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Publications of year 2020
Books
  1. Stanislas Dehaene. How We Learn: The New Science of Education and the Brain. Penguin UK, 2020. [bibtex-entry]


Theses
  1. Jordy Tasserie. Thalamic DBS restores consciousness in anesthetized macaques. PhD Thesis, Univerité Paris Saclay, 2020. [bibtex-entry]


Articles in journals
  1. Parvaneh Adibpour, Jessica Lebenberg, Claire Kabdebon, Ghislaine Dehaene-Lambertz, and Jessica Dubois. Anatomo-functional correlates of auditory development in infancy. Developmental Cognitive Neuroscience, pp 100752, 2020. [PDF] [bibtex-entry]


  2. Fosca Al Roumi, Sébastien Marti, Liping Wang, Marie Amalric, and Stanislas Dehaene. Mental compression of spatial sequences in human working memory using numerical and geometrical primitives. bioRxiv, 2020. [WWW] [bibtex-entry]


  3. Irene Altarelli, Ghislaine Dehaene-Lambertz, and Daphne Bavelier. Individual differences in the acquisition of non-linguistic audio-visual associations in 5 year olds. Developmental science, 23(4):e12913, 2020. [bibtex-entry]


  4. Shohini Bhattasali, Murielle Popa-Fabre, Christophe Pallier, and John Hale. Modeling Conventionalization and Predictability within MWEs at the Brain Level. Proceedings of the Society for Computation in Linguistics, 3(1):110--119, 2020. [WWW] [PDF]
    Abstract: While expressions have traditionally been binarized as compositional and noncompositional in linguistic theory, Multiword Expressions (MWEs) demonstrate finer-grained distinctions. Using Association Measures like Pointwise Mutual Information and Dice's Coefficient, MWEs can be characterized as having different degrees of conventionalization and predictability. Our goal is to investigate how these gradiences could reflect cognitive processes. In this study, fMRI recordings of naturalistic narrative comprehension is used to probe to what extent these computational measures and the cognitive processes they could operationalize are observable during on-line sentence processing. Our results show that Dice's Coefficent, representing lexical predictability, is a better predictor of neural activation for processing MWEs. Overall our experimental approach demonstrates how we can test the cognitive plausibility of computational metrics by comparing it against neuroimaging data.
    [bibtex-entry]


  5. Elisa Castaldi, Marco Turi, Sahawanatou Gassama, Manuela Piazza, and Evelyn Eger. Excessive visual crowding effects in developmental dyscalculia. Journal of Vision, August 2020, Vol.20, 7, 2020. [WWW] [PDF] [bibtex-entry]


  6. Elisa Castaldi, Alexandre Vignaud, and Evelyn Eger. Mapping subcomponents of numerical cognition in relation to functional and anatomical landmarks of human parietal cortex. NeuroImage, 221:117210, 2020. [PDF] [bibtex-entry]


  7. Claire HC Chang, Stanislas Dehaene, Denise H Wu, Wen-Jui Kuo, and Christophe Pallier. Cortical encoding of linguistic constituent with and without morphosyntactic cues. Cortex, 129:281 295, 2020. [PDF]
    Abstract: This study examined the brain areas involved in combining words into larger units when there are few or no morphosyntactic cues. We manipulated constituent length in word strings of the same length under two conditions: Mandarin sentence, which had sparse morphosyntactic cues, and nominal phrase that had no morphosyntactic cues [e.g., ((honey mustard) (chicken burger))]. Contrasting sentences to word lists revealed a network that largely overlapped with the one reported in languages with rich morphosyntactic cues, including left IFGorb/IFGtri and areas along left STG/STS. Both conditions showed increased activation in left IFGtri/IFGorb in functional ROIs defined based on previous study in sentence processing, while the nominal phrases additionally revealed a constituent length effect in bilateral dorsal IFGtri, left IFGoper, left pMTG/pSTG, left IPL, and several subcortical areas, which might reflect an increased reliance on semantic and pragmatic information. Moreover, in upper left IFGtri/IFGoper and left thalamus/caudate, this effect increased with the participants' tendency to combine nouns into phrases. The absence of syntactic constraints on linguistic composition might highlight individual differences in cognitive control, which helps to integrate non-syntactic information.
    [bibtex-entry]


  8. Lorenzo Ciccione and Stanislas Dehaene. Grouping mechanisms in numerosity perception. Open Mind, 4:102--118, 2020. [bibtex-entry]


  9. Dror Dotan and Stanislas Dehaene. Parallel and serial processes in number-to-quantity conversion. Cognition, 204:104387, 2020. [PDF] [bibtex-entry]


