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Publications of year 2019
Theses
  1. Maxime Maheu. Perceiving regularity in sequences: Behavioural, neural and computational signatures. PhD Thesis, Université Paris Descartes, 2019.
    Abstract: As human beings, we can understand spoken language, recognize the opening bars of Beethoven’s 5th Symphony, notice the tide-induced fluctuations of the level of the ocean, predict the color of traffic lights, and identify many more of the ubiquitous temporal regularities that characterize our daily environment. How does the human brain detect, identify, process and leverage those regularities in spite of their striking diversity? In this dissertation, I studied the mechanisms through which the human brain acquires knowledge of sequences and of regularities they may entail. To do so, I recorded behavioural and neural responses to auditory binary sequences characterized by various types of regularities. In parallel, I derived mathematical models of sequence processing that rest upon normative principles of probabilistic inference, but that are yet characterized by different computational architectures, and used human data to arbitrate among them. Using this same general approach, I investigated three different facets of the human sensitivity to sequences. Firstly, I demonstrated that a simple machinery for inferring transition structures between sequence items supports various aspects of the human perception of sequences encountered in seemingly disparate studies. Secondly, I then found that this learning algorithm was implemented in distinct brain systems which extracted statistical trends over different timescales, thereby providing a mechanistic explanation for the human sensitivity to both global statistical biases and to the recent history of observations. In addition to statistical learning, humans also possess the ability to quickly detect and identify deterministic rules. Thirdly, I showed that statistics and rules correspond to two distinct hypothesis spaces, instead of a continuum; and that human subjects could rationally arbitrate among them given the observed sequence. Altogether, my investigations of the cognitive foundations, computational principles and neural architectures supporting sequence processing suggest that the human brain is equipped with several systems that conform to normative principles of probabilistic inference but that are specialized in different aspects of sequences, thereby providing a putative explanation for the human perception of a vast repertoire of temporal regularities.
    [bibtex-entry]


Book chapters
  1. Stanislas Dehaene. Human Singularity and Symbolic Tree Structures. In The Neocortex, volume 27, pages 294--310. W. Singer, T. J. Sejnowski and P. Rakic, 2019. [WWW] [PDF] [bibtex-entry]


Articles in journals
  1. Fosca Al Roumi, Dror Dotan, Tianming Yang, Liping Wang, and Stanislas Dehaene. Acquisition and processing of an artificial mini-language combining semantic and syntactic elements. Cognition, 185:49--61, 2019. [WWW] [PDF] [bibtex-entry]


  2. Marie Amalric and Stanislas Dehaene. A distinct cortical network for mathematical knowledge in the human brain. NeuroImage, 189:19--31, 2019. [WWW] [PDF] [bibtex-entry]


  3. Lucie Berkovitch and Stanislas Dehaene. Subliminal syntactic priming. Cognitive psychology, 109:26--46, 2019. [WWW] [PDF] [bibtex-entry]


  4. Shohini Bhattasali, Murielle Fabre, Wen-Ming Luh, Hazem Al Saied, Matthieu Constant, Christophe Pallier, Jonathan Brennan, R. Nathan Spreng, and John Hale. Localising Memory Retrieval and Syntactic Composition: A fMRI study of naturalistic language comprehension. Language, Cognition and Neuroscience, 2019. [WWW] [PDF]
    Abstract: This study examines memory retrieval and syntactic composition using fMRI while participants listen to a book, The Little Prince. These two processes are quantified drawing on methods from computational linguistics. Memory retrieval is quantified via multi-word expressions that are likely to be stored as a unit, rather than built-up compositionally. Syntactic composition is quantified via bottom-up parsing that tracks tree-building work needed in composed syntactic phrases. Regression analyses localise these to spatially-distinct brain regions. Composition mainly correlates with bilateral activity in anterior temporal lobe and inferior frontal gyrus. Retrieval of stored expressions drives right-lateralised activation in the precuneus. Less cohesive expressions activate well-known nodes of the language network implicated in composition. These results help to detail the neuroanatomical bases of two widely-assumed cognitive operations in language processing.
    [bibtex-entry]


