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Publications of year 2024
Books
  1. Stanislas Dehaene. Seeing the Mind: Spectacular Images from Neuroscience, and what They Reveal about Our Neuronal Selves. MIT Press, 2024. [bibtex-entry]


Theses
  1. Chloé Gomez. Projet DeepStim. Modeling states of consciousness and their modulation by deep brain stimulation: from experimental data to computational models. PhD Thesis, Paris Saclay University, 2024. [bibtex-entry]


  2. Yvan Nedelec. How to best assess duration perception in the lab and the wild? An exploratory journey into measuring time perception in train travels, and challenging the automaticity of duration deviance with neuroimaging. PhD Thesis, Paris VI, 2024. [bibtex-entry]


  3. Alexis Thual. Comparing cortical surfaces with functional magnetic resonance imaging and optimal transport: An application to decoding perceived visual semantics across individuals and species. PhD Thesis, Paris Saclay University, 2024. [bibtex-entry]


Articles in journals
  1. Aakash Agrawal and Stanislas Dehaene. Cracking the neural code for word recognition in convolutional neural networks. PLOS Computational Biology, 20(9):e1012430, 2024. [WWW] [bibtex-entry]


  2. Marie E. Bellet, Marion Gay, Joachim Bellet, Bechir Jarraya, Stanislas Dehaene, Timo van Kerkoerle, and Theofanis I. Panagiotaropoulos. Spontaneously emerging internal models of visual sequences combine abstract and event-specific information in the prefrontal cortex. Cell Reports, Volume 43 Issue 3 (March 2024), 2024. [bibtex-entry]


  3. Lucas Benjamin, Mathias Sable-Meyer, Ana Flo, Ghislaine Dehaene-Lambertz, and Fosca Al Roumi. Long-horizon associative learning explains human sensitivity to statistical and network structures in auditory sequences. bioRxiv, pp 2024--01, 2024. [WWW] [bibtex-entry]


  4. Laurent Bonnasse-Gahot and Christophe Pallier. fMRI predictors based on language models of increasing complexity recover brain left lateralization. Neurips, 2024. [WWW] [PDF]
    Abstract: Over the past decade, studies of naturalistic language processing where participants are scanned while listening to continuous text have flourished. Using word embeddings at first, then large language models, researchers have created encoding models to analyze the brain signals. Presenting these models with the same text as the participants allows to identify brain areas where there is a significant correlation between the functional magnetic resonance imaging (fMRI) time series and the ones predicted by the models' artificial neurons. One intriguing finding from these studies is that they have revealed highly symmetric bilateral activation patterns, somewhat at odds with the well-known left lateralization of language processing. Here, we report analyses of an fMRI dataset where we manipulate the complexity of large language models, testing 28 pretrained models from 8 different families, ranging from 124M to 14.2B parameters. First, we observe that the performance of models in predicting brain responses follows a scaling law, where the fit with brain activity increases linearly with the logarithm of the number of parameters of the model (and its performance on natural language processing tasks). Second, although this effect is present in both hemispheres, it is stronger in the left than in the right hemisphere. Specifically, the left-right difference in brain correlation follows a scaling law with the number of parameters. This finding reconciles computational analyses of brain activity using large language models with the classic observation from aphasic patients showing left hemisphere dominance for language.
    [bibtex-entry]


  5. Nicolas Boulant, Franck Mauconduit, Vincent Gras, Alexis Amadon, Caroline Le Ster, Michel Luong, Aurélien Massire, Christophe Pallier, Laure Sabatier, Michel Bottlaender, Alexandre Vignaud, and Denis Le Bihan. In vivo imaging of the human brain with the Iseult 11.7-T MRI scanner. Nature Methods, 21(11):2013--2016, November 2024. [WWW] [PDF]
    Abstract: The understanding of the human brain is one of the main scientific challenges of the twenty-first century. In the early 2000s, the French Atomic Energy Commission launched a program to conceive and build a human magnetic resonance imaging scanner operating at 11.7 T. We have now acquired human brain images in vivo at such a magnetic field. We deployed parallel transmission tools to mitigate the radiofrequency field inhomogeneity problem and tame the specific absorption rate. The safety of human imaging at such high field strength was demonstrated using physiological, vestibular, behavioral and genotoxicity measurements on the imaged volunteers. Our technology yields T2 and T2*-weighted images reaching mesoscale resolutions within short acquisition times and with a high signal and contrast-to-noise ratio.
    [bibtex-entry]