  10. Xiaoxia Feng, Irene Altarelli, Karla Monzalvo, Guosheng Ding, Franck Ramus, Hua Shu, Stanislas Dehaene, Xiangzhi Meng, and Ghislaine Dehaene-Lambertz. A universal reading network and its modulation by writing system and reading ability in French and Chinese children. Elife, 9:e54591, 2020. [WWW] [PDF] [bibtex-entry]


  11. Arnaud Gloaguen, Cathy Philippe, Vincent Frouin, Giulia Gennari, Ghislaine Dehaene-Lambertz, Laurent Le Brusquet, and Arthur Tenenhaus. Multiway generalized canonical correlation analysis. Biostatistics, 2020. [bibtex-entry]


  12. Peng Gui, Yuwei Jiang, Di Zang, Zengxin Qi, Jiaxing Tan, Hiromi Tanigawa, Jian Jiang, Yunqing Wen, Long Xu, Jizong Zhao, Y. Mao, M. Poo, N. Ding, S. Dehaene, X. Wu, and L. Wang. Assessing the depth of language processing in patients with disorders of consciousness. Nature neuroscience, 23(6):761--770, 2020. [WWW] [bibtex-entry]


  13. Gerald Hahn, Gorka Zamora-López, Lynn Uhrig, Enzo Tagliazucchi, Helmut Laufs, Dante Mantini, Morten L Kringelbach, Bechir Jarraya, and Gustavo Deco. Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals. NeuroImage, 226:117470, 2020. [WWW] [bibtex-entry]


  14. Daria La Rocca, Philippe Ciuciu, Denis-Alexander Engemann, and Virginie Van Wassenhove. Emergence of β and γ networks following multisensory training. NeuroImage, 206:116313, 2020. [PDF] [bibtex-entry]


  15. Yair Lakretz, Stanislas Dehaene, and Jean-Rémi King. What Limits Our Capacity to Process Nested Long-Range Dependencies in Sentence Comprehension?. Entropy, 22(4):446, 2020. [bibtex-entry]


  16. Yair Lakretz, Dieuwke Hupkes, Alessandra Vergallito, Marco Marelli, Marco Baroni, and Stanislas Dehaene. Exploring processing of nested dependencies in neural-network language models and humans. arXiv preprint arXiv:2006.11098, 2020. [bibtex-entry]


  17. Yann Le Guen, François Leroy, Cathy Philippe, IMAGEN consortium, Jean-François Mangin, Ghislaine Dehaene-Lambertz, and Vincent Frouin. Enhancer locus in ch14q23. 1 modulates brain asymmetric temporal regions involved in language processing. Cerebral Cortex, 30(10):5322--5332, 2020. [PDF] [bibtex-entry]


  18. Diana López-Barroso, Michel Thiebaut de Schotten, José Morais, Régine Kolinsky, Lucia W Braga, Alexandre Guerreiro-Tauil, Stanislas Dehaene, and Laurent Cohen. Impact of literacy on the functional connectivity of vision and language related networks. NeuroImage, pp 116722, 2020. [WWW] [bibtex-entry]


  19. Maxime Maheu, Florent Meyniel, and Stanislas Dehaene. Rational arbitration between statistics and rules in human sequence learning. bioRxiv, 2020. [WWW] [PDF]
    Abstract: Detecting and learning temporal regularities is essential to accurately predict the future. Past research indicates that humans are sensitive to two types of sequential regularities: deterministic rules, which afford sure predictions, and statistical biases, which govern the probabilities of individual items and their transitions. How does the human brain arbitrate between those two types? We used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence learning. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over several distinct hypothesis spaces, underlies the human capability to learn both statistics and rules.
    [bibtex-entry]


  20. Raphaëlle Malassis, Stanislas Dehaene, and Joël Fagot. Baboons (Papio papio) Process a Context-Free but Not a Context-Sensitive Grammar. Scientific Reports (Nature Publisher Group), 10(1), 2020. [WWW] [PDF] [bibtex-entry]


  21. George A Mashour, Pieter Roelfsema, Jean-Pierre Changeux, and Stanislas Dehaene. Conscious Processing and the Global Neuronal Workspace Hypothesis. Neuron, 105(5):776--798, 2020. [WWW] [PDF] [bibtex-entry]


  22. Florent Meyniel. Brain dynamics for confidence-weighted learning. PLOS Computational Biology, 16(6):e1007935, June 2020. [WWW] [PDF]
    Abstract: Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects’ confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15–30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations.
    [bibtex-entry]


