Publications

Giavazzi, M., Daland, R., Palminteri, S., Peperkamp, S., Brugières, P., Jacquemot, C., Schramm, C., Cleret de Langavant, L. & Bachoud-Levi, A. (2018 ). The role of the striatum in linguistic selection: Evidence from Huntington’s disease and computational modeling. Cortex, 109, 189-204. doi:10.1016/j.cortex.2018.08.031

Pischedda, D. , Palminteri, S. & Giorgio, C. (2020). The role of counterfactual information on outcome value coding in mPFC and cingulate cortex: from an absolute to an adaptive neural code. . The Journal of Neuroscience, 40 (16), 3268-3277. doi:10.1523/JNEUROSCI.1712-19.2020

Mamassian, P., Knill, D. & Kersten, D. (1998). The perception of cast shadows. Trends in Cognitive Sciences, 2(8), 288-295. doi:10.1016/S1364-6613(98)01204-2

Park, H. & Tallon-Baudry, C. (2014). The neural subjective frame: from bodily signals to perceptual consciousness. Philosophical Transactions Royal Society B, 369, 20130208. doi:10.1098/rstb.2013.0208

Tallon-Baudry, C., Florence, C., Hyeong-Dong, P. & Babo-Rebelo, M. (2018). The neural monitoring of visceral inputs, rather than attention, accounts for first-person perspective in conscious vision. Cortex, . doi:10.1016/j.cortex.2017.05.019

Fernandez-Velasco, P. & Casati, R. (2020). The many faces of disorientation: a response to Daniel R. Montello. Spatial Cognition & Computation , 20(4), 314-327. doi:10.1080/13875868.2020.1772267

E. Santos, P. , Cabalar, P. & Casati, R. (2019). The knowledge of knots: an interdisciplinary literature review. Spatial Cognition & Computation , 19(4), 334-358. doi:10.1080/13875868.2019.1667998

Mello, M. , Dupont, L. , Engelen, T., Acciarino, A. , W. de Borst, A. & de Gelder, B. (2022). The influence of body expression, group affiliation and threat proximity on interactions in virtual reality. Current Research in Behavioral Sciences, 3, 100075. doi:10.1016/j.crbeha.2022.100075

Palminteri, S., Wyart, V. & Koechlin, E. (2017). The Importance of Falsification in Computational Cognitive Modeling. Trends in Cognitive Sciences, 21(6), 425-433. doi:10.1016/j.tics.2017.03.011

Bavard, S. & Palminteri, S. (2023). The functional form of value normalization in human reinforcement learning. eLife, 12, e83891. doi:10.7554/eLife.83891

Neri, P. (2018). The empirical characteristics of human pattern vision defy theoretically-driven expectations . PLoS Computational Biology , 14(12), e1006585. doi:10.1371/journal.pcbi.1006585

Pischedda, D. , Palminteri, S. & Giorgio, C. (2020). The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: from an absolute to a relative neural code. Journal of Neuroscience, 40(16), 3268-3277. doi:10.1523/JNEUROSCI.1712-19.2020

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

Arcangeli, M. (2021). The conceptual nature of imaginative content. Synthese, 199(1-2), 3189-3205. doi:10.1007/s11229-020-02930-7

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

Fernandez-Velasco, P., Perroy, B. & Casati, R. (2021). The collective disorientation of the COVID-19 crisis. Global Discourse: An interdisciplinary journal of current affairs, 11(3), 44&-462. doi:10.1332/204378921X16146158263164

A Cooper, E., Casati, R., Farid, H. & Cavanagh, P. (2023). The art of the float. Journal of vision, 23(8), 13. doi:10.1167/jov.23.8.13

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

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%)

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

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