Publications

de Vignemont, F. (2007). How Many Representations of the Body? Behavioral and Brain Sciences , 30(2), 204–205

Delorme, C., Salvador, A., Valabrègue, R., Roze, E., Palminteri, S., Vidailhet, M., De Wit, S., Robbins, T., Hartmann, A. & Worbe, Y. (2016). Enhanced habit formation in Gilles de la Tourette syndrome. Brain : a journal of neurology, 139(Pt 2), 605-15. doi:10.1093/brain/awv307

Lebreton, M. & Palminteri, S. (2016). When are inter-individual brain-behavior correlations informative? bioRxiv. doi:10.1101/036772

Palminteri, S. & Pessiglione, M. (2016). Opponent brain systems for reward and punishment learning: Causal evidence from drug and lesion studies in humans. Decision Neuroscience: An Integrative Perspective, 291-303. doi:10.1016/B978-0-12-805308-9.00023-3

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

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

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

Hertz, U., Palminteri, S., Brunetti, S., Olesen, C., Frith, C. & Bahrami, B. (2017). Neural computations underpinning the strategic management of influence in advice giving. Nature communications, 8(1), 2191. doi:10.1038/s41467-017-02314-5

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

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

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

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

de Vignemont, F. & Sackur, J. (2018). La conscience. In Collins, Thérèse, Tallon-Baudry, Catherine, Andler, Daniel (Eds.), La cognitionGallimard

de Vignemont, F. (2018). Schéma corporel et image corporelle. In De Boeck/Solal (Eds.), Manuel de psychomotricité

de Vignemont, F. (2018). Peripersonal perception in action. Synthese, 1-18. doi:10.1007/s11229-018-01962-4

Pitron, V., Alsmith, A. & de Vignemont, F. (2018). How do the body schema and the body image interact? . Consciousness and Cognition, 65, 352-358. doi:10.1016/j.concog.2018.08.007

de Vignemont, F. (2018). Was Descartes right after all? An affective background for bodily awareness. . In Tsakiris, Manos de Preester, Helena (Eds.), The Interoceptive Basis of the Mind: from homeostasis to awareness Oxford: Oxford University Press

de Vignemont, F. (2018). Varieties of bodily feelings. In Kriegel, Uriah (Eds.), The Oxford handbook of consciousness Oxford: Oxford University Press

de Vignemont, F. (2018). The first-person in pain. In Bain, D., Brady, M., Corns, J. (Eds.), Philosophy of Pain: Unpleasantness, Emotion, and DevianceRoutledge

Correa, C. , Noorman, S. , Jiang, J. , Palminteri, S., Cohen, M. , Lebreton, M. & van Gaal, S. (2018). How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning. Journal of Neuroscience , 0457-18. doi:10.1523/JNEUROSCI.0457-18.2018

Bavard, S., Lebreton, M., Khamassi, M., Coricelli, G. & Palminteri, S. (2018). Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nature Communications, 9(4503)

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

Alsmith, A. & Longo, M. (2019). Using VR technologies to investigate the flexibility of human self-conception. In E. Fischer and M. Curtis (Eds.), Methodological Advances in Experimental Philosophy (pp. 153-174). London: Bloomsbury

Alsmith, A. (2019). Bodily structure and body representation. Synthese. doi:10.1007/s11229-019-02200-1

van der Veer, A. , Longo, M. , Alsmith, A., Wong, H. & Mohler, B. (2019). Self and body part localization in virtual reality: Comparing a headset and a large-screen immersive display. Frontiers in Robotics and AI, 6, 33. doi:10.3389/frobt.2019.00033

van der Veer, A. , Alsmith, A., Longo, M. , Wong, H. , Diers, D. , Bues, M. , Giron, A. & Mohler, B. (2019). The influence of the viewpoint in a self-avatar on body part and self-localization. In ACM Symposium on Applied Perception 2019, 1-11. doi:10.1145/3343036.3343124

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

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