Publications

Maldonado, M., Chemla, E. & Spector, B. (2019). Revealing abstract semantic mechanisms through priming: The distributive/collective contrast. Cognition, 182, 171-176. doi:10.1016/j.cognition.2018.09.009

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, 31(5). doi:10.1016/j.cub.2020.11.042

Caze, R., Humphries, M., Gutkin, B. & Schultz, S. (2013). A difficult classification for neurons without dendrites. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on, San Diego, CA, USA, IEEE, 215-218. doi:10.1109/NER.2013.6695910

Lunven, M., Hernandez Dominguez, K., Youssov, K., Hamet Bagnou, J., Fliss, R. , Vandendriessche, H., Bapst, B. , Morgado, G., Remy, P., Schubert, R. , Reilmann, R., Busse, M., Craufurd, D., Massart, R., Rosser, A. & Bachoud-Levi, A. (2023). A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington's disease. Brain communications, 5(2), fcad043. doi:10.1093/braincomms/fcad043

Oster, A. & Gutkin, B. (2011). A reduced model of DA neuronal dynamics that displays quiescence, tonic firing and bursting. Journal of Physiology Paris, 105, 53-58. doi:10.1016/j.jphysparis.2011.07.012

Keramati, M. & Gutkin, B. (2011). A Reinforcement Learning Theory for Homeostatic Regulation. NIPS, .

Mcdonnell, M., Iannella, N., To, M., Tuckwell, H., Jost, J., Gutkin, B. & Ward, L. (2015). A review of methods for identifying stochastic resonance in simulations of single neuron models. Network: Computation in Neural Systems, 26(2), 35-71. doi:10.3109/0954898X.2014.990064

Nair, A. , B. Johnson, E. , Gregory, S. , Osborne-Crowley, K. , Zeun, P. , Scahill, R. , Lowe, J. , Papoutsi, M., Palminteri, S., Rutledge, R. , Rees, G. & Tabrizi, S. (2021). Aberrant Striatal Value Representation in Huntington's Disease Gene Carriers 25 Years Before Onset. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 910-918. doi:10.1016/j.bpsc.2020.12.015

Jedlicka, P., Deller, T., Gutkin, B. & Backus, K. (2011). Activity-dependent intracellular chloride accumulation and diffusion controls GABA(A) receptor-mediated synaptic transmission. Hippocampus, 21(8), 885-98. doi:10.1002/hipo.20804

Ghambaryan, A., Gutkin, B., Klucharev, V. & Koechlin, E. (2021). Additively Combining Utilities and Beliefs: Research Gaps and Algorithmic Developments. Frontiers in Neuroscience, . doi:10.3389/fnins.2021.704728

Fontolan, L., Krupa, M., Hyafil, A. & Gutkin, B. (2013). Analytical insights on theta-gamma coupled neural oscillators. Journal of Mathematical Neuroscience, 3(1), 1-20. doi:10.1186/2190-8567-3-16

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

Chemla, E. & R. George, B. (2016). Can We Agree About Agree? Review of Philosophy and Psychology, 7 (1), 243–264

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

Chemla, E., Mintz, T., Bernal, S. & Christophe, A. (2009). Categorizing words using 'frequent frames': what cross-linguistic analyses reveal about distributional acquisition strategies. Developmental science, 12(3), 396-406. doi:10.1111/j.1467-7687.2009.00825.x

Cremers, A., Tieu, L. & Chemla, E. (2017). Children's exhaustive readings of embedded questions. Language Acquisition: A Journal of Developmental Linguistics, 24, 343-360. doi:10.1080/10489223.2016.1176173

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

Gutkin, B. & Stiefel, K. (2014). Cholinergic Neuromodulation of Phase Response Curves. In Schultheiss et al (eds) (Eds.), Phase Response Cruves in NeuroscienceSpringer

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, .

Versteegh, M., Kuhn, J., Synnaeve, G., Ravaux, L., Chemla, E., Cäsar, C., Fuller, J., Murphy, D., Schel, A. & Dunbar, E. (2016). Classification and automatic transcription of primate calls. The Journal of the Acoustical Society of America, 140(1), EL26. doi:10.1121/1.4954887

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

Tieu, L., Pasternak , R., Schlenker, P. & Chemla, E. (2018). Co-speech gesture projection: Evidence from inferential judgments. Glossa: a journal of general linguistics , 3(1), 109. doi:10.5334/gjgl.580

Tieu, L., Pasternak , R., Schlenker, P. & Chemla, E. (2017). Co-speech gesture projection: Evidence from truth-value judgment and picture selection tasks. Glossa, 2, 102. doi:10.5334/gjgl.334

Hansen , N. & Chemla, E. (2017). Color Adjectives, Standards, and Thresholds: An Experimental Investigation. Linguistics \& Philosophy, 40, 239-278

Buccola, B., Dautriche, I. & Chemla, E. (2018). Competition and symmetry in an artificial word learning task. Frontiers in Psychology, 9, 2176. doi:10.3389/fpsyg.2018.02176

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

Gutkin, B. & Ahmed, S. (2012). Computational Neuroscience of Drug Addiction.

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