  5. Valentina Borghesani, Marco Buiatti, Evelyn Eger, and Manuela Piazza. Conceptual and perceptual dimensions of word meaning are recovered rapidly and in parallel during reading. Journal of cognitive neuroscience, 31(1):95--108, 2019. [WWW] [PDF] [bibtex-entry]


  6. Valentina Borghesani, Maria Dolores de Hevia, Arnaud Viarouge, Pedro Pinheiro-Chagas, Evelyn Eger, and Manuela Piazza. Processing number and length in the parietal cortex: Sharing resources, not a common code. Cortex, 114:17--27, 2019. [WWW] [PDF] [bibtex-entry]


  7. Florence Bouhali, Zoé Bézagu, Stanislas Dehaene, and Laurent Cohen. A mesial-to-lateral dissociation for orthographic processing in the visual cortex. Proceedings of the National Academy of Sciences, 116(43):21936--21946, 2019. [bibtex-entry]


  8. Elisa Castaldi, Manuela Piazza, Stanislas Dehaene, Alexandre Vignaud, and Evelyn Eger. Attentional amplification of neural codes for number independent of other quantities along the dorsal visual stream. eLife, 8, 2019. [WWW] [PDF] [bibtex-entry]


  9. Elisa Castaldi, Alexandre Vignaud, and Evelyn Eger. Mapping numerical perception and operations in relation to functional and anatomical landmarks of human parietal cortex. bioRxiv, pp 602599, 2019. [bibtex-entry]


  10. Farveh Daneshvarfard, Hamid Abrishami Moghaddam, Ghislaine Dehaene-Lambertz, Guy Kongolo, Fabrice Wallois, and Mahdi Mahmoudzadeh. Neurodevelopment and asymmetry of auditory-related responses to repetitive syllabic stimuli in preterm neonates based on frequency-domain analysis. Scientific Reports, 9(1):10654, 2019. [WWW] [bibtex-entry]


  11. Athena Demertzi, Enzo Tagliazucchi, Stanislas Dehaene, Gustavo Deco, Pablo Barttfeld, Federico Raimondo, Charlotte Martial, Davinia Fernández-Espejo, Benjamin Rohaut, HU Voss, and others. Human consciousness is supported by dynamic complex patterns of brain signal coordination. Science advances, 5(2):eaat7603, 2019. [WWW] [PDF] [bibtex-entry]


  12. Moira R. Dillon, Marianne Duyck, Stanislas Dehaene, Marie Amalric, and Véronique Izard. Geometric categories in cognition.. Journal of Experimental Psychology: Human Perception and Performance, 45(9):1236--1247, 2019. [PDF] [bibtex-entry]


  13. Dror Dotan, Pedro Pinheiro-Chagas, Fosca Al Roumi, and Stanislas Dehaene. Track It to Crack It: Dissecting Processing Stages with Finger Tracking. Trends in Cognitive Sciences, 23(12):1058 -- 1070, 2019. [WWW] [PDF]
    Abstract: A central goal in cognitive science is to parse the series of processing stages underlying a cognitive task. A powerful yet simple behavioral method that can resolve this problem is finger trajectory tracking: by continuously tracking the finger position and speed as a participant chooses a response, and by analyzing which stimulus features affect the trajectory at each time point during the trial, we can estimate the absolute timing and order of each processing stage, and detect transient effects, changes of mind, serial versus parallel processing, and real-time fluctuations in subjective confidence. We suggest that trajectory tracking, which provides considerably more information than mere response times, may provide a comprehensive understanding of the fast temporal dynamics of cognitive operations.
    [bibtex-entry]


  14. Xiaoxia Feng, Irene Altarelli, Karla Monzalvo, Guosheng Ding, Franck Ramus, Hua Shu, Stanislas Dehaene, Xiangzhi Meng, and Ghislaine Dehaene-Lambertz. Shared anomalies in cortical reading networks in Chinese and French dyslexic children. bioRxiv, pp 834945, 2019. [WWW] [bibtex-entry]


  15. Baptiste Gauthier, Karin Pestke, and Virginie van Wassenhove. Building the Arrow of Time… Over Time: A Sequence of Brain Activity Mapping Imagined Events in Time and Space. Cerebral Cortex, 29(10):4398--4414, 2019. [WWW] [bibtex-entry]


  16. Laetitia Grabot, Tadeusz W Kononowicz, Tom Dupré La Tour, Alexandre Gramfort, Valérie Doyère, and Virginie Van Wassenhove. The Strength of Alpha–Beta Oscillatory Coupling Predicts Motor Timing Precision. Journal of Neuroscience, 39(17):3277--3291, 2019. [bibtex-entry]


  17. Micha Heilbron and Florent Meyniel. Confidence resets reveal hierarchical adaptive learning in humans. PLoS computational biology, 15(4):e1006972, 2019. [bibtex-entry]


  18. Claire Kabdebon and Ghislaine Dehaene-Lambertz. Symbolic labeling in 5-month-old human infants. Proceedings of the National Academy of Sciences, 116(12):5805--5810, 2019. [PDF] [bibtex-entry]


  19. Daria La Rocca, Philippe Ciuciu, Denis-Alexander Engemann, and Virginie Van Wassenhove. Emergence of β and γ networks following multisensory training. bioRxiv, pp 560235, 2019. [WWW] [bibtex-entry]


  20. Yair Lakretz, German Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, and Marco Baroni. The emergence of number and syntax units in LSTM language models. arXiv preprint arXiv:1903.07435, 2019. [PDF] [bibtex-entry]


  21. Caroline Le Ster, Antonio Moreno, Franck Mauconduit, Vincent Gras, Ruediger Stirnberg, Benedikt A Poser, Alexandre Vignaud, Evelyn Eger, Stanislas Dehaene, Florent Meyniel, and others. Comparison of SMS-EPI and 3D-EPI at 7T in an fMRI localizer study with matched spatiotemporal resolution and homogenized excitation profiles. Plos one, 14(11):e0225286, 2019. [WWW] [bibtex-entry]


  22. Maxime Maheu, Stanislas Dehaene, and Florent Meyniel. Brain signatures of a multiscale process of sequence learning in humans. Elife, 8:e41541, 2019. [PDF] [bibtex-entry]


  23. Matthias Michel, Diane Beck, Ned Block, Hal Blumenfeld, Richard Brown, David Carmel, Marisa Carrasco, Mazviita Chirimuuta, Marvin Chun, Axel Cleeremans, and others. Opportunities and challenges for a maturing science of consciousness. Nature human behaviour, 3(2):104, 2019. [PDF] [bibtex-entry]


  24. Eric Moulton, Florence Bouhali, Karla Monzalvo, Cyril Poupon, Hui Zhang, Stanislas Dehaene, Ghislaine Dehaene-Lambertz, and Jessica Dubois. Connectivity between the visual word form area and the parietal lobe improves after the first year of reading instruction: a longitudinal MRI study in children. Brain Structure and Function, pp 1--18, 2019. [PDF] [bibtex-entry]


  25. Philippe Pinel, Baudouin Forgeot d’Arc, Stanislas Dehaene, Thomas Bourgeron, Bertrand Thirion, Denis Le Bihan, and Cyril Poupon. The functional database of the ARCHI project: Potential and perspectives. NeuroImage, 197:527--543, 2019. [PDF] [bibtex-entry]


  26. Pedro Pinheiro-Chagas, Manuela Piazza, and Stanislas Dehaene. Decoding the processing stages of mental arithmetic with magnetoencephalography. cortex, 114:124--139, 2019. [PDF] [bibtex-entry]


  27. Rishi Rajalingham, Kohitij Kar, Sachi Sanghavi, Stanislas Dehaene, and James J DiCarlo. A precursor of reading: Neural responses to letters strings in the untrained primate inferior temporal cortex predict word recognition behavior. Journal of Vision, 19(10):172b--172b, 2019. [WWW] [bibtex-entry]


  28. Moti Salti, Asaf Harel, and Sébastien Marti. Conscious perception: Time for an update?. Journal of cognitive neuroscience, 31(1):1--7, 2019. [PDF] [bibtex-entry]


  29. Mélanie Strauss and Stanislas Dehaene. Detection of arithmetic violations during sleep. Sleep, 42(3):zsy232, 2019. [bibtex-entry]


  30. M Strauss, F Raimondo, J Sitt, L Naccache, and S Dehaene. De l’éveil au sommeil: quand perdons-nous connaissance?. Médecine du Sommeil, 16(1):24, 2019. [WWW] [bibtex-entry]


  31. Darinka Trübutschek, Sébastien Marti, and Stanislas Dehaene. Temporal-order information can be maintained in non-conscious working memory. Scientific reports, 9(1):6484, 2019. [bibtex-entry]


  32. Darinka Trübutschek, Sébastien Marti, Henrik Ueberschär, and Stanislas Dehaene. Probing the limits of activity-silent non-conscious working memory. Proceedings of the National Academy of Sciences, 116(28):14358--14367, 2019. [PDF] [bibtex-entry]


  33. Liping Wang, Marie Amalric, Wen Fang, Xinjian Jiang, Christophe Pallier, Santiago Figueira, Mariano Sigman, and Stanislas Dehaene. Representation of spatial sequences using nested rules in human prefrontal cortex. NeuroImage, 186:245--255, 2019. [PDF]
    Abstract: Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing.
    [bibtex-entry]


  34. Qi Zhu and Wim Vanduffel. Submillimeter fMRI reveals a layout of dorsal visual cortex in macaques, remarkably similar to New World monkeys. Proceedings of the National Academy of Sciences, 116(6):2306--2311, 2019. [WWW] [bibtex-entry]


Conference proceedings
  1. Yair Lakretz, German Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, and Marco Baroni. The emergence of number and syntax units in LSTM language models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pages 11--20, June 2019. Association for Computational Linguistics. [WWW] [PDF]
    Abstract: Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this remarkable feat. Some have conjectured it depends on heuristics that do not truly take hierarchical structure into account. We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two “number units”. Importantly, the behaviour of these units is partially controlled by other units independently shown to track syntactic structure. We conclude that LSTMs are, to some extent, implementing genuinely syntactic processing mechanisms, paving the way to a more general understanding of grammatical encoding in LSTMs.
    [bibtex-entry]


  2. Hervé Lemaître, Yann Le Guen, Amanda Tilot, Jason J Stein, Cathy Philippe, Jean-François Mangin, Vincent Frouin, Simon Fischer, and Stanislas Dehaene. Unearthing the Evolutionary History of Genetic Variants Influencing Human Sulcal Widening Authors: Introduction. In OHBM (Organization for Human Brain Mapping), 2019. [WWW] [bibtex-entry]


  3. Lucia Melloni, Elizabeth A Buffalo, Stanislas Dehaene, Karl J Friston, Asif A Ghazanfar, Anne-Lise Giraud, Scott T Grafton, Saskia Haegens, Bijan Pesaran, Christopher I Petkov, and others. Computation and its neural implementation in human cognition. In Strüngmann Forum Reports, volume 27, pages 323--46, 2019. [WWW] [bibtex-entry]


  4. Cassandra Potier Watkins, Olivier Dehaene, and Stanislas Dehaene. Automatic Construction of a Phonics Curriculum for Reading Education Using the Transformer Neural Network. In International Conference on Artificial Intelligence in Education, pages 226--231, 2019. Springer. [WWW] [PDF] [bibtex-entry]


Miscellaneous
  1. Lionel Naccache Jacobo Sitt, Jean-Remi King, Laurent Cohen and Stanislas Dehaene. Methods to monitor consciousness, 2019. [bibtex-entry]



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