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Ian Charest

Associate Academic Member
Assistant Professor, Université de Montréal, Department of Psychology
Research Topics
Computational Neuroscience
Computer Vision
Deep Learning
Natural Language Processing
Website

Biography

Ian Charest is a cognitive computational neuroscientist whose general research interests are high-level vision and audition.

He leads the Charest Lab at the Université de Montréal, where he and his team investigate visual recognition in the brain using neuroimaging techniques, such as magneto-electroencephalography (M-EEG) and functional magnetic resonance imaging (fMRI).

Charest’s work makes use of advanced computational modelling and analysis techniques, including machine learning, representational similarity analysis (RSA) and artificial neural networks (ANNs), to better understand human brain function.

Current topics of research in the lab include information processing in the brain during perception, memory, and visual consciousness when recognizing and interpreting natural scenes and visual objects.

The Charest lab is currently funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to study the interaction between vision and semantics. Charest also holds a Courtois chair in cognitive and computational neuroscience, which is supporting the development of an online platform for the cross-disciplinary investigation of behavioural, computational and neuroimaging datasets.

Current Students

Peter Brotherwood Brotherwood
PhD - Université de Montréal
Co-supervisor :
Shahab Bakhtiari
Website
Github
Google Scholar
Morteza Mahdiani
Master's Research - Université de Montréal
Website
Google Scholar

