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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.

Publications

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 (published)
doi.org
Neural computations in prosopagnosia
Simon Faghel-Soubeyrand
Anne-Raphaelle Richoz
Delphine Waeber
Jessica Woodhams
Roberto Caldara
Frédéric Gosselin
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
2024-05-24
Cerebral Cortex (New York, NY) (published)
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… (see more)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 (published)
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 (published)
doi.org
Neural representation of occluded objects in visual cortex
Courtney Mansfield
Tim Kietzmann
Jasper JF van den Bosch
Ian Charest
Marieke Mur
Nikolaus Kriegeskorte
Fraser Smith
2023-08-01
Journal of Vision (published)
doi.org
Reconstructing mental images using Bubbles and electroencephalography
Audrey Lamy-Proulx
Jasper JF van den Bosch
Catherine Landry
Peter Brotherwood
Vincent Taschereau-Dumouchel
Frédéric Gosselin
Ian Charest
2023-08-01
Journal of Vision (published)
doi.org
The semantic distance between a linguistic prime and a natural scene target predicts reaction times in a visual search experiment
Katerina Marie Simkova
Jasper JF van den Bosch
Damiano Grignolio
Clayton Hickey
Ian Charest
2023-08-01
Journal of Vision (published)
doi.org
Do visual mental imagery and exteroceptive perception rely on the same mechanisms?
Catherine Landry
Jasper JF van den Bosch
Ian Charest
Frédéric Gosselin
Vincent Taschereau-Dumouchel
2023-08-01
Journal of Vision (published)
doi.org
Improving the accuracy of single-trial fMRI response estimates using GLMsingle
Jacob S Prince
Ian Charest
Jan W Kurzawski
John A Pyles
Michael J Tarr
Kendrick Kay
2022-11-29
eLife (published)
doi.org
Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales
Futing Zou
Wanjia Guo
Emily J. Allen
Yihan Wu
Ian Charest
Thomas Naselaris
Kendrick Kay
Brice A. Kuhl
J. Benjamin Hutchinson
Sarah DuBrow
Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limit… (see more)ed evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in multiple temporal contexts, potentially creating interference. Here, using 7T fMRI, we measured CA1 and entorhinal activity patterns as human participants viewed thousands of natural scene images distributed, and repeated, across many months. We show that memory for an image’s original temporal context was predicted by the degree to which CA1/entorhinal activity patterns from the first encounter with an image were re-expressed during re-encounters occurring minutes to months later. Critically, temporal memory signals were dissociable from predictors of recognition confidence, which were carried by distinct medial temporal lobe expressions. These findings suggest that CA1 and entorhinal cortex preserve temporal memories across long timescales by coding for and reinstating temporal context information.
2022-09-03
bioRxiv (preprint)
doi.org
Researcher perspectives on ethics considerations in epigenetics: an international survey
Charles Dupras
Terese Knoppers
Nicole Palmour
Elisabeth Beauchamp
Stamatina Liosi
Reiner Siebert
Alison May Berner
Stephan Beck
Ian Charest
Yann Joly
2022-09-02
Clinical Epigenetics (published)
doi.org
Sleep spindles track cortical learning patterns for memory consolidation
Marit Petzka
Alex Chatburn
Ian Charest
George M. Balanos
Bernhard P. Staresina
2021-09-03
Current Biology (published)
doi.org