Publications

Paraouty, N., Stasiak, A., Lorenzi, C., Varnet, L. & Winter, I. (2018). Dual coding of frequency modulation in the ventral cochlear nucleus. Journal of Neuroscience , 17. doi:10.1523/JNEUROSCI.2107-17.2018

Paraouty, N., Ewert, S., Wallaert, N. & Lorenzi, C. (2016). Interactions between amplitude modulation and frequency modulation processing: Effects of age and hearing loss. The Journal of the Acoustical Society of America, 140(1), 121. doi:10.1121/1.4955078

Paraouty, N. & Lorenzi, C. (2017). Using individual differences to assess modulation-processing mechanisms and age effects. Hearing research, 344, 38-49. doi:10.1016/j.heares.2016.10.024

Otto, T. & Mamassian, P. (2017). Multisensory decisions: The test of a race model, its logic, and power. Multisensory Research, 30(1), 1-24. doi:10.1163/22134808-00002541

Otto, T. & Mamassian, P. (2012). Noise and correlations in parallel perceptual decision making. Current Biology, 22(15), 1391-1396. doi:10.1016/j.cub.2012.05.031

Otto, T., Dassy, B. & Mamassian, P. (2013). Principles of multisensory behavior. The Journal of Neuroscience, 33(17), 7463-7474. doi:10.1523/JNEUROSCI.4678-12.2013

Ota, K. , Wu, Q. & Mamassian, P. (2020). Visual cue estimation with non-gaussian distribution. Journal of vision, 20(11). doi:10.1167/jov.20.11.1436

Osses , A. & Kohlrausch, A. (2021). Perceptual similarity between piano notes: Simulations with a template-based perception model. The Journal of the Acoustical Society of America, 149, 3534. doi:10.1121/10.0004818

Osses , A. & Varnet, L. (2021). Consonant-in-noise discrimination using an auditory model with different speech- based decision devices. In DAGA, Vienna Austria.

Occelli, F., Suied, C., Pressnitzer, D., Edeline, J. & Gourévitch, B. (2016). A Neural Substrate for Rapid Timbre Recognition? Neural and Behavioral Discrimination of Very Brief Acoustic Vowels. Cerebral Cortex, 26(6), 2483-2496. doi:10.1093/cercor/bhv071

O’sullivan, A., Crosse, M., Di Liberto, G., de Cheveigné, A. & Lalor, E. (2021). Neurophysiological indices of audiovisual speech integration are enhanced at the phonetic level for speech in noise. Journal of Neuroscience, 41, 4991-5003. doi:10.1523/JNEUROSCI.0906-20.2021

O'Sullivan, J., Power, A., Mesgarani, N., Rajaram, S., Foxe, J., Shinn-Cunningham, B., Slaney, M., Shamma, S. & Lalor, E. (2015). Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cerebral cortex (New York, N.Y. : 1991), 25(7), 1697-706. doi:10.1093/cercor/bht355

O. Deroy, ., U. Noppeney, ., N. Faivre, ., Lunghi, C., C. Spence, . & M. Aller, . (2016). The Complex Interplay Between Multisensory Integration and Perceptual Awareness. Multisensory Research, 29(6-7), 585-606. doi:10.1163/22134808-00002529

Novikov, N., Zakharov, D., Moiseeva, V. & Gutkin, B. (2021). Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs. Front. Neural Circuits. doi:10.3389/fncir.2021.647944

Norman-Haignere, S., Mcdermott, J., Kanwisher, N. & Conway, B. (2019). Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones. Nature Neuroscience , 22, 1057–1060. doi:10.1038/s41593-019-0410-7

Neuert, V., Pressnitzer, D., Patterson, R. & Winter, I. (2001). The responses of single units in the inferior colliculus of the guinea pig to damped and ramped sinusoids. Hearing Research, 159, 36-52. doi:10.1016/S0378-5955(01)00318-5

Neri, P. (2021). Optimal templates for signal extraction by noisy ideal detectors and human observers. Journal of Computational Neuroscience, 49(1-20). doi:10.1007/s10827-020-00768-z

Neri, P. (2020). Complex visual analysis of ecologically relevant signals in Siamese fighting fish. Anim Cogn ., 23(1), 41-53. doi:10.1007/s10071-019-01313-x

Neri, P. (2018). The empirical characteristics of human pattern vision defy theoretically-driven expectations . PLoS Computational Biology , 14(12), e1006585. doi:10.1371/journal.pcbi.1006585

Neri, P. (2018). Classification images as descriptive statistics. "Journal of Mathematical Psychology", "82"("1), "26–37"

Neri, P. (2017). Object segmentation controls image reconstruction from natural scenes. PLoS biology, 15(8), e1002611. doi:10.1371/journal.pbio.1002611

Neri, P. (2004). Estimation of nonlinear psychophysical kernels. Journal of vision, 4(2), 82-91. doi:10.1167/4.2.2

Neri, P. & Heeger, D. (2002). Spatiotemporal mechanisms for detecting and identifying image features in human vision. Nature Neuroscience, 5(8), 812-816. doi:10.1038/nn886

Nelken, I. & de Cheveigné, A. (2013). An ear for statistics. Nature neuroscience, 16(4), 381-2. doi:10.1038/nn.3360

Narain, D., Smeets, J., Mamassian, P., Brenner, E. & Van Beers, R. (2014). Structure learning and the Occam's razor principle: a new view of human function acquisition. Frontiers in Computational Neuroscience, 8(121), 1-13. doi:10.3389/fncom.2014.00121

Narain, D., Mamassian, P., Van Beers, R., Smeets, J. & Brenner, E. (2013). How the statistics of sequential presentation influence the learning of structure. PLoS One, 8(4), e62276. doi:10.1371/journal.pone.0062276

Myers, C., Sheynin, J., Balsdon, T., Luzardo, A., Beck, K., Hogarth, L., Haber, P. & Moustafa, A. (2016). Probabilistic reward- and punishment-based learning in opioid addiction: Experimental and computational data. Behavioural brain research, 296, 240-248. doi:10.1016/j.bbr.2015.09.018

Moustafa, A., Kéri, S., Somlai, Z., Balsdon, T., Frydecka, D., Misiak, B. & White, C. (2015). Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data. Behavioural brain research, 291, 147-154. doi:10.1016/j.bbr.2015.05.024

Moon, I., Won, J., Park, M., Ives, D., Nie, K., Heinz, M., Lorenzi, C. & Rubinstein, J. (2014). Optimal combination of neural temporal envelope and fine structure cues to explain speech identification in background noise. The Journal of neuroscience : the official journal of the Society for Neuroscience, 34(36), 12145-54. doi:10.1523/JNEUROSCI.1025-14.2014

Montagnini, A., Mamassian, P., Perrinet, L., Castet, E. & Masson, G. (2007). Bayesian modeling of dynamic motion integration. Journal of Physiology, Paris, 101(1-3), 64-77. doi:10.1016/j.jphysparis.2007.10.013