  6. Lorenzo Ciccione, Thomas Dighiero Brecht, Nicolas Claidiere, Joel Fagot, and Stanislas Dehaene. The baboon as a statistician: Can non-human primates perform linear regression on a graph?. bioRxiv, pp 2024--06, 2024. [WWW] [bibtex-entry]


  7. Paola Crespo-Bojorque, Elodie Cauvet, Christophe Pallier, and Juan M. Toro. Recognizing structure in novel tunes: differences between human and rats. Animal Cognition, 27(1):17, March 2024. [WWW] [PDF]
    Abstract: A central feature in music is the hierarchical organization of its components. Musical pieces are not a simple concatenation of chords, but are characterized by rhythmic and harmonic structures. Here, we explore if sensitivity to music structure might emerge in the absence of any experience with musical stimuli. For this, we tested if rats detect the difference between structured and unstructured musical excerpts and compared their performance with that of humans. Structured melodies were excerpts of Mozart's sonatas. Unstructured melodies were created by the recombination of fragments of different sonatas. We trained listeners (both human participants and Long-Evans rats) with a set of structured and unstructured excerpts, and tested them with completely novel excerpts they had not heard before. After hundreds of training trials, rats were able to tell apart novel structured from unstructured melodies. Human listeners required only a few trials to reach better performance than rats. Interestingly, such performance was increased in humans when tonality changes were included, while it decreased to chance in rats. Our results suggest that, with enough training, rats might learn to discriminate acoustic differences differentiating hierarchical music structures from unstructured excerpts. More importantly, the results point toward species-specific adaptations on how tonality is processed.
    [bibtex-entry]


  8. Sébastien Czajko, Alexandre Vignaud, and Evelyn Eger. Human brain representations of internally generated outcomes of approximate calculation revealed by ultra-high-field brain imaging. Nature Communications, 15(1):572, 2024. [bibtex-entry]


  9. Stanislas Dehaene. Stanislas Dehaene. Neuron, 112(10):1527--1530, 2024. [bibtex-entry]


  10. Ghislaine Dehaene-Lambertz. Perceptual Awareness in Human Infants: What is the Evidence?. Journal of Cognitive Neuroscience, pp 1--11, 2024. [WWW] [PDF] [bibtex-entry]


  11. Théo Desbordes, Jean-Rémi King, and Stanislas Dehaene. Tracking the neural codes for words and phrases during semantic composition, working-memory storage, and retrieval. Cell Reports, 43(3), 2024. [WWW] [bibtex-entry]


  12. Doris E Dijksterhuis, Matthew W Self, Jessy K Possel, Judith C Peters, ECW van Straaten, Sander Idema, Johannes C Baaijen, Sandra MA van der Salm, Erik J Aarnoutse, Nicole CE van Klink, P van Eijsden, S. Hanslmayr, R Chelvarajah, F. Roux, LD Kolibius, V. Sawlani, DT Rollings, Stanislas Dehaene, and Roelfsema PR. Pronouns reactivate conceptual representations in human hippocampal neurons. Science, 385(6716):1478--1484, 2024. [WWW] [bibtex-entry]


  13. Scott Ensel, Lynn Uhrig, Ayberk Ozkirli, Guylaine Hoffner, Jordy Tasserie, Stanislas Dehaene, Dimitri Van De Ville, Béchir Jarraya, and Elvira Pirondini. Transient brain activity dynamics discriminate levels of consciousness during anesthesia. Communications biology, 7(1):716, 2024. [WWW] [bibtex-entry]


  14. Cedric Foucault and Florent Meyniel. Two determinants of dynamic adaptive learning for magnitudes and probabilities. Open Mind, 8:615--638, 2024. [WWW] [bibtex-entry]


  15. Guylaine Hoffner, Pablo Castro, Lynn Uhrig, Camilo M Signorelli, Morgan Dupont, Jordy Tasserie, Alain Destexhe, Rodrigo Cofre, Jacobo Sitt, and Bechir Jarraya. Transcranial direct current stimulation modulates primate brain dynamics across states of consciousness. bioRxiv, pp 2024--03, 2024. [WWW] [bibtex-entry]