  23. Michael Milham, Christopher I. Petkov, Daniel S. Margulies, Charles E. Schroeder, Michele A. Basso, Pascal Belin, Damien A. Fair, Andrew Fox, Sabine Kastner, Rogier B. Mars, Adam Messinger, Colline Poirier, Wim Vanduffel, David C. Van Essen, Ashkan Alvand, Yannick Becker, Suliann Ben Hamed, Austin Benn, Clementine Bodin, Susann Boretius, Bastien Cagna, Olivier Coulon, Sherif Hamdy El-Gohary, Henry Evrard, Stephanie J. Forkel, Patrick Friedrich, Sean Froudist-Walsh, Eduardo A. Garza-Villarreal, Yang Gao, Alessandro Gozzi, Antoine Grigis, Renee Hartig, Takuya Hayashi, Katja Heuer, Henrietta Howells, Dirk Jan Ardesch, Béchir Jarraya, Wendy Jarrett, Hank P. Jedema, Igor Kagan, Clare Kelly, Henry Kennedy, P. Christiaan Klink, Sze Chai Kwok, Robert Leech, Xiaojin Liu, Christopher Madan, Wasana Madushanka, Piotr Majka, Ann-Marie Mallon, Kevin Marche, Adrien Meguerditchian, Ravi S. Menon, Hugo Merchant, Anna Mitchell, Karl-Heinz Nenning, Aki Nikolaidis, Michael Ortiz-Rios, Marco Pagani, Vikas Pareek, Mark Prescott, Emmanuel Procyk, Reza Rajimehr, Ioana-Sabina Rautu, Amir Raz, Anna Wang Roe, Román Rossi-Pool, Lea Roumazeilles, Tomoko Sakai, Jerome Sallet, Pamela García-Saldivar, Chika Sato, Stephen Sawiak, Marike Schiffer, Caspar M. Schwiedrzik, Jakob Seidlitz, Julien Sein, Zhi-ming Shen, Amir Shmuel, Afonso C. Silva, Luciano Simone, Nikoloz Sirmpilatze, Julia Sliwa, Jonathan Smallwood, Jordy Tasserie, Michel Thiebaut de Schotten, Roberto Toro, Regis Trapeau, Lynn Uhrig, Julien Vezoli, Zheng Wang, Sara Wells, Bella Williams, Ting Xu, Augix Guohua Xu, Essa Yacoub, Ming Zhan, Lei Ai, Céline Amiez, Fabien Balezeau, Mark G. Baxter, Erwin L. A. Blezer, Thomas Brochier, Aihua Chen, Paula L. Croxson, Christienne G. Damatac, Stanislas Dehaene, Stefan Everling, Lazar Fleysher, Winrich Freiwald, Timothy D. Griffiths, Carole Guedj, Fadila Hadj-Bouziane, Noam Harel, Bassem Hiba, Benjamin Jung, Bonhwang Koo, Kevin N. Laland, David A. Leopold, Patrik Lindenfors, Martine Meunier, Kelvin Mok, John H. Morrison, Jennifer Nacef, Jamie Nagy, Mark Pinsk, Simon M. Reader, Pieter R. Roelfsema, David A. Rudko, Matthew F. S. Rushworth, Brian E. Russ, Michael Christoph Schmid, Elinor L. Sullivan, Alexander Thiele, Orlin S. Todorov, Doris Tsao, Leslie Ungerleider, Charles R. E. Wilson, Frank Q. Ye, Wilbert Zarco, and Yong-di Zhou. Accelerating the Evolution of Nonhuman Primate Neuroimaging. Neuron, 105(4):600--603, 2020. [WWW]
    Abstract: Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions.
    [bibtex-entry]


  24. Julia Moser, Franziska Schleger, Magdalene Weiss, Katrin Sippel, Ghislaine Dehaene-Lambertz, and Hubert Preissl. Magnetoencephalographic signatures of hierarchical rule learning in newborns. Developmental cognitive neuroscience, 46:100871, 2020. [PDF] [bibtex-entry]


  25. Theofanis I. Panagiotaropoulos, Abhilash Dwarakanath, and Vishal Kapoor. Prefrontal Cortex and Consciousness: Beware of the Signals. Trends in Cognitive Science, March 2020. [WWW] [PDF] [bibtex-entry]


  26. Theofanis I. Panagiotaropoulos, Liping Wang, and Stanislas Dehaene. Hierarchical architecture of conscious processing and subjective experience. Cognitive Neuropsychology, 0(0):1--4, 2020. [WWW] [PDF] [bibtex-entry]


