Publications

Hosseinizaveh, N. & Mamassian, P. (2022). The effect of confidence on visual perceptual learning. Journal of Vision, 22(14). doi:10.1167/jov.22.14.3950

Prosper, A., Pasqualetti, M., Concetta Morrone, M. & Lunghi, C. (2023). The duration effect of short-term monocular deprivation measured by binocular rivalry and binocular combination. Vision research, 211, 108278. doi:10.1016/j.visres.2023.108278

Garcia, B., Cerrotti, F. & Palminteri, S. (2021). The description–experience gap: a challenge for the neuroeconomics of decision-making under uncertainty. Phil. Trans. R. Soc. B, 376, 20190665. doi:10.1098/rstb.2019.0665

Garcia, B., Cerrotti, F. & Palminteri, S. (2021). The description-experience gap: a challenge for the neuroeconomics of decision-making under uncertainty. Philosophical transactions of the royal society B, 376(1819). doi:10.1098/rstb.2019.0665

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

Palminteri, S. & Maël, L. (2022). The computational roots of positivity and confirmation biases in reinforcement-learning. Trends in Cognitive Sciences, 26(7), 607-621. doi:10.1016/j.tics.2022.04.005

Palminteri, S., Kilford, E., Coricelli, G. & Blakemore, S. (2016). The Computational Development of Reinforcement Learning during Adolescence. PLoS Computational Biology, 12(6). doi:10.1371/journal.pcbi.1004953

Najar, A., Bonnet, E., Bahador, B. & Palminteri, S. (2020). The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning. PLoS Biology, 18 (12), e3001028. doi:10.1371/journal.pbio.3001028

Ponsot, E., Susini, P. & Oberfeld, D. (2016). Temporal weighting of loudness: Comparison between two different psychophysical tasks. The Journal of the Acoustical Society of America, 139(1), 406-17. doi:10.1121/1.4939959

Ponsot, E., Susini, P., Saint Pierre, G. & Meunier, S. (2013). Temporal loudness weights for sounds with increasing and decreasing intensity profiles. The Journal of the Acoustical Society of America, 134(4), EL321-6. doi:10.1121/1.4819184

Maex, R. & Gutkin, B. (2017). Temporal integration and 1/f power scaling in a circuit model of cerebellar interneurons. Journal of neurophysiology, 118(1), 471-485. doi:10.1152/jn.00789.2016

Jovanovic, L. & Mamassian, P. (2020). Temporal context affects the perceived time of visual events. Psychonomic Bulletin & Review, 27, 56–61. doi:10.3758/s13423-019-01682-x

Bouchacourt, F., Palminteri, S., Koechlin, E. & Ostojic, S. (2020). Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets. eLife, 9, e50469. doi:10.7554/eLife.50469

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. (2019). Temporal attention causes systematic biases in visual confidence. Scientific Reports, 9(1), 11622. doi:10.1038/s41598-019-48063-x

Deneux, T., Kempf, A., Daret, A., Ponsot, E. & Bathellier, B. (2016). Temporal asymmetries in auditory coding and perception reflect multi-layered nonlinearities. Nature communications, 7, 12682. doi:10.1038/ncomms12682

Balsdon, T. & Clifford, C. (2018). Task Dependent Effects of Head Orientation on Perceived Gaze Direction. Frontiers in Psychology, 9, 2491. doi:10.3389/fpsyg.2018.02491

Zakharov, D., Lapish, C., Gutkin, B. & Kuznetsov, A. (2016). Synergy of AMPA and NMDA Receptor Currents in Dopaminergic Neurons: A Modeling Study. Frontiers in computational neuroscience, 10, 48. doi:10.3389/fncom.2016.00048

Jeong, H. & Gutkin, B. (2007). Synchrony of neuronal oscillations controlled by GABAergic reversal potentials. Neural Computation, 19(3), 706-729. doi:10.1162/neco.2007.19.3.706

Wexler, M., Mamassian, P. & Schütz, A. (2022). Structure of visual biases revealed by individual differences. Vision Research, 195, 108014. doi:10.1016/j.visres.2022.108014

Lussange, J., Vrizzi, S., Bourgeois-Gironde, S., Palminteri, S. & Gutkin, B. (2022). Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model. Comput Econ. doi:10.1007/s10614-022-10249-3

Caze, R., Humphries, M. & Gutkin, B. (2012). Spiking and saturating dendrites differentially expand single neuron computation capacity. , Vol. 13: In Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA.

Lazarevich, I. , Prokin, I., Gutkin, B. & Kazantsev, V. (2023). Spikebench: An open benchmark for spike train time-series classification. PLoS computational biology, 19(1), e1010792. doi:10.1371/journal.pcbi.1010792

Muller, L., Brette, R. & Gutkin, B. (2011). Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike phase-locking. Frontiers in Computational Neuroscience, 15(5), 45. doi:10.3389/fncom.2011.00045

Gekas, N., Perrinet, L., Mamassian, P., Montagnini, A. & Masson, G. (2018). Speed uncertainty and motion perception with naturalistic random textures. Journal of Vision, 18(10), 345

Meso, A., Gekas, N., Mamassian, P. & Masson, G. (2022). Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions. eNeuro, 13(9), 3. doi:10.1523/ENEURO.0511-21.2022

Hyafil, A., Fontolan, L., Kabdebon, C., Gutkin, B. & Giraud, A. (2015). Speech encoding by coupled cortical theta and gamma oscillations. eLife, 4, 1-45. doi:10.7554/eLife.06213

Salvador, A., Worbe, Y., Delorme, C., Coricelli, G., Gaillard, R., Robbins, T., Hartmann, A. & Palminteri, S. (2017). Specific effect of a dopamine partial agonist on counterfactual learning: evidence from Gilles de la Tourette syndrome. Scientific reports, 7(1), 6292. doi:10.1038/s41598-017-06547-8

Safra, L., Palminteri, S. & Chevallier, C. (2019). Social information impairs reward learning in depressive subjects: behavioral and computational characterization. Plos Computational Biology, 15(7), e1007224. doi:10.1101/378281

Gurnani, H. & Cayco Gajic, A. (2023). Signatures of task learning in neural representations. Signatures of task learning in neural representations, Current Opinion in Neurobiology,, 83, 102759. doi:10.1016/j.conb.2023.102759