Publications

Jacquet, P., Safra, L., Wyart, V., Baumard, N. & Chevallier, C. (2019). The ecological roots of human susceptibility to social influence: a pre-registered study investigating the impact of early-life adversity. Royal Society Open Science, 6(1), 180454. doi:10.1098/rsos.180454

Skvortsova , V., Palminteri, S., Buot, A., Karachi, C., Welter, M., Grabli, D. & Pessiglione, M. (2020). A Causal Role for the Pedunculopontine Nucleus in Human Instrumental Learning. Current Biology. doi:10.1016/j.cub.2020.11.042

Schaffer, E., Ostojic, S. & Abbott, L. (2013). A complex-valued firing-rate model that approximates the dynamics of spiking networks. PLoS computational biology, 9(10), e1003301. doi:10.1371/journal.pcbi.1003301

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

Leptourgos, P., Bouttier, V., Jardri, R. & Denève, S. (2021). A functional theory of bistable perception based on dynamical circular inference. Plos Computational Biology. doi:10.1371/journal.pcbi.1008480

Mastrogiuseppe, F. & Ostojic, S. (2019). A geometrical description of global dynamics in trained feedback networks. Neural Computation, 31(6), 1139-1182. doi:10.1162/neco_a_01187

Saez, A., Rigotti, M., Ostojic, S., Fusi, S. & Salzman, C. (2015). Abstract Context Representations in Primate Amygdala and Prefrontal Cortex. Neuron, 87(4), 869-81. doi:10.1016/j.neuron.2015.07.024

Le Bars, S., Devaux, A., Nevidal, T., Chambon, V. & Pacherie, E. (2020). Agents' pivotality and reward fairness modulate sense of agency in cooperative joint action. Cognition, 195, 104117. doi:10.1016/j.cognition.2019.104117

Lebreton, M., Bavard, S., Daunizeau, J. & Palminteri, S. (2019). Assessing inter-individual differences with task-related functional neuroimaging. Nature Human Behaviour. doi:10.1038/s41562-019-0681-8

Lefebvre, G., Lebreton, M., Meyniel, F., Bourgeois-Gironde, S. & Palminteri, S. (2017). Behavioural and neural characterization of optimistic reinforcement learning. Nature Human Behaviour , 1(4), 0067

Chambon, V., Thero, H., Findling, C. & Koechlin, E. (2018). Believing in one's power: a counterfactual heuristic for goal-directed control. bioRxiv, 498675. doi:10.1101/498675

Palminteri, S. & Chevallier, C. (2018). Can We Infer Inter-Individual Differences in Risk-Taking From Behavioral Tasks? Frontiers in psychology, 9, 2307. doi:10.3389/fpsyg.2018.02307

Chambon, V., Thero, H., Vidal, M., Vandendriessche, C., Haggard, P. & Palminteri, S. (2020). Choosing and learning: outcome valence differentially affects learning from free versus forced choices. Nature Human Behaviour, .

Jardri, R., Leptourgos, P., Bouttier, V. & Denève, S. (2020). Circular Inference and Hallucinations. Biological Psychiatry, 87(9), S41-S42. doi:10.1016/j.biopsych.2020.02.131

Leptourgos, P., Notredame, C., Jardri, R., Denève, S. & Eck, M. (2020). Circular inference in bistable perception. Journal of Vision, 20(12), 521195. doi:10.1167/jov.20.4.12

Bouttier, V., Duttagupta, S. , Denève, S. & Jardri, R. (2021). Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes. Schizophrenia Research, S0920-9964(21), 00031-1. doi:10.1016/j.schres.2020.12.045.

Ostojic, S., Panja, D. & Nienhuis, B. (2004). Clustering in a one-dimensional inelastic lattice gas. Physical Review E, 69(4 Pt 1), 041301. doi:10.1103/PhysRevE.69.041301

Bondanelli, G. & Ostojic, S. (2020). Coding with transient trajectories in recurrent neural networks. PLoS Computational Biology, 16 (2), e1007655. doi:10.1371/journal.pcbi.1007655

Findling, C. & Wyart, V. (2021). Computation noise in human learning and decision-making: origin, impact, function. Current Opinion in Behavioral Sciences, 38, 124-132. doi:10.1016/j.cobeha.2021.02.018

Findling, C. & Wyart, V. (2021). Computation noise in human learning and decision-making: origin, impact, function. , 38, 124-132. doi:10.1016/j.cobeha.2021.02.018

Findling, C., Skvortsova , V., Dromnelle, R. , Palminteri, S. & Wyart, V. (2019). Computational noise in reward-guided learning drives behavioral variability in volatile environments. Nature Neuroscience , 22, 2066–2077. doi:10.1101/439885

Balsdon, T., Wyart, V. & Mamassian, P. (2020). Confidence controls perceptual evidence accumulation. Nature Communications, 11(1), 1753. doi:10.1038/s41467-020-15561-w

Palminteri, S., Lefebvre, G., Kilford, E. & Blakemore, S. (2017). Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing. PLoS computational biology, 13(8), e1005684. doi:10.1371/journal.pcbi.1005684

Lebreton, M., Bacily , K., Palminteri, S. & Engelmann, J. (2019). Contextual influence on confidence judgments in human reinforcement learning. PLoS Comput Biol, 15(4), e1006973. doi:10.1371/journal.pcbi.1006973

Lefebvre, G., Nioche, A., Bourgeois-Gironde, S. & Palminteri, S. (2018). Contrasting temporal difference and opportunity cost reinforcement learning in an empirical money-emergence paradigm. PNAS, 115(49), E11446-E11454. doi:10.1073/pnas.1813197115

Beiran, M. & Ostojic, S. (2019). Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks . PLoS Computational Biology, 15(3), e1006893. doi:10.1371/journal.pcbi.1006893

Martí, D., Brunel, N. & Ostojic, S. (2018). Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks. Physical Review E, 97, 062314. doi:https://doi.org/10.1103/PhysRevE.97.062314

Sidarus, N., Palminteri, S. & Chambon, V. (2019). Cost-benefit trade-offs in decision-making and learning. PLOS Computational Biology, 15(9), e1007326. doi:10.1371/journal.pcbi.1007326

Oswald, A. & Ostojic, S. (2020). Curating more diverse scientific conferences. Nat Rev Neurosci. doi:10.1038/s41583-020-0373-4

Fontanesi , L., Palminteri, S. & Lebreton, M. (2019). Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: A meta-analytical approach using diffusion decision modeling. Cognitive Affective Behavioral Neuroscience, 1-13. doi:10.3758/s13415-019-00723-1