  27. Ana Luísa Pinho, Alexis Amadon, Baptiste Gauthier, Nicolas Clairis, André Knops, Sarah Genon, Elvis Dohmatob, Juan Jesús Torre, Chantal Ginisty, Séverine Becuwe-Desmidt, Séverine Roger, Yann Lecomte, Valérie Berland, Laurence Laurier, Véronique Joly-Testault, Gaëlle Médiouni-Cloarec, Christine Doublé, Bernadette Martins, Eric Salmon, Manuela Piazza, David Melcher, Mathias Pessiglione, Virginie van Wassenhove, Evelyn Eger, Gaël Varoquaux, Stanislas Dehaene, Lucie Hertz-Pannier, and Bertrand Thirion. Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping. Scientific Data, 7(1):353, 2020. [WWW] [PDF]
    Abstract: We present an extension of the Individual Brain Charting dataset –a high spatial-resolution, multi-task, functional Magnetic Resonance Imaging dataset, intended to support the investigation on the functional principles governing cognition in the human brain. The concomitant data acquisition from the same 12 participants, in the same environment, allows to obtain in the long run finer cognitive topographies, free from inter-subject and inter-site variability. This second release provides more data from psychological domains present in the first release, and also yields data featuring new ones. It includes tasks on e.g. mental time travel, reward, theory-of-mind, pain, numerosity, self-reference effect and speech recognition. In total, 13 tasks with 86 contrasts were added to the dataset and 63 new components were included in the cognitive description of the ensuing contrasts. As the dataset becomes larger, the collection of the corresponding topographies becomes more comprehensive, leading to better brain-atlasing frameworks. This dataset is an open-access facility; raw data and derivatives are publicly available in neuroimaging repositories. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12958181
    [bibtex-entry]


  28. Samuel Planton, Timo van Kerkoerle, Leïla Abbih, Maxime Maheu, Florent Meyniel, Mariano Sigman, Liping Wang, Santiago Figueira, Sergio Romano, and Stanislas Dehaene. Mental compression of binary sequences in a language of thought. 2020. [WWW] [bibtex-entry]


  29. Rishi Rajalingham, Kohitij Kar, Sachi Sanghavi, Stanislas Dehaene, and James J DiCarlo. The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature communications, 11(1):3886, 2020. [WWW] [bibtex-entry]


  30. Mathias Sablé-Meyer, Serge Caparos, Timo van Kerkoerle, Marie Amalric, Stanislas Dehaene, and others. A signature of human uniqueness in the perception of geometric shapes. 2020. [WWW] [bibtex-entry]


  31. Camilo Miguel Signorelli and Daniel Meling. Towards new concepts for a biological neuroscience of consciousness. 2020. [PDF] [bibtex-entry]


  32. Jordy Tasserie, Antoine Grigis, Lynn Uhrig, Morgan Dupont, Alexis Amadon, and Béchir Jarraya. Pypreclin: An automatic pipeline for macaque functional MRI preprocessing. NeuroImage, pp 116353, 2020. [WWW] [bibtex-entry]


  33. Cassandra Potier Watkins, Julien Caporal, Clément Merville, Sid Kouider, and Stanislas Dehaene. Accelerating reading acquisition and boosting comprehension with a cognitive science-based tablet training. Journal of Computers in Education, pp 1--30, 2020. [PDF] [bibtex-entry]


  34. Niklas Wilming, Peter R Murphy, Florent Meyniel, and Tobias H Donner. Large-scale dynamics of perceptual decision information across human cortex. Nature Communications, 11(1):1--14, 2020. [WWW] [bibtex-entry]


  35. Oscar Woolnough, Cristian Donos, Patrick S Rollo, Kiefer J Forseth, Yair Lakretz, Nathan E Crone, Simon Fischer-Baum, Stanislas Dehaene, and Nitin Tandon. Spatiotemporal dynamics of orthographic and lexical processing in the ventral visual pathway. Nature Human Behaviour, pp 1--10, 2020. [PDF] [bibtex-entry]


Miscellaneous
  1. Ghislaine Dehaene-Lambertz, Ana Fló, and Marcela Peña. Infants' early competence for language and symbols, 2020. [PDF]
    Abstract: Humans have much more sophisticated communication skills than other species. They are not limited to emotional cries, alarm calls, and soothing demands; they also interpret the inner and outer world in a symbolic way, resulting in a collective intelligence and an accumulation of knowledge called culture. This culture permeates the child and fosters efficient learning, based on the knowledge accumulated through generations. To develop this collective intelligence, it requires (a) a social brain predisposed to learn from conspecifics, (b) awareness of one’s mental state and knowledge and those of others, (c) a shared common language of thought, and (d) a communication system for exchanging this information. We insist on the value of symbolic representations as a compressed, necessary format for representing information to ourselves and exchanging information with others. We propose that human cognition has been boosted beyond the cognition of other primates by the multiplicative advantage of codevelopment of social cognition, language but also symbolic thinking that can be observed from the first months of life on
    [bibtex-entry]


  2. Stéphane Dufau, Ludivine Javourey-Drevet, Johannes C Ziegler, and Stanislas Dehaene. Hibou: une ressource d'entraînement à la lecture, 2020. [WWW] [bibtex-entry]



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Last modified: Tue Apr 8 15:45:11 2025
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