Publications

Zeldenrust, F., De Knecht, S., Wadman, W., Denève, S. & Gutkin, B. (2017). Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series. Frontiers in computational neuroscience, 11, 49. doi:10.3389/fncom.2017.00049

Zeldenrust, F., Gutkin, B. & Denève, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. Plos Computational Biology, 17(4), e1008673. doi:10.1371/journal.pcbi.1008673

Wexler, M., Duyck, M. & Mamassian, P. (2015). Persistent states in vision break universality and time invariance. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14990-14995. doi:10.1073/pnas.1508847112

Van Der Groen, O., Van Der Burg, E., Lunghi, C. & Alais, D. (2013). Touch Influences Visual Perception with a Tight Orientation-Tuning. Plos One, 8(11). doi:10.1371/journal.pone.0079558

Vacher, J. , Mamassian, P. & Coen-Cagli, R. (2020). Measuring and Modeling Human Probabilistic Segmentation Maps. Journal of Vision, 20(11), 260. doi:10.1167/jov.20.11.260

Vacher, J. , Davila, A., Kohn, A. & Coen-Cagli, R. (2020). Texture Interpolation for Probing Visual Perception. Advances in Neural Information Processing Systems (Spotlight – top 5%)

Vacher, J. , Mamassian, P. & Coen-Cagli, R. (2019). An Ideal Observer Model for Grouping and Contour Integration in Natural Images. Vision, 48, 88-88

Mamassian, P. & Coen-Cagli, R. (2018). An Ideal Observer Model to Probe Human Visual Segmentation of Natural Images. arXiv

Udden, J. , Martins, M. , Zuidema, W. & Tecumseh Fitch, W. (2019). Hierarchical Structure in Sequence Processing: How to Measure It and Determine Its Neural Implementation. Topics in Cognitive Science . doi:10.1111/tops.12442

Toscani, M. , Mamassian, P. & Valsecchi, M. (2021). Underconfidence in peripheral vision. Journal of Vision, 21(2). doi:10.1167/jov.21.6.2

Toscani, M. , Gegenfurtner, K. , Mamassian, P. & Valsecchi, M. (2019). Under-confidence in peripheral vision. Journal of Vision, 19(10), 67c. doi:10.1167/19.10.67c

Stefanini, F. , A. Kheirbek, M. , Kushnir, L., C. Jimenez, J. , H. Jennings, J. , D. Stuber, G. , Hen, R. & Fusi, S. (2018). A distributed neural code in the dentate gyrus and in CA1. . doi:10.1101/292953

Sperber, D. & Mercier, H. (2017). The Enigma of Reason .

Simeone, O. , Rajendran, B. , Gruning, A. , Eleftheriou, E. , Davies, M. , Denève, S. & Huang, G. (2019). Learning Algorithms and Signal Processing for Brain-Inspired Computing. IEEE Signal Processing Magazine, 36(6), 12-15. doi:10.1109/MSP.2019.2935557

Schweitzer, R. , Watson, T. , Balsdon, T. & Rolfs, M. (2018). From retinal to world-centered perception of intra-saccadic motion streaks: Evidence for high-fidelity eye position information during saccades. , 18(10), 1285

Schütz, A. & Mamassian, P. (2016). Early, local motion signals generate directional preferences in depth ordering of transparent motion. Journal of vision, 16(10), 24. doi:10.1167/16.10.24

Schaffhauser, T., Boubenec, Y. & Mamassian, P. (2019). Temporal binding across senses facilitates change detection within senses. Journal of vision, 19(10), 19a-19a. doi:10.1167/19.10.19a

Recht, S. , Mamassian, P. & de Gardelle, V. (2018). Confidence blinks before attention. Journal of Vision, 18(10), 1112. doi:10.1167/18.10.1112

Recht, S. , de Gardelle, V. & Mamassian, P. (2021). Metacognitive blindness in temporal selection during the deployment of spatial attention. Cognition, 216, 104864. doi:10.1016/j.cognition.2021.104864

