no code implementations • 18 Oct 2023 • Jonathan Vacher, Pascal Mamassian
Here, we demonstrate the value of measuring the perceptual scale of classical (spatial frequency, orientation) and less classical physical variables (interpolation between textures) by embedding it in recent probabilistic modeling of perception.
no code implementations • 18 Jan 2023 • Jonathan Vacher, Claire Launay, Pascal Mamassian, Ruben Coen-Cagli
We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features.
1 code implementation • NeurIPS 2020 • Jonathan Vacher, Aida Davila, Adam Kohn, Ruben Coen-Cagli
We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.
no code implementations • 25 May 2019 • Jonathan Vacher, Claire Launay, Ruben Coen-Cagli
Our flexible approach can be easily generalized to adapt probabilistic mixture models to arbitrary data topologies.
no code implementations • 31 May 2018 • Jonathan Vacher, Pascal Mamassian, Ruben Coen-Cagli
Following this hypothesis, we propose a probabilistic generative model of visual segmentation that combines knowledge about 1) the sensitivity of neurons in the visual cortex to statistical regularities in natural images; and 2) the preference of humans to form contiguous partitions of visual space.
no code implementations • 2 Nov 2016 • Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré
We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus.
no code implementations • NeurIPS 2015 • Jonathan Vacher, Andrew Meso, Laurent U. Perrinet, Gabriel Peyré
We study here the principled construction of a generative model specifically crafted to probe motion perception.