Publications

Vandendriessche, H., Demmou, A. , Bavard, S., Yadak, J. , Lemogne, C. , Mauras, T. & Palminteri, S. (2022). Contextual influence of reinforcement learning performance of depression: evidence for a negativity bias? Psychological Medicine, ., 1-11. doi:10.1017/S0033291722001593

Palminteri, S. & Maël, L. (2021). Context-dependent outcome encoding in human reinforcement learning. Current Opinion in Behavioral Sciences, 41, 144-151. doi:10.1016/j.cobeha.2021.06.006

Cleeremans, A. & Tallon-Baudry, C. (2022). Consciousness matters: phenomenal experience has functional value. Neuroscience of Consciousness., 2022(1), niac007. doi:10.1093/nc/niac007

Florence, C., Rebollo, I., Anne, U., Wyart, V. & Tallon-Baudry, C. (2016). Conscious Vision Proceeds from Global to Local Content in Goal-Directed Tasks and Spontaneous Vision. The Journal of Neuroscience, 36(19), 5200-5213. doi:10.1523/JNEUROSCI.3619-15.2016

Chierchia, G. , Soukupová, M., Kilford, E., Griffin, C. , Leung, J. , Palminteri, S. & Blakemore, S. (2022). Confirmatory reinforcement learning changes with age during adolescence. Developmental Science, .(e13330). doi:10.1111/desc.13330

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

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

Zamani, A. , Novikov, N. & Gutkin, B. (2020). Concomitance of inverse stochastic resonance and stochastic resonance in a minimal bistable spiking neural circuit. Communications in Nonlinear Science and Numerical Simulation, 82, 105024. doi:10.1016/j.cnsns.2019.105024

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

Koechlin, E. (2021). Computational models of adaptive behavior and prefrontal cortex. Neuropsychopharmacology, 47, 58–71. doi:10.1038/s41386-021-01123-1

Tegnér, J., Compte, A., Auffray, C., An, G., Cedersund, G., Clermont, G., Gutkin, B., Oltvai, Z., Stephan, K., Thomas, R. & Villoslada, P. (2009). Computational disease modeling - Fact or fiction? BMC Systems Biology, 3. doi:10.1186/1752-0509-3-56

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

Bobashev, G., Costenbader, E. & Gutkin, B. (2007). Comprehensive mathematical modeling in drug addiction sciences. Drug and Alcohol Dependence, 89(1), 102-106. doi:10.1016/j.drugalcdep.2006.12.029

Grèzes, J. & Dezecache, G. (2012). Communication émotionnelle: mécanismes cognitifs et cérébraux. In P. Allain, G. Aubin & D. Le Gal (Eds.), Cognition Sociale et NeuropsychologieSolal

Baumard, N., Safra, L., Martins, M. & Chevallier, C. (2023). Cognitive fossils: using cultural artifacts to reconstruct psychological changes throughout history. Trends in Cognitive Sciences, . doi:10.1016/j.tics.2023.10.001

Keramati, M., Durand, A., Girardeau, P., Gutkin, B. & Ahmed, S. (2017). Cocaine addiction as a homeostatic reinforcement learning disorder. Psychological review, 124(2), 130-153. doi:10.1037/rev0000046

Tolu, S., Eddine, R., Marti, F., David, V., Graupner, M., Pons, S., Baudonnat, M., Husson, M., Besson, M., Reperant, C., Zemdegs, J., Pagès, C., Hay, Y., Lambolez, B., Caboche, J., Gutkin, B., Gardier, A., Changeux, J., Faure, P. & Maskos, U. (2013). Co-activation of VTA da and GABA neurons mediates nicotine reinforcement. Molecular Psychiatry, 18(3), 382-393. doi:10.1038/mp.2012.83

Mell, H., Safra, L., Baumard, N. & Jacquet, P. (2017). Climate is not a good candidate to account for variations in aggression and violence across space and time. The Behavioral and brain sciences, 40, e91. doi:10.1017/S0140525X16001060

Chanel, G., Pichon, S., Conty, L., Berthoz, S., Chevallier, C. & Grèzes, J. (2015). Classification of autistic individuals and controls using cross-task characterization of fMRI activity. NeuroImage. Clinical, 10, 78-88. doi:10.1016/j.nicl.2015.11.010

Chanel, G., Pichon, S., Conty, L., Berthoz, S., Chevallier, C. & Grèzes, J. (2016). Classification of autistic individuals and controls using cross-task characterization of fMRI activity. Neuroimage-Clinical, 10, 78-88. doi:10.1016/j.nicl.2015.11.010

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, .

Stiefel, K., Gutkin, B. & Sejnowski, T. (2008). Cholinergic neuromodulation changes phase response curve shape and type in cortical pyramidal neurons. PLoS ONE, 3(12). doi:10.1371/journal.pone.0003947

Rooy, M., Lazarevich, I. , Koukouli, F., Maskos, U. & Gutkin, B. (2021). Cholinergic modulation of hierarchical inhibitory control over cortical resting state dynamics: Local circuit modeling of schizophrenia-related hypofrontality. Current Research in Neurobiology, 2, 1000018. doi:10.1016/j.crneur.2021.100018

Palminteri, S. (2023). Choice-confirmation bias and gradual perseveration in human reinforcement learning. Behavioral neuroscience, 137(1), 78-88. doi:10.1037/bne0000541

Safra, L., Algan, Y., Tecu, T., Grèzes, J., Baumard, N. & Chevallier, C. (2017). Childhood harshness predicts long-lasting leader preferences. Evolution and Human Behavior, 38(5), 645-651. doi:10.1016/j.evolhumbehav.2017.05.001

Lettinga, N., Mell, H., Algan, Y., Jacquet, P. & Chevallier, C. (2021). Childhood environmental adversity is not linked to lower levels of cooperative behaviour in economic games. Evolutionary Human Sciences, 3, E29. doi:10.1017/ehs.2021.21

Jacquet, P., Roy, A., Chambon, V., Borghi, A., Salemme, R., Farnè, A. & Reilly, K. (2016). Changing ideas about others' intentions: updating prior expectations tunes activity in the human motor system. Scientific reports, 6, 26995. doi:10.1038/srep26995

Lanore, F. , Cayco Gajic, A., Gurnani, H. , Coyle, D. & Angus Silver, R. (2021). Cerebellar granule cell axons support high-dimensional representations. Nature Neuroscience, 24, 1142-1150. doi:10.1038/s41593-021-00873-x

Chanes, L., Quentin, R., Tallon-Baudry, C. & Valero-Cabré, A. (2013). Causal frequency-specific contributions of frontal spatiotemporal patterns induced by non-invasive neurostimulation to human visual performance. Journal of Neuroscience, 33(11), 5000-5005. doi:10.1523/JNEUROSCI.4401-12.2013

Tallon-Baudry, C. (2013). Catherine Tallon-Baudry. Current Biology, 23(14), R588-R590. doi:10.1016/j.cub.2013.06.010