Predicting and selecting sensory events: inference for smooth eye movements (PhD: 2015 - 2018)
- PhD supervisors
Guillaume Masson, Laurent Perrinet and Anna Montagnini
In everyday life, we constantly need to track relevant moving targets in complex environments with our eyes such as, for instance, when we try to catch someone running in the crowd. However, this seemingly simple task demands to deal with several dynamic sources of uncertainty, related to intrinsic, target-related properties or to external, environment-related factors. In addition, one single object has to be selected at a time for accurate visual processing and ocular tracking in presence of a multitude of competing signals.
The PhD project aims at understanding the dynamic inference and decision processes underlying smooth eye movements. The PhD fellow will conduct psychophysics and oculomotor recordings on healthy subjects, as well as modeling work, in order to elucidate the effects of sensory uncertainty on the accuracy and the dynamics of visuomotor decisions. Bayesian Inference will provide a general and solid framework for behavioral models. Oculomotor decision times, such as those characterizing the dynamic switch between smooth pursuit and saccades during motion tracking, or transitions between two alternative tracking solutions, will be modeled and benchmarked against the predictions of current models of choice reaction times ("accumulation-to-threshold" models).
InVIBE Team (Inference in Vision and Behavior) - Institut de Neuroscience de la Timone, CNRS & Aix-Marseille Université, Marseille FRANCE – http://www.int.univ-amu.fr/
- Position details
This position is funded by the Marie Skodowska-Curie program of the H2020 European Union program, as part of the Innovative Training Network PACE (Perception and Action in Complex Environments).
List of publications
- Kiana Mansour Pour, Nikos Gekas, Pascal Mamassian, Laurent U. Perrinet, Anna Montagnini, Guillaume S. Masson. Speed uncertainty and motion perception with naturalistic random textures, URL . In VSS, 2018, 2018 abstractIt is still not fully understood how visual system integrates motion energy across different spatial and temporal frequencies to build a coherent percept of the global motion under the complex, noisy naturalistic conditions. We addressed this question by manipulating local speed variability distribution (i. e. speed bandwidth) using a well-controlled class of broadband random-texture stimuli called Motion Clouds (MCs) with continuous naturalistic spatiotemporal frequency spectra (Sanz-Leon et al., 2012, ; Simoncini et al., 2012). In a first 2AFC experiment on speed discrimination, participants had to compare the speed of a broad speed bandwidth MC (range: 0.05-8°/s) moving at 1 of 5 possible mean speeds (ranging from 5 to 13 °/s) to that of another MC with a small speed bandwidth (SD: 0.05 °/s), always moving at a mean speed of 10°/s . We found that MCs with larger speed bandwidth (between 0.05-0.5°/s) were perceived moving faster. Within this range, speed uncertainty results in over-estimating stimulus velocity. However, beyond a critical bandwidth (SD: 0.5 °/s), perception of a coherent speed was lost. In a second 2AFC experiment on direction discrimination, participants had to estimate the motion direction of moving MCs with different speed bandwidths. We found that for large band MCs participant could no longer discriminate motion direction. These results suggest that when increasing speed bandwidth from small to large range, the observer experiences different perceptual regimes. We then decided to run a Maximum Likelihood Difference Scaling (Knoblauch & Maloney, 2008) experiment with our speed bandwidth stimuli to investigate these different possible perceptual regimes. We identified three regimes within this space that correspond to motion coherency, motion transparency and motion incoherency. These results allow to further characterize the shape of the interactions kernel observed between different speed tuned channels and different spatiotemporal scales (Gekas et al ., 2017) that underlies global velocity estimation..
- Kiana Mansour Pour, Laurent U. Perrinet, Guillaume S. Masson, Anna Montagnini. Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit , URL . In GDR Vision, Lille, 2017, 2017 abstract
- Kiana Mansour Pour, Laurent U. Perrinet, Guillaume S. Masson, Anna Montagnini. How the dynamics of human smooth pursuit is influenced by speed uncertainty, URL . In Proceedings of ECVP, 2017 abstract
- Kiana Mansour Pour, Laurent U. Perrinet, Guillaume S. Masson, Anna Montagnini. Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit , URL . In Proceedings of SfN 2016, pages 2P045. 2016 abstract
- Kiana Mansour Pour, Laurent U. Perrinet, Guillaume S. Masson, Anna Montagnini. Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit , URL . In GDR Vision, Toulouse, Nov 3rd, 2016, 2016 abstract
- Kiana Mansour Pour, Laurent U. Perrinet, Guillaume S. Masson, Anna Montagnini. Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit , URL . In Proceedings of ECVP, pages 2P045. 2016 abstract