Publications

Derome, M., Kozuharova, P., O Diaconescu, A., Denève, S., Jardri, R. & Allen, P. (2023). Functional connectivity and glutamate levels of the medial prefrontal cortex in schizotypy are related to sensory amplification in a probabilistic reasoning task. NeuroImage, 278, 120280. doi:10.1016/j.neuroimage.2023.120280

Angeletos Chrysaitis, N. , Jardri, R., Denève, S. & Seriès, P. (2021). No increased circular inference in adults with high levels of autistic traits or autism. Plos Computational Biology, 17(9), e1009006. doi:10.1371/journal.pcbi.1009006

Leptourgos, P., Bouttier, V., Denève, S. & Jardri, R. (2022). From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis. Neuroscience & Biobehavioral Reviews, 135, 104593. doi:10.1016/j.neubiorev.2022.104593

Bouttier, V., Duttagupta, S. , Denève, S. & Jardri, R. (2022). Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes. Schizophrenia Research, 245, 59-67. doi:10.1016/j.schres.2020.12.045

Yesilaltay , S., Hacquin, A., Chevallier, C. & Mercier, H. (2021). Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions. Journal of Experimental Psychology: Applied. doi:10.1037/xap0000400

Alemi, A., Machens, C., Denève, S. & Slotine, J. (2018). Learning nonlinear dynamics in efficient, balanced spiking networks using local plasticity rules. In Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, AAAI Press.

Brendel, W. , Bourdoukan, R., Vertechi, P. , Machens, C. & Denève, S. (2020). Learning to represent signals spike by spike. PLOS Computational Biology, 16(3), e1007692. doi:10.1371/journal.pcbi.1007692

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

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

Caze, R., Humphries, M. & Gutkin, B. (2013). Modulation of computational capacity of neurons due to dendritic synaptic interactions. PLoS Comput. Biol., 9(2)

Caze, R., Humphries, M. & Gutkin, B. (2012). Spiking and saturating dendrites differentially expand single neuron computation capacity. , Vol. 13: In Twenty First Annual Computational Neuroscience Meeting: CNS*2012, Decatur, GA, USA.

Caze, R., Humphries, M. & Gutkin, B. (2013). Dendrites enhance both single neuron and network computation. In Remme et al (eds) (Eds.), Dendritic ComputationSpringer

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, .

Charnavel , I. & Chemla, E. (2017). More Hybrid Agreement: simultaneous agreement with two competing triggers. Snippets, 31, 7-8. doi:10.7358/snip-2017-031-char

Chemla, E. & Egré, P. (2019). Suszko's problem: mixed consequence and compositionality. Review of Symbolic Logic, 12(4), 736-767. doi:10.1017/S1755020318000503

Chemla, E., Buccola, B. & Dautriche, I. (2019). Connecting content and logical words. Journal of Semantics, ffz001. doi:10.1093/jos/ffz001

Chemla, E. & Spector, B. (2010 ). Experimental Detection of Embedded Implicatures. In Aloni, Maria and Bastiaanse, Harald and de Jager, Tikitu and Schulz, Katrin (Eds.), In Logic, Language and Meaning: 17th Amsterdam Colloquium, Amsterdam, The Netherlands, Springer Berlin Heidelberg, 53–62. doi:10.1007/978-3-642-14287-1_6

Chemla, E. & Bott, L. (2014). Processing inferences at the semantics/pragmatics frontier: disjunctions and free choice. Cognition, 130(3), 380-96. doi:10.1016/j.cognition.2013.11.013

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

Chemla, E. & Schlenker, P. (2012). Incremental Vs. Symmetric Accounts of Presupposition Projection: An Experimental Approach. Natural Language Semantics, 20(2), 177–226 . doi:10.1007/s11050-012-9080-7

Chemla, E., Egré, P. & Schlenker, P. (2015). Predicting Moral Judgments From Causal Judgments . Philosophical Psychology, 28(1), 21–48. doi:10.1080/09515089.2013.863145

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

Chemla, E. & Spector, B. (2011). Experimental Evidence for Embedded Scalar Implicatures. Journal of Semantics, 28(3), 359-400. doi:10.1093/jos/ffq023

Chemla, E. & Singh, R. (2017). Training and timing local scalar enrichments under global pragmatic pressures. Journal of Semantics, 34(1), 107-126. doi:10.1093/jos/ffw006

Chevallier, C., Hacquin, A. & Mercier, H. (2021). COVID-19 Vaccine Hesitancy: Shortening the Last Mile. Trends in Cognitive Sciences, 25(5), 331–333. doi:10.1016/j.tics.2021.02.002

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

Cremers, A. & Chemla, E. (2017). Experiments on the acceptability and possible readings of questions embedded under emotive-factives. Natural Language Semantics, 25, 223-261. doi:10.1007/s11050-017-9135-x

Cremers, A. & Chemla, E. (2017). Probability Judgments of Gappy Sentences. In Pistoia-Reda, Salvatore and Filippo Domaneschi (Eds.), Linguistic and Psycholinguistic Approaches on Implicatures and Presuppositions (pp. 111-150).Palgrave

Cremers, A. & Chemla, E. (2017). Experiments on the Acceptability and Possible Readings of Questions Embedded Under Emotive-Factives . Natural Language Semantics, 25(3), 223–261. doi:10.1007/s11050-017-9135-x

Dautriche, I., Chemla, E. & Christophe, A. (2016). Word Learning: Homophony and the Distribution of Learning Exemplars. Language Learning and Development, 12(3), 231-251. doi:10.1080/15475441.2015.1127163