdeM

morteza.mahdiani@umontreal.ca

Site web

Google Scholar

Publications

A Python Toolbox for Representational Similarity Analysis

Jasper JF van den Bosch

Tal Golan

Benjamin Peters

JohnMark Taylor

Mahdiyar Shahbazi

Baihan Lin

Ian Charest

Jörn Diedrichsen

Nikolaus Kriegeskorte

Marieke Mur

Heiko H. Schütt

2025-05-27

bioRxiv (prépublication)

doi.org

Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review

Andrii Zahorodnii

Jasper J.F. van den Bosch

Ian Charest

Christopher Summerfield

Ila Rani Fiete

This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up p… (voir plus)rocess that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process.

2025-01-22

ArXiv (prépublication)

doi.org

arxiv.org

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Julia Lifanov

Benjamin J. Griffiths

Juan Linde-Domingo

Catarina S. Ferreira

Martin Wilson

Stephen D. Mayhew

Ian Charest

Maria Wimber

2024-09-26

eNeuro (publié)

doi.org

Neural computations in prosopagnosia

Simon Faghel-Soubeyrand

Anne-Raphaelle Richoz

Delphine Waeber

Jessica Woodhams

Frédéric Gosselin

Roberto Caldara

Ian Charest

We aimed to identify neural computations underlying the loss of face identification ability by modelling the brain activity of brain-lesione… (voir plus)d patient PS, a well-documented case of acquired pure prosopagnosia. We collected a large dataset of high-density electrophysiological (EEG) recordings from PS and neurotypicals while they completed a one-back task on a stream of face, object, animal and scene images. We found reduced neural decoding of face identity around the N170 window in PS, and conjointly revealed normal non-face identification in this patient. We used Representational Similarity Analysis (RSA) to correlate human EEG representations with those of deep neural network (DNN) models of vision and caption-level semantics, offering a window into the neural computations at play in patient PS’s deficits. Brain representational dissimilarity matrices (RDMs) were computed for each participant at 4 ms steps using cross-validated classifiers. PS’s brain RDMs showed significant reliability across sessions, indicating meaningful measurements of brain representations with RSA even in the presence of significant lesions. Crucially, computational analyses were able to reveal PS’s representational deficits in high-level visual and semantic brain computations. Such multi-modal data-driven characterisations of prosopagnosia highlight the complex nature of processes contributing to face recognition in the human brain. Highlights We assess the neural computations in the prosopagnosic patient PS using EEG, RSA, and deep neural networks Neural dynamics of brain-lesioned PS are reliably captured using RSA Neural decoding shows normal evidence for non-face individuation in PS Neural decoding shows abnormal neural evidence for face individuation in PS PS shows impaired high-level visual and semantic neural computations

2024-05-24

Cerebral Cortex (New York, NY) (publié)

doi.org

Decoding face recognition abilities in the human brain

Simon Faghel-Soubeyrand

Meike Ramon

Eva Bamps

Matteo Zoia

Jessica Woodhams

Anne-Raphaelle Richoz

Roberto Caldara

Frédéric Gosselin

Ian Charest

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusiv… (voir plus)e. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities—super-recognisers—and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared computations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain computations of super-recognisers and mid-level computations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and computations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. Significance The ability to robustly recognise faces is crucial to our success as social beings. Yet, we still know little about the brain mechanisms allowing some individuals to excel at face recognition. This study builds on a sizeable neural dataset measuring the brain activity of individuals with extraordinary face recognition abilities—super-recognisers—to tackle this challenge. Using state-of-the-art computational methods, we show robust prediction of face recognition abilities in single individuals from a mere second of brain activity, and revealed specific brain computations supporting individual differences in face recognition ability. Doing so, we provide direct empirical evidence for an association between semantic computations and face recognition abilities in the human brain—a key component of prominent face recognition models.

2024-03-01

PNAS Nexus (publié)

doi.org

Are vividness judgments in mental imagery correlated with perceptual thresholds?

Ian Charest

Clémence Bertrand Pilon

Hugo Delhaye

Vincent Taschereau-Dumouchel

Frédéric Gosselin

2023-08-01

Journal of Vision (publié)

doi.org