Publications

Representations in vision and language converge in a shared, multidimensional space of perceived similarities
Katerina M. Simkova
Adrien Doerig
Clayton Hickey
Ian Charest
Humans can effortlessly describe what they see, yet establishing a shared representational format between vision and language remains a sign… (see more)ificant challenge. Emerging evidence suggests that human brain representations in both vision and language are well predicted by semantic feature spaces obtained from large language models (LLMs). This raises the possibility that sensory systems converge in their inherent ability to transform their inputs onto shared, embedding-like representational space. However, it remains unclear how such a space manifests in human behavior. To investigate this, 63 participants performed behavioral similarity judgments separately on 100 natural scene images and 100 corresponding sentence captions from the Natural Scenes Dataset. We found that visual and linguistic similarity judgments not only converge at the behavioral level but also predict a remarkably similar network of functional magnetic resonance imaging brain responses evoked by viewing the natural scene images. Furthermore, computational models trained to map images onto LLM-embeddings outperformed both category-trained and AlexNet controls in predicting the behavioral similarity structure. These findings demonstrate that human visual and linguistic similarity judgments are grounded in a shared, modality-agnostic representational structure that mirrors how the visual system encodes experience. The convergence between sensory and artificial systems observed here suggests a common capacity of how conceptual representations are formed-not as arbitrary products of first order, modality-specific input, but as structured representations that reflect the stable, relational properties of the external world.
2026-05-19
Journal of Vision (published)
doi.org
A French Canadian adaptation and validation of the Vividness of Visual Imagery Questionnaire and Plymouth Sensory Imagery Questionnaire.
Catherine Landry
Laurence Lessard
Jeffrey Saint-Louis
Frédéric Gosselin
Ian Charest
Vincent Taschereau-Dumouchel
Guillaume T. Vallet
Mental imagery plays a central role in various cognitive processes and is increasingly investigated in cognitive science. Yet standardized t… (see more)ools for its assessment in French-speaking populations remain scarce. This study examined the psychometric properties of two widely used self-report instruments of mental imagery within the French Canadian population: the Vividness of Visual Imagery Questionnaire (VVIQ) and the Plymouth Sensory Imagery Questionnaire (Psi-Q)-here termed VVIQ-Québec (QC) and Psiq-QC. A total of 328 adults completed the VVIQ-QC and Psiq-QC, with a randomly selected subsample (n = 73) repeating the assessment 1 month later. Exploratory factor analysis of the VVIQ-QC (eyes-open) revealed, as in the original VVIQ, distinct factors corresponding to each prompt cluster (i.e., relative, sunrise, landscape, and storefront). The Psiq-QC yielded a six-factor solution after excluding the Body modality, diverging from the original seven-factor model. Both instruments showed strong internal consistency, temporal stability, and convergent validity. No significant effects of age, sex, or education were observed on imagery scores. These findings provide the first validated French Canadian versions of the VVIQ and Psi-Q, available via the Open Science Framework at https://osf.io/wuhja, offering reliable tools for both research and clinical applications. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
2025-12-14
Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale (published)
doi.org
Neural activity resolved in space and time through fusion of large-scale EEG and fMRI datasets.
Ian Charest
Peter Brotherwood
Mathias Salvas-Hébert
Kendrick Kay
Frédéric Gosselin
2025-07-14
Journal of Vision (published)
doi.org
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
2025-05-26
bioRxiv (preprint)
doi.org
What makes a theory of consciousness unscientific?
IIT-Concerned
Derek H. Arnold
Mark G. Baxter
Tristan A. Bekinschtein
Yoshua Bengio
James W. Bisley
Jacob Browning
Dean V. Buonomano
David Carmel
Marisa Carrasco
Peter Carruthers
Olivia Carter
Dorita H. F. Chang
Ian Charest
Mouslim Cherkaoui
Axel Cleeremans
Michael A. Cohen
Philip R. Corlett
Kalina Christoff
Sam Cumming … (see 80 more)
Cody A. Cushing
Beatrice de Gelder
Felipe De Brigard
Daniel C. Dennett
Nadine Dijkstra
Adrien Doerig
Paul E. Dux
Stephen M. Fleming
Keith Frankish
Chris Frith
Sarah Garfinkel
Melvyn A. Goodale
Jacqueline Gottlieb
Jake R. Hanson
Ran R. Hassin
Michael H. Herzog
Cecilia Heyes
Po‐Jang Hsieh
Shao‐Min Hung
Robert W. Kentridge
Tomas Knapen
Nikos Konstantinou
Konrad P. Kording
Timo L. Kvamme
Sze Chai Kwok
Renzo C. Lanfranco
Hakwan Lau
Joseph E. LeDoux
Alan Lee
Camilo Libedinsky
Matthew D. Lieberman
Ying-Tung Lin
Kayuet Liu
Maro G. Machizawa
Julio Martínez-Trujillo
Janet Metcalfe
Matthias Michel
Kenneth D. Miller
Partha P. Mitra
Dean Mobbs
Robert M. Mok
Jorge Morales
Myrto Mylopoulos
Brian Odegaard
Charles C.-F. Or
Adrian M. Owen
David Pereplyotchik
Franco Pestilli
Megan A. K. Peters
Ian Phillips
Rosanne L. Rademaker
Dobromir Rahnev
Geraint Rees
Dario L. Ringach
Adina L. Roskies
Daniela Schiller
Aaron Schurger
D. Samuel Schwarzkopf
R. B. Y. Scott
Aaron R. Seitz
Joshua Shepherd
Juha Silvanto
Heleen A. Slagter
Barry Smith
Guillermo Solovey
David Soto
Hugo J. Spiers
Timo Stein
Vincent Taschereau‐Dumouchel
Frank Tong
Peter U. Tse
Jonas Vibell
Sebastian Watzl
Taylor W. Webb
Josh Weisberg
Thalia Wheatley
Michał Wierzchoń
Martijn E. Wokke
Karen Yan
Michał Klincewicz
2025-03-09
Nature Neuroscience (unknown)
doi.org
Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review
Andrii Zahorodnii
Jasper 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… (see more)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.
2025-01-21
ArXiv (preprint)
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
2024-09-25
eNeuro (published)
doi.org
Aftereffects following adaptation to face mental images
Mathias Salvas-Hébert
Vincent Taschereau-Dumouchel
Ian Charest
Frédéric Gosselin
2024-09-14
Journal of Vision (published)
doi.org
Exploiting large-scale neuroimaging datasets to reveal novel insights in vision science
Ian Charest
Peter Brotherwood
Catherine Landry
Jasper van den Bosch
Shahab Bakhtiari
Tim Kietzmann
Frédéric Gosselin
Adrien Doerig
2024-09-14
Journal of Vision (published)
doi.org
Neural responses in space and time to a massive set of natural scenes
Peter Brotherwood
Emmanuel Lebeau
Mathias Salvas-Hébert
Marin Coignard
Shahab Bakhtiari
Frédéric Gosselin
Kendrick Kay
Ian Charest
2024-09-14
Journal of Vision (published)
doi.org
Unveiling Mental Imagery: Enhanced Mental Images Reconstruction using EEG and the Bubbles Method
Audrey Lamy-Proulx
Laurence Leblond
Jasper van den Bosch
Catherine Landry
Peter Brotherwood
Vincent Taschereau-Dumouchel
Frédéric Gosselin
Ian Charest
2024-09-14
Journal of Vision (published)
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… (see more)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
2024-05-23
Cerebral Cortex (New York, NY) (published)
doi.org