Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

Xivry, Jean-Jacques Orban de; Coppe, Sébastien; Blohm, Gunnar; Lefèvre, Philippe

doi:10.1523/jneurosci.2321-13.2013

Cited by 109 publications

(96 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For optimal control and other planning models, with few exceptions [7,8], the focus has been on the cost of the movement per se, a highly motor-centric view of action. This stands in stark contrast to the field of active sensing [for a review see 9] in which actions are used to gather sensory information relevant for a given task.…”

Section: Introductionmentioning

confidence: 99%

When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing

2016

View full text Add to dashboard Cite

Movement planning is thought to be primarily determined by motor costs such as inaccuracy and effort. Solving for the optimal plan that minimizes these costs typically leads to specifying a time-varying feedback controller which both generates the movement and can optimally correct for errors that arise within a movement. However, the quality of the sensory feedback during a movement can depend substantially on the generated movement. We show that by incorporating such state-dependent sensory feedback, the optimal solution incorporates active sensing and is no longer a pure feedback process but includes a significant feedforward component. To examine whether people take into account such state-dependency in sensory feedback we asked people to make movements in which we controlled the reliability of sensory feedback. We made the visibility of the hand state-dependent, such that the visibility was proportional to the component of hand velocity in a particular direction. Subjects gradually adapted to such a sensory perturbation by making curved hand movements. In particular, they appeared to control the late visibility of the movement matching predictions of the optimal controller with state-dependent sensory noise. Our results show that trajectory planning is not only sensitive to motor costs but takes sensory costs into account and argues for optimal control of movement in which feedforward commands can play a significant role.

show abstract

Section: Introductionmentioning

confidence: 99%

When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing

2016

View full text Add to dashboard Cite

show abstract

“…Therefore, it is likely that the same mechanism, based on an internal representation of target velocity, is used to correct catch-up saccade amplitude both in head-restrained and head-unrestrained conditions. This internal representation of target trajectory could rely on an internal model based on a Kalman filter as recently proposed for modeling predictive smooth pursuit eye movements (Orban de Xivry, Coppe, Blohm, and Lefèvre, 2013).…”

Section: Saccade Amplitudementioning

confidence: 99%

Catch-up saccades in head-unrestrained conditions reveal that saccade amplitude is corrected using an internal model of target movement

2014

Self Cite

View full text Add to dashboard Cite

This study analyzes how human participants combine saccadic and pursuit gaze movements when they track an oscillating target moving along a randomly oriented straight line with the head free to move. We found that to track the moving target appropriately, participants triggered more saccades with increasing target oscillation frequency to compensate for imperfect tracking gains. Our sinusoidal paradigm allowed us to show that saccade amplitude was better correlated with internal estimates of position and velocity error at saccade onset than with those parameters 100 ms before saccade onset as head-restrained studies have shown. An analysis of saccadic onset time revealed that most of the saccades were triggered when the target was accelerating. Finally, we found that most saccades were triggered when small position errors were combined with large velocity errors at saccade onset. This could explain why saccade amplitude was better correlated with velocity error than with position error. Therefore, our results indicate that the triggering mechanism of head-unrestrained catch-up saccades combines position and velocity error at saccade onset to program and correct saccade amplitude rather than using sensory information 100 ms before saccade onset.

show abstract

“…A model relies on two Kalman filters: (1): one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion was developed (Orban de Xivry et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…The model had the capability to learn the experienced values of the target velocity for ramp and sinusoidal signals, but only in the case of target dynamics already experienced by the system can add the learning component. A model relies on two Kalman filters: (1): one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion was developed (Orban de Xivry et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Information fusion control with time delay for smooth pursuit eye movement

Zhang

Qin

et al. 2016

Physiol Rep

View full text Add to dashboard Cite

Smooth pursuit eye movement depends on prediction and learning, and is subject to time delays in the visual pathways. In this paper, an information fusion control method with time delay is presented, implementing smooth pursuit eye movement with prediction and learning as well as solving the problem of time delays in the visual pathways. By fusing the soft constraint information of the target trajectory of eyes and the ideal control strategy, and the hard constraint information of the eye system state equation and the output equation, optimal estimations of the co‐state sequence and the control variable are obtained. The proposed control method can track not only constant velocity, sinusoidal target motion, but also arbitrary moving targets. Moreover, the absolute value of the retinal slip reaches steady state after 0.1 sec. Information fusion control method elegantly describes in a function manner how the brain may deal with arbitrary target velocities, how it implements the smooth pursuit eye movement with prediction, learning, and time delays. These two principles allowed us to accurately describe visually guided, predictive and learning smooth pursuit dynamics observed in a wide variety of tasks within a single theoretical framework. The tracking control performance of the proposed information fusion control with time delays is verified by numerical simulation results.

show abstract

Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

Cited by 109 publications

References 115 publications

When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing

When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing

Catch-up saccades in head-unrestrained conditions reveal that saccade amplitude is corrected using an internal model of target movement

Information fusion control with time delay for smooth pursuit eye movement

Contact Info

Product

Resources

About