Recht, S. , de Gardelle, V. & Mamassian, P. (2019). Metacognitive estimates of time during spatial orienting of attention. Journal of vision, 19(10), 214c-214c. doi:10.1167/19.10.214c

Recht, S. , Mamassian, P. & de Gardelle, V. (2019). Temporal attention causes systematic biases in visual confidence. Scientific Reports, 9(1), 11622. doi:10.1038/s41598-019-48063-x

Recht, S. , Mamassian, P. & de Gardelle, V. (2018). When temporal attention lacks confidence. bioRxiv, 496232

Recanatesi, S., Farrell, M., Lajoie, G., Denève, S., Rigotti, M. & Shea-Brown, E. (2018). Signatures and mechanisms of low-dimensional neural predictive manifolds. bioRxiv. doi:10.1101/471987

Recanatesi, S., Farrell, M., Lajoie, G., Denève, S., Rigotti, M. & Shea-Brown, E. (2021). Predictive learning as a network mechanism for extracting low-dimensional latent space representations. Nature Communications, 12(1417). doi:10.1038/s41467-021-21696-1

Rahnev, D., Desender, K., Lee, A., Adler, W., Aguilar-Lleyda, D., Akdoğan, B., Arbuzova, P., Atlas, L., Balcı, F., Bang, J., Bègue, I., Birney, D., Brady, T., Calder-Travis, J., Chetverikov, A., Clark, T., Davranche, K., Denison, R., Dildine, T., Double, K., Duyan, Y., Faivre, N., Fallow, K., Filevich, E., Gajdos, T., Gallagher, R., de Gardelle, V., Gherman, S., Haddara, N., Hainguerlot, M., Hsu, T., Hu, X., Iturrate, I., Jaquiery, M., Kantner, J., Koculak, M., Konishi, M., Koß, C., Kvam, P., Kwok, S., Lebreton, M., Lempert, K., Ming Lo, C., Luo, L., Maniscalco, B., Martin, A., Massoni, S., Matthews, J., Mazancieux, A., Merfeld, D., O'Hora, D., Palser, E., Paulewicz, B., Pereira, M., Peters, C., Philiastides, M., Pfuhl, G., Prieto, F., Rausch, M., Recht, S. , Reyes, G., Rouault, M., Sackur, J., Sadeghi, S., Samaha, J., Seow, T., Shekhar, M., Sherman, M., Siedlecka, M., Skóra, Z., Song, C., Soto, D., Sun, S., Van Boxtel, J., Wang, S., Weidemann, C., Weindel, G., Wierzchoń, M., Xu, X., Ye, Q., Yeon, J., Zou, F. & Zylberberg, A. (2020). The Confidence Database. Nature Human Behaviour, 4, 317–325. doi:10.1038/s41562-019-0813-1

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

Ponsot, E. & Neri, P. (2020). Inter-individual differences in spectrotemporal modulation processing despite similar audiograms : could they be used to assess supra-threshold hearing distortions? In e-Forum Acusticum 2020. doi:10.48465/fa.2020.0808

Ponsot, E., Burred, J., Belin, P. & Aucouturier, J. (2018). Reply to Knight et al.: The complexity of inferences from speech prosody should be addressed using data-driven approaches. Proceedings of the National Academy of Sciences of the United States of America, 115(27), E6104-E6105. doi:10.1073/pnas.1806857115

Ponsot, E., Susini, P. & Meunier, S. (2017). Global loudness of rising- and falling-intensity tones: How temporal profile characteristics shape overall judgments. The Journal of the Acoustical Society of America, 142(1), 256. doi:10.1121/1.4991901

Ponsot, E., Arias, P. & Aucouturier, J. (2018). Uncovering mental representations of smiled speech using reverse correlation. The Journal of the Acoustical Society of America, 143(1), EL19. doi:10.1121/1.5020989