  16. Sara Jamali, Sophie Bagur, Enora Bremont, Timo Van Kerkoerle, Stanislas Dehaene, and Brice Bathellier. Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex. bioRxiv, pp 2024--08, 2024. [WWW] [bibtex-entry]


  17. Marianna Lamprou-Kokolaki, Yvan Nédélec, Simon Lhuillier, and Virginie van Wassenhove. Distinctive features of experiential time: Duration, speed and event density. Consciousness and Cognition, 118:103635, 2024. [WWW]
    Abstract: William James's use of "time in passing" and "stream of thoughts" may be two sides of the same coin that emerge from the brain segmenting the continuous flow of information into discrete events. Herein, we investigated how the density of events affects two temporal experiences: the felt duration and speed of time. Using a temporal bisection task, participants classified seconds-long videos of naturalistic scenes as short or long (duration), or slow or fast (passage of time). Videos contained a varying number and type of events. We found that a large number of events lengthened subjective duration and accelerated the felt passage of time. Surprisingly, participants were also faster at estimating their felt passage of time compared to duration. The perception of duration scaled with duration and event density, whereas the felt passage of time scaled with the rate of change. Altogether, our results suggest that distinct mechanisms underlie these two experiential times.
    [bibtex-entry]


  18. Marie Lubineau, Cassandra Potier Watkins, Hervé Glasel, and Stanislas Dehaene. Examining the Impact of Reading Fluency on Lexical Decision Results in French 6th Graders. Open Mind, 8:535--557, 2024. [WWW] [bibtex-entry]


  19. Boris New, Jessica Bourgin, Julien Barra, and Christophe Pallier. UniPseudo: A universal pseudoword generator. Quarterly Journal of Experimental Psychology, 77(2):278--286, 2024. [WWW] [PDF]
    Abstract: seudowords are letter strings that look like words but are not words. They are used in psycholinguistic research, particularly in tasks such as lexical decision. In this context, it is essential that the pseudowords respect the orthographic statistics of the target language. Pseudowords that violate them would be too easy to reject in a lexical decision and would not enforce word recognition on real words. We propose a new pseudoword generator, UniPseudo, using an algorithm based on Markov chains of orthographic n-grams. It generates pseudowords from a customizable database, which allows one to control the characteristics of the items. It can produce pseudowords in any language, in orthographic or phonological form. It is possible to generate pseudowords with specific characteristics, such as frequency of letters, bigrams, trigrams, or quadrigrams, number of syllables, frequency of biphones, and number of morphemes. Thus, from a list of words composed of verbs, nouns, adjectives, or adverbs, UniPseudo can create pseudowords resembling verbs, nouns, adjectives, or adverbs in any language using an alphabetic or syllabic system.
    [bibtex-entry]


  20. Arthur Prat-Carrabin, Florent Meyniel, and Rava Azeredo da Silveira. Resource-rational account of sequential effects in human prediction. Elife, 13:e81256, 2024. [WWW] [bibtex-entry]


  21. Gaetano Valenza, Mariano Alcañiz, Vladimir Carli, Gabriela Dudnik, Claudio Gentili, Jaime Guixeres Provinciale, Simone Rossi, Nicola Toschi, and Virginie van Wassenhove. The EXPERIENCE Project: Automatic virtualization of extended personal reality through biomedical signal processing and explainable artificial intelligence [Applications Corner]. IEEE Signal Processing Magazine, 41(1):60--66, 2024. [WWW] [bibtex-entry]


  22. Timo van Kerkoerle, Louise Pape, Milad Ekramnia, Xiaoxia Feng, Jordy Tasserie, Morgan Dupont, Xiaolian Li, Bechir Jarraya, Wim Vanduffel, Stanislas Dehaene, and Ghislaine Dehaene-Lambertz. Brain areas for reversible symbolic reference, a potential singularity of the human brain. eLife, eLife 12:RP87380, July 2024. [WWW] [bibtex-entry]


  23. Anna MA Wagelmans and Virginie van Wassenhove. The day-of-the-week effect is resilient to routine change. Memory & Cognition, pp 1--12, 2024. [WWW] [bibtex-entry]


  24. Cassandra Potier Watkins, Stanislas Dehaene, and Naama Friedmann. Characterizing different types of developmental dyslexias in French: The Malabi screener. Cognitive neuropsychology, pp 1--32, 2024. [WWW] [bibtex-entry]



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Last modified: Sat Jan 4 12:12:18 2025
Author: cp983411.

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