Publications

Ladenbauer, J., McKenzie , S., English , D., Hagens , O. & Ostojic, S. (2019). Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nature Communications, 10(4933). doi:10.1038/s41467-019-12572-0

Balsdon, T., Wyart, V. & Mamassian, P. (2019). Graded, multidimensional representations of sensory evidence allow for dissociable performance in second-choice and confidence judgments. Journal of Vision, 19(10), 289a-289a. doi:10.1167/19.10.289a

Novikov, N. & Gutkin, B. (2020). Role of synaptic nonlinearity in persistent firing rate shifts caused by external periodic forcing. PHYSICAL REVIEW E, 101(5), 052408. doi:10.1103/PhysRevE.101.052408

Chevallier, C. (2019). Theory of mind and autism: Revisiting Baron-Cohen et al.’s Sally-Anne study. In A. Slater and P. Quinn (Eds.), Developmental Psychology: Revisiting the Classic Studies 2nd edition (pp. 148-163).Sage

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

Safra, L., Chevallier, C. & Palminteri, S. (2019). Depressive symptoms are associated with blunted reward learning in social contexts. PLOS Computational Biology, 15(7), e1007224. doi:10.1371/journal.pcbi.1007224

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

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

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

Najar, A., Sigaud, O. & Chetouani , M. (2020). Interactively shaping robot behaviour with unlabeled human instructions. Auton Agent Multi-Agent Syst, 34(35). doi:10.1007/s10458-020-09459-6

Delorme, C., Salvador, A., Valabrègue, R., Roze, E., Palminteri, S., Vidailhet, M., De Wit, S., Robbins, T., Hartmann, A. & Worbe, Y. (2015). Enhanced habit formation in Gilles de la Tourette syndrome. Brain, 139(2), 605-615. doi:10.1093/brain/awv307

Sidarus, N., Palminteri, S. & Chambon, V. (2018). Trading off the cost of conflict against expected rewards. Plos Computational Biology, 42809. doi:10.1371/journal.pcbi.1007326

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

Ladenbauer, J. & Obermayer, K. (2019). Weak electric fields promote resonance in neuronal spiking activity: Analytical results from two-compartment cell and network models. PLOS Computational Biology, 15(4), e1006974. doi:10.1371/journal.pcbi.1006974

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

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

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

Azzalini, D., Rebollo, I. & Tallon-Baudry, C. (2019). Visceral Signals Shape Brain Dynamics and Cognition. Trends in Cognitive Sciences , 23(6), 488-509. doi:10.1016/j.tics.2019.03.007

Dumont, G. & Gutkin, B. (2019). Macroscopic phase resetting-curves determine oscillatory coherence and signal transfer in inter-coupled neural circuits. PLOS Computational Biology . doi:10.1371/journal.pcbi.1007019

Collins, T., Andler, D. & Tallon-Baudry, C. (2018). La Cognition. Du Neurone à la société.

Batty, M., Kovarski, K. & Meaux, E. (2018). De l’exploration à la perception des visages chez les personnes avec un trouble du spectre de l’autisme. Neuropsychologie et remédiations des troubles du spectre de l’autismeEditions deboeck Supérieur

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

Balsdon, T., Summersby, S., Kemp, R. & White, D. (2018). Improving face identification with specialist teams. Cognitive Research: Principles and Implications, 3(1), 25. doi:10.1186/s41235-018-0114-7

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

Balsdon, T., Schweitzer, R. , Watson, T. & Rolfs, M. (2018). All is not lost: Post-saccadic contributions to the perceptual omission of intra-saccadic streaks. Consciousness and Cognition , 64, 19-31. doi:10.1016/j.concog.2018.05.004

Balsdon, T. & Clifford, C. (2018). How wide is the cone of direct gaze? Royal Society open science 6, 5(8), 180249

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

Balsdon, T. & Clifford, C. (2018). Visual processing: conscious until proven otherwise. , 5(1). doi:10.1098/rsos.171783

Sidarus, N., Haggard, P. & Beyer, F. (2018). How social contexts affect cognition: mentalizing interferes with sense of agency during voluntary action. PsyArXiv. doi:10.31234/osf.io/wj3ep

Patron, E., Mennella, R., Messerotti Benvenuti, S. & Thayer, J. (2018). The frontal cortex is a heart-brake: Reduction in delta oscillations is associated with heart rate deceleration. NeuroImage, 188, 403-410. doi:10.1016/j.neuroimage.2018.12.035