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).
Phd in Computational Neuroscience, 2019