Publications

Di Liberto, G., Wong, D., Melnik, G. & de Cheveigné, A. (2019 ). Low-frequency cortical responses to natural speech reflect probabilistic phonotactics. NeuroImage, 196, 237-247. doi:10.1016/j.neuroimage.2019.04.037

Peiffer-Smadja, N. , Maatoug, R. , Lescure, F. , D’Ortenzio, E. , Pineau, J. & King, J. (2020). Machine Learning for COVID-19 needs global collaboration and data-sharing. Nat Mach Intell, . doi:10.1038/s42256-020-0181-6

Dipoppa, M., Krupa, M., Torcini, A. & Gutkin, B. (2012). Marginally Stable States and Quasi-periodic minor attractors in excitable pulse-coupled networks. SIADS , 63(1), 62-97

Ego-Stengel, V. , Abbasi, A. , Larroche, M. , Lassagne, H. , Boubenec, Y. & Shulz, D. (2019). Mechanical coupling through the skin affects whisker movements and tactile information encoding. Journal of neurophysiology, 122(4), 1606-1622. doi:10.1152/jn.00863.2018

Oster, A., Faure, P. & Gutkin, B. (2015). Mechanisms for multiple activity modes of VTA dopamine neurons. Frontiers in computational neuroscience, 9, 95. doi:10.3389/fncom.2015.00095

Ponsot, E., Varnet, L., Wallaert, N., Daoud, E., Shamma, S., Lorenzi, C. & Neri, P. (2021). Mechanisms of Spectrotemporal Modulation Detection for Normal- and Hearing-Impaired Listeners. Trends Hear, 25, 2331216520978029. doi:10.1177/2331216520978029

Kang, H. , Lancelin, D. & Pressnitzer, D. (2018). Memory for Random Time Patterns in Audition, Touch, and Vision. Neuroscience, in press. doi:10.1016/j.neuroscience.2018.03.017

Kim, S., Senju, A., Sodian, B., Paulus, M., Itakura, S., Okuno , A., Ueno, M. & Proust, J. (2021). Memory Monitoring and Control in Japanese and German Preschoolers. Memory & Cognition. doi:10.3758/s13421-021-01263-1

Proust, J. (2010). Metacognition. Philosophy Compass, 5 (11), 989–998

Proust, J. (2018). Metacognition. In T. Crane (Eds.), Rootledge Encyclopedia of Philosophy

Kim, S., Sodian, B., Senju, A., Okuno , A., Ueno, M., Itakura, S. & Proust, J. (2020). Metacognition and mindreading in young children: a cross-cultural study. Consciousness and Cognition , 85, 103017. doi:10.1016/j.concog.2020.103017

Proust, J. & Fortier, M. (2018). Metacognitive Diversity - An Interdisciplinary approaches. Oxford: Oxford University Press

Paulus, M., Tsalas, N., Proust, J. & Sodian, B. (2014). Metacognitive monitoring of oneself and others: developmental changes during childhood and adolescence. Journal of experimental child psychology, 122, 153-165. doi:10.1016/j.jecp.2013.12.011

Proust, J. (1999). Mind, Space and Objectivity in Non-Human Animals. Erkenntnis, 51(1 ), 545–562

Goodman, D., Winter, I., Léger, A., de Cheveigné, A. & Lorenzi, C. (2018). Modelling firing regularity in the ventral cochlear nucleus: Mechanisms, and effects of stimulus level and synaptopathy. Hearing research, 358, 98-100. doi:10.1016/j.heares.2017.09.010

Caze, R., Humphries, M. & Gutkin, B. (2013). Modulation of computational capacity of neurons due to dendritic synaptic interactions. PLoS Comput. Biol., 9(2)

Maravall, M., Ostojic, S., Pressnitzer, D. & Chait, M. (2018). More than the Sum of its Parts: Perception and Neuronal Underpinnings of Sequence Processing. Neuroscience, in press. doi:10.1016/j.neuroscience.2018.07.043

Bimbard, C., Demene, C. , Girard, C. , Radtke-Schuller, S., Shamma, S., Tanter, M. & Boubenec, Y. (2018). Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret. eLIFE. doi:10.7554/eLife.35028.001

Schwartz, J., Grimault, N., Hupé, J., Moore, B. & Pressnitzer, D. (2012). Multistability in perception: binding sensory modalities, an overview. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 367(1591), 896-905. doi:10.1098/rstb.2011.0254

de Cheveigné, A., Di Liberto, G., Arzounian, D., Wong, D., Hjortkjaer, J., Fuglsang , S. & Parra, L. (2019). Multiway canonical correlation analysis of brain signals. NeuroImage, 186, 728-740. doi:10.1016/j.neuroimage.2018.11.026

Pressnitzer, D. (2023). Music cognition: Can you carry over that tune? Current biology : CB, 33(8), R296-R298. doi:10.1016/j.cub.2023.03.029

Patil, K., Pressnitzer, D., Shamma, S. & Elhilali, M. (2012). Music in our ears: the biological bases of musical timbre perception. PLoS computational biology, 8(11), e1002759. doi:10.1371/journal.pcbi.1002759

Pressnitzer, D., Bestel, J. & Fraysse, B. (2005). Music to electric ears: pitch and timbre perception by cochlear implant patients. Annals of the New York Academy of Sciences, 1060, 343-5. doi:10.1196/annals.1360.050

Di Liberto, G., PELOFI, C., Shamma, S. & de Cheveigné, A. (2020). Musical expertise enhances the cortical tracking of the acoustic envelope during naturalistic music listening. Acoustical Science and Technology (Japan), 41 (1), 361-364. doi:10.1250/ast.41.361

Müller, B., Tsalas, N., Van Schie, H., Meinhardt, J., Proust, J., Sodian, B. & Paulus, M. (2016). Neural correlates of judgments of learning - An ERP study on metacognition. Brain research, 1652, 170-177. doi:10.1016/j.brainres.2016.10.005

Hyafil, A., Giraud, A., Fontolan, L. & Gutkin, B. (2015). Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. Trends in neurosciences, 38(11), 725-40. doi:10.1016/j.tins.2015.09.001

Gwilliams, L., King, J. , Marantz, A. & Poeppel, D. (2022). Neural dynamics of phoneme sequences reveal position-invariant code for content and order. Nature Communications, 13(6606). doi:10.1038/s41467-022-34326-1

Mikutta, C., Leonard, M. , Hungate, D., Koelsch, S., Shamma, S., Chang, E., Millán, J., Knight, R. & Pasley, B. (2017). Neural Encoding of Auditory Features during Music Perception and Imagery. Cerebral Cortex, 1(12). doi:10.1093/cercor/bhx277

Martin , S., Mikutta, C., Leonard, M. , Hungate, D., Koelsch, S., Shamma, S., Chang, E., Millán, J., Knight, R. & Pasley, B. (2018). Neural Encoding of Auditory Features during Music Perception and Imagery. Cerebral cortex (New York, N.Y. : 1991), 28(12), 4222-4233. doi:10.1093/cercor/bhx277

Di Liberto, G., Nie, J. , Yeaton, J. , Khalighinejad, B. , Shamma, S. & Mesgarani, N. (2021). Neural representation of linguistic feature hierarchy reflects second-language proficiency. NeuroImage, 222, 117586. doi:10.1016/j.neuroimage